diff --git a/depletion.ipynb b/depletion.ipynb
index 0ab2f1a..09feeda 100644
--- a/depletion.ipynb
+++ b/depletion.ipynb
@@ -125,7 +125,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x15140e8c8cd0>"
+       "<matplotlib.image.AxesImage at 0x7f8a97aecf70>"
       ]
      },
      "execution_count": 6,
@@ -134,9 +134,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 258.065x264.901 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -284,11 +284,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "operator = openmc.deplete.Operator(geometry, settings, \"./chain_simple.xml\")"
+    "model = openmc.Model(geometry=geometry, settings=settings)\n",
+    "operator = openmc.deplete.CoupledOperator(model, \"./chain_simple.xml\")"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "raw",
    "metadata": {},
    "source": [
     "We will then simulate our fuel pin operating at linear power of 174 W/cm, or 174 W given a unit height for our problem."
@@ -346,7 +347,684 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                %%%%%%%%%%%%%%%\n",
+      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
+      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
+      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
+      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
+      "                #######################     %%%%%%%%%%%%%%%%%%\n",
+      "                 #######################     %%%%%%%%%%%%%%%%%\n",
+      "                 ######################     %%%%%%%%%%%%%%%%%\n",
+      "                  ####################     %%%%%%%%%%%%%%%%%\n",
+      "                    #################     %%%%%%%%%%%%%%%%%\n",
+      "                     ###############     %%%%%%%%%%%%%%%%\n",
+      "                       ############     %%%%%%%%%%%%%%%\n",
+      "                          ########     %%%%%%%%%%%%%%\n",
+      "                                      %%%%%%%%%%%\n",
+      "\n",
+      "                 | The OpenMC Monte Carlo Code\n",
+      "       Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n",
+      "         License | https://docs.openmc.org/en/latest/license.html\n",
+      "         Version | 0.13.1\n",
+      "        Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n",
+      "       Date/Time | 2022-10-05 19:02:05\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 U234 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U234.h5\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 O17 from /home/pshriwise/data/xs/openmc/nndc_hdf5/O17.h5\n",
+      " Reading U236 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U236.h5\n",
+      " Reading Zr90 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr90.h5\n",
+      " Reading Zr91 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr91.h5\n",
+      " Reading Zr92 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr92.h5\n",
+      " Reading Zr94 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr94.h5\n",
+      " Reading Zr96 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr96.h5\n",
+      " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n",
+      " Reading H2 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H2.h5\n",
+      " Reading c_H_in_H2O from /home/pshriwise/data/xs/openmc/nndc_hdf5/c_H_in_H2O.h5\n",
+      " Minimum neutron data temperature: 294 K\n",
+      " Maximum neutron data temperature: 294 K\n",
+      " Preparing distributed cell instances...\n",
+      " Reading plot XML file...\n",
+      " Writing summary.h5 file...\n",
+      "[openmc.deplete] t=0.0 s, dt=2592000 s, source=174\n",
+      " Reading I135 from /home/pshriwise/data/xs/openmc/nndc_hdf5/I135.h5\n",
+      " Reading Xe135 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Xe135.h5\n",
+      " Reading Xe136 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Xe136.h5\n",
+      " Reading Cs135 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Cs135.h5\n",
+      " Reading Gd157 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Gd157.h5\n",
+      " Reading Gd156 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Gd156.h5\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    1.49721\n",
+      "        2/1    1.49226\n",
+      "        3/1    1.44838\n",
+      "        4/1    1.47163\n",
+      "        5/1    1.41429\n",
+      "        6/1    1.42100\n",
+      "        7/1    1.43656\n",
+      "        8/1    1.41211\n",
+      "        9/1    1.45570\n",
+      "       10/1    1.33919\n",
+      "       11/1    1.47822\n",
+      "       12/1    1.47425    1.47624 +/- 0.00198\n",
+      "       13/1    1.39919    1.45056 +/- 0.02571\n",
+      "       14/1    1.35785    1.42738 +/- 0.02946\n",
+      "       15/1    1.38972    1.41985 +/- 0.02403\n",
+      "       16/1    1.47812    1.42956 +/- 0.02189\n",
+      "       17/1    1.50905    1.44092 +/- 0.02171\n",
+      "       18/1    1.48299    1.44618 +/- 0.01952\n",
+      "       19/1    1.49089    1.45114 +/- 0.01792\n",
+      "       20/1    1.50543    1.45657 +/- 0.01692\n",
+      "       21/1    1.41183    1.45251 +/- 0.01584\n",
+      "       22/1    1.49487    1.45604 +/- 0.01488\n",
+      "       23/1    1.45853    1.45623 +/- 0.01369\n",
+      "       24/1    1.45628    1.45623 +/- 0.01268\n",
+      "       25/1    1.37824    1.45103 +/- 0.01290\n",
+      "       26/1    1.40298    1.44803 +/- 0.01243\n",
+      "       27/1    1.48608    1.45027 +/- 0.01189\n",
+      "       28/1    1.46773    1.45124 +/- 0.01125\n",
+      "       29/1    1.46127    1.45177 +/- 0.01066\n",
+      "       30/1    1.38587    1.44847 +/- 0.01063\n",
+      "       31/1    1.42115    1.44717 +/- 0.01020\n",
+      "       32/1    1.45934    1.44772 +/- 0.00974\n",
+      "       33/1    1.40481    1.44586 +/- 0.00949\n",
+      "       34/1    1.47602    1.44711 +/- 0.00917\n",
+      "       35/1    1.47601    1.44827 +/- 0.00887\n",
+      "       36/1    1.44354    1.44809 +/- 0.00853\n",
+      "       37/1    1.54954    1.45185 +/- 0.00902\n",
+      "       38/1    1.46876    1.45245 +/- 0.00872\n",
+      "       39/1    1.51863    1.45473 +/- 0.00872\n",
+      "       40/1    1.51529    1.45675 +/- 0.00866\n",
+      "       41/1    1.39841    1.45487 +/- 0.00858\n",
+      "       42/1    1.58314    1.45888 +/- 0.00923\n",
+      "       43/1    1.51678    1.46063 +/- 0.00911\n",
+      "       44/1    1.44328    1.46012 +/- 0.00886\n",
+      "       45/1    1.42208    1.45903 +/- 0.00867\n",
+      "       46/1    1.50118    1.46020 +/- 0.00850\n",
+      "       47/1    1.50796    1.46150 +/- 0.00837\n",
+      "       48/1    1.44889    1.46116 +/- 0.00816\n",
+      "       49/1    1.48715    1.46183 +/- 0.00797\n",
+      "       50/1    1.49559    1.46267 +/- 0.00782\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 1.1968e+00 seconds\n",
+      "   Reading cross sections          = 1.1906e+00 seconds\n",
+      " Total time in simulation          = 1.3040e+01 seconds\n",
+      "   Time in transport only          = 1.3031e+01 seconds\n",
+      "   Time in inactive batches        = 2.2501e+00 seconds\n",
+      "   Time in active batches          = 1.0790e+01 seconds\n",
+      "   Time synchronizing fission bank = 4.3167e-03 seconds\n",
+      "     Sampling source sites         = 3.9236e-03 seconds\n",
+      "     SEND/RECV source sites        = 3.6606e-04 seconds\n",
+      "   Time accumulating tallies       = 2.4592e-04 seconds\n",
+      "   Time writing statepoints        = 1.4237e-03 seconds\n",
+      " Total time for finalization       = 5.2430e-05 seconds\n",
+      " Total time elapsed                = 1.4251e+01 seconds\n",
+      " Calculation Rate (inactive)       = 4444.21 particles/second\n",
+      " Calculation Rate (active)         = 3707.27 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 1.46857 +/- 0.00652\n",
+      " k-effective (Track-length)  = 1.46267 +/- 0.00782\n",
+      " k-effective (Absorption)    = 1.46527 +/- 0.00455\n",
+      " Combined k-effective        = 1.46478 +/- 0.00392\n",
+      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n0.h5...\n",
+      "[openmc.deplete] t=2592000.0 s, dt=2592000 s, source=174\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    1.53241\n",
+      "        2/1    1.40513\n",
+      "        3/1    1.45199\n",
+      "        4/1    1.48296\n",
+      "        5/1    1.45516\n",
+      "        6/1    1.49568\n",
+      "        7/1    1.41651\n",
+      "        8/1    1.41637\n",
+      "        9/1    1.41213\n",
+      "       10/1    1.43048\n",
+      "       11/1    1.43207\n",
+      "       12/1    1.51388    1.47297 +/- 0.04091\n",
+      "       13/1    1.44591    1.46395 +/- 0.02528\n",
+      "       14/1    1.50325    1.47378 +/- 0.02040\n",
+      "       15/1    1.44463    1.46795 +/- 0.01684\n",
+      "       16/1    1.45528    1.46583 +/- 0.01391\n",
+      "       17/1    1.39473    1.45568 +/- 0.01554\n",
+      "       18/1    1.41153    1.45016 +/- 0.01454\n",
+      "       19/1    1.36173    1.44033 +/- 0.01616\n",
+      "       20/1    1.48965    1.44527 +/- 0.01527\n",
+      "       21/1    1.42710    1.44361 +/- 0.01391\n",
+      "       22/1    1.41558    1.44128 +/- 0.01291\n",
+      "       23/1    1.39316    1.43758 +/- 0.01244\n",
+      "       24/1    1.46698    1.43968 +/- 0.01171\n",
+      "       25/1    1.42137    1.43846 +/- 0.01097\n",
+      "       26/1    1.44859    1.43909 +/- 0.01028\n",
+      "       27/1    1.39159    1.43630 +/- 0.01005\n",
+      "       28/1    1.51030    1.44041 +/- 0.01033\n",
+      "       29/1    1.58514    1.44802 +/- 0.01239\n",
+      "       30/1    1.33291    1.44227 +/- 0.01309\n",
+      "       31/1    1.38694    1.43963 +/- 0.01272\n",
+      "       32/1    1.46008    1.44056 +/- 0.01217\n",
+      "       33/1    1.39234    1.43847 +/- 0.01181\n",
+      "       34/1    1.40957    1.43726 +/- 0.01138\n",
+      "       35/1    1.47587    1.43881 +/- 0.01102\n",
+      "       36/1    1.43015    1.43847 +/- 0.01059\n",
+      "       37/1    1.38802    1.43661 +/- 0.01036\n",
+      "       38/1    1.33953    1.43314 +/- 0.01057\n",
+      "       39/1    1.48062    1.43478 +/- 0.01033\n",
+      "       40/1    1.44053    1.43497 +/- 0.00998\n",
+      "       41/1    1.46435    1.43592 +/- 0.00970\n",
+      "       42/1    1.49916    1.43789 +/- 0.00960\n",
+      "       43/1    1.46589    1.43874 +/- 0.00934\n",
+      "       44/1    1.40238    1.43767 +/- 0.00913\n",
+      "       45/1    1.44733    1.43795 +/- 0.00887\n",
+      "       46/1    1.35951    1.43577 +/- 0.00889\n",
+      "       47/1    1.45303    1.43623 +/- 0.00866\n",
+      "       48/1    1.43982    1.43633 +/- 0.00843\n",
+      "       49/1    1.53025    1.43874 +/- 0.00855\n",
+      "       50/1    1.45253    1.43908 +/- 0.00834\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 0.0000e+00 seconds\n",
+      "   Reading cross sections          = 0.0000e+00 seconds\n",
+      " Total time in simulation          = 1.2879e+01 seconds\n",
+      "   Time in transport only          = 1.2870e+01 seconds\n",
+      "   Time in inactive batches        = 2.1932e+00 seconds\n",
+      "   Time in active batches          = 1.0686e+01 seconds\n",
+      "   Time synchronizing fission bank = 4.6210e-03 seconds\n",
+      "     Sampling source sites         = 4.2406e-03 seconds\n",
+      "     SEND/RECV source sites        = 3.5471e-04 seconds\n",
+      "   Time accumulating tallies       = 2.2210e-04 seconds\n",
+      "   Time writing statepoints        = 2.5594e-03 seconds\n",
+      " Total time for finalization       = 5.4901e-05 seconds\n",
+      " Total time elapsed                = 1.2893e+01 seconds\n",
+      " Calculation Rate (inactive)       = 4559.54 particles/second\n",
+      " Calculation Rate (active)         = 3743.15 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 1.43941 +/- 0.00730\n",
+      " k-effective (Track-length)  = 1.43908 +/- 0.00834\n",
+      " k-effective (Absorption)    = 1.43889 +/- 0.00475\n",
+      " Combined k-effective        = 1.43891 +/- 0.00466\n",
+      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n1.h5...\n",
+      "[openmc.deplete] t=5184000.0 s, dt=2592000 s, source=174\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    1.35648\n",
+      "        2/1    1.41942\n",
+      "        3/1    1.36608\n",
+      "        4/1    1.46674\n",
+      "        5/1    1.47455\n",
+      "        6/1    1.40320\n",
+      "        7/1    1.36282\n",
+      "        8/1    1.41123\n",
+      "        9/1    1.45872\n",
+      "       10/1    1.43235\n",
+      "       11/1    1.43551\n",
+      "       12/1    1.42639    1.43095 +/- 0.00456\n",
+      "       13/1    1.38410    1.41533 +/- 0.01584\n",
+      "       14/1    1.40010    1.41152 +/- 0.01183\n",
+      "       15/1    1.45122    1.41946 +/- 0.01212\n",
+      "       16/1    1.46395    1.42688 +/- 0.01237\n",
+      "       17/1    1.50374    1.43786 +/- 0.01516\n",
+      "       18/1    1.41657    1.43520 +/- 0.01340\n",
+      "       19/1    1.39988    1.43127 +/- 0.01245\n",
+      "       20/1    1.48773    1.43692 +/- 0.01248\n",
+      "       21/1    1.50428    1.44304 +/- 0.01285\n",
+      "       22/1    1.40603    1.43996 +/- 0.01213\n",
+      "       23/1    1.44173    1.44009 +/- 0.01115\n",
+      "       24/1    1.47605    1.44266 +/- 0.01064\n",
+      "       25/1    1.41864    1.44106 +/- 0.01004\n",
+      "       26/1    1.42455    1.44003 +/- 0.00944\n",
+      "       27/1    1.43444    1.43970 +/- 0.00888\n",
+      "       28/1    1.39999    1.43749 +/- 0.00866\n",
+      "       29/1    1.45318    1.43832 +/- 0.00823\n",
+      "       30/1    1.46514    1.43966 +/- 0.00792\n",
+      "       31/1    1.49160    1.44213 +/- 0.00793\n",
+      "       32/1    1.34013    1.43750 +/- 0.00887\n",
+      "       33/1    1.48829    1.43971 +/- 0.00876\n",
+      "       34/1    1.47141    1.44103 +/- 0.00849\n",
+      "       35/1    1.39193    1.43906 +/- 0.00838\n",
+      "       36/1    1.39036    1.43719 +/- 0.00826\n",
+      "       37/1    1.44249    1.43739 +/- 0.00795\n",
+      "       38/1    1.48357    1.43904 +/- 0.00784\n",
+      "       39/1    1.40641    1.43791 +/- 0.00765\n",
+      "       40/1    1.42094    1.43734 +/- 0.00741\n",
+      "       41/1    1.40773    1.43639 +/- 0.00723\n",
+      "       42/1    1.45978    1.43712 +/- 0.00704\n",
+      "       43/1    1.42308    1.43669 +/- 0.00683\n",
+      "       44/1    1.47645    1.43786 +/- 0.00673\n",
+      "       45/1    1.51314    1.44001 +/- 0.00688\n",
+      "       46/1    1.48159    1.44117 +/- 0.00679\n",
+      "       47/1    1.48586    1.44238 +/- 0.00671\n",
+      "       48/1    1.38670    1.44091 +/- 0.00669\n",
+      "       49/1    1.53726    1.44338 +/- 0.00697\n",
+      "       50/1    1.44556    1.44344 +/- 0.00680\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 0.0000e+00 seconds\n",
+      "   Reading cross sections          = 0.0000e+00 seconds\n",
+      " Total time in simulation          = 1.2995e+01 seconds\n",
+      "   Time in transport only          = 1.2986e+01 seconds\n",
+      "   Time in inactive batches        = 2.2152e+00 seconds\n",
+      "   Time in active batches          = 1.0780e+01 seconds\n",
+      "   Time synchronizing fission bank = 4.3730e-03 seconds\n",
+      "     Sampling source sites         = 3.9823e-03 seconds\n",
+      "     SEND/RECV source sites        = 3.6477e-04 seconds\n",
+      "   Time accumulating tallies       = 2.3376e-04 seconds\n",
+      "   Time writing statepoints        = 2.6625e-03 seconds\n",
+      " Total time for finalization       = 6.8176e-03 seconds\n",
+      " Total time elapsed                = 1.3016e+01 seconds\n",
+      " Calculation Rate (inactive)       = 4514.3 particles/second\n",
+      " Calculation Rate (active)         = 3710.67 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 1.43781 +/- 0.00627\n",
+      " k-effective (Track-length)  = 1.44344 +/- 0.00680\n",
+      " k-effective (Absorption)    = 1.43243 +/- 0.00527\n",
+      " Combined k-effective        = 1.43604 +/- 0.00493\n",
+      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n2.h5...\n",
+      "[openmc.deplete] t=7776000.0 s, dt=2592000 s, source=174\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    1.52145\n",
+      "        2/1    1.45763\n",
+      "        3/1    1.38715\n",
+      "        4/1    1.49491\n",
+      "        5/1    1.45451\n",
+      "        6/1    1.41857\n",
+      "        7/1    1.36661\n",
+      "        8/1    1.35323\n",
+      "        9/1    1.32219\n",
+      "       10/1    1.45559\n",
+      "       11/1    1.56651\n",
+      "       12/1    1.37746    1.47198 +/- 0.09452\n",
+      "       13/1    1.41360    1.45252 +/- 0.05794\n",
+      "       14/1    1.46543    1.45575 +/- 0.04110\n",
+      "       15/1    1.36700    1.43800 +/- 0.03645\n",
+      "       16/1    1.41995    1.43499 +/- 0.02991\n",
+      "       17/1    1.48070    1.44152 +/- 0.02611\n",
+      "       18/1    1.46150    1.44402 +/- 0.02275\n",
+      "       19/1    1.34499    1.43302 +/- 0.02288\n",
+      "       20/1    1.47485    1.43720 +/- 0.02089\n",
+      "       21/1    1.50424    1.44329 +/- 0.01985\n",
+      "       22/1    1.41394    1.44085 +/- 0.01829\n",
+      "       23/1    1.43248    1.44020 +/- 0.01683\n",
+      "       24/1    1.38295    1.43611 +/- 0.01611\n",
+      "       25/1    1.42331    1.43526 +/- 0.01503\n",
+      "       26/1    1.44462    1.43585 +/- 0.01407\n",
+      "       27/1    1.43415    1.43575 +/- 0.01321\n",
+      "       28/1    1.48582    1.43853 +/- 0.01276\n",
+      "       29/1    1.44884    1.43907 +/- 0.01209\n",
+      "       30/1    1.34656    1.43444 +/- 0.01236\n",
+      "       31/1    1.44433    1.43492 +/- 0.01177\n",
+      "       32/1    1.48062    1.43699 +/- 0.01141\n",
+      "       33/1    1.37226    1.43418 +/- 0.01126\n",
+      "       34/1    1.31991    1.42942 +/- 0.01179\n",
+      "       35/1    1.48850    1.43178 +/- 0.01155\n",
+      "       36/1    1.47446    1.43342 +/- 0.01122\n",
+      "       37/1    1.49478    1.43569 +/- 0.01103\n",
+      "       38/1    1.35729    1.43289 +/- 0.01099\n",
+      "       39/1    1.37560    1.43092 +/- 0.01079\n",
+      "       40/1    1.41980    1.43055 +/- 0.01043\n",
+      "       41/1    1.36832    1.42854 +/- 0.01029\n",
+      "       42/1    1.41065    1.42798 +/- 0.00997\n",
+      "       43/1    1.36825    1.42617 +/- 0.00984\n",
+      "       44/1    1.37304    1.42461 +/- 0.00967\n",
+      "       45/1    1.40813    1.42414 +/- 0.00940\n",
+      "       46/1    1.39351    1.42329 +/- 0.00918\n",
+      "       47/1    1.46699    1.42447 +/- 0.00900\n",
+      "       48/1    1.41202    1.42414 +/- 0.00877\n",
+      "       49/1    1.45476    1.42493 +/- 0.00858\n",
+      "       50/1    1.38327    1.42388 +/- 0.00842\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 0.0000e+00 seconds\n",
+      "   Reading cross sections          = 0.0000e+00 seconds\n",
+      " Total time in simulation          = 1.3031e+01 seconds\n",
+      "   Time in transport only          = 1.3022e+01 seconds\n",
+      "   Time in inactive batches        = 2.2077e+00 seconds\n",
+      "   Time in active batches          = 1.0824e+01 seconds\n",
+      "   Time synchronizing fission bank = 4.5578e-03 seconds\n",
+      "     Sampling source sites         = 4.1598e-03 seconds\n",
+      "     SEND/RECV source sites        = 3.7168e-04 seconds\n",
+      "   Time accumulating tallies       = 2.3355e-04 seconds\n",
+      "   Time writing statepoints        = 2.7490e-03 seconds\n",
+      " Total time for finalization       = 4.9850e-05 seconds\n",
+      " Total time elapsed                = 1.3045e+01 seconds\n",
+      " Calculation Rate (inactive)       = 4529.68 particles/second\n",
+      " Calculation Rate (active)         = 3695.64 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 1.42700 +/- 0.00561\n",
+      " k-effective (Track-length)  = 1.42388 +/- 0.00842\n",
+      " k-effective (Absorption)    = 1.43128 +/- 0.00502\n",
+      " Combined k-effective        = 1.42959 +/- 0.00429\n",
+      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n3.h5...\n",
+      "[openmc.deplete] t=10368000.0 s, dt=2592000 s, source=174\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    1.38925\n",
+      "        2/1    1.42976\n",
+      "        3/1    1.41226\n",
+      "        4/1    1.39211\n",
+      "        5/1    1.50564\n",
+      "        6/1    1.47182\n",
+      "        7/1    1.40604\n",
+      "        8/1    1.47053\n",
+      "        9/1    1.38703\n",
+      "       10/1    1.40210\n",
+      "       11/1    1.49465\n",
+      "       12/1    1.38331    1.43898 +/- 0.05567\n",
+      "       13/1    1.36733    1.41510 +/- 0.04004\n",
+      "       14/1    1.43836    1.42091 +/- 0.02891\n",
+      "       15/1    1.28111    1.39295 +/- 0.03582\n",
+      "       16/1    1.46121    1.40433 +/- 0.03138\n",
+      "       17/1    1.33978    1.39511 +/- 0.02808\n",
+      "       18/1    1.41161    1.39717 +/- 0.02440\n",
+      "       19/1    1.39461    1.39689 +/- 0.02152\n",
+      "       20/1    1.47066    1.40426 +/- 0.02062\n",
+      "       21/1    1.42437    1.40609 +/- 0.01874\n",
+      "       22/1    1.46622    1.41110 +/- 0.01782\n",
+      "       23/1    1.37032    1.40797 +/- 0.01669\n",
+      "       24/1    1.41432    1.40842 +/- 0.01546\n",
+      "       25/1    1.38921    1.40714 +/- 0.01445\n",
+      "       26/1    1.40164    1.40679 +/- 0.01352\n",
+      "       27/1    1.42757    1.40802 +/- 0.01276\n",
+      "       28/1    1.32544    1.40343 +/- 0.01288\n",
+      "       29/1    1.45468    1.40613 +/- 0.01247\n",
+      "       30/1    1.49292    1.41047 +/- 0.01261\n",
+      "       31/1    1.38744    1.40937 +/- 0.01204\n",
+      "       32/1    1.46078    1.41171 +/- 0.01171\n",
+      "       33/1    1.42757    1.41240 +/- 0.01122\n",
+      "       34/1    1.44109    1.41359 +/- 0.01080\n",
+      "       35/1    1.39698    1.41293 +/- 0.01038\n",
+      "       36/1    1.47438    1.41529 +/- 0.01025\n",
+      "       37/1    1.45001    1.41658 +/- 0.00995\n",
+      "       38/1    1.42864    1.41701 +/- 0.00960\n",
+      "       39/1    1.44516    1.41798 +/- 0.00931\n",
+      "       40/1    1.46250    1.41946 +/- 0.00912\n",
+      "       41/1    1.36719    1.41778 +/- 0.00898\n",
+      "       42/1    1.32118    1.41476 +/- 0.00920\n",
+      "       43/1    1.48217    1.41680 +/- 0.00915\n",
+      "       44/1    1.46043    1.41808 +/- 0.00897\n",
+      "       45/1    1.47425    1.41969 +/- 0.00886\n",
+      "       46/1    1.46925    1.42107 +/- 0.00872\n",
+      "       47/1    1.46076    1.42214 +/- 0.00854\n",
+      "       48/1    1.34478    1.42010 +/- 0.00856\n",
+      "       49/1    1.38493    1.41920 +/- 0.00839\n",
+      "       50/1    1.34114    1.41725 +/- 0.00841\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 0.0000e+00 seconds\n",
+      "   Reading cross sections          = 0.0000e+00 seconds\n",
+      " Total time in simulation          = 1.3126e+01 seconds\n",
+      "   Time in transport only          = 1.3116e+01 seconds\n",
+      "   Time in inactive batches        = 2.2380e+00 seconds\n",
+      "   Time in active batches          = 1.0888e+01 seconds\n",
+      "   Time synchronizing fission bank = 4.1247e-03 seconds\n",
+      "     Sampling source sites         = 3.7224e-03 seconds\n",
+      "     SEND/RECV source sites        = 3.7812e-04 seconds\n",
+      "   Time accumulating tallies       = 2.2789e-04 seconds\n",
+      "   Time writing statepoints        = 3.0614e-03 seconds\n",
+      " Total time for finalization       = 5.4060e-05 seconds\n",
+      " Total time elapsed                = 1.3140e+01 seconds\n",
+      " Calculation Rate (inactive)       = 4468.2 particles/second\n",
+      " Calculation Rate (active)         = 3673.84 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 1.41574 +/- 0.00666\n",
+      " k-effective (Track-length)  = 1.41725 +/- 0.00841\n",
+      " k-effective (Absorption)    = 1.42868 +/- 0.00429\n",
+      " Combined k-effective        = 1.42755 +/- 0.00464\n",
+      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n4.h5...\n",
+      "[openmc.deplete] t=12960000.0 s, dt=2592000 s, source=174\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    1.41046\n",
+      "        2/1    1.38415\n",
+      "        3/1    1.41886\n",
+      "        4/1    1.49276\n",
+      "        5/1    1.51088\n",
+      "        6/1    1.47652\n",
+      "        7/1    1.35478\n",
+      "        8/1    1.35145\n",
+      "        9/1    1.49048\n",
+      "       10/1    1.44388\n",
+      "       11/1    1.46152\n",
+      "       12/1    1.46663    1.46408 +/- 0.00255\n",
+      "       13/1    1.47189    1.46668 +/- 0.00299\n",
+      "       14/1    1.39703    1.44927 +/- 0.01754\n",
+      "       15/1    1.41472    1.44236 +/- 0.01524\n",
+      "       16/1    1.42886    1.44011 +/- 0.01265\n",
+      "       17/1    1.45836    1.44272 +/- 0.01100\n",
+      "       18/1    1.44504    1.44301 +/- 0.00953\n",
+      "       19/1    1.35868    1.43364 +/- 0.01259\n",
+      "       20/1    1.36823    1.42710 +/- 0.01302\n",
+      "       21/1    1.44284    1.42853 +/- 0.01186\n",
+      "       22/1    1.52754    1.43678 +/- 0.01362\n",
+      "       23/1    1.44989    1.43779 +/- 0.01257\n",
+      "       24/1    1.48022    1.44082 +/- 0.01202\n",
+      "       25/1    1.40460    1.43840 +/- 0.01145\n",
+      "       26/1    1.36586    1.43387 +/- 0.01163\n",
+      "       27/1    1.35671    1.42933 +/- 0.01183\n",
+      "       28/1    1.54670    1.43585 +/- 0.01292\n",
+      "       29/1    1.41323    1.43466 +/- 0.01228\n",
+      "       30/1    1.41355    1.43361 +/- 0.01170\n",
+      "       31/1    1.56992    1.44010 +/- 0.01288\n",
+      "       32/1    1.35745    1.43634 +/- 0.01284\n",
+      "       33/1    1.42131    1.43569 +/- 0.01229\n",
+      "       34/1    1.45210    1.43637 +/- 0.01179\n",
+      "       35/1    1.36098    1.43335 +/- 0.01170\n",
+      "       36/1    1.45621    1.43423 +/- 0.01128\n",
+      "       37/1    1.47355    1.43569 +/- 0.01095\n",
+      "       38/1    1.44393    1.43598 +/- 0.01055\n",
+      "       39/1    1.52203    1.43895 +/- 0.01061\n",
+      "       40/1    1.43143    1.43870 +/- 0.01025\n",
+      "       41/1    1.44187    1.43880 +/- 0.00991\n",
+      "       42/1    1.27834    1.43379 +/- 0.01083\n",
+      "       43/1    1.38906    1.43243 +/- 0.01058\n",
+      "       44/1    1.41543    1.43193 +/- 0.01028\n",
+      "       45/1    1.39747    1.43095 +/- 0.01003\n",
+      "       46/1    1.38755    1.42974 +/- 0.00982\n",
+      "       47/1    1.40362    1.42904 +/- 0.00958\n",
+      "       48/1    1.32535    1.42631 +/- 0.00971\n",
+      "       49/1    1.38622    1.42528 +/- 0.00952\n",
+      "       50/1    1.40968    1.42489 +/- 0.00928\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 0.0000e+00 seconds\n",
+      "   Reading cross sections          = 0.0000e+00 seconds\n",
+      " Total time in simulation          = 1.3098e+01 seconds\n",
+      "   Time in transport only          = 1.3089e+01 seconds\n",
+      "   Time in inactive batches        = 2.2179e+00 seconds\n",
+      "   Time in active batches          = 1.0881e+01 seconds\n",
+      "   Time synchronizing fission bank = 4.5930e-03 seconds\n",
+      "     Sampling source sites         = 4.1842e-03 seconds\n",
+      "     SEND/RECV source sites        = 3.7955e-04 seconds\n",
+      "   Time accumulating tallies       = 2.4737e-04 seconds\n",
+      "   Time writing statepoints        = 2.7292e-03 seconds\n",
+      " Total time for finalization       = 5.2371e-05 seconds\n",
+      " Total time elapsed                = 1.3112e+01 seconds\n",
+      " Calculation Rate (inactive)       = 4508.68 particles/second\n",
+      " Calculation Rate (active)         = 3676.29 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 1.42490 +/- 0.00784\n",
+      " k-effective (Track-length)  = 1.42489 +/- 0.00928\n",
+      " k-effective (Absorption)    = 1.42584 +/- 0.00494\n",
+      " Combined k-effective        = 1.42575 +/- 0.00483\n",
+      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n5.h5...\n",
+      "[openmc.deplete] t=15552000.0 (final operator evaluation)\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    1.46692\n",
+      "        2/1    1.42016\n",
+      "        3/1    1.35815\n",
+      "        4/1    1.38064\n",
+      "        5/1    1.44504\n",
+      "        6/1    1.36182\n",
+      "        7/1    1.31279\n",
+      "        8/1    1.47632\n",
+      "        9/1    1.48287\n",
+      "       10/1    1.40492\n",
+      "       11/1    1.34730\n",
+      "       12/1    1.46660    1.40695 +/- 0.05965\n",
+      "       13/1    1.47009    1.42800 +/- 0.04036\n",
+      "       14/1    1.41037    1.42359 +/- 0.02888\n",
+      "       15/1    1.35040    1.40895 +/- 0.02673\n",
+      "       16/1    1.41945    1.41070 +/- 0.02190\n",
+      "       17/1    1.54521    1.42992 +/- 0.02668\n",
+      "       18/1    1.45638    1.43322 +/- 0.02334\n",
+      "       19/1    1.38313    1.42766 +/- 0.02132\n",
+      "       20/1    1.48971    1.43386 +/- 0.02006\n",
+      "       21/1    1.43673    1.43412 +/- 0.01814\n",
+      "       22/1    1.38255    1.42983 +/- 0.01711\n",
+      "       23/1    1.47338    1.43318 +/- 0.01609\n",
+      "       24/1    1.43212    1.43310 +/- 0.01490\n",
+      "       25/1    1.47334    1.43578 +/- 0.01413\n",
+      "       26/1    1.44139    1.43613 +/- 0.01322\n",
+      "       27/1    1.47399    1.43836 +/- 0.01262\n",
+      "       28/1    1.45271    1.43916 +/- 0.01192\n",
+      "       29/1    1.41352    1.43781 +/- 0.01136\n",
+      "       30/1    1.40013    1.43593 +/- 0.01094\n",
+      "       31/1    1.44858    1.43653 +/- 0.01042\n",
+      "       32/1    1.38137    1.43402 +/- 0.01025\n",
+      "       33/1    1.37217    1.43133 +/- 0.01015\n",
+      "       34/1    1.36865    1.42872 +/- 0.01007\n",
+      "       35/1    1.35863    1.42592 +/- 0.01005\n",
+      "       36/1    1.41942    1.42567 +/- 0.00966\n",
+      "       37/1    1.47196    1.42738 +/- 0.00945\n",
+      "       38/1    1.50806    1.43026 +/- 0.00956\n",
+      "       39/1    1.38029    1.42854 +/- 0.00938\n",
+      "       40/1    1.32528    1.42510 +/- 0.00969\n",
+      "       41/1    1.43717    1.42549 +/- 0.00938\n",
+      "       42/1    1.40786    1.42494 +/- 0.00910\n",
+      "       43/1    1.45468    1.42584 +/- 0.00887\n",
+      "       44/1    1.32809    1.42296 +/- 0.00907\n",
+      "       45/1    1.37289    1.42153 +/- 0.00892\n",
+      "       46/1    1.39638    1.42083 +/- 0.00870\n",
+      "       47/1    1.45351    1.42172 +/- 0.00851\n",
+      "       48/1    1.37909    1.42059 +/- 0.00836\n",
+      "       49/1    1.41817    1.42053 +/- 0.00814\n",
+      "       50/1    1.36996    1.41927 +/- 0.00803\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     = 0.0000e+00 seconds\n",
+      "   Reading cross sections          = 0.0000e+00 seconds\n",
+      " Total time in simulation          = 1.3234e+01 seconds\n",
+      "   Time in transport only          = 1.3225e+01 seconds\n",
+      "   Time in inactive batches        = 2.2456e+00 seconds\n",
+      "   Time in active batches          = 1.0989e+01 seconds\n",
+      "   Time synchronizing fission bank = 4.2442e-03 seconds\n",
+      "     Sampling source sites         = 3.8368e-03 seconds\n",
+      "     SEND/RECV source sites        = 3.8075e-04 seconds\n",
+      "   Time accumulating tallies       = 2.4804e-04 seconds\n",
+      "   Time writing statepoints        = 2.7128e-03 seconds\n",
+      " Total time for finalization       = 5.0970e-05 seconds\n",
+      " Total time elapsed                = 1.3248e+01 seconds\n",
+      " Calculation Rate (inactive)       = 4453.13 particles/second\n",
+      " Calculation Rate (active)         = 3640.13 particles/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     = 1.42429 +/- 0.00588\n",
+      " k-effective (Track-length)  = 1.41927 +/- 0.00803\n",
+      " k-effective (Absorption)    = 1.42275 +/- 0.00472\n",
+      " Combined k-effective        = 1.42368 +/- 0.00460\n",
+      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
+      "\n",
+      " Creating state point openmc_simulation_n6.h5...\n"
+     ]
+    }
+   ],
    "source": [
     "integrator.integrate()"
    ]
@@ -369,10 +1047,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "depletion_results.h5\t openmc_simulation_n3.h5  statepoint.50.h5\r\n",
-      "openmc_simulation_n0.h5  openmc_simulation_n4.h5  summary.h5\r\n",
-      "openmc_simulation_n1.h5  openmc_simulation_n5.h5\r\n",
-      "openmc_simulation_n2.h5  openmc_simulation_n6.h5\r\n"
+      "c5g7.h5\t\t\t openmc_simulation_n2.h5  openmc_simulation_n6.h5\n",
+      "depletion_results.h5\t openmc_simulation_n3.h5  statepoint.50.h5\n",
+      "openmc_simulation_n0.h5  openmc_simulation_n4.h5  summary.h5\n",
+      "openmc_simulation_n1.h5  openmc_simulation_n5.h5\n"
      ]
     }
    ],
@@ -393,7 +1071,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = openmc.deplete.ResultsList.from_hdf5(\"./depletion_results.h5\")"
+    "results = openmc.deplete.Results(\"./depletion_results.h5\")"
    ]
   },
   {
@@ -402,7 +1080,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "time, k = results.get_eigenvalue()"
+    "time, k = results.get_keff()"
    ]
   },
   {
@@ -422,13 +1100,13 @@
     {
      "data": {
       "text/plain": [
-       "array([[1.4618427 , 0.00459795],\n",
-       "       [1.43996612, 0.00407154],\n",
-       "       [1.4293619 , 0.0051392 ],\n",
-       "       [1.4262837 , 0.00367395],\n",
-       "       [1.41334423, 0.00460271],\n",
-       "       [1.42301155, 0.0045317 ],\n",
-       "       [1.41417596, 0.0045945 ]])"
+       "array([[1.46477526, 0.00392422],\n",
+       "       [1.43890826, 0.00465543],\n",
+       "       [1.43604211, 0.00493173],\n",
+       "       [1.42958815, 0.00429288],\n",
+       "       [1.42754967, 0.00463556],\n",
+       "       [1.42575323, 0.00483288],\n",
+       "       [1.42368305, 0.00459654]])"
       ]
      },
      "execution_count": 20,
@@ -463,7 +1141,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -511,7 +1189,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -535,7 +1213,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -575,7 +1253,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -625,7 +1303,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1513fef13f10>"
+       "<matplotlib.image.AxesImage at 0x7f8a8c4de9d0>"
       ]
      },
      "execution_count": 29,
@@ -634,9 +1312,9 @@
     },
     {
      "data": {
-      "image/png": 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GU0wqhmE4ZbRS2b5+ResRiC4GJ9kYleHi4rNt+x3r8r12xWil0iesOzk+9hnUJ9S0pweKyObs8e+JyJGFxzZky7eJyKku2mNMsJ6f/bF94p9Q056uB55W1VXAlcCns+e+mcnV998CnAb8RfZ6hmH0lCDTnmb3b8hu3wScLCKSLd+kqntU9TFgIXs9wzB6SqhpT19eJ5vS4xlgec3nAlPTnj63y0GzDcPwQW8KtftMe/qaZbGbYxhGCaGmPX15HRFZBLweeKrmcw3D6BFBpj3N7p+b3f4AcLuqarb87Kx3aCVwNPB3DtpkgJ3nMgPbJ/4JNe3p9cByEVkAPgpcnD33AeDLTOZP/gZwgaru7dqmobHurG2xmzB67DOoT6hpT58Hzix57qeAT7loRxOOuX4n0O66nqvOOLjzqNrdG1baqNqBsnvDys6vseqMg1s9L/9ex6Q3hVrDMPqBScUwDKeYVAzDcMpgrvyWX7nKrgC3LwsXHWPnu2RYz8/+uLziW45FKoZhOKWXUnlp96OxmzAXH1NaWpdmPHzs+9SnPe1yjPVSKkYzLOy3fRASk0oJP776HO/v4WI8g5EWIT7TEN/NLoxCKmdecL7z12w7OMkwqvDx3fJxDJQxCqnMI4URiHXpktuPOfzvsu19qmWl8l0eTJdyauSFuNSntzTCknqB1gWDi1R89Lv7JGRdZeGiY0YVsYTe3r7VyHwdK4OTimEYcemtVP7t5rO8v0eKVXYXOf4YohUX25hiPSXEd7LrsdVbqaRAnSr9GHJoox51vgtD6FUcjVTmdanFrpo3zcUtWplPjCgldj1l3nc4ZHcyjEgqxv4MUSxD3Ka+MUipuKxqV+WwrlKgGNEKDOsgdLUtTfbt7g0ra312LlIfl/UUn72knaQiIstE5DYReTj7v3TGOm8Vke+KyAMicq+InFV4bKOIPCYi92R/b23y/iGKtWNgCGIZwjakgItjqmukcjHwLVU9GvhWdn+a54DfVNV8atOrRGRJ4fGPqepbs797OrZnLinXVaD+r17OurO2WcSC2wilaZSSAinVU6C7VIrTmd4AnD69gqpuV9WHs9v/DDwJ/EzH9w1KqBSoLWMWS4yUpymppT6+6SqVN6hqrskfAm+Yt7KInAgsBh4pLP5UlhZdKSIHznnuK9Oe7n2hsmF9G1lbJOYvYJ/EErOtqUQpbfB9bFRKRUS+KSL3z/jbZxL2bHIwnfM6K4D/BfyWqr6ULd4AvAn4ZWAZ8PGy5+8z7ekBr355edMcMGYK1IdoBdIfzu+6fbGjlC64TH1c1SgrTyhU1VPKHhORH4nIClXdmUnjyZL1fhr4GvAJVd1aeO18j+wRkf8JXNSo9QHJw8/XXvjFmY+7mAuoSJt5gdadtY2Nm4911ob8wE3lGrc+RNdGKK6jlHmpT5/Snpyu6U9xOtNzgZunV8imQv0/wBdU9aapx1Zk/4VJPeb+ju0xDCMyXaVyOfAeEXkYOCW7j4isFpHrsnV+HfgVYN2MruP/LSL3AfcBhwB/0rE9+5BiXcV3OOwjlI+dDvl6f9/n9qR4ikaIY6LT9VRU9Sng5BnL7wY+nN3+K+CvSp7/7i7v35YzLzifGz97zczHjrl+Z6upUCGNFMgnxQPbd0qUak0nZOpTRWpdyTmDGFEbchCcixy3yS9Ymy9xiLNr8+jB5cHv4zXL8F1LcRGlhKynuDyGBn/ltxu2nhZ0gjHX0UpbXBdt51EmgbJoJnYUksolDUKfkRyqHCCTnuB+sfygg/XUw39xn2WvW7u5dP15UilLg6pSoLJeIKC2VJpcarJtGhRKLH2hrVB8RClden3KUp95ac88qUxHKrc+fh9PPf+szG1ECYNIf2CyU1I5F8jHL1DbXD6VX+UUCCGUuqRy3RQfx81gpDKPNmFf1UC40LWVLrg8R6iPhNz+ELWUNoM0Q/aEDk4qLkfYdmHVGQc7Pyeo6y/m2OTiYntdpz11vxdtiDWCdprBScUlIaKVprgIxccgFhfbGOP8Hh9RSmhGI5V54Z/PPn0fZzC7EssQ5eJqu5ru49jXn21boPXBIKXiMqxzEa2kKhYYVtTialuapjyuhBIySvHZqTFIqRiGEY9RSSVWClSXur96OU2vFFdG31MhlylP6FGzLkgp9YEBS6WPKVBbXKZCfZKLy/b6LMqOKfWBAUulDF/RikuxtPkFdHlQpC4X1+1rs+9cjJrN6dKLmFqUAiM498cV+S9F2zOYm/KxLe9tNIwf3J/VPH3gxhry71NwPoXigj50IU8zmHN/yig7J6jN+UA588Qy75ygnCYnHDYVS06ISyb4kkyIKKltZNdEKF2jlCqhtIlS6qY+Xc79sUjFMT+++pxKsTQ5k7lNxAJhrsUy7+CvEk7M9KoPQukzg49UoN0ZzL6jFQgTsUCYqKUPdKk7uRYK+IlSmpyJPA87S9kDvou20KxHqEse3+fpJFzRJ6FUkcLwh3l0ilREZBmwGTgS+Afg11X16Rnr7WVyHVqAf1LVNdnylcAmYDnwfeBDqvqTqvdtGqlA+GglJ5UaS5GxRC4uZBoj5YkdpUDcSKXOtKcA/16Y2nRNYfmngStVdRXwNLC+Y3tKadM3X/WLUKcy73oMi4ueB1eD5lLF1fb1SSjzCH2dIe/TnpaRTcvxbiCftqPR813SZeyKqy6/pmIxueyPS5k0GYfiamCjj96eGISa9vSgbMrSrSJyerZsObBbVV/M7j8BHFr2Rk2nPZ3FPGP7/FCa1FhMLs2JIRNo9kPgs6fHZdrjgsouZRH5JvBzMx76RPGOqqqIlBVojlDVHSJyFHB7NtfPM00aqqrXAtfCpKbS5LmGYYSjMlJR1VNU9T/O+LsZ+FFhlsHSaU9VdUf2/1HgTuA44ClgiYjkYjsM2NF5izwQqraS0zScdjXCs28Ri8v2Nt2HrqMUH7WUWHTt/bkCeEpVLxeRi4FlqvoHU+ssBZ5T1T0icgjwXWCtqj4oIjcCX1HVTSJyDXCvqv5F1fu26f0p0vbK+zC/R8hVb1BOm6k+XPQOTZNKb5EP4bURckihVMnEV+rTpfenq1SWA18Gfh74RyZdyrtEZDVwvqp+WETeAXwOeIlJZHSVql6fPf8oJl3Ky4AfAL+hqnuq3rerVMD9lB45QxVLkVCS8R01pS4UcDflRlOiSSUWMaUC/RAL+JdLGXWlEyvVapsupiQUMKk4xYVUctqccAjd0yAIIxaIJ5fU6FJ78tHT4yPtcdXbY8P0PVDVxTzvQ687dsVn8baIq67nvtJ1+/silFQYfaQC/uor4D4Vgm5RS87QoxcXEm0qchcpD8SroxSxSKUjbT+MOt18rrubwc2lKYcavbjarhSFMo9UpvwFi1T2wUd9JcdHxJLjInKB/kYvruTYRtYu0p2c2HWUIlaodYTPHiFodinKGClRkdQF4zrK8hGZ5PgUCphUnOBLKjBfLBCmxpLTJnJxLZec2JLxlar5jE7Abw0F/KU9VlMxDCMZLFKZQd+jFfAXsRTxFb2EKCC3LXaPIUoBS3+84FssMBy59IlUZALpCgUs/fFClw+tbrdgkws8tb0eh8uLCPWZLvshtFCqSKn7eBYWqcyhS7SS4zodgvZRS85YopeuMm0qclfjUGJHKWDpj1eGKpacoQnGVVQ2ZqGAScU7ocSSY4JpRsoiyemTUMCkEoTUxQLu5ALpC8ZlnahNvWrIQgGTSlC6jLrN8dE7VMSlXKYJLRufRea2xW/XxdjULlwNJpXg9CFqAb9ymUdd8cTqlfItE+hndFLEpBKB0GKB/sklNULIBPovFIh7jdrKaU9F5NeAKwuL3gScrap/LSIbgV/llek61qnqPVXvm4JUoFoskE7UUmQsknEx107o6ATiCwUSn/ZUVe/IpzxlMiPhc8D/LazyscKUqPd0bI9hGJHpGqlsA96lqjuzeX/uVNVj56x/HvCrqvpfs/sbga+q6k1lz5lFKpEKxIlWwE3EAsOLWlzNBBgj5YE0ohSIm/7sVtUl2W0Bns7vl6x/O/A/VPWr2f2NwNuBPWSRTqgpOlziSiw5JphmpCySnD4JBTxLpWLa0xuKEhGRp1V1acnrrADuBd6oqi8Ulv0QWMxkStNHVPXSkuefB5wH8JpFi09Ye+Tx87csMHXEAu67nYu4kktOqpJxPS9xU5mAm+7iIikJBeJGKrXTHxH5feAtqnpeyePvAi5S1f9c9b6pRSpFYkct4F4u04SSjc9JzcGvTKB/0UmRmFKpnPa0sO5WYIOq3lFYtiITkjDpIXpeVfcr9k6TslQgDbGAf7n0lTYygfEIBRKf9jRb70jgb4HDVfWlwvNvB34GEOCe7DmVI6dSlwq4T4egvVzABNNWJOBeJpC2UMAGvyWN66gFusklZ+iS6SKRnKbXPOl7dFLEpNIDUpULDEcwLkQC44xMpjGp9AQfKVGOK8FAfyTjSiLQ7kpsQxUKmFR6RV2xQHy5TBNLNi7lMY1PmUA/hQImlV7iWy7gVzDzqCsfn7KYR9vrw45BJjl24WvDMJLBIpXIhIhYIF7Ukgpdrl4/pgglx9KfntNELNBNLjlDl0wXieQ0kQkMRyhgUhkMMeQCwxGMC5HAOCOTaUwqAySWYKA/knElERh3VDILk8pAaSqWHJeCmSa0cFyKY5qmIskZulDApDJ42soF/Aqmj7QVCYxDJjkmlZHQRS4wXsF0EQmMSyY5JpUR0lUwMFzJdJUIjFMkRUwqI8eFYKC/knEhETCRFDGpGC/jSjBFUpGNK3kUMZHMxqRi7IcPuZThSjo+pFGGyWQ+du6PYRjJYJHKCAgZtaSMRSf16RKpLHLdGCM9Zh1MQxeNCSQenaQiImcCnwR+AThRVe8uWe804M+AA4DrVPXybPlKYBOwHPg+8CFV/UmXNhn1mD7o+i4Zk0g6dI1U7gf+C/C5shVE5ADgs8B7gCeAu0Rki6o+CHwauFJVN4nINcB64C87tsloQZ+iGRNI2nSSiqo+BDCZtqeUE4EFVX00W3cTsFZEHmIyYXs+Y9QNTKIek0oiVB28vqRj0ug3IWoqhwKPF+4/AbyNScqzW1VfLCw/tOxFitOeAnu+tLD1fg9tjc0hwL/GbkRt/vsRTdbu17bVZ6jbNXOm0TpUSmXeXMqqenPbN26Kql7LZL5lRORuVV0d6r1DMdTtguFu25C3q+1zK6Wiqqe0ffGMHcDhhfuHZcueApaIyKIsWsmXG4bRY0IMfrsLOFpEVorIYuBsYItOBsjcAXwgW+9cIFjkYxiGHzpJRUTeLyJPAG8HviYit2bL3ygitwBkUchHgFuBh4Avq+oD2Ut8HPioiCwwqbFcX/Otr+3S7oQZ6nbBcLfNtmuKXo6oNQwjXezcH8MwnGJSMQzDKb2QioicKSIPiMhLIlLafScip4nINhFZEJGLQ7axDSKyTERuE5GHs/9LS9bbKyL3ZH9bQrezLlX7X0QOFJHN2ePfE5EjIzSzFTW2bZ2I/Evhc/pwjHY2QUQ+LyJPisjMMV8y4epsm+8VkeNrvbCqJv/H5NyiY4E7gdUl6xwAPAIcBSwG/h54c+y2V2zXnwIXZ7cvBj5dst6zsdtaY1sq9z/wu8A12e2zgc2x2+1w29YBfx67rQ2361eA44H7Sx5/H/B1QICTgO/Ved1eRCqq+pCqbqtY7eXTAXRyUuImYK3/1nViLZPTE8j+nx6vKZ2ps/+L23sTcLJUnOORCH38blWiqt8Gds1ZZS3wBZ2wlcm4shVVr9sLqdRk1ukApcP+E+ENqrozu/1D4A0l6x0kIneLyFYROT1M0xpTZ/+/vI5Ohho8w2QoQerU/W6dkaUJN4nI4TMe7xutjqlkrqeSyukArpm3XcU7qqoiUta/f4Sq7hCRo4DbReQ+VX3EdVuNTvwN8CVV3SMiv8MkInt35DZFIRmpqL/TAaIyb7tE5EciskJVd2Zh5ZMlr7Ej+/+oiNwJHMckx0+JOvs/X+cJEVkEvJ7J6RqpU7ltqlrcjuuY1Mv6Tqtjakjpz8zTASK3qYotTE5PgJLTFERkqYgcmN0+BHgn8GCwFtanzv4vbu8HgNs1qwgmTuW2TdUa1jAZPd53tgC/mfUCnQQ8U0jXy4ldga5ZpX4/k3xuD/Aj4NZs+RuBW6aq1duZ/Ip/Ina7a2zXcuBbwMPAN4Fl2fLVTK6QB/AO4D4mPQ73Aetjt3vO9uy3/4FLgTXZ7YOAG4EF4O+Ao2K32eG2XQY8kH1OdwBvit3mGtv0JWAn8EJ2fK0HzgfOzx4XJhdYeyT77s3seZ3+s2H6hmE4ZUjpj2EYCWBSMQzDKSYVwzCcYlIxDMMpJhXDMJxiUjEMwykmFcMwnPL/AeihhY1ti+deAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 258.065x264.901 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -701,10 +1379,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<?xml version='1.0' encoding='utf-8'?>\r\n",
-      "<cross_sections>\r\n",
-      "  <depletion_chain path=\"chain_simple.xml\" type=\"depletion_chain\" />\r\n",
-      "</cross_sections>\r\n"
+      "<?xml version='1.0' encoding='utf-8'?>\n",
+      "<cross_sections>\n",
+      "  <depletion_chain path=\"chain_simple.xml\" type=\"depletion_chain\" />\n",
+      "</cross_sections>\n"
      ]
     }
    ],
@@ -725,7 +1403,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "new_op = openmc.deplete.Operator(geometry, settings)"
+    "model = openmc.Model(geometry=geometry, settings=settings)\n",
+    "new_op = openmc.deplete.CoupledOperator(model, \"./chain_simple.xml\")"
    ]
   },
   {
@@ -736,7 +1415,7 @@
     {
      "data": {
       "text/plain": [
-       "228"
+       "9"
       ]
      },
      "execution_count": 35,
@@ -756,7 +1435,7 @@
     {
      "data": {
       "text/plain": [
-       "['B10', 'B11', 'O16', 'Br81', 'Br82', 'Kr82', 'Kr83', 'Kr84', 'Kr85', 'Kr86']"
+       "['I135', 'Xe135', 'Xe136', 'Cs135', 'Gd157', 'Gd156', 'U234', 'U235', 'U238']"
       ]
      },
      "execution_count": 36,
@@ -776,16 +1455,7 @@
     {
      "data": {
       "text/plain": [
-       "['Am242',\n",
-       " 'Am242_m1',\n",
-       " 'Am243',\n",
-       " 'Am244',\n",
-       " 'Am244_m1',\n",
-       " 'Cm242',\n",
-       " 'Cm243',\n",
-       " 'Cm244',\n",
-       " 'Cm245',\n",
-       " 'Cm246']"
+       "['I135', 'Xe135', 'Xe136', 'Cs135', 'Gd157', 'Gd156', 'U234', 'U235', 'U238']"
       ]
      },
      "execution_count": 37,
@@ -864,7 +1534,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -878,9 +1548,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/hexagonal-lattice.ipynb b/hexagonal-lattice.ipynb
index 07c3ab8..87f2a2a 100644
--- a/hexagonal-lattice.ipynb
+++ b/hexagonal-lattice.ipynb
@@ -247,7 +247,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAGQCAIAAAAP3aGbAAAUTklEQVR4nO3d3dLbtg6FYbjTC++dqwdOHMf2J1P8AbGA95kc7JntNjUFroEombwdx2FAm9tt/r+TAkS7f3f/ByCWFZE08jcSZ3hGYBXlH0x9fvrvJMhqIrCqUEmoRi9fh/wqgsBKK1lCnSO/iiCw8iiVUOfIr6wILG2EVIvnUSK8pBFYksipbo+hI7kUEVgyCKm5aLsUEVjRkVMOaLtUEFhBkVNbkFzBEVixkFNBkFwxEVghkFNhkVyhEFibEVUq7leK2NqLwNqDnBJFw7UXgeWNqMqBhmsLAssJOZUSDZczAms5oqoCGi4fBNZCRFU1xNZqBNYSRFVlxNY6BNZkRBXuiK0VCKxpiCq8I7bmIrAmIKpwjtiahcAaQlShHbE1jsDqRFShD7E1gsC6jKjCOGKrzz+7/wPEkFaYiHK6ig6rFbWFFWi1LiGwviOqsBqx1YjAOkNUwROx9RWB9SPS6qrj+DxktxtT8ILbjcz60e1gbN4QVe1+CqmfEF7tmJrvCKxXpFWjq1H1jNhqxOx8QWD9QVS1G0mrOzKrHXP0gcD6hbRqNB5Vz4itRkzTOwKLqLpgblrdkVntyk/W8m+6k1btVqTVun9tSpRr3Q6La3/J6lihz7qk6qyt2mGRVpBWtoArBlbZi93N4a6NG8OrapZxrVvCmtd4kGeUcGPYodIMrtRhkVZIqVRhVwmsUhd1Iuc7NW4M+9Qp7/w/fq5zLVFZkZ0ekndYpBVKSV/wmQMr/cUD3uUu+7SBlfuy+diyosQy1rjExZ8zsBJfMKBF1imQbdE963UCrkq5DJ+qwyKtgBfJJkWewEp2YYBZMk2NJIGV6ZIA06WZIBkCK83FANbJMU3kAyvHZQAcJJgs2oGV4AJEtmXvBDZsWEp9yggHlvrQA1tITxzVwJIedGAv3ekjGVi6ww0EITqJ9AJLdKBFOa8osYDlSXEqiQWW4hADYclNKKXAkhvcHNy6HtqrLbSmlUxgaQ1rMg5RQlptJDS5NAJLaEABRSpTTCCwVIYyt6UdEO1VBBITLXpgSQxiEYtihbSKI/50C32Qavzhi+a//16H7L//Jl/fuVsYT08rhxFIL3AkBA4s0qrd+yx9+0C42HKIqrcPBC31gKKmQtRbQtKq3deJ2viZSwbjxj+tGj+Du7ATMGKHFXawAro0CVe0GFdbrRUrVtsHIat42RAvsEirdh0tw9Lp+lN4LV1WjzYIyQSLh6i3hFhk6W3R7XZ8/LPub+Qur5pYgUV71Y652o2haxdtSgYKrGhDA8CCTcwogRVqUHJL01+k+SLxxZmeIQIrznAA+CjIJN0fWEEGAsC5CFN1f2ABQKPNgRUhs6tJ8xZSmi8iZPuE3RlY2788gKv2TtttgUVaDaK/6MbQDdo4eVnDqmX1T3M+/ln3NxI91ez5LSHt1Szbf/fLj5/L2vIzww2BRVrN1ThdQ22JtWV7GSOtZvPPLO9bQtJqupZJGCqtxv/xd1sGAf7T2bvDIrCWYotktkh25txkuQYWaaVuxQo6h1Co88wsv1tC0krdoud9Sx8jwoHn1Oa1BjRZGitkFho5BRbtFZCY2wT3CCzSSp1DB0STpc5nmv/r8ZfU8PFVoASPqNyi5DhuCRbgs5ZBEMufElZor3If4enZ+0gHVu4yaLT6ieHaW0LSqv0zMTnfqeneGOYug3arpzxPCYe0l2CFYi2LMnCzMLDSt1dXi49iTYkyeLF04q8KLNIK+En64lk3/bkldKVVqVtWlLSWsbQuaAJLAit9ewXg3KIQoMMCIGN+YFVor7gRwKAKJbQiCuiwAMiYHFgV2iur8coylipSQtMDYWZgFUkrAO3mxgK3hABkTAss2qsWWjcCW36KrPX7Z60LusvEcKDD6kSlohvF021OYNVsr66WHWWaEmXQYlZE0GENaS++mmVaBGXgZkJg1WyvHnIf4em8oqS1gPUsdxlMMSUoJuw4WjywHrLujcuOo5dkLYMpxvcjHQ0s0qoCn8xKkFb4ajCzWMPCdw5RQlqhxVBg0V4BuGQwNOiw0GRpB0R7hUb9gUV7Vc2iWCGtqhmJjjwHqb4/nSn4aGb1INxux9wF+OlpRRlY6kHofEoYqr063wstzaU64X+E53hsOUTV2weqV0KoEeh7XKgdWFfOgwt0qeZqHIRQmeWfVr8/Vr0MLMwg+AWWXFr9/nyI6zTXpUFYMQJXY2vFitX2QdhOdC50ZBZPCYVFOMLzdjsef0Y+0y3CIMDN5cASba/6/pF8lg7CczAtDakHrqkpz4WOMKHDUhWk5hQxdLokA6u74KhUJFNtLlwLrCD3gxghWqnv0nyRyq5GimSHBaCmC4FFewVgukvBQocFQAaBVU6QlwbHpfkiaNcaWKHuB7srlRJHMjnmQnu80GGpClVwWhg6XU2BFaq9uuuoOcrUFg/Ccdw+/ln3N3JNLctcaAwZOixhEY7wbAmmpeEVYRDgRjiwLlVe1jLdeIRnXwCtiC3OMa0zF75vLxPwfvCF0KZli7CBn7GBn5npz4WvG85kCKy7xNvCtnMYBLZIjk93EAoFFhysWITiEAo8fA2sL2tYpBUeFj3v8zlWGhK+Bo7wojs8LY0VMguNCCwAMs4Ci/tB3Dl0QDRZuDuPnTwHqW738YmyytOZE25Rchy3BAvwWcsgiLOnhHRYjXK/AeTZ+0gHVu4y8HTyrPDHW0LSqlHLRr26m/k636np3hjmLgNnJ+HDovuQK8ftUqxpUQZuCKx+HOEJowx8EVidKDt0o3i6fQ4sFrAW0arULStKWstYWhdUyE8RRIcFQAaBBUDGh8DifvArbgQwiBL66mMQ0WEBkEFg9eCVZQyihPoQWABkvAYWC1gAgniPIzosV1o3Alt+iqz1+2etC5oAgdWJSkU3iqcbgdWPIzxhlIEvAmsIR3jCKANHfwUWK+4dWkpQt0ydV5S0FrCe5S6DjV5C6a8dRwmsEVn3xmXH0UuylsFGzxuQElj4ziezEqQVVngOLNaw8J1DlJBWaEFgAZDxJ7C4H8SJpR0Q7RVOPEcTHRZaLYoV0grt8hyk+v50puCjmdWDcLsdcxfgp6cVZWCpB+HPU0LdW8LzvdDSXKoT/kd4jseWQ1S9faB6JeiOwONBoXZgXTkPTvVSfdU4CKEyyz+tfn+sehmY5iC8BlbutPr9eb3r9NWlQVgxAldja8WK1fZB2K7CXLhnFovuwiIc4Xm7HY8/I5/pFmEQ4EY1sDrKjkq1xYPwHExLQ+qBa2rF5oJqYEG35rZj6HRJBlZ3wVGpSKbaXJAMLIwQrdR3ab4I2hFYAGQQWABk/GOaL2EBKOUeU3RY5Si+NPhRmi+CdpKB1V2plDiSqTYXJAMLJltwETB0ulQDq6PmKFNbPAjHcfv4Z93fyDW1YnNBNbBgMY7wbAmmpeEVYRDgRjiwLlVe1jLdeIRnXwCtiC3OMa0zF27Hcai/1pB107J2bOBnbOBnZtnnwnGkCKy7xNvCtnMYBLZIji/rIKQKLDhYsQjFIRRodBx2M6Nc0GTd8z4yC42EF93haenbCUv/5ciEwAIgg8DCdw4dEE0WWrCGNc3HJ8oJns54RkmCxaysZRAEgTVB7jeACKxGucsgCG4JR7Vs1Ku7ma/znZrujWHuMoiDwBpy5bhdijUtysANgdWPIzxhlIEvAqsTZYduFE83AsuVVqVuWVHSWsbSuqAJEFgAZBBYAGQQWD24EcAgSqgPgQVABoHVg1eWMYgS6kNgAZBBYAGQQWC50roR2PJTZK3fP2td0AQIrE5UKrpRPN0IrH4c4QmjDHwRWEM4whNGGTgisEa1lKBumTqvKGktYD3LXQZxsOPoNFn3xmXH0UuylkEQBBa+88msBGmF1bglxHcOUUJaoQWBBUAGgYUmSzsg2is0+uegVNBmUayQVmh0HPbv7v+Gad6fzhR8NLN6EG63Y+4C/PS0ogws9SDcjuO4iW8ldr4XWppLdcL/CM/x2HKIqrcPVK8E9RE4DvHAunIenPalOtE4CKEyyz+tfn+sehmY8iAch/Ki+6VNZrPuSLvxCM/b7ejInb5/6hznmNaZC8KBhQhHeN4D6DyGWj7TLcIgwI1qYHWUHZVqiwfhOZiWhtQD19SKzQXVwIJuzW3H0OmSDKzugqNSkUy1uSAZWBghWqnv0nwRtPvHzHjZHUBw95iiwwIgg8ACIIPAKkf3RecXab4I2kkGVnelUuJIptpckAwsmGzBRcDQ6VINrI6ao0wt3SAk+zp9Ss0F1cCCcYSnmTEIxfwKLMVXsS5VXtYy5QhPYxAKzIVHQN2O3/8z665YipfnKvauMwbBzPLOhVSBdZd4W9h2DAIjYBkHIWFgAcjqEVgsugOQ8SewFNfdAaT3HE10WABkEFgAZOQ5SHW7j0+U1Z/OXJXv+dRVlMFSf54SGg8Ke/EGkOV9A6gdZbDI8xrWX4FlZNZ1HOFZ4QjPryiDRV4eBrKGNYQjPOsc4XmCMnBDYPXjCE8YZeCLwOpE2VmxIzwnYhC6EViuqNRkuKDOXgOL993RqNoRnvD3Hkd0WABkEFg96BEwiBLqQ2ABkPEhsFjG+orX/zCIEvrqYxDRYQGQQWChU7UjPBHB58DirnAR5moyXNBFfoogOqxOVKoVO8JzIgahG4HVjyM8YZSBLwJrCEd4pj/CswVl4OZ1P6y//j9ebWvDzm3GBn6UwTwna+gE1jTsjWtskUwZzEBgAZBxElhna1i83ADA2XnssOgOQAaBBUDGl8DirhCAm6+Bk+cgVZ5PGYPACJhZ6kE4e0r46xPhnxXyBhBvABmDYGb6c+Frh/U9sCxwZnGEp3GEp5kxCCnmQssClPCiO0d4Gkd4mhmDUGkuCAcWOMLTGIRimgIr4LNCjvDsk2wQkn2dPjnmQmPI0GGpClhzKhg6Xa2BFarJ4ghP4C7HXGiPFzqsckJV6og0XwTtCCwAMi4EVqi7QgA5XAoWOiwAMq4FFk1WAmFfdL4qzRep7GqkSHZYHOEJ3FWbC5KBBZMtuAgYOl2XAyvIXSFHePZJNgjJvk4f3bnQESZ0WMI4wtMYhGJ6AkuxycpaphzhaQyC5lzoi5Gm/bA+/GOR3jFW37RsHHvXGYNgZlJzwTWwLFhmWeptYdsxCIyAKQxC911ansACoKI7sPoX3YOsZAHQMhIdPCUEIGMosGiyAFwyGBp0WABkjB6kehysvv/y8YlytKczq8V/PrUaZXBi/J6s/ynhn39F+cDiDSCTegNoEcrgqxCBZbUziyM8ExzhOY4y+GrKkjdrWEM4wrPOEZ4nKAM3cwKr5uNCjvCEUQZtZkUEHVanmmX3IscRnv4YhG7TAqtmk3UVlZoMF7TFxHCgw0KnHEd4QsvMwKLJAvBibixM7rCKZBY9AgYVKaHpgcAtIQAZ8wOrQpNV+fU/TFGhhFZEAR0WABlLAqtCk4VqR3jikkUhQIflirmaDBfU2arASt9kUammfITnXukHYd30X9hhkVmDn4cEyuDF0onPLeEQjvBUPMJzOsrAzZz9sM7+ggLvx7Fzm7GBH2VgZuvvq5YHltXILGNvXDNji+TaZeCwCkRgAZjDIbA81rDSr74D8JnmTovuZBaQmNsE5ykhABl+gUWTBaTkObVHD1K9hFNXV+MhHSPgzLkR8XhK+NffR2AtwBtAxiBs4hxY3mtY3BhO17J3Zfr9LRmELfyn84ZFdzJrIo7wNAZhky0Tec9TQjJrCo7wNAZhk11TmNcaakk2XZN9HXy1LbBosgYxV7sxdIM2Tt6dHRaZBcjZO2033xKSWf7S9BdpvoiQ7ROWNSwAMvYH1vbMBtAiwlTdH1gWYyAAnAgySUMEloUZjgrS/EIlzReJL870jBJYFmlQADyEmpiBAsuCDU1w9BfdGLp20aZkrMDCasnmarKvg6/CBVa0RI+MIzyNQVgp4GQMF1gWcpjC4ghPYxDWiDkNIwaWRR2smFomYfqJyiDMFXYCeu84egnbk17FBsGMwLjAkRA7sIzMAnzFzoOot4QPwYcPyCT+dIseWKYwiEACEhNNILBMZCgBXSpTTCOwTGdAATlCk8v1INVBnMMaHw/p5AillcV/SviOzIrpfP9PYismtdkvGFhGZgVz5VhAvWJLTHDq66xhPVMc6KwubazOLuxxiE4iycAy2eEGItCdPqqBZcqDnkZHx0STtZ30xBEOLBMfesCf+pTRDizTvwC6unslmqxdEkwW+cCyFJcBWC3HNMkQWJblYgCLpJkgSQLLEl0SYK5MUyNPYFmuCwNMkWxSKP2WsMX98vAqPJAsqu5SdVgPKS9VNN2/s+EHOg6yToGcgWV5LxjwVeLiTxtYlvqyBdHRK9FerZa77DMHlmW/eMCL9AUvub1MB5bh12F7mQhqzOMygWVk1mJs4LdRmUlcKbCMzFqPLZL9VZrBxQLrjthCDvXmbvZF948KXmbkU7OMKwaWVb3YSKNsAWf7aU47fsQDRWWj6q5oh/VQ/PJDC+Vat8N6oNVCfETVXfUO64GCQFgU5wMd1h+0WoiGqHpBh/WKEkEQlOI7OqwPaLWwF1H1EzqsH1E02ILCO0GHdYZWC56Iqq8IrO+ILaxGVDUisFoRW1iBqLqENaxrKC9MRDldRYd1Ga0WxhFVfQisTsQW+hBVIwisIcQW2hFV4wisCYgtnCOqZiGwpiG28I6omovAmozYwh1RtQKBtQSxVRlRtQ6BtRCxVQ1RtRqBtRyxVQFR5YPAcvIoaJIrE3LKGYHljYYrB6JqCwJrDxouUeTUXgTWZjRcKoiqCAisEGi4wiKnQiGwYiG5giCnYiKwgiK5tiCngiOwoiO5HJBTKggsGc+TivAaR0gpIrAk0XZ1I6ekEVjaaLtaEFJpEFh5vEzLyvlFQmVFYKVVKr9IqCIIrCqS5RcJVROBVdRPEz5akBFMeEZg4S/nAbEizogktPsfghVILVnPjJgAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -291,7 +291,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -362,7 +362,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAGQCAIAAAAP3aGbAAAU4klEQVR4nO3dTZLcxhGG4RKDZxDDd5B39Am89GmsCK4VXDNCPo2XPoG0s+7goC9BL0D3YHoa6ERVZVb+vM9KYsw0MqarvkpUo4Efvn371gAggnerCwAAKQILQBgEFoAwCCwAYRBYAMIgsACEQWABCIPAAhAGgQUgDAILQBgEFoAwCCwAYRBYAMIgsACEQWABCIPAAhAGgQUgDAILQBgEFoAwCCwAYRBYAMIgsACEQWABCIPAAhAGgQUgDAILQBjvVxcQyZcPv68uAQl9+vpxdQlh0GFJkVZQwtCSI7BEGFJQxQATIrAAhEFgPcfqBwMMMwkCC0AYBNYTrHsww2B7isACEAaBdYYVD8YYcucIrEMMHSzBwDtBYAEIg8B6jFUOCzH8jhBYAMIgsB5gfcNyDMKHCCwAYfzw7du31TX4or2yffr6l92xflM9lhxVyVlWxZ1n7hBYr6im1X6gvz7oyqlIVXJLqiKz9gisV5QC62igvz609VSkKrmFVRFYe+xhvViYVvIfm4Wq5NZWxe77Hh3Wi+kjo28E62+LUJWUk6posm4IrO/mptX4YqsxFalKzltVZNaGwGptalrNPS+YNeipSs5nVY3Maq2xhzXX9F2MKS9IVcYvovqCxdFhzWmvtMdl30JNVXI+q7pDk0VgjQaW5RIqH/RUFb2qhwis6oE1klaruv3zQU9VexGrOlc8s9jD6rRwb+Lk0FQlP7TPqnCudIfV1175GW37hZqqzvmvSq5yk1U3sDrSys9ARyYdsVU2szglBBBG0cDi+1kIrewALhpYACKqGFhlVydkUnMYv19dQC2fP9//yy+/rKjjNaqS81lVHeU+JVx1pejbgb63atBTlZxeVVxHKlcrsJZ8C+d8oO9ZTkWqktOuiu/ryFXcw7IkH+tXf3gEVSkdyKyqsgoFVs1NSqRXamAXCix7HeutwRJNVaqHoMlSVSWwSq1CqKbO8C4RWEvezu6VVnWJpiqDF1/SZBXJrBKBBSCH/IFVZOUBKgz1/IEFII3kgVVhzQFu0g/45IEFIJPMgbV2ten+7ojql06oyuDF134dOneTlTawcr9twInEgz9tYHnQsdIaLM5UpXoI7jajKmdgJV5hAImsUyBnYPlxab01W5ypSulAtFfaEt4PS29t4QZ+NqpVNXg/rBP5bpVFYF0w/lxCnzfYpSo5jaoILLlsgaV66s6DVKFBL7BausxKtYeVdaMR6JZsUqQKLAC55QmsZCsJMEumqZEnsACkl2TTXXsNYbsd2lS33luW3Xc6rOdIKxhgmElk6LA8X3gFXMVlWSfCB5ZSWhFVWEsptqJn1vvVBbhDVMGDbRxqb2yFE3sPa3p7RVrBlekDMvolDnRY3xFV8IlWay/wHtastYKoQhSzYivuTlbUwJqSVkQVIpoSW0EzK/Ye1gjSCkFVHrohO6zB9qry+41MBlutiE1WrU13ogqZFNyPj3dK2N1ekVZIqXtgR7zEoUSHRVQhtzqtVrAOq2NNIK1QRMdQD9dkRQqscH9cwL9Y0ypSYAEoLkxgxVoHgEACTa4wgbXc589PHqW5BFXJUVUCMS4cHVkBeFyzDaqS8/kQ6RDXkdJhHXq67i1ZGKlKjqryCRBYS06whYPGeGxRlRxVXRViJ8t7YHlOq44fHkFVSgcqXtWe/8zyHlj2OgaKwdiiKtVDlK0qHNeB5T/vgWScTzrXgWWve01TXQypyuDFC1YVkd/Acp70QFaep57fwAKAO04Dy3PGA+m5nYAeA8vtHwuow+c09BhYAPCQu8DymetAQQ4no7vAWqv7e6eqX6OlKoMXL1hVRL4Cy2GiA5V5m5KOAsvJn6ZjTTNYBqlK9RBlq5JwMjE3jgLLj0sDxWxUUZXSgYpXFYuXwHKV4k08XIxHFVXJUdVEfqanlzuO6v1FBh/zdfRlrrVDiqrkSlWl92hCJ/cjdRFYqvnNcwlRh+qzVD1klpdTQgB4an1g+Tk9BnDCw1RdHFge/gQAhJZP2PUdFgAIrQwsg7Rmxx2lGAz4tU3W+4XHVkVUoaZt5Kt+XLjQsssa3F54BaSR77KsbHtYpBVwk286rOmwNNqrfO8NMItGq7WkyVoQWNPTiqgCJKbHln1mxd50J6oAuQT78dZ7WBPbK9IK6DBx4thf4hCywyKqgBFxWy3TPazxPCaqgLnGY8tyJyvSZQ2kFTBdrGll12GNtFex/qZARCOtllmTZRRY3WlFVAGWumPLJrP8broTVYA95/vxFntYHe0VaQUs1DEBbS5xiLTpDqA49cBafotCADYMJrtuYJFWQCnaU55TQgBhKH5KuLC9KvXszEFUJUdVEl8+/K53iYPidVhLrhQ9evP27N9IqpKjKjnVqnxeR6p1SrikvZK8f/Ifm4Wq5KhKzmdVG73pn2cP69IbY/YuUpXSgahK6YedUwks+/aq4y0xeBepSvUQVKX6K4OUQmB+YHEpA4CmEwV5TgkBpDc5sDzvtU/8RdUXpyqDX1R98YJVHZkeCHRYAMKYGVjsXgG4MzcW6LAAhDEtsGivADw0MRzmBBZpBeDErIjIcErY/W0p1S9/UZXBi1OVwS+6MiGwaK8APDUlKDJ0WK1r9TBYcKhK9RBUpforPo0Glp/26tJbYvb+UZXSgahK6YdVjcfFUGD5SauN8I0xfv+oSo6q5HxW9dRgaCQ5Jbx5+vYsef+oSo6q5HxWpar/jqOq7dWU5xLuvzzl552jKjmqkpteleqzVLtvSZo5sAB08xlYnaeE3navAATSHSDZ9rAAJNYTWLRXAAb1xcjlwDJIKzawovv5jx9//uPH1VXcoyq5n//48T//+pv2UTrCxN0pIWkV3W36uZqHVCV3K8Ygs6669ikhnwzixNGs+/Wn/xpXskdVckdV/emv/9Q76KVPDF0EFlEVnaRBsJ+KVCUnqUoptrQCSyOtiKrorp7L2ExFqpK7WpVGbMkza+UeFmkVXcfOi8FmDVWpHmLtxpa0w5rbXhFV0Y3PJY32garkxqua22oJmyxRYE1MK6IqurnL/qypSFVyc6uaGFuSzDI9JSStopt+kjLlBanK+EX2jM8Qn3dYU9oroio6yUD/x5+/Pvz3v//7w9Pf7WsfqEpOu6oprdbTJks9sIiq6M4H+tEQP3I+9OVTkap8VjUYW6OBNZJWRFV0cwf63shUpKo9n1WNxNZ5ZmntYZFW0emN9ae/fnJoqpIfemFVehtbZx1WX3tFVEWnOtDvyNsHqopYVV+rddJkHQZWR1oRVdE93ZedO9Y3kg3dc1Qlt6Sqjtg6yix3d2uAWxpjffxlqcrs141f9qHHgcUt+nBHdVB2vzhVGfzikhc/iiA6LABhPAgs2ivcMej5Ow5BVaq/svwQD4PoftN97YVX+2erbTw8961OVQ833S13KOSbylQVqKqJl2W9736hud5Ov/2/rwoIqgJcedVhLfkWztH0e8tyKtas6m2HZbk4bySNA1VtolQ18fs6izfd5TPw6g+PoCrAp5fAYq8dgEP7aFrZYXV0AQaNA1UBbn0PLNorvGW//SE5KFXJD+qzqg63gHrXFqVV9/qv2jhQFeDTFlNc6Q4gjHecDAII4cuH3+mwAITx7tJzogFglU9fP9JhAQjjXbvyYPuJur/RovpVGKoCfNpi6t3+f4C98RvyahyUquQH9VlVh1tArTwl7Fj/DVoGqgLcegksmiwADjm6W8OlLsCsZaAqwCcvdxw9/xKJt1vlbfJVxR1HH6IquSp3HN2mmbebEVMV4MqDB6l2N1k8SDW6owep2izRVz9doiq5tVV1d1hvN9Yf7GGx+447Bp+OdxyCqlR/ZfkhHgYRV7oDCONxYNFk4Y7qEt394lRl8ItLXvwoguiwIKU04gdflqrMft34ZR96sOl+07f7ztZ7dEdb75u527fnY/3Xn/57+2+qilhV33b7yRneWWA1PjEsTHvQy6ffHlW95bOqiZ8M7mmdEn758JvSK8PG0TTYqJ6bnByaquSHXljV4JNTTzzpsNqix0HDj7kLdff0u0NVPqua+JDnh9QD6/91EFuxnQ/6zdHQlyzm8um3R1Vy2lVN6aomBFab9xwwYis6yaC/qm/67VGVnEZVs04AJVdTmV7WwMZWdOMTRuMFqcr4Rfb0tqseEnVYbfbDVmm1ohtfqKfPnEZVV4xXNTeqhBerSwOrKTwgmtiKrm/Qa0y/PaqS66tqelcl/2rNyivdOUOMrmM6ac/AvkNQlZzxOeCdCx1WU2iyNrRa0UkWaoPpd4eq5CRVKUXVpW8uuwisDbEV3dGgt59+e1Qld1SValelGFhNObMasRXfftCvnX57VCW3r0r7BPDqjWHc3a2Bja3obhPPzwxsVHXFrZi121UPXe6wmn6T1eizgNUMWoeO++71dFjc3g/AoL4YcXdKCABHOgOLJgtAt+4A8fJcQg37J/f5eWYfVclRlZzPqqbr2XS/0dt95yHSNqhKrlpVepvuI+dn2fawzt8/yQ9ooCo5qpLzWZWqocDytpMlfHuM30WqkqMqOZ9VPTUYGqMdlp/MuvTGmL2LVKV0IKpS+mFV43GR5JSw4y0xeBepSvUQVKX6Kz5NCCw/TRYAt6YERYYOq3v1UF12qMrgxanK4BddmRNYNFkATsyKiGkdFpkF4KGJ4ZDhlBBAETMDiyYLwJ25sUCHBSCMyYG1pMnq/sKU6ve/qMrgxanK4BdHTA8EOiwAYcwPLHayADSdKFDpsOwzq6PdNeiQqUr1EFSl+iuDlEIgzynhpbfE7P2jKqUDUZXSDzunFVied9+N3z+qkqMqOZ9VbfSm/9AdR8+N3I908DFfR1+bWrvUUJUcVckpVTVyx9GQgdUGMovnEgJrdQeW6tlVnj0sAOnpBhaXOAClaE959Q6LzAKKMJjsnBICCMMisDpyV++ZaACe6piANudSfp/8vP3J+LgQsOS8V9C9rGFv4WVZAJ7yeeHVHbvAasOPtie2AA2DXZXlB2uRNt2dN6tARLGmlWmH1YabrA2tFjBuSlQZX7fkd9P9BPvxwIhYXdWe9SnhxDyO+0cHFpo4cewvCw/ZYd3QagFyCdZ46z2szZSdrDvEFnBEI6qWfOtuTWA1ncxqxBbwmlJXteo7wpEua5BI0PQCs+SbDss6rKbWZG1otVCZalQtvAVL7E33E+zHo6Z8XdXeylNCg5zO/eYBdwwG/No73GXbwwKQ2OLA4n6kQCDLJ+z6Dmv5nwCAhIepuj6wAEDIxaeEn75+VL3EYUSpZ2cOoio5n1Wd8NBetbXXYe3pBVb3lQ1HQ2rPfnhRlVzBqvQ+JXQSWF5OCZ38OW4ko0r+Y7NQlRxVTeRnenoJLFcuDRezsUVVSgcqXlUsjgLLSYp3DBSDsUVVqocoW5WEk4m5cRRYzdmfBoC3KekrsJbrXtNUF0OqMnjxglVF5C6wvCU6UJbDyegusADgiMfAcpjrQDU+p6HHwGpe/1hAEW4noNPAAoC3/AaW24wHcvM89fwG1hLd3+FS/UoaVRm8eMGqInIdWJ6THkjJ+aRzHVhLdKxpBssgVakeomxV4XgPrCV5f2mgmI0qqlI6UPGq9py3V81/YDXfmWU8qqhKjqqu8p9WLURgrfJ00CwZVVQlR1X5eLnj6FPdtySd9SzV7Wuo3gYTVclVqKr7jqMh2qtGYAGZpA+sMKeEUf6gQDiBJleYwAKASIEVaB0Aoog1rSIFVuv64+o9+AhwpWOox0qr5uRBqtq2N5Ldd2RVZ1UO1mG1gTWhzpuKUtJ/MrhXosO6odVCJgXX4DDXYd0Zf7Q9sYW4xqMqYnvVIp4SzlJwdUIOlYdu1A6rzWiyNrRaiGJWVAVtr1rowGrzMqsRW/BtYlcVN61atU33E+zHw6fKJ4Bvxd7Dmr5WMDjgyvQBGbq9anRYb9FqwQPWzodi72FtJu5k3SG2YE8vqqK3Vy1HYDXNzGrEFqyodlUJ0qpF38OyQXMOAwwziSQdVlNusja0WtBgEFU52qtGhwUgkDyBlWYNAebKNDXyBBaA9FIFVqaVBJgi2aTIduHop68fDXbfu20Podvz8Jg8qpLzWdWRZGnVMn1KeOPzOtK3A31v1aCnKjm9qrhSVC5hYDW1zOoLrPOBvmc5FalKTrsqpcDKl1Yt2R6WQ/KxfvWHR1CV0oHMqiorZ2ClXFsAuaxTIGdgOdGx3hos0VSlegiaLFVpAyvrCgM8lXjwpw2stvpt615pVZdoqjJ48bVNVuK0arkDC0AyyQMr92oD3Ek/4JMHFoBM8gdW+jUH2FQY6vkDC0AaJQJrycrT/d0R1S+dUJXBiy/5tmOF9qoVCaxW5u1ETXWGd5XAWqJjpTVYnKlK9RCe7zaTQKHAqrMKoZRSA7tQYC1xab01W5ypSulAtFfact4P68TIrbK4gZ+NalWN3A+rVHvVCgZWG8is8ecS+rzBLlXJaVTVHVjV0qoRWJfwIFVoILDkKu5hFXybkU/NYVwxsAAEVTSwaq5OSKPsAC4aWAAiqrjpftO3++5n632/WUtV5/xXJVe2vWrFA6uF/cTwaKBT1Vuxqnqqclo1Tgm76T2td+TQVCU/tM+qcK56h9WGHxNtuVDLBzpVRa/qoeLtVSOw2qTn2msP+t7NDqqS8lnVHQKLwGptUmY1nUE/PtCpSs5nVRvSqrGHNdf0vYkpL0hVxi+i+oLF0WF9N6vJ2owv1BoDnarkvFVFe7UhsF7MzazWO+i112SqknNSFWl1Q2C9mB5YG/mgtzx9oCq55VURWDfsYb1QGhbCQWy82UFVcmurIq326LBeUWqyNkcL9dp9WaqSW1IVgbVHYN1Tzaz2etD7+QiJquQsqyKt7hBYD2hnFiBBWr3FHhaAMAisB1jZsByD8CECC0AYBNZjrG9YiOF3hMACEAaBdYhVDksw8E4QWGcYOjDGkDtHYAEIg8B6ghUPZhhsTxFYAMIgsJ5j3YMBhpkEgQUgDAJLhNUPqhhgQgSWFEMKShhactxeBkAYdFgAwiCwAIRBYAEIg8ACEAaBBSAMAgtAGAQWgDAILABhEFgAwiCwAIRBYAEIg8ACEAaBBSAMAgtAGAQWgDAILABhEFgAwiCwAIRBYAEIg8ACEAaBBSAMAgtAGAQWgDAILABhEFgAwiCwAITxP9SzpAKNTdw1AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -389,7 +389,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -403,9 +403,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/mdgxs-part-i.ipynb b/mdgxs-part-i.ipynb
index c37547a..b311465 100644
--- a/mdgxs-part-i.ipynb
+++ b/mdgxs-part-i.ipynb
@@ -169,8 +169,7 @@
    "outputs": [],
    "source": [
     "# Instantiate a Materials collection and export to XML\n",
-    "materials_file = openmc.Materials([inf_medium])\n",
-    "materials_file.export_to_xml()"
+    "materials = openmc.Materials([inf_medium])"
    ]
   },
   {
@@ -230,10 +229,7 @@
    "outputs": [],
    "source": [
     "# Create Geometry and set root Universe\n",
-    "openmc_geometry = openmc.Geometry([cell])\n",
-    "\n",
-    "# Export to \"geometry.xml\"\n",
-    "openmc_geometry.export_to_xml()"
+    "geometry = openmc.Geometry([cell])"
    ]
   },
   {
@@ -255,19 +251,16 @@
     "particles = 5000\n",
     "\n",
     "# Instantiate a Settings object\n",
-    "settings_file = openmc.Settings()\n",
-    "settings_file.batches = batches\n",
-    "settings_file.inactive = inactive\n",
-    "settings_file.particles = particles\n",
-    "settings_file.output = {'tallies': True}\n",
+    "settings = openmc.Settings()\n",
+    "settings.batches = batches\n",
+    "settings.inactive = inactive\n",
+    "settings.particles = particles\n",
+    "settings.output = {'tallies': True}\n",
     "\n",
     "# Create an initial uniform spatial source distribution over fissionable zones\n",
     "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
     "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings_file.source = openmc.Source(space=uniform_dist)\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
+    "settings.source = openmc.Source(space=uniform_dist)"
    ]
   },
   {
@@ -333,8 +326,8 @@
    "outputs": [],
    "source": [
     "# Instantiate a few different sections\n",
-    "chi_prompt = mgxs.Chi(domain=cell, groups=energy_groups, by_nuclide=True, prompt=True)\n",
-    "prompt_nu_fission = mgxs.FissionXS(domain=cell, groups=energy_groups, by_nuclide=True, nu=True, prompt=True)\n",
+    "chi_prompt = mgxs.Chi(domain=cell, energy_groups=energy_groups, by_nuclide=True, prompt=True)\n",
+    "prompt_nu_fission = mgxs.FissionXS(domain=cell, energy_groups=energy_groups, by_nuclide=True, nu=True, prompt=True)\n",
     "chi_delayed = mgxs.ChiDelayed(domain=cell, energy_groups=energy_groups, by_nuclide=True)\n",
     "delayed_nu_fission = mgxs.DelayedNuFissionXS(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n",
     "beta = mgxs.Beta(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n",
@@ -406,43 +399,43 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=3.\n",
       "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=5.\n",
       "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=4.\n",
       "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=7.\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=7.\n",
       "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=13.\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=13.\n",
       "  warn(msg, IDWarning)\n"
      ]
     }
    ],
    "source": [
     "# Instantiate an empty Tallies object\n",
-    "tallies_file = openmc.Tallies()\n",
+    "tallies = openmc.Tallies()\n",
     "\n",
     "# Add chi-prompt tallies to the tallies file\n",
-    "tallies_file += chi_prompt.tallies.values()\n",
+    "tallies += chi_prompt.tallies.values()\n",
     "\n",
     "# Add prompt-nu-fission tallies to the tallies file\n",
-    "tallies_file += prompt_nu_fission.tallies.values()\n",
+    "tallies += prompt_nu_fission.tallies.values()\n",
     "\n",
     "# Add chi-delayed tallies to the tallies file\n",
-    "tallies_file += chi_delayed.tallies.values()\n",
+    "tallies += chi_delayed.tallies.values()\n",
     "\n",
     "# Add delayed-nu-fission tallies to the tallies file\n",
-    "tallies_file += delayed_nu_fission.tallies.values()\n",
+    "tallies += delayed_nu_fission.tallies.values()\n",
     "\n",
     "# Add beta tallies to the tallies file\n",
-    "tallies_file += beta.tallies.values()\n",
+    "tallies += beta.tallies.values()\n",
     "\n",
     "# Add decay rate tallies to the tallies file\n",
-    "tallies_file += decay_rate.tallies.values()\n",
+    "tallies += decay_rate.tallies.values()\n",
     "\n",
     "# Export to \"tallies.xml\"\n",
-    "tallies_file.export_to_xml()"
+    "tallies.export_to_xml()"
    ]
   },
   {
@@ -456,6 +449,19 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tie geometry, materials, settings, and tallies together into a model object\n",
+    "model = openmc.Model(geometry=geometry,\n",
+    "                     materials=materials,\n",
+    "                     settings=settings,\n",
+    "                     tallies=tallies)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -485,112 +491,113 @@
       "                          ########     %%%%%%%%%%%%%%\n",
       "                                      %%%%%%%%%%%\n",
       "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-28 08:47:21\n",
-      "    OpenMP Threads | 2\n",
+      "                 | The OpenMC Monte Carlo Code\n",
+      "       Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n",
+      "         License | https://docs.openmc.org/en/latest/license.html\n",
+      "         Version | 0.13.1\n",
+      "        Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n",
+      "       Date/Time | 2022-10-03 23:19:19\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 H1 from /opt/data/xs/nndc_hdf5/H1.h5\n",
-      " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n",
-      " Reading U235 from /opt/data/xs/nndc_hdf5/U235.h5\n",
-      " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n",
-      " Reading Pu239 from /opt/data/xs/nndc_hdf5/Pu239.h5\n",
-      " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
+      " Reading H1 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/H1.h5\n",
+      " Reading O16 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/O16.h5\n",
+      " Reading U235 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U235.h5\n",
+      " Reading U238 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U238.h5\n",
+      " Reading Pu239 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Pu239.h5\n",
+      " Reading Zr90 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr90.h5\n",
+      " Minimum neutron data temperature: 294 K\n",
+      " Maximum neutron data temperature: 294 K\n",
       " Reading tallies XML file...\n",
       " Preparing distributed cell instances...\n",
+      " Reading plot XML file...\n",
       " Writing summary.h5 file...\n",
-      " Maximum neutron transport energy: 20000000.0 eV for H1\n",
+      " Maximum neutron transport energy: 20000000 eV for H1\n",
       " Initializing source particles...\n",
       "\n",
       " ====================>     K EIGENVALUE SIMULATION     <====================\n",
       "\n",
       "  Bat./Gen.      k            Average k\n",
       "  =========   ========   ====================\n",
-      "        1/1    1.26527\n",
-      "        2/1    1.23398\n",
-      "        3/1    1.25383\n",
-      "        4/1    1.24224\n",
-      "        5/1    1.22254\n",
-      "        6/1    1.20745\n",
-      "        7/1    1.22674\n",
-      "        8/1    1.22724\n",
-      "        9/1    1.23185\n",
-      "       10/1    1.22019\n",
-      "       11/1    1.24342\n",
-      "       12/1    1.23258    1.23800 +/- 0.00542\n",
-      "       13/1    1.20626    1.22742 +/- 0.01103\n",
-      "       14/1    1.19410    1.21909 +/- 0.01141\n",
-      "       15/1    1.24556    1.22439 +/- 0.01030\n",
-      "       16/1    1.27632    1.23304 +/- 0.01207\n",
-      "       17/1    1.25083    1.23558 +/- 0.01051\n",
-      "       18/1    1.23155    1.23508 +/- 0.00912\n",
-      "       19/1    1.27501    1.23952 +/- 0.00918\n",
-      "       20/1    1.24863    1.24043 +/- 0.00827\n",
-      "       21/1    1.18837    1.23569 +/- 0.00885\n",
-      "       22/1    1.21978    1.23437 +/- 0.00819\n",
-      "       23/1    1.22815    1.23389 +/- 0.00754\n",
-      "       24/1    1.24244    1.23450 +/- 0.00701\n",
-      "       25/1    1.21128    1.23295 +/- 0.00671\n",
-      "       26/1    1.22836    1.23267 +/- 0.00628\n",
-      "       27/1    1.21573    1.23167 +/- 0.00598\n",
-      "       28/1    1.19115    1.22942 +/- 0.00607\n",
-      "       29/1    1.24854    1.23042 +/- 0.00583\n",
-      "       30/1    1.24486    1.23115 +/- 0.00558\n",
-      "       31/1    1.23967    1.23155 +/- 0.00532\n",
-      "       32/1    1.24406    1.23212 +/- 0.00511\n",
-      "       33/1    1.24808    1.23282 +/- 0.00493\n",
-      "       34/1    1.23056    1.23272 +/- 0.00472\n",
-      "       35/1    1.24209    1.23310 +/- 0.00454\n",
-      "       36/1    1.23203    1.23305 +/- 0.00437\n",
-      "       37/1    1.21629    1.23243 +/- 0.00425\n",
-      "       38/1    1.22928    1.23232 +/- 0.00409\n",
-      "       39/1    1.23665    1.23247 +/- 0.00395\n",
-      "       40/1    1.24100    1.23276 +/- 0.00383\n",
-      "       41/1    1.26373    1.23375 +/- 0.00384\n",
-      "       42/1    1.25002    1.23426 +/- 0.00375\n",
-      "       43/1    1.24100    1.23447 +/- 0.00364\n",
-      "       44/1    1.25701    1.23513 +/- 0.00359\n",
-      "       45/1    1.23027    1.23499 +/- 0.00349\n",
-      "       46/1    1.25747    1.23562 +/- 0.00345\n",
-      "       47/1    1.24960    1.23599 +/- 0.00338\n",
-      "       48/1    1.23535    1.23598 +/- 0.00329\n",
-      "       49/1    1.21318    1.23539 +/- 0.00325\n",
-      "       50/1    1.27184    1.23630 +/- 0.00330\n",
+      "        1/1    1.24970\n",
+      "        2/1    1.21100\n",
+      "        3/1    1.19756\n",
+      "        4/1    1.25555\n",
+      "        5/1    1.24295\n",
+      "        6/1    1.21186\n",
+      "        7/1    1.25633\n",
+      "        8/1    1.24522\n",
+      "        9/1    1.21656\n",
+      "       10/1    1.26028\n",
+      "       11/1    1.20898\n",
+      "       12/1    1.23185    1.22041 +/- 0.01143\n",
+      "       13/1    1.25350    1.23144 +/- 0.01285\n",
+      "       14/1    1.22167    1.22900 +/- 0.00941\n",
+      "       15/1    1.24549    1.23230 +/- 0.00800\n",
+      "       16/1    1.24976    1.23521 +/- 0.00715\n",
+      "       17/1    1.18269    1.22771 +/- 0.00963\n",
+      "       18/1    1.23822    1.22902 +/- 0.00845\n",
+      "       19/1    1.23413    1.22959 +/- 0.00747\n",
+      "       20/1    1.21361    1.22799 +/- 0.00687\n",
+      "       21/1    1.24244    1.22930 +/- 0.00635\n",
+      "       22/1    1.21414    1.22804 +/- 0.00593\n",
+      "       23/1    1.21809    1.22727 +/- 0.00551\n",
+      "       24/1    1.19780    1.22517 +/- 0.00552\n",
+      "       25/1    1.24190    1.22628 +/- 0.00526\n",
+      "       26/1    1.24078    1.22719 +/- 0.00500\n",
+      "       27/1    1.21557    1.22651 +/- 0.00475\n",
+      "       28/1    1.26431    1.22861 +/- 0.00494\n",
+      "       29/1    1.27196    1.23089 +/- 0.00520\n",
+      "       30/1    1.24033    1.23136 +/- 0.00496\n",
+      "       31/1    1.24532    1.23203 +/- 0.00476\n",
+      "       32/1    1.22646    1.23177 +/- 0.00455\n",
+      "       33/1    1.23791    1.23204 +/- 0.00436\n",
+      "       34/1    1.21230    1.23122 +/- 0.00425\n",
+      "       35/1    1.22857    1.23111 +/- 0.00408\n",
+      "       36/1    1.22386    1.23083 +/- 0.00393\n",
+      "       37/1    1.25504    1.23173 +/- 0.00388\n",
+      "       38/1    1.24488    1.23220 +/- 0.00377\n",
+      "       39/1    1.24251    1.23255 +/- 0.00366\n",
+      "       40/1    1.19482    1.23130 +/- 0.00375\n",
+      "       41/1    1.20078    1.23031 +/- 0.00376\n",
+      "       42/1    1.24233    1.23069 +/- 0.00366\n",
+      "       43/1    1.29614    1.23267 +/- 0.00406\n",
+      "       44/1    1.23726    1.23281 +/- 0.00394\n",
+      "       45/1    1.24222    1.23307 +/- 0.00384\n",
+      "       46/1    1.24097    1.23329 +/- 0.00374\n",
+      "       47/1    1.27425    1.23440 +/- 0.00380\n",
+      "       48/1    1.25510    1.23495 +/- 0.00374\n",
+      "       49/1    1.23654    1.23499 +/- 0.00364\n",
+      "       50/1    1.23369    1.23495 +/- 0.00355\n",
       " Creating state point statepoint.50.h5...\n",
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     = 2.6337e-01 seconds\n",
-      "   Reading cross sections          = 2.5312e-01 seconds\n",
-      " Total time in simulation          = 2.6057e+01 seconds\n",
-      "   Time in transport only          = 2.6028e+01 seconds\n",
-      "   Time in inactive batches        = 9.8414e-01 seconds\n",
-      "   Time in active batches          = 2.5072e+01 seconds\n",
-      "   Time synchronizing fission bank = 1.1907e-02 seconds\n",
-      "     Sampling source sites         = 9.8847e-03 seconds\n",
-      "     SEND/RECV source sites        = 1.9926e-03 seconds\n",
-      "   Time accumulating tallies       = 1.1168e-03 seconds\n",
-      "   Time writing statepoints        = 7.9423e-03 seconds\n",
-      " Total time for finalization       = 6.1998e-03 seconds\n",
-      " Total time elapsed                = 2.6333e+01 seconds\n",
-      " Calculation Rate (inactive)       = 50805.8 particles/second\n",
-      " Calculation Rate (active)         = 7976.89 particles/second\n",
+      " Total time for initialization     = 8.7385e-01 seconds\n",
+      "   Reading cross sections          = 8.6672e-01 seconds\n",
+      " Total time in simulation          = 1.3402e+02 seconds\n",
+      "   Time in transport only          = 1.3397e+02 seconds\n",
+      "   Time in inactive batches        = 4.7414e+00 seconds\n",
+      "   Time in active batches          = 1.2927e+02 seconds\n",
+      "   Time synchronizing fission bank = 2.1579e-02 seconds\n",
+      "     Sampling source sites         = 1.9879e-02 seconds\n",
+      "     SEND/RECV source sites        = 1.6605e-03 seconds\n",
+      "   Time accumulating tallies       = 1.0377e-02 seconds\n",
+      "   Time writing statepoints        = 4.2323e-03 seconds\n",
+      " Total time for finalization       = 1.3320e-02 seconds\n",
+      " Total time elapsed                = 1.3492e+02 seconds\n",
+      " Calculation Rate (inactive)       = 10545.3 particles/second\n",
+      " Calculation Rate (active)         = 1547.09 particles/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
-      " k-effective (Collision)     = 1.23688 +/- 0.00324\n",
-      " k-effective (Track-length)  = 1.23630 +/- 0.00330\n",
-      " k-effective (Absorption)    = 1.23291 +/- 0.00216\n",
-      " Combined k-effective        = 1.23399 +/- 0.00202\n",
+      " k-effective (Collision)     = 1.23445 +/- 0.00332\n",
+      " k-effective (Track-length)  = 1.23495 +/- 0.00355\n",
+      " k-effective (Absorption)    = 1.23293 +/- 0.00238\n",
+      " Combined k-effective        = 1.23332 +/- 0.00230\n",
       " Leakage Fraction            = 0.00000 +/- 0.00000\n",
       "\n"
      ]
@@ -598,7 +605,7 @@
    ],
    "source": [
     "# Run OpenMC\n",
-    "openmc.run()"
+    "statepoint_filename = model.run()"
    ]
   },
   {
@@ -617,12 +624,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Load the last statepoint file\n",
-    "sp = openmc.StatePoint('statepoint.50.h5')"
+    "sp = openmc.StatePoint(statepoint_filename)"
    ]
   },
   {
@@ -641,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -679,26 +686,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[[5.15913423e-06, 1.16842422e-06]],\n",
+       "array([[[5.14603413e-06, 1.16498857e-06]],\n",
        "\n",
-       "       [[2.66298632e-05, 7.60931827e-06]],\n",
+       "       [[2.65622446e-05, 7.58694377e-06]],\n",
        "\n",
-       "       [[2.54231809e-05, 5.75848069e-06]],\n",
+       "       [[2.53586263e-05, 5.74154841e-06]],\n",
        "\n",
-       "       [[5.70008690e-05, 1.05132542e-05]],\n",
+       "       [[5.68561321e-05, 1.04823410e-05]],\n",
        "\n",
-       "       [[2.33695515e-05, 5.47610066e-06]],\n",
+       "       [[2.33102114e-05, 5.45999869e-06]],\n",
        "\n",
-       "       [[9.78942387e-06, 1.65741521e-06]]])"
+       "       [[9.76456653e-06, 1.65254173e-06]]])"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -716,7 +723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "scrolled": true
    },
@@ -757,8 +764,8 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>9.493755e-08</td>\n",
-       "      <td>9.395351e-08</td>\n",
+       "      <td>9.479196e-08</td>\n",
+       "      <td>5.863577e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>199</th>\n",
@@ -766,8 +773,8 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
-       "      <td>1.602797e-08</td>\n",
-       "      <td>1.586127e-08</td>\n",
+       "      <td>1.600306e-08</td>\n",
+       "      <td>9.891470e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>398</th>\n",
@@ -775,8 +782,8 @@
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>4.900384e-07</td>\n",
-       "      <td>4.849591e-07</td>\n",
+       "      <td>4.892869e-07</td>\n",
+       "      <td>3.026598e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>399</th>\n",
@@ -784,8 +791,8 @@
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
-       "      <td>1.043816e-07</td>\n",
-       "      <td>1.032959e-07</td>\n",
+       "      <td>1.042193e-07</td>\n",
+       "      <td>6.441782e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>598</th>\n",
@@ -793,8 +800,8 @@
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>4.678332e-07</td>\n",
-       "      <td>4.629841e-07</td>\n",
+       "      <td>4.671158e-07</td>\n",
+       "      <td>2.889454e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>599</th>\n",
@@ -802,8 +809,8 @@
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
-       "      <td>7.899251e-08</td>\n",
-       "      <td>7.817094e-08</td>\n",
+       "      <td>7.886975e-08</td>\n",
+       "      <td>4.874928e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>798</th>\n",
@@ -811,8 +818,8 @@
        "      <td>4</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>1.048921e-06</td>\n",
-       "      <td>1.038048e-06</td>\n",
+       "      <td>1.047312e-06</td>\n",
+       "      <td>6.478393e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>799</th>\n",
@@ -820,8 +827,8 @@
        "      <td>4</td>\n",
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
-       "      <td>1.442166e-07</td>\n",
-       "      <td>1.427166e-07</td>\n",
+       "      <td>1.439925e-07</td>\n",
+       "      <td>8.900152e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
@@ -829,8 +836,8 @@
        "      <td>5</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>4.300427e-07</td>\n",
-       "      <td>4.255852e-07</td>\n",
+       "      <td>4.293832e-07</td>\n",
+       "      <td>2.656050e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
@@ -838,8 +845,8 @@
        "      <td>5</td>\n",
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
-       "      <td>7.511894e-08</td>\n",
-       "      <td>7.433765e-08</td>\n",
+       "      <td>7.500220e-08</td>\n",
+       "      <td>4.635875e-08</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -847,19 +854,19 @@
       ],
       "text/plain": [
        "     cell  delayedgroup  group in nuclide          mean     std. dev.\n",
-       "198     1             1         1    U235  9.493755e-08  9.395351e-08\n",
-       "199     1             1         1   Pu239  1.602797e-08  1.586127e-08\n",
-       "398     1             2         1    U235  4.900384e-07  4.849591e-07\n",
-       "399     1             2         1   Pu239  1.043816e-07  1.032959e-07\n",
-       "598     1             3         1    U235  4.678332e-07  4.629841e-07\n",
-       "599     1             3         1   Pu239  7.899251e-08  7.817094e-08\n",
-       "798     1             4         1    U235  1.048921e-06  1.038048e-06\n",
-       "799     1             4         1   Pu239  1.442166e-07  1.427166e-07\n",
-       "998     1             5         1    U235  4.300427e-07  4.255852e-07\n",
-       "999     1             5         1   Pu239  7.511894e-08  7.433765e-08"
+       "198     1             1         1    U235  9.479196e-08  5.863577e-08\n",
+       "199     1             1         1   Pu239  1.600306e-08  9.891470e-09\n",
+       "398     1             2         1    U235  4.892869e-07  3.026598e-07\n",
+       "399     1             2         1   Pu239  1.042193e-07  6.441782e-08\n",
+       "598     1             3         1    U235  4.671158e-07  2.889454e-07\n",
+       "599     1             3         1   Pu239  7.886975e-08  4.874928e-08\n",
+       "798     1             4         1    U235  1.047312e-06  6.478393e-07\n",
+       "799     1             4         1   Pu239  1.439925e-07  8.900152e-08\n",
+       "998     1             5         1    U235  4.293832e-07  2.656050e-07\n",
+       "999     1             5         1   Pu239  7.500220e-08  4.635875e-08"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -871,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -909,7 +916,7 @@
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
        "      <td>0.013336</td>\n",
-       "      <td>0.000056</td>\n",
+       "      <td>0.000061</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -917,7 +924,7 @@
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>0.013271</td>\n",
-       "      <td>0.000050</td>\n",
+       "      <td>0.000059</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -925,7 +932,7 @@
        "      <td>2</td>\n",
        "      <td>U235</td>\n",
        "      <td>0.032739</td>\n",
-       "      <td>0.000137</td>\n",
+       "      <td>0.000150</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -933,7 +940,7 @@
        "      <td>2</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>0.030881</td>\n",
-       "      <td>0.000116</td>\n",
+       "      <td>0.000136</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -941,7 +948,7 @@
        "      <td>3</td>\n",
        "      <td>U235</td>\n",
        "      <td>0.120780</td>\n",
-       "      <td>0.000504</td>\n",
+       "      <td>0.000552</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -949,7 +956,7 @@
        "      <td>3</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>0.113370</td>\n",
-       "      <td>0.000427</td>\n",
+       "      <td>0.000501</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -957,7 +964,7 @@
        "      <td>4</td>\n",
        "      <td>U235</td>\n",
        "      <td>0.302780</td>\n",
-       "      <td>0.001264</td>\n",
+       "      <td>0.001383</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -965,7 +972,7 @@
        "      <td>4</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>0.292500</td>\n",
-       "      <td>0.001101</td>\n",
+       "      <td>0.001292</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -973,7 +980,7 @@
        "      <td>5</td>\n",
        "      <td>U235</td>\n",
        "      <td>0.849490</td>\n",
-       "      <td>0.003545</td>\n",
+       "      <td>0.003880</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -981,7 +988,7 @@
        "      <td>5</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>0.857490</td>\n",
-       "      <td>0.003228</td>\n",
+       "      <td>0.003787</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
@@ -989,7 +996,7 @@
        "      <td>6</td>\n",
        "      <td>U235</td>\n",
        "      <td>2.853000</td>\n",
-       "      <td>0.011907</td>\n",
+       "      <td>0.013029</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
@@ -997,7 +1004,7 @@
        "      <td>6</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>2.729700</td>\n",
-       "      <td>0.010276</td>\n",
+       "      <td>0.012056</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1005,21 +1012,21 @@
       ],
       "text/plain": [
        "    cell  delayedgroup nuclide      mean  std. dev.\n",
-       "0      1             1    U235  0.013336   0.000056\n",
-       "1      1             1   Pu239  0.013271   0.000050\n",
-       "2      1             2    U235  0.032739   0.000137\n",
-       "3      1             2   Pu239  0.030881   0.000116\n",
-       "4      1             3    U235  0.120780   0.000504\n",
-       "5      1             3   Pu239  0.113370   0.000427\n",
-       "6      1             4    U235  0.302780   0.001264\n",
-       "7      1             4   Pu239  0.292500   0.001101\n",
-       "8      1             5    U235  0.849490   0.003545\n",
-       "9      1             5   Pu239  0.857490   0.003228\n",
-       "10     1             6    U235  2.853000   0.011907\n",
-       "11     1             6   Pu239  2.729700   0.010276"
+       "0      1             1    U235  0.013336   0.000061\n",
+       "1      1             1   Pu239  0.013271   0.000059\n",
+       "2      1             2    U235  0.032739   0.000150\n",
+       "3      1             2   Pu239  0.030881   0.000136\n",
+       "4      1             3    U235  0.120780   0.000552\n",
+       "5      1             3   Pu239  0.113370   0.000501\n",
+       "6      1             4    U235  0.302780   0.001383\n",
+       "7      1             4   Pu239  0.292500   0.001292\n",
+       "8      1             5    U235  0.849490   0.003880\n",
+       "9      1             5   Pu239  0.857490   0.003787\n",
+       "10     1             6    U235  2.853000   0.013029\n",
+       "11     1             6   Pu239  2.729700   0.012056"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1038,9 +1045,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mgxs/mdgxs.py:749: FutureWarning: As the xlwt package is no longer maintained, the xlwt engine will be removed in a future version of pandas. This is the only engine in pandas that supports writing in the xls format. Install openpyxl and write to an xlsx file instead. You can set the option io.excel.xls.writer to 'xlwt' to silence this warning. While this option is deprecated and will also raise a warning, it can be globally set and the warning suppressed.\n",
+      "  df.to_excel(filename + '.xls', index=False)\n"
+     ]
+    }
+   ],
    "source": [
     "beta.export_xs_data(filename='beta', format='excel')"
    ]
@@ -1054,7 +1070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1084,22 +1100,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f361f5a1198>"
+       "<matplotlib.legend.Legend at 0x7f0aa9759e20>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x432 with 1 Axes>"
       ]
@@ -1146,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1185,8 +1201,8 @@
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>8.808003e-08</td>\n",
-       "      <td>4.352878e-10</td>\n",
+       "      <td>8.785604e-08</td>\n",
+       "      <td>4.659397e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1194,8 +1210,8 @@
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>7.175417e-09</td>\n",
-       "      <td>3.446159e-11</td>\n",
+       "      <td>7.154286e-09</td>\n",
+       "      <td>3.857161e-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -1203,8 +1219,8 @@
        "      <td>2</td>\n",
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>9.559207e-07</td>\n",
-       "      <td>4.724119e-09</td>\n",
+       "      <td>9.534897e-07</td>\n",
+       "      <td>5.056780e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1212,8 +1228,8 @@
        "      <td>2</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>1.307826e-07</td>\n",
-       "      <td>6.281133e-10</td>\n",
+       "      <td>1.303974e-07</td>\n",
+       "      <td>7.030245e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1221,8 +1237,8 @@
        "      <td>3</td>\n",
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.361643e-07</td>\n",
-       "      <td>1.167114e-09</td>\n",
+       "      <td>2.355637e-07</td>\n",
+       "      <td>1.249299e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -1230,8 +1246,8 @@
        "      <td>3</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.040175e-08</td>\n",
-       "      <td>9.798408e-11</td>\n",
+       "      <td>2.034167e-08</td>\n",
+       "      <td>1.096700e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -1239,8 +1255,8 @@
        "      <td>4</td>\n",
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>4.735711e-07</td>\n",
-       "      <td>2.340368e-09</td>\n",
+       "      <td>4.723668e-07</td>\n",
+       "      <td>2.505171e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -1248,8 +1264,8 @@
        "      <td>4</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.635713e-08</td>\n",
-       "      <td>1.265862e-10</td>\n",
+       "      <td>2.627951e-08</td>\n",
+       "      <td>1.416833e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -1257,8 +1273,8 @@
        "      <td>5</td>\n",
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.837213e-08</td>\n",
-       "      <td>1.402138e-10</td>\n",
+       "      <td>2.829997e-08</td>\n",
+       "      <td>1.500873e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -1266,8 +1282,8 @@
        "      <td>5</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.439290e-09</td>\n",
-       "      <td>1.171525e-11</td>\n",
+       "      <td>2.432107e-09</td>\n",
+       "      <td>1.311246e-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
@@ -1275,8 +1291,8 @@
        "      <td>6</td>\n",
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>1.482388e-09</td>\n",
-       "      <td>7.325898e-12</td>\n",
+       "      <td>1.478618e-09</td>\n",
+       "      <td>7.841770e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
@@ -1284,8 +1300,8 @@
        "      <td>6</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>7.019358e-11</td>\n",
-       "      <td>3.371208e-13</td>\n",
+       "      <td>6.998687e-11</td>\n",
+       "      <td>3.773271e-13</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1307,21 +1323,21 @@
        "11     1             6   Pu239   \n",
        "\n",
        "                                                score     mean  std. dev.  \n",
-       "0   (((delayed-nu-fission / nu-fission) * (delayed... 8.81e-08   4.35e-10  \n",
-       "1   (((delayed-nu-fission / nu-fission) * (delayed... 7.18e-09   3.45e-11  \n",
-       "2   (((delayed-nu-fission / nu-fission) * (delayed... 9.56e-07   4.72e-09  \n",
-       "3   (((delayed-nu-fission / nu-fission) * (delayed... 1.31e-07   6.28e-10  \n",
-       "4   (((delayed-nu-fission / nu-fission) * (delayed... 2.36e-07   1.17e-09  \n",
-       "5   (((delayed-nu-fission / nu-fission) * (delayed... 2.04e-08   9.80e-11  \n",
-       "6   (((delayed-nu-fission / nu-fission) * (delayed... 4.74e-07   2.34e-09  \n",
-       "7   (((delayed-nu-fission / nu-fission) * (delayed... 2.64e-08   1.27e-10  \n",
-       "8   (((delayed-nu-fission / nu-fission) * (delayed... 2.84e-08   1.40e-10  \n",
-       "9   (((delayed-nu-fission / nu-fission) * (delayed... 2.44e-09   1.17e-11  \n",
-       "10  (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09   7.33e-12  \n",
-       "11  (((delayed-nu-fission / nu-fission) * (delayed... 7.02e-11   3.37e-13  "
+       "0   (((delayed-nu-fission / nu-fission) * (delayed... 8.79e-08   4.66e-10  \n",
+       "1   (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09   3.86e-11  \n",
+       "2   (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07   5.06e-09  \n",
+       "3   (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07   7.03e-10  \n",
+       "4   (((delayed-nu-fission / nu-fission) * (delayed... 2.36e-07   1.25e-09  \n",
+       "5   (((delayed-nu-fission / nu-fission) * (delayed... 2.03e-08   1.10e-10  \n",
+       "6   (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07   2.51e-09  \n",
+       "7   (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08   1.42e-10  \n",
+       "8   (((delayed-nu-fission / nu-fission) * (delayed... 2.83e-08   1.50e-10  \n",
+       "9   (((delayed-nu-fission / nu-fission) * (delayed... 2.43e-09   1.31e-11  \n",
+       "10  (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09   7.84e-12  \n",
+       "11  (((delayed-nu-fission / nu-fission) * (delayed... 7.00e-11   3.77e-13  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1345,15 +1361,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Beta (U-235) : 0.006504 +/- 0.000006\n",
-      "Beta (Pu-239): 0.002245 +/- 0.000002\n"
+      "Beta (U-235) : 0.006504 +/- 0.000007\n",
+      "Beta (Pu-239): 0.002245 +/- 0.000003\n"
      ]
     },
     {
@@ -1362,13 +1378,13 @@
        "(0.0, 7.0)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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p06bx3HPP0aNHjzU3Seax2WabccMNNzBjxgzuvfdeTjnlFN5++20ALr74Yp555hmmT5/Odtttt2Z+su9///scffTRTJ8+nbPPPpszzzyzzY/JCcbMrIPZZ599mDNnDg899BAHH3zwmvITTzyR6667bp36O+6445opYrbddlu23nprGpYjaZgIMyJYtmzZmjv4Z86cyZgxYwDYb7/9uPvuu9v8ODxVjJlZCZ1b3BQqcU75qQ1WrlzJH//4R8aOHbte+5g6dSrvv//+WlPMHHvssdxzzz0MHz6ciWkt65EjR3LnnXdy8sknc9ddd7FkyRIWLlzYpnOU+QzGzKwDWLZsGaNGjaKmpobtttuO4447rtVtvPrqqxx11FFce+21bLTRh3/er732Wl555RV22mmnNeMzF154IQ8//DCjR4/m4YcfZuDAgXTr1q3NjgecYMzMOoSGMZhp06Zx6aWX0qNHD7p3787q1avX1Fm+fDkAU6ZMWTOF/+TJ2TJbixcv5gtf+AITJkzgk5/85Drtd+vWjfHjx6+Zwn/bbbflzjvv5Omnn2bChGzi+r59+7bpMbmLzMysRJ5urPay/fbbM3PmTFasWMGyZcu4//772Xvvvdljjz2YNm3amnrvv/8+hx56KEcffTSHH374mvKI4IUXXmCHHXYgIpg8efKaKfzffPNNttxySzbaaCPOP/98vvnNb7Z5/E4wZmYd1ODBg/nqV7/KLrvswtChQxk9enST9W677TYeeeQRFi5cuOYigOuuu44RI0ZwzDHHsHjxYiKCkSNHcuWVVwLw0EMPceaZZyKJT3/601x++eVtHr+n6/d0/dbBVON096WqMX5P1988T9dvZmYdjhOMmZkVwgnGzIxsQNzWtqGfiROMmXV5PXv2ZOHChU4yJSKChQsX0rNnz/Vuw1eRmVmXN2jQIOrr69dMr2KZnj17MmjQoPXe3gnGzLq8jTfemKFDh1Y6jE6nbIKR1BM4GNgH2BZYBjwH/CEiZhQbnpmZVasWE4ykc8mSy0PAFOANoCewI3BBSj6nRcT0guM0M7MqU+4MZmpEnNPMexdJ2hrYro1jMjOzTqDFBBMRf2hcJmkjoFdELI6IN8jOaszMzNaS6zJlSb+V1EfS5mTjLzMlnV5saGZmVs3y3gczPCIWA4cAfwSGAkcVFZSZmVW/vAlmY0kbkyWYyRHxAeA7kszMrFl5E8yvgLnA5sAjkrYHFhcVlJmZVb9cN1pGxH8C/1lSNE/SfsWEZGZmnUHeQf5NJP2zpB9JOlvS2cCPcmw3VtJsSXMkndFMu7em96dIGlLy3pmpfLakz6eywZIelDRT0gxJJ5fU31LSnyU9n37+Q55jMzOzYuTtIrsbGAesBN4teTRLUjfgcuBAYDhwhKThjaodByyKiB2Ai4GfpW2HA+OBnYGxwBWpvZVkN3YOBz4JnFDS5hnA/RExDLg/vTYzswrJOxfZoIgY28q2dwfmRMSLAJImkSWpmSV1xgG16fntwGWSlMonRcQK4CVJc4DdI+Ix4FWAiFgiaRYwMLU5Dtg3tXU92ewDP2xlzGZm1kbynsH8r6RdW9n2QGB+yev6VNZknYhYCbwD9MuzbepOG002hQ3ANhHxanr+GrBNU0FJOl5SnaQ6z5xqZlacvAlmb+DJNB4yXdKzkio2/5ikXsAdwCnp/py1RLaoQ5OXUUfEVRFRExE1/fv3LzhSM7OuK28X2YHr0fbLwOCS14NSWVN16iV1B7YAFra0bbof5w7g5oi4s6TO65IGRMSrkgbgKWzMzCoq1xlMRMwD+gJfTI++qawlTwDDJA2V1INs0H5yozqTgWPS88OBB9LZx2RgfLrKbCgwDJiaxmeuBmZFxEUttHUM2YUJZmZWIXkvUz4ZuBnYOj1uknRSS9ukMZUTgfuAWcBtETFD0nmSvpSqXQ30S4P4p5Ku/ErrzNxGNnh/L3BCRKwC9iKbomaMpGnpcVBq6wLgAEnPA59Nr83MrEKUZw3qNN6yZ0S8m15vDjwWESMKjq9QNTU1UVdXV+kwzNYiffi8GpeIr/b4rTxJT0ZETbl6eQf5Bawqeb0qlZmZmTUp7yD/tcAUSXel14eQdW+ZmZk1qWyCSQuMPU524+LeqfjYiHi6wLjMzKzKlU0wEbFa0uURMRp4qh1iMjOzTiDvGMz9kg5LlwmbmZmVlTfBfBv4HbBC0mJJSyR5PRgzM2tW3vVgehcdiJmZdS55b7S8P0+ZmZlZgxbPYCT1BDYDtkoLeDWMwfRh3ZmRzczM1ijXRfZt4BRgW9a+gmwxcFlBMZmZWSfQYoKJiEuASySdFBGXtlNMZmbWCeS9k/8dSUc3LoyIG9o4HjMz6yTyJphPlDzvCexP1mXmBGNmZk3Ke5nyWlPzS+oLTCoiIDMz6xzy3mjZ2LvA0LYMxMzMOpdcZzCSfs+Ha9xvBAwnWxDMzMysSXnHYC4seb4SmBcR9QXEY2ZmnUSuLrKIeBiYC2wcEY8CCyV5+hgzM2tW3qlivgXcDvwqFQ0C/qugmMzMrBPIO8h/ArAX2R38RMTzwNZFBWVmZtUvb4JZERHvN7yQ1J0PB/3NzMzWkTfBPCzpR8Cmkg4gWxvm98WFZWZm1S5vgjkDWAA8SzYB5j3AvxUVlJmZVb+8d/KvBn6dHmZmZmXlvdFyL6AW2D5tIyAi4iPFhWZmZtUs742WVwPfA54EVhUXjpmZdRa5p+uPiD8WGomZmXUqeRPMg5L+A7gTWNFQGBFPNb+JmZl1ZXkTzB7pZ01JWQBj2jYcMzPrLPJeRbZf0YGYmVnnsr7rwZiZmbXICcbMzApRaIKRNFbSbElzJJ3RxPubSLo1vT9F0pCS985M5bMlfb6k/BpJb0h6rlFbtZJeljQtPQ4q8tjMzKxlLY7BSPpyS+9HxJ0tbNsNuBw4AKgHnpA0OSJmllQ7DlgUETtIGg/8DPiapOHAeGBnYFvgL5J2jIhVwHXAZcANTez24oi4sIlyMzNrZ+UG+b/YwntBdtlyc3YH5kTEiwCSJgHjgNIEM45shgDI1pu5TJJS+aSIWAG8JGlOau+xiHik9EzHzMw6phYTTEQcuwFtDwTml7yu58PLndepExErJb0D9EvljzfadmCOfZ4o6WigDjgtIhY1riDpeOB4gO222y7fkZiZWavlvQ8GSV8g67Lq2VAWEecVEdR6uhL4CdmZ1U+AicA3G1eKiKuAqwBqamq8po2ZWUHyLpn8S+BrwElkE11+hWziy5a8DAwueT0olTVZJy1itgWwMOe2a4mI1yNiVcnMz7uXic/MzAqU9yqyT0XE0WQD8ucCewI7ltnmCWCYpKGSepAN2k9uVGcycEx6fjjwQEREKh+frjIbCgwDpra0M0kDSl4eCjzXXF0zMyte3i6yZenne5K2JTvLGNBC/YYxlROB+4BuwDURMUPSeUBdREwmm6X5xjSI/xZZEiLVu43sgoCVwAnpCjIk3QLsC2wlqR44JyKuBn4uaRRZF9lcsoXRzMysQpSdMJSpJP0YuBTYn+zS4wB+ExE/Lja8YtXU1ERdXV2lwzBbi/Th8xz/PTucao/fypP0ZETUlKuX9wzm5+mS4Tsk/TfZQP/yDQnQzMw6t7xjMI81PImIFRHxTmmZmZlZY+Xu5P9HsvtPNpU0muwKMoA+wGYFx2ZmZlWsXBfZ54FvkF0mfFFJ+RLgRwXFZGZmnUC5O/mvB66XdFhE3NFOMZmZWSeQdwzmfkkXSapLj4mStig0MjMzq2p5E8zVZN1iX02PxcC1RQVlZmbVL+9lyh+NiMNKXp8raVoB8ZiZWSeR9wxmmaS9G15I2osP7+43MzNbR94zmO8AN5SMuyziwznEzMzM1pE3wSyOiJGS+gBExOI0CaWZmVmT8naR3QFZYomIxans9mJCMjOzzqDcnfwfJ1tkbAtJXy55qw8lC4+ZmZk1Vq6L7GPAwUBf4Isl5UuAbxUUk5mZdQLl7uS/G7hb0p4R4cktzcwst1xjME4uZmbWWnkH+c3MzFrFCcbMzAqR6z4YSZsAhwFDSreJiPOKCcvMzKpd3jOYu4FxwErg3ZKHdVITJ0Lv3tn66tX66N07Ow4zqwxFRPlK0nMRsUs7xNOuampqoq6urtJhdEi9e8PSpZWOYsP16gVLllQ6itaRPnye479nh1Pt8Vt5kp6MiJpy9fKewfyvpF03MCarIp0huUDnOQ6zapR3LrK9gW9IeglYAQiIiBhRWGTWYVTjt9DSb9FmVhl5E8yBhUZhViAnG7PKyHuj5Tw+nC7mi0DfVGbWIfXqVekINlxnOAbr2nIlGEknAzcDW6fHTZJOKjIwsw1RW1vdf6B79cqOwaya5b2KbDqwZ0S8m15vDjxW7WMwvoqseb4SyNaXf3c6v7a+ikzAqpLXq1KZmZlZk/IO8l8LTJF0V3p9CHB1IRGZmVmnkCvBRMRFkh4iu1wZ4NiIeLqwqMzMrOqVW9GyT0QslrQlMDc9Gt7bMiLeKjY8MzOrVuXGYH6bfj4J1JU8Gl63SNJYSbMlzZF0RhPvbyLp1vT+FElDSt47M5XPlvT5kvJrJL0h6blGbW0p6c+Snk8//6FcfGZWrErPR+d57CqrxQQTEQenn0Mj4iMlj6ER8ZGWtpXUDbic7CbN4cARkoY3qnYcsCgidgAuBn6Wth0OjAd2BsYCV6T2AK5LZY2dAdwfEcOA+9NrM2tn1Xx5eIOlS32ZeFvIex/M/XnKGtkdmBMRL0bE+8AkshmZS40Drk/Pbwf2l6RUPikiVkTES8Cc1B4R8QjQVNdcaVvXk12IYGbtrNrvQWrgeew2XIsJRlLPNP6ylaR/SN1QW6aurIFl2h4IzC95Xd/ENmvqRMRK4B2gX85tG9smIl5Nz18DtmnmmI6XVCepbsGCBWWaNLPWOu20bAbriOp8WNspdxXZt4FTgG3Jxl0a7n1ZDFxWXFgbJiJCUpO/KhFxFXAVZDdatmtgZmZdSIsJJiIuAS6RdFJEXNrKtl8GBpe8HpTKmqpTL6k7sAWwMOe2jb0uaUBEvCppAPBGK+M1M7M2lPdO/tWS+ja8SN1l/1JmmyeAYZKGSupBNmg/uVGdycAx6fnhwAORzV0zGRifrjIbCgwDppbZX2lbx5CtwmlmZhWSN8F8KyLebngREYuAb7W0QRpTORG4D5gF3BYRMySdJ+lLqdrVQD9Jc4BTSVd+RcQM4DZgJnAvcEJErAKQdAvwGPAxSfWSjkttXQAcIOl54LPptZmZVUjeyS6fBUaks4uGS5CnR8TOBcdXKE922TxPWGhdlX/3y8s72WXeucjuBW6V9Kv0+tupzMzMrEl5E8wPyZLKd9PrPwO/KSQiMzPrFPJOdrkauDI9zMzMysqVYCQNA84nm/KlZ0N5uelizMys68p7Fdm1ZGcvK4H9gBuAm4oKyszMql/eBLNpRNxPdtXZvIioBb5QXFhmZlbt8g7yr5C0EfC8pBPJ7qrvBNPZmZlZUfKewZwMbAb8K/BPwNf58K55MzOzdZQ9g0k3VX4tIr4PLAWOLTwqMzOremXPYNIULXu3QyxmZtaJ5B2DeVrSZOB3wLsNhRFxZyFRmZlZ1cubYHqSTaM/pqQsACcYMzNrUosJRtLPIuKHwD0R8bt2isnMzDqBcmMwB0kScGZ7BGNmZp1HuS6ye4FFQC9Ji0vKRbYycZ/CIjMzs6pWbsnk04HTJd0dEePaKSazLm3i/06k9uFalr6/tNKhrLdePXpR+5laTvvUaZUOxSqoxS6y1D1GS8mloY6ZtY1qTy4AS99fSu3DtZUOwyqs3BjMg5JOkrRdaaGkHpLGSLoe39Fv1qaqPbk06CzHYeuv3BjMWOCbwC2ShgJvA5uSJaY/Ab+IiKcLjdCsC4tzqm/NXp3rTg3LlBuDWQ5cAVwhaWNgK2BZRLzdDrGZmVkVy3ujJRHxgaRVQB9JfVLZ3wuLzMzMqlqu2ZQlfUnS88BLwMPAXOCPBcZlZmZVLu90/T8BPgn8X0QMBfYHHi8sKjMzq3p5E8wHEbEQ2EjSRhHxIFBTYFxmZlbl8o7BvC2pF/AIcLOkNyiZVdnMzKyxvGcw44D3gO+RTR/zAnBwUUGZmVn1y5tgzo6I1RGxMiKuj4j/BH5YZGBmZlbd8iaYA5ooO7AtAzEzs86l3How3wX+BfiIpOklb/UGHi0yMDMzq27lBvl/S3a/y/nAGSXlSyLircKiMjOzqtdiF1lEvBMRcyPiCGAwMCYi5pFdrjy0XSI0M7OqlOsyZUnnkN338jHgWqAHcBOwV3GhWUXtORH2rYVNlqJzKx3M+vGaJGaVlXeQ/1DgS6R7XyLiFbJxmBZJGitptqQ5ks5o4v1NJN2a3p8iaUjJe2em8tmSPl+uTUnXSXpJ0rT0GJXz2KwpKblUM69JYlZZeRPM+xERQABI2rzcBpK6AZeTXW02HDhC0vBG1Y4DFkXEDsDFwM/StsOB8cDOZEsGXCGpW442T4+IUekxLeexWVOqPLk08JokZpWT907+2yT9Cugr6Vtka8T8usw2uwNzIuJFAEmTyG7YnFlSZxxQm57fDlyWVsgcB0yKiBXAS5LmpPbI0aa1Ma9JYmbrI9cZTERcSJYA7iAbhzk7Ii4ts9lAYH7J6/pU1mSdiFgJvAP0a2Hbcm1OkDRd0sWSNmkqKEnHS6qTVLdgwYIyh2BmZusrbxcZEfHniDgduAD4S3EhrbczgY8DnwC2pJmZBiLiqoioiYia/v37t2d8ZmZdSosJRtInJT0k6U5JoyU9BzwHvC5pbJm2Xya7tLnBoFTWZB1J3YEtgIUtbNtsmxHxamRWkF3ptjtmZlYx5c5gLgN+CtwCPAD8v4j4R+DTZDdftuQJYJikoZJ6kA3aT25UZzJwTHp+OPBAuphgMjA+XWU2FBgGTG2pTUkD0k8Bh5AlQjMzq5Byg/zdI+JPAJLOi4jHASLib9nf8eZFxEpJJwL3Ad2AayJihqTzgLqImAxcDdyYBvHfIksYpHq3kQ3erwROiIhVKY512ky7vFlSf0DANOA7rfgczMzWUebPXIcXFb4+p1yCWV3yfFmj98qGHhH3APc0Kju75Ply4CvNbDsBmJCnzVQ+plw8Zmbl9OoFS311e5so10U2UtJiSUuAEel5w+td2yE+M7N2VVubJRnbcC2ewUREt/YKxKwovifGWuO007KHbbjclymbVZNePar/K2hnOAbr2vLeyW9WVWo/U0vtw7VVO1VMw0Sd1a5azx49UWrbUFT6MoMKqqmpibq6ukqH0SGV/mGoxqlirHJ6n9+7ahN7qV49erHkzCWVDqNDkvRkRNSUq+cuMjNrU7Wfqe0U3XudIUlWmrvIzKxNnfap06q6a6lau/U6Ip/BmJlZIZxgzMysEE4wZmZWCCcYMzMrhBOMmZkVwgnGzMwK4QRjZmaFcIIxM7NCOMEURKruh5nZhnKCMTOzQjjBmJlZIZxgChJR3Q8zsw3lBGNmZoVwgjEzs0I4wZiZWSGcYMzMrBBOMGZmVgivaGlm1oxqX90yzqnsJaE+gzEzK9GrR69Kh9BpOMGYmZWo/Uytk0wbUXThu+pqamqirq6ukLar/dS6VKVPs82sY5H0ZETUlKvnMxhrkb/Jmdn6coKxZvXq0Yvaz9RWOgwzq1K+iqwg7lYys66u0DMYSWMlzZY0R9IZTby/iaRb0/tTJA0pee/MVD5b0ufLtSlpaGpjTmqzR5HHZmZmLSsswUjqBlwOHAgMB46QNLxRteOARRGxA3Ax8LO07XBgPLAzMBa4QlK3Mm3+DLg4tbUotW1mZhVS5BnM7sCciHgxIt4HJgHjGtUZB1yfnt8O7C9JqXxSRKyIiJeAOam9JttM24xJbZDaPKS4QzMzs3KKTDADgfklr+tTWZN1ImIl8A7Qr4VtmyvvB7yd2mhuX2Zm1o663FVkko6XVCepbsGCBZUOx8ys0yoywbwMDC55PSiVNVlHUndgC2BhC9s2V74Q6JvaaG5fAETEVRFRExE1/fv3X4/DMjOzPIpMME8Aw9LVXT3IBu0nN6ozGTgmPT8ceCCyqQUmA+PTVWZDgWHA1ObaTNs8mNogtXl3gcdmZmZlFDpVjKSDgF8A3YBrImKCpPOAuoiYLKkncCMwGngLGB8RL6ZtzwK+CawETomIPzbXZir/CNmg/5bA08DXI2JFmfiWALPb9KDb11bAm5UOYgNUc/zVHDs4/kqr9vg/FhG9y1Xq0nORSarLM59OR+X4K6eaYwfHX2ldJf4uN8hvZmbtwwnGzMwK0dUTzFWVDmADOf7KqebYwfFXWpeIv0uPwZiZWXG6+hmMmZkVxAnGzMwK0SUTTLllBDo6SddIekPSc5WOpbUkDZb0oKSZkmZIOrnSMbWGpJ6Spkp6JsV/bqVjWh9pdvKnJf13pWNpLUlzJT0raZqkYtY8L4ikvpJul/Q3SbMk7VnpmPKS9LH0mTc8Fks6pcVtutoYTJry//+AA8gmxXwCOCIiZlY0sFaQ9GlgKXBDROxS6XhaQ9IAYEBEPCWpN/AkcEi1fP5p5u7NI2KppI2BvwInR8TjFQ6tVSSdCtQAfSLi4ErH0xqS5gI1EVF1NypKuh74n4j4TZqNZLOIeLvCYbVa+jv6MrBHRMxrrl5XPIPJs4xAhxYRj5DNfFB1IuLViHgqPV8CzKKKZr6OzNL0cuP0qKpvaZIGAV8AflPpWLoSSVsAnwauBoiI96sxuST7Ay+0lFygayaYPMsIWDtIK5iOBqZUOJRWSd1L04A3gD9HRFXFTzbV0g+A1RWOY30F8CdJT0o6vtLBtMJQYAFwbeqe/I2kzSsd1HoaD9xSrlJXTDDWAUjqBdxBNs/c4krH0xoRsSoiRpHN2r27pKrpppR0MPBGRDxZ6Vg2wN4RsRvZyrYnpC7jatAd2A24MiJGA+8C1TgG3AP4EvC7cnW7YoLJs4yAFSiNXdwB3BwRd1Y6nvWVujceJFvWu1rsBXwpjWNMAsZIuqmyIbVORLycfr4B3EXW7V0N6oH6kjPe28kSTrU5EHgqIl4vV7ErJpg8ywhYQdIg+dXArIi4qNLxtJak/pL6puebkl0s8reKBtUKEXFmRAyKiCFkv/sPRMTXKxxWbpI2TxeHkLqXPgdUxdWUEfEaMF/Sx1LR/kBVXNzSyBHk6B6D7JStS4mIlZJOBO7jwyn/Z1Q4rFaRdAuwL7CVpHrgnIi4urJR5bYXcBTwbBrHAPhRRNxTuZBaZQBwfbqKZiPgtoioukt9q9g2wF3Z9xS6A7+NiHsrG1KrnATcnL7cvggcW+F4WiUl9QOAb+eq39UuUzYzs/bRFbvIzMysHTjBmJlZIZxgzMysEE4wZmZWCCcYMzMrhBOMGSBpVZohdkaaKfk0SS3+/5A0pOgZrSVdJ+nwZt47Nc3K+2yK+aJ0E6tZh9Dl7oMxa8ayNP0LkrYGfgv0Ac6pZFDNkfQdspsMPxkRb6f7Kk4FNgU+aFS3W0SsqkCY1sX5DMaskTQFyfHAicp0k/Qfkp6QNF3SOjeZpbOZ/5H0VHp8KpXfIOmQkno3SxrXXJtpf5cpW6/oL8DWzYR5FvDdhtl408y8FzTM6yZpqaSJkp4B9kxnO8+lxyklMa85A5P0fUm16flDki5JZ3XPSaqW6VisA/EZjFkTIuLFdLf+1mTLObwTEZ+QtAnwqKQ/sfY0/W8AB0TEcknDyKbSqCGbFud7wH+l6do/BRwDHNdMm6OBjwHDye5anwlcUxqbpD5Ar4h4qYVD2ByYEhGnSfonsjvG9wAETJH0MLCozMewWUSMSpNJXgNUzaSe1jH4DMasvM8BR6epbaYA/YBhjepsDPxa0rNks8wOB4iIh8nmvutPNofTHRGxsoU2Pw3ckmZsfgV4oFxwkj6fzjTmNpw5AavIJhQF2Bu4KyLeTWvZ3Ansk+O4b0nH8AjQp2EONrO8fAZj1gRJHyH7I/0G2bf+kyLivkZ1hpS8/B7wOjCS7Ivb8pL3bgC+Tja5ZMPcU821eVC52CJiceoCGxoRL6U27lO2/HGPVG15jnGXlaz9JbNn412VeW3WIp/BmDWSzjZ+CVwW2WR99wHfbbhCS9KOTSwUtQXwakSsJpvMs1vJe9cBpwCULA3dXJuPAF9LYzQDgP2aCfN84MqSmZ3Fugmiwf8Ah0jaLO3j0FT2OrC1pH6pm67x0slfS23vTdad904z7Zs1yWcwZplNU3fVxmTf7G8EGpYT+A0wBHgq/SFfABzSaPsrgDskHQ3cS7aYFAAR8bqkWcB/ldRvrs27gDFkYy9/Bx5rJt4rSeMsklYAS4FHgacbV4yIpyRdB0xt2HdEPA0g6bxU/jLrLjuwXNLT6TP5ZjNxmDXLsymbFUzSZsCzwG7VchYg6SHg+xFRV+lYrHq5i8ysQJI+C8wCLq2W5GLWVnwGY2ZmhfAZjJmZFcIJxszMCuEEY2ZmhXCCMTOzQjjBmJlZIf4/cpIq9nsrMe8AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1416,7 +1432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1425,13 +1441,13 @@
        "(1000.0, 20000000.0)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1469,7 +1485,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1483,9 +1499,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/mdgxs-part-ii.ipynb b/mdgxs-part-ii.ipynb
index a9102df..91d0285 100644
--- a/mdgxs-part-ii.ipynb
+++ b/mdgxs-part-ii.ipynb
@@ -83,8 +83,7 @@
    "outputs": [],
    "source": [
     "# Create a materials collection and export to XML\n",
-    "materials = openmc.Materials((fuel, water, zircaloy))\n",
-    "materials.export_to_xml()"
+    "materials = openmc.Materials((fuel, water, zircaloy))"
    ]
   },
   {
@@ -267,8 +266,7 @@
    "outputs": [],
    "source": [
     "# Create Geometry and export to XML\n",
-    "geometry = openmc.Geometry(root_universe)\n",
-    "geometry.export_to_xml()"
+    "geometry = openmc.Geometry(root_universe)"
    ]
   },
   {
@@ -299,10 +297,24 @@
     "# Create an initial uniform spatial source distribution over fissionable zones\n",
     "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n",
     "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings.source = openmc.Source(space=uniform_dist)\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings.export_to_xml()"
+    "settings.source = openmc.Source(space=uniform_dist)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll tie the materials, geometry, and settings into a single model object and export the necessary XML files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = openmc.Model(geometry, materials, settings)\n",
+    "model.export_to_xml()"
    ]
   },
   {
@@ -314,12 +326,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6CAIAAAAHjs1qAAAUJklEQVR4nO2dzZEkuQ2FuR1zU4wXa4ccUMxtDuvIhGSCIsaRPeytQw7IjvViHdChQlXZJAg+EACJbOKdZjJfIb8EWT/JepX9yz//9mdJpc7Q226AVGqdvlT///n7r9f//viNe+2/mnmnyHxrBpE5PoPIHJ/hl+uHmZ+///r+9ft197e//iCrP4riZtC5mKF0ercMOAIDb47QNEOGN8ZaSnn/+r16ujy3g2ZRWZG5NMMmZSBrOgE/nJX5sSWbJmUgMZCyb4z1+YC2LukUqT09KUPPrGEgj9UzmzCIlE271plggC5V8fldHdJwQkwzkFv0RxkyTJfKpvkxzKzMLH5Jm5DVABiKaVrAfgZsYKW5ps1M98e1ArJreMXtpF3HZYQ3LYKCDJx508bTnbm+RoRj8UfBGdojihgYs4hhumk8A44kYlA2bXrv3BHJoyAPfxO5ebO0v625x9BuZ4DbslbAOANJtbhpSoYeld/AtTJvGrfuzixwkmbGWZr1ZtAsZdgCnAytOebA/dJmZp6XKcgT6GHGnXvNERhKNk1hVpYlpnsq9VmVEbHUQcrpnjpIOd1TB4kOAL9//c5fLJfLBcFTQ/OzrMiMMOBmc4Y54Kc2Mmxs2gTDVdMMr0tVMqqBxzJNzMp4qp+5NyTZNMYcsGmDDzN4LHPC3DviSgZl8HUlgwgjm0ZqJgAsEvNwUZZ1TWhJxMAgZdN6DOubtjQADD7QUJosq6HwLGs27amIAeBU6qaCpvt0jipIEHdLwnY6y7qlaUFCyHjT+Af29EpE9hzKRpiEb5XRRVwihi1NaxlMmiZiaJG2Dxz/cGJlRhQNdTKTIh/uxyBKqK5vGg62eOBAtvUDd91YB4DL/68P8C8gPMzXHGkEBsYcoWlt+DZC03YxMOZMRKYOUq7MpA5STvfUQcrpnjpIxHT/+fuv+Bd7ruZkmDAnA2PurswUux/MltmFjgkzz3DdW/3XkIE334ihhBk4q5+HDwLAvep+WVY/BrKCBwNjvhFDr/L6gTNkeH2YEcVxnDKcfgxkK3/89ud0+o1hIGXFoGwaziDqcE940/STB2F4BYAZtyajZxJqU7YY/x6bn8EaBpHWN61tkf6l57kXrKNpGshgfEvUCFnWjcdlGJinXLUrIPyu4+IzDZTxLVGDKEgS876K38C5SegeAF7/3GiPqHlPnxb+mo2/DygZGLV4WwZuXQCYyXCChxSJzNOKwrcmYG2bTBicsqxKBpMnUpCBI481ZHi9upMcTrHMHhnO0KvTYyBfXMmN6xlWNi0CA1nHb6ZxiUjRGytudip7O4bKHIGBN38yhgwApw5SRsRSBymne+og5XRPHaQv1f/xC6k15ggMQ3MytOYIDK2Z+9tMZbSeVcUyeyh42UItivEpWZyhBQbLWpldmzZtNmza9MAxlW2b9sZYCxszqszvir9UfzWLVqxJhl6wrjWDZa3MhWoaafNj8GuaZuCYyiRDawPLjm+JCtZ97gKLkKfXezHAw80iifK0rdmkadKwJ86gTCzjDMVt4JhXjbmmjS9VleFbkyzrcO8aBlya1LTJpHyWAp2fYOAQs3EAuIWYqK9XhAxtJfMsq6viD1zEAPCuHGnA/CrTtICB6vgD5xUA5n82O4Swem5EYMAlalrLYPUEiNC0ZQyIeRwAxktLnxjkdQxpJq+N9HOCZHDKsjo1TRoAdmqa08CZN21y3b01R1i+jbDuzjCQZsa5HfjzDRyRiAz1xdgtGETm+zKIzDEZMgCcOkgZEUsdpJzuqYOU0z11kLgAcJH8glBkvvtPHq0YROYIwBEYROZuALhd8SkWN6okzSbBVyWDFBhcJnNlKNk0OUO5NO3DhxkwlikK9/Vime3DGXMvGqph6FUQMZBFsmk8A1lkTdPGAWCnAJM0y9qaTTDaOiYBYGXTnBicmiYdOL8ZNWSALlWnc2p+54YzkFt6MkwCVqXwRKQfAyNN06zUPjfMB843ALxLAQPA91L8BoYIAFe7guRItyRscYYITYvA0B7XPDX9SkQydTUnb5LhVIaQcfF5vTUMoofnwA0ZCrky4xTLNMlwRmDA87ROd/fsmXPgeF0rDP7y3nA9HzdfnYWNyFZlReYbMdwOOD7D0EwnIpmkskaisn5mXMngzbAYOAPAqYOUEbHUQcrpnjpIOd1TB+lDAHh6ZQa5Xn7ufTzQ/AIfYXgaeIaiW5H4ZAwF6/DEwF2nhOFMg1ZmXKOh7fbeRvLL4d7NHpwYCvUd9UoGUeWes3Ru0biMgTF7MCDm14cZk2goaSbJfnRuDwvKlQHPYzgxiMK3OJgVg3LgnBiQpr0CwD0+5emJlmDxHCkjTYLNKoTsFABuD2rVNBFDi2TCoFmA57O3sgBwpS1ZVo22BJ6qc2cYql1bmrYrE1ZpOjUNyveWqKmUk1SJSF7TOaoIWdYieb00BMZfs/H3ASUDoxYveGqaf2BP41uiKk+bGeYKUZQj9XsiiRicmhaBgZHTwCk/xSEMb9UOxvqUNMNJnga5kazcW3UyYQDPTjR1nBj4Iy5jIDeKBs6EYW7g6ogY/sbaQuNm/rXZyRwBOBkmzIbAmYhMHaTMzKQOUk731EHK6Z46SF/aTc8P+8PFvutlAW5G1hCdzKEYROYIwBEYRObWyf1tpmGGE/yVcZsx5M3V98O4mQGWMkwDR2ialKFnjjBwpHm6aYMAcK+6XzR0pTkZ4jCsAR7fErWVSTSUNDPBOpCBlBVwBAa8aREYojVtfKmqnBOic5vea8iAh295humvxKUTwoThcw/cU763RN0VAA4SPL7KPMvqqvgDFzEAvCtFHSS9fRXTtICB6vgDFzEATG6Z0xoGq2Hmfxw9ZDi2ad4MMwFgk/FQZlmdGMhj9cwiBmY8RJPyqKaZM7xe3UUZzrb6t7/+EMUyybJO5oezBW5rugKTDPo8bQ4cXpYLADt91zU0T+dIIwAnw4R5GXAGgFMHKSNiqYOU0z11kHK6pw5SHQB2utqI8JPHEg84GSbMmrJcALjY3RK1LdvjFjGU5svkxQxSYGXTTBjK7qYpGaTAV+cbYy39mJHyRpW9wIOIgawjYiAxRAy4WRTuc2IonbO+18D1MBCGcQCYzHCSTpHa05MymCSrRFlWDbBITgxOA0ceq2feO3DQpep0Ts3q3DQM5Bb9UaSl8ESkHwOjCE1rnxvmDL4B4F0KGAC+l+I3MEQAuNoVP0e6jCF+0yIwTDcN1Hi6a7KsxSjDKWJQ5kgZs3cae+Lh2TSE4alxABgvLe0vnuFUMlgBg2VJZdP4sqTMm1avuxfJH0+7fn7iV4XLxw9buNmKQQMsZTABzqZ5MBCJyOdlCvIEepjBpxpuToYJcwTg4AwZAE4dpIyIpQ5STvfUQcrpnjpIHwLAz8/471+/8xfL5eNVMGh+GIbXHM/KySCtjAMfwlCZB7dEtYplttt7G0vz/fBiBlHlZGDMvam5kqEyDz7MmGRZSbLeE9QpT9tjwCuDtK4MIjA/BtDco13JUGkmACzS8H3n+l88R8pIAyxiYJCcAsDtQa2aJmJokUyapgzqhAgAgw901ZbAU3XuDEO1a0vTIuTSiiQ1zT+wp1yZSR0k31uixnzNWCP8NRt/H/BTzPdhk2ToVTO3RBUJ/6zGM2zPkbYMDJJfANipaSKGFsmkacqnHMIweHXXR0PJK+7SP7fhXS1tGfDKIK0rgwjMjwE092hXMlQiAsAlzNcrySCtHOornggMlTkTkamDlCszqYOU0z11kHK6pw7Sl3YT89unnhlc8MLNyTBhjgAcnCF/mm3PYAKcTfNgGASAe9WV8VQ/sz4tHAE4AsPtgJGy40RkK1EssyfS7MRgBQyWJZVN48uSMm/a+FJ1ZY7UhAEPXpMMePhWxCBSNk0q8FlkfEvUIAHgCIEnPMsapGkRGKabBsr4lqhBFCSJeV/Fb+DcJHQPAFs9NzSJSPMc6UQphsEvNX2vprUXoCECwCanR0ZDRQwmT6T2XERZVqfUtBOD08CRx+qZ9w7c69W9l+Eke+QUDRUxkHVEDCSGiAE3m+RplQylc9b3GrgeBsJQJyJFv6y5mnFnEHMyxGEQmTVlMwCcOkgZEUsdpJzuqYOU0z11kOoAMH5N4Gd2uojxMydD6xSZlwHXAeDqu1kmK1eaeGoPBS/rZ44AnAzbgd8Ya2FjRpX5/et3PFjXK+tkfjhb4LamKzDJQGL4MZSzB27mlqiiDGdP7elFYCCP1TNLs6wTuzQMn6Bp5gy+t0RlIKRalmUVMLEM06VMJsSzFOgM0jRvBuMA8ByEuSJkaCuZZ1ldFX/gIgaAd+VIA+ZX8URkBMUfOK8AMP/T3SGE1XNjGQNjFjFMzxhRZpA5Sg5cq3EAGC8t7a8owwkykLICjsCANy0CQ7SmcevuzAInaWacRXIPienlW35FVsQwDRyhaVKGnjnCwJHm6aYRicjn5YLTd13I89LJHIpBZI4AHIFBZG6dGQBOHaSMiKUOUk731EHK6Z46SFwAuLCXBe0XYLh5y08eIwAnw4TZEPjDLVFL890sE8tsv8VlzO12ciNZOQhDobq8kqFXWTpwHgykOQhDuQzchw8zolhme7CemSTDz43J0+oZ8OBr+/CenBj4Iy5j0CeWTRjmBm4mACwS/r7DM7RmDRUjkyyrsmkRGBg5DZwyqLM/ALwlWNeeNt5HQ2D8s2m1y4+BUYu3JadZAd8sAJxKOSlEALjaFSSIu+VNBn/NFi1oOCnILwSqczdPTb8SkUxdzQCI3k+ZYJ13EnqCgW/aNAP/8JbBpGkihhbJhEHzlOODlsTKjPKGnYyZPI3eKgEoVwZ8pJ0YmLPTgFkxKAfOiQFp2uAv7w3XVR5mPsBZnczjgWCGcxgNFTE8DTxD+bjagDMUrGn3YihYhycG7jolDGcaw5CJyNRBysxM6iDldE8dpJzuqYNET3enVVhRWT9zMsRhWAxsszKDmKcXGUTmGzHcDjg+w9D8IQBMfjHbC51qYpmisgcy3A74LgyvDzOiOI4yltkz41rP0FbwYxCZc+B4XSu8AsCMW3N6+sGQMmgSbAxDUSRDpTJJAubAXfc+/mGciIwQAG6PGzzLGqFpERja4+JNA+V7S9RdCpLEvK/iN1CViOQ1nUbkc2pOao8oCvdZYUxnWf0YGGmaZqX2AtR84Ma3RHU6bTJPaxK+FamtI2JwapoTg1PTpAPnN6OGDG/VDsb6lD6W2SPrmfGY7nQ0dI6BLJJN4xnIImuaVici8V/WtFczuJl/mXEyB2QQmSMAR2AQmStnBoBTBykjYqmDlNM9dZByuqcO0pd20/PD/nDd6npZ4GS+BYPIfF8GkTkmA/e3mQq7ltRmWXsoVdpzWLbaxfxu14/Bow+kGYzIbgH+fAPnHgCWlu2ds6ay/tT8zBEYSHOEgeuZtQFgPlAG1pXG3/D1VPzcRBLlaVtzhKbxaUSQQaSVA2fetPGlqjLDaZJlHe5dw4BLE741mZTPUqDzEwwcYva9JWqQHGkEmWdZXRV/4CIGgHflSAPmV/FEZATFHzivADD/s9khhNVzIwIDLlHTWgarJ0CEpi1jQMzjALCotOjcyOsY0kxeG+nnBMnglGUVhft60geAnZrmNHBMoHKuaa9Xd2WG89tff+ijoT86f6CnZyYZetHQ1gyWtTIXqmmkzY/Br2magWMqkwytDSzLBYBjfjG2nmFoTobWHIGhNWcAOHWQMiKWOkg53VMHKad76iDVAeBzfvK4haHMXn3uMn8yhskAMJ5T65XtcYsYSvNlMmMW5fhiMhSLpkVgKJKmKWfa1fnGWIvb3T17gQecoVenx9BbAI7AsLJpERjIOn4z7eocB4CdAkzSLKsmfMvIKQCsZHNiMAl+BRk48lhDBuhSdTqn5nduOEORRBsMk4D4J85qlx8DoxZvy8BVwObZW98A8C4FDADfS/EbGCIAXO2KnyNdxsBMINHKwxoFGTjz1PQrEcnU1Zy8SY5UGUIWvaczwBoGkdY3rW2RVfh2TdNAhteru1OWVWT2YyBnfG+1hJQyT2vFoGwaziDqcE940/STB2EY/OW9YnEPico8LCtiqMw8w3UvM8+UDLz5RgwlzMCBM23IQCQiH48Bnz2u5mSYMCcDY84AcOogZUQsdZByuqcOUk731EH6EAC+XtjyV9beZvyafQ0DY47QNOlChx9wBAbGPLglqlMss2fuOQt1hpGBD2Qo1Bf70YBfH2aUSUsTMyny4X4Moljb+qbhYIsHDmRbP3DXja8AcI/PKcvaSpQjNTmikmFL0zThWyuGFmn7wPEPXxcABh/oqi0RTjzLGqFpQVKuEQPAqdRNBU336UjQlhwpeee09RjTWdZs2gQD2LTxLVH9sqwVIs+wZhKIGPjwrQYjmzYhhGHw6q6PZTJmENqVATSLXguyaVJaV+Cr6Fuivn/9Dq7nV8fjzc+yIjPCgJvNGeaAn9rIsLFpEwxXTTNkIjJ1kHJlJnWQcrqnDlJO99RB+tJuel4ZIMtY+E+qJsqamyMwlGyawqwsy90S1eqn2W1cTvPrWp5hC3AytOaYAzcIAPeqK+Oprub//uPf7cawwGEZSil//8+/ljHogZGy41uittJnentmJlgHMhRqmdYKGGcgqRY3TcnQo/IbuFbmTRtfqq7MkU7v5Y9okmWVMkw3TTohTBiUTZveO3dE8ijIw41viRohy7rxuIzwpkVQkIEzb5rxLVGDaEuokBeeiIyggA2sNNc09wCw1VhOM5Bb9EcZMkyXyqb5McwEgE1Oj4yGihj0c4KclKIs6+IX5mzatc4Ew+vVvZfhJCf3t+Yv1ffMorIic9EFX3vdcQJ+OCvzY0s2TcpAYiBl6QDwtURblzTjX86Jyt6OQWSOzyAyx2fIAHDqIGVELHWQ/gcg1yPxJKXdEwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -359,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -381,28 +393,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=1.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=6.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=17.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=23.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Instantiate a tally mesh                                                                                                                                                                  \n",
     "mesh = openmc.RegularMesh(mesh_id=1)\n",
@@ -429,8 +422,8 @@
     "mgxs_lib.build_library()\n",
     "\n",
     "# Create a \"tallies.xml\" file for the MGXS Library\n",
-    "tallies_file = openmc.Tallies()\n",
-    "mgxs_lib.add_to_tallies_file(tallies_file, merge=True)\n",
+    "tallies = openmc.Tallies()\n",
+    "mgxs_lib.add_to_tallies_file(tallies, merge=True)\n",
     "\n",
     "# Instantiate a current tally\n",
     "mesh_filter = openmc.MeshSurfaceFilter(mesh)\n",
@@ -439,10 +432,7 @@
     "current_tally.filters = [mesh_filter]\n",
     "\n",
     "# Add current tally to the tallies file\n",
-    "tallies_file.append(current_tally)\n",
-    "\n",
-    "# Export to \"tallies.xml\"\n",
-    "tallies_file.export_to_xml()"
+    "tallies.append(current_tally)"
    ]
   },
   {
@@ -454,9 +444,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tie geometry, materials, settings, and tallies together into a model object\n",
+    "model = openmc.Model(geometry=geometry,\n",
+    "                     materials=materials,\n",
+    "                     settings=settings,\n",
+    "                     tallies=tallies)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=1.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=2.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=5.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=6.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=17.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=23.\n",
+      "  warn(msg, IDWarning)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -485,112 +506,113 @@
       "                          ########     %%%%%%%%%%%%%%\n",
       "                                      %%%%%%%%%%%\n",
       "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-26 13:44:53\n",
-      "    OpenMP Threads | 2\n",
+      "                 | The OpenMC Monte Carlo Code\n",
+      "       Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n",
+      "         License | https://docs.openmc.org/en/latest/license.html\n",
+      "         Version | 0.13.1\n",
+      "        Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n",
+      "       Date/Time | 2022-10-03 23:30:12\n",
+      "  OpenMP Threads | 2\n",
       "\n",
       " Reading settings XML file...\n",
       " Reading cross sections XML file...\n",
       " Reading materials XML file...\n",
       " Reading geometry XML file...\n",
-      " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n",
-      " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n",
-      " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n",
-      " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n",
-      " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n",
-      " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
+      " Reading U235 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U235.h5\n",
+      " Reading U238 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U238.h5\n",
+      " Reading O16 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/O16.h5\n",
+      " Reading H1 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/H1.h5\n",
+      " Reading B10 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/B10.h5\n",
+      " Reading Zr90 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr90.h5\n",
+      " Minimum neutron data temperature: 294 K\n",
+      " Maximum neutron data temperature: 294 K\n",
       " Reading tallies XML file...\n",
       " Preparing distributed cell instances...\n",
+      " Reading plot XML file...\n",
       " Writing summary.h5 file...\n",
-      " Maximum neutron transport energy: 20000000.0 eV for U235\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    1.03409\n",
-      "        2/1    1.02768\n",
-      "        3/1    1.01526\n",
-      "        4/1    1.03910\n",
-      "        5/1    1.00144\n",
-      "        6/1    1.00485\n",
-      "        7/1    1.03532\n",
-      "        8/1    1.01159\n",
-      "        9/1    0.97707\n",
-      "       10/1    1.06530\n",
-      "       11/1    1.08615\n",
-      "       12/1    1.00438    1.04527 +/- 0.04089\n",
-      "       13/1    1.03145    1.04066 +/- 0.02405\n",
-      "       14/1    1.04785    1.04246 +/- 0.01710\n",
-      "       15/1    1.04634    1.04323 +/- 0.01327\n",
-      "       16/1    1.02264    1.03980 +/- 0.01137\n",
-      "       17/1    1.00157    1.03434 +/- 0.01105\n",
-      "       18/1    1.09443    1.04185 +/- 0.01216\n",
-      "       19/1    1.02810    1.04032 +/- 0.01084\n",
-      "       20/1    1.01800    1.03809 +/- 0.00995\n",
-      "       21/1    1.02815    1.03719 +/- 0.00904\n",
-      "       22/1    1.00064    1.03414 +/- 0.00880\n",
-      "       23/1    0.99013    1.03076 +/- 0.00877\n",
-      "       24/1    1.01182    1.02940 +/- 0.00823\n",
-      "       25/1    1.07537    1.03247 +/- 0.00826\n",
-      "       26/1    1.02924    1.03227 +/- 0.00772\n",
-      "       27/1    1.01499    1.03125 +/- 0.00733\n",
-      "       28/1    1.01749    1.03049 +/- 0.00695\n",
-      "       29/1    1.04072    1.03102 +/- 0.00660\n",
-      "       30/1    0.99990    1.02947 +/- 0.00645\n",
-      "       31/1    1.03239    1.02961 +/- 0.00613\n",
-      "       32/1    1.02613    1.02945 +/- 0.00585\n",
-      "       33/1    1.04340    1.03006 +/- 0.00562\n",
-      "       34/1    1.05081    1.03092 +/- 0.00545\n",
-      "       35/1    1.02511    1.03069 +/- 0.00524\n",
-      "       36/1    0.99923    1.02948 +/- 0.00517\n",
-      "       37/1    0.97758    1.02756 +/- 0.00534\n",
-      "       38/1    0.99628    1.02644 +/- 0.00526\n",
-      "       39/1    1.06004    1.02760 +/- 0.00521\n",
-      "       40/1    1.08287    1.02944 +/- 0.00536\n",
-      "       41/1    1.02731    1.02937 +/- 0.00518\n",
-      "       42/1    1.02634    1.02928 +/- 0.00502\n",
-      "       43/1    1.04269    1.02968 +/- 0.00488\n",
-      "       44/1    1.05044    1.03029 +/- 0.00478\n",
-      "       45/1    1.05183    1.03091 +/- 0.00468\n",
-      "       46/1    1.02770    1.03082 +/- 0.00455\n",
-      "       47/1    1.07099    1.03191 +/- 0.00455\n",
-      "       48/1    1.04467    1.03224 +/- 0.00444\n",
-      "       49/1    1.02996    1.03218 +/- 0.00433\n",
-      "       50/1    1.03418    1.03223 +/- 0.00422\n",
+      "        1/1    0.99225\n",
+      "        2/1    0.99354\n",
+      "        3/1    1.02644\n",
+      "        4/1    1.06300\n",
+      "        5/1    1.03396\n",
+      "        6/1    1.00753\n",
+      "        7/1    1.04194\n",
+      "        8/1    1.04023\n",
+      "        9/1    1.03320\n",
+      "       10/1    1.04267\n",
+      "       11/1    1.02172\n",
+      "       12/1    1.07125    1.04648 +/- 0.02477\n",
+      "       13/1    1.04987    1.04761 +/- 0.01434\n",
+      "       14/1    1.01403    1.03922 +/- 0.01317\n",
+      "       15/1    1.04432    1.04024 +/- 0.01025\n",
+      "       16/1    1.06785    1.04484 +/- 0.00955\n",
+      "       17/1    1.04639    1.04506 +/- 0.00807\n",
+      "       18/1    1.06538    1.04760 +/- 0.00744\n",
+      "       19/1    1.00283    1.04263 +/- 0.00823\n",
+      "       20/1    1.00930    1.03929 +/- 0.00808\n",
+      "       21/1    1.02698    1.03817 +/- 0.00740\n",
+      "       22/1    1.04975    1.03914 +/- 0.00682\n",
+      "       23/1    1.03265    1.03864 +/- 0.00629\n",
+      "       24/1    0.98957    1.03513 +/- 0.00680\n",
+      "       25/1    1.01457    1.03376 +/- 0.00648\n",
+      "       26/1    1.00560    1.03200 +/- 0.00631\n",
+      "       27/1    1.00933    1.03067 +/- 0.00608\n",
+      "       28/1    1.01275    1.02967 +/- 0.00581\n",
+      "       29/1    1.04347    1.03040 +/- 0.00555\n",
+      "       30/1    1.03126    1.03044 +/- 0.00526\n",
+      "       31/1    1.05165    1.03145 +/- 0.00511\n",
+      "       32/1    1.02594    1.03120 +/- 0.00488\n",
+      "       33/1    1.00047    1.02987 +/- 0.00485\n",
+      "       34/1    1.04045    1.03031 +/- 0.00466\n",
+      "       35/1    1.01414    1.02966 +/- 0.00452\n",
+      "       36/1    1.03056    1.02970 +/- 0.00434\n",
+      "       37/1    1.05870    1.03077 +/- 0.00431\n",
+      "       38/1    0.97655    1.02883 +/- 0.00458\n",
+      "       39/1    1.05223    1.02964 +/- 0.00450\n",
+      "       40/1    1.08089    1.03135 +/- 0.00467\n",
+      "       41/1    1.05155    1.03200 +/- 0.00456\n",
+      "       42/1    1.01221    1.03138 +/- 0.00446\n",
+      "       43/1    1.03906    1.03161 +/- 0.00433\n",
+      "       44/1    1.00455    1.03082 +/- 0.00427\n",
+      "       45/1    1.00183    1.02999 +/- 0.00423\n",
+      "       46/1    1.05348    1.03064 +/- 0.00416\n",
+      "       47/1    1.06321    1.03152 +/- 0.00414\n",
+      "       48/1    1.05862    1.03224 +/- 0.00410\n",
+      "       49/1    1.04610    1.03259 +/- 0.00401\n",
+      "       50/1    1.02808    1.03248 +/- 0.00391\n",
       " Creating state point statepoint.50.h5...\n",
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     = 2.5756e-01 seconds\n",
-      "   Reading cross sections          = 2.4233e-01 seconds\n",
-      " Total time in simulation          = 1.2099e+01 seconds\n",
-      "   Time in transport only          = 1.2046e+01 seconds\n",
-      "   Time in inactive batches        = 8.7582e-01 seconds\n",
-      "   Time in active batches          = 1.1223e+01 seconds\n",
-      "   Time synchronizing fission bank = 4.8051e-03 seconds\n",
-      "     Sampling source sites         = 4.1028e-03 seconds\n",
-      "     SEND/RECV source sites        = 6.7903e-04 seconds\n",
-      "   Time accumulating tallies       = 3.0247e-02 seconds\n",
-      "   Time writing statepoints        = 1.4253e-02 seconds\n",
-      " Total time for finalization       = 5.3700e-07 seconds\n",
-      " Total time elapsed                = 1.2369e+01 seconds\n",
-      " Calculation Rate (inactive)       = 28544.8 particles/second\n",
-      " Calculation Rate (active)         = 8910.20 particles/second\n",
+      " Total time for initialization     = 7.2704e-01 seconds\n",
+      "   Reading cross sections          = 7.1697e-01 seconds\n",
+      " Total time in simulation          = 6.0982e+01 seconds\n",
+      "   Time in transport only          = 6.0103e+01 seconds\n",
+      "   Time in inactive batches        = 4.4142e+00 seconds\n",
+      "   Time in active batches          = 5.6568e+01 seconds\n",
+      "   Time synchronizing fission bank = 1.0706e-02 seconds\n",
+      "     Sampling source sites         = 9.8511e-03 seconds\n",
+      "     SEND/RECV source sites        = 8.2068e-04 seconds\n",
+      "   Time accumulating tallies       = 8.5453e-01 seconds\n",
+      "   Time writing statepoints        = 8.0261e-03 seconds\n",
+      " Total time for finalization       = 2.0600e-06 seconds\n",
+      " Total time elapsed                = 6.1735e+01 seconds\n",
+      " Calculation Rate (inactive)       = 5663.53 particles/second\n",
+      " Calculation Rate (active)         = 1767.79 particles/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
-      " k-effective (Collision)     = 1.02658 +/- 0.00374\n",
-      " k-effective (Track-length)  = 1.03223 +/- 0.00422\n",
-      " k-effective (Absorption)    = 1.02640 +/- 0.00361\n",
-      " Combined k-effective        = 1.02849 +/- 0.00321\n",
+      " k-effective (Collision)     = 1.03096 +/- 0.00285\n",
+      " k-effective (Track-length)  = 1.03248 +/- 0.00391\n",
+      " k-effective (Absorption)    = 1.02246 +/- 0.00327\n",
+      " Combined k-effective        = 1.02749 +/- 0.00267\n",
       " Leakage Fraction            = 0.00000 +/- 0.00000\n",
       "\n"
      ]
@@ -598,7 +620,7 @@
    ],
    "source": [
     "# Run OpenMC\n",
-    "openmc.run()"
+    "statepoint_filename = model.run()"
    ]
   },
   {
@@ -617,7 +639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -634,7 +656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -668,7 +690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -720,8 +742,8 @@
        "      <td>1</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000079</td>\n",
-       "      <td>1.583613e-05</td>\n",
+       "      <td>0.000101</td>\n",
+       "      <td>2.537729e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -731,8 +753,8 @@
        "      <td>2</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.001001</td>\n",
-       "      <td>1.983614e-04</td>\n",
+       "      <td>0.001282</td>\n",
+       "      <td>3.179682e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -742,8 +764,8 @@
        "      <td>3</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000636</td>\n",
-       "      <td>1.248704e-04</td>\n",
+       "      <td>0.000813</td>\n",
+       "      <td>2.001150e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -753,8 +775,8 @@
        "      <td>4</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000503</td>\n",
-       "      <td>9.721681e-05</td>\n",
+       "      <td>0.000641</td>\n",
+       "      <td>1.556332e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -764,8 +786,8 @@
        "      <td>5</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000018</td>\n",
-       "      <td>3.397815e-06</td>\n",
+       "      <td>0.000023</td>\n",
+       "      <td>5.409817e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -775,8 +797,8 @@
        "      <td>6</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>2.710107e-07</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>4.317338e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -786,8 +808,8 @@
        "      <td>1</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000092</td>\n",
-       "      <td>2.407283e-05</td>\n",
+       "      <td>0.000105</td>\n",
+       "      <td>2.667910e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -797,8 +819,8 @@
        "      <td>2</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.001165</td>\n",
-       "      <td>3.017676e-04</td>\n",
+       "      <td>0.001332</td>\n",
+       "      <td>3.344146e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -808,8 +830,8 @@
        "      <td>3</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000739</td>\n",
-       "      <td>1.899840e-04</td>\n",
+       "      <td>0.000845</td>\n",
+       "      <td>2.105053e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -819,8 +841,8 @@
        "      <td>4</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000582</td>\n",
-       "      <td>1.478539e-04</td>\n",
+       "      <td>0.000665</td>\n",
+       "      <td>1.637544e-04</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -842,19 +864,19 @@
        "\n",
        "                                               score      mean     std. dev.  \n",
        "                                                                              \n",
-       "0  (((delayed-nu-fission / nu-fission) * (delayed...  0.000079  1.583613e-05  \n",
-       "1  (((delayed-nu-fission / nu-fission) * (delayed...  0.001001  1.983614e-04  \n",
-       "2  (((delayed-nu-fission / nu-fission) * (delayed...  0.000636  1.248704e-04  \n",
-       "3  (((delayed-nu-fission / nu-fission) * (delayed...  0.000503  9.721681e-05  \n",
-       "4  (((delayed-nu-fission / nu-fission) * (delayed...  0.000018  3.397815e-06  \n",
-       "5  (((delayed-nu-fission / nu-fission) * (delayed...  0.000001  2.710107e-07  \n",
-       "6  (((delayed-nu-fission / nu-fission) * (delayed...  0.000092  2.407283e-05  \n",
-       "7  (((delayed-nu-fission / nu-fission) * (delayed...  0.001165  3.017676e-04  \n",
-       "8  (((delayed-nu-fission / nu-fission) * (delayed...  0.000739  1.899840e-04  \n",
-       "9  (((delayed-nu-fission / nu-fission) * (delayed...  0.000582  1.478539e-04  "
+       "0  (((delayed-nu-fission / nu-fission) * (delayed...  0.000101  2.537729e-05  \n",
+       "1  (((delayed-nu-fission / nu-fission) * (delayed...  0.001282  3.179682e-04  \n",
+       "2  (((delayed-nu-fission / nu-fission) * (delayed...  0.000813  2.001150e-04  \n",
+       "3  (((delayed-nu-fission / nu-fission) * (delayed...  0.000641  1.556332e-04  \n",
+       "4  (((delayed-nu-fission / nu-fission) * (delayed...  0.000023  5.409817e-06  \n",
+       "5  (((delayed-nu-fission / nu-fission) * (delayed...  0.000002  4.317338e-07  \n",
+       "6  (((delayed-nu-fission / nu-fission) * (delayed...  0.000105  2.667910e-05  \n",
+       "7  (((delayed-nu-fission / nu-fission) * (delayed...  0.001332  3.344146e-04  \n",
+       "8  (((delayed-nu-fission / nu-fission) * (delayed...  0.000845  2.105053e-04  \n",
+       "9  (((delayed-nu-fission / nu-fission) * (delayed...  0.000665  1.637544e-04  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -899,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -972,8 +994,8 @@
        "      <td>x-max out</td>\n",
        "      <td>total</td>\n",
        "      <td>current</td>\n",
-       "      <td>0.03154</td>\n",
-       "      <td>0.000643</td>\n",
+       "      <td>0.03118</td>\n",
+       "      <td>0.000831</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -983,8 +1005,8 @@
        "      <td>x-max in</td>\n",
        "      <td>total</td>\n",
        "      <td>current</td>\n",
-       "      <td>0.03030</td>\n",
-       "      <td>0.000662</td>\n",
+       "      <td>0.03188</td>\n",
+       "      <td>0.000783</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1016,8 +1038,8 @@
        "      <td>y-max out</td>\n",
        "      <td>total</td>\n",
        "      <td>current</td>\n",
-       "      <td>0.02981</td>\n",
-       "      <td>0.000595</td>\n",
+       "      <td>0.03172</td>\n",
+       "      <td>0.000610</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -1027,8 +1049,8 @@
        "      <td>y-max in</td>\n",
        "      <td>total</td>\n",
        "      <td>current</td>\n",
-       "      <td>0.03083</td>\n",
-       "      <td>0.000732</td>\n",
+       "      <td>0.03070</td>\n",
+       "      <td>0.000660</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -1061,17 +1083,17 @@
        "       x  y  z       surf                                    \n",
        "0      1  1  1  x-min out   total  current  0.00000  0.000000\n",
        "1      1  1  1   x-min in   total  current  0.00000  0.000000\n",
-       "2      1  1  1  x-max out   total  current  0.03154  0.000643\n",
-       "3      1  1  1   x-max in   total  current  0.03030  0.000662\n",
+       "2      1  1  1  x-max out   total  current  0.03118  0.000831\n",
+       "3      1  1  1   x-max in   total  current  0.03188  0.000783\n",
        "4      1  1  1  y-min out   total  current  0.00000  0.000000\n",
        "5      1  1  1   y-min in   total  current  0.00000  0.000000\n",
-       "6      1  1  1  y-max out   total  current  0.02981  0.000595\n",
-       "7      1  1  1   y-max in   total  current  0.03083  0.000732\n",
+       "6      1  1  1  y-max out   total  current  0.03172  0.000610\n",
+       "7      1  1  1   y-max in   total  current  0.03070  0.000660\n",
        "8      1  1  1  z-min out   total  current  0.00000  0.000000\n",
        "9      1  1  1   z-min in   total  current  0.00000  0.000000"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1096,7 +1118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1105,13 +1127,13 @@
        "Text(0.5, 1.0, 'Beta - delayed group 6')"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1296x648 with 12 Axes>"
       ]
@@ -1164,7 +1186,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1178,9 +1200,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/mgxs-part-i.ipynb b/mgxs-part-i.ipynb
index 10e02e9..1257424 100644
--- a/mgxs-part-i.ipynb
+++ b/mgxs-part-i.ipynb
@@ -144,6 +144,16 @@
     "import openmc.mgxs as mgxs"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a model object to tie geometry, materials, settings, and tallies together\n",
+    "model = openmc.Model()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -153,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,13 +186,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Instantiate a Materials collection and export to XML\n",
-    "materials_file = openmc.Materials([inf_medium])\n",
-    "materials_file.export_to_xml()"
+    "model.materials = openmc.Materials([inf_medium])"
    ]
   },
   {
@@ -194,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -237,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -254,15 +263,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Create Geometry and set root Universe\n",
-    "openmc_geometry = openmc.Geometry(root_universe)\n",
-    "\n",
-    "# Export to \"geometry.xml\"\n",
-    "openmc_geometry.export_to_xml()"
+    "model.geometry = openmc.Geometry(root_universe)"
    ]
   },
   {
@@ -274,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,19 +290,18 @@
     "particles = 2500\n",
     "\n",
     "# Instantiate a Settings object\n",
-    "settings_file = openmc.Settings()\n",
-    "settings_file.batches = batches\n",
-    "settings_file.inactive = inactive\n",
-    "settings_file.particles = particles\n",
-    "settings_file.output = {'tallies': True}\n",
+    "settings = openmc.Settings()\n",
+    "settings.batches = batches\n",
+    "settings.inactive = inactive\n",
+    "settings.particles = particles\n",
+    "settings.output = {'tallies': True}\n",
     "\n",
     "# Create an initial uniform spatial source distribution over fissionable zones\n",
     "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
     "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings_file.source = openmc.Source(space=uniform_dist)\n",
+    "settings.source = openmc.Source(space=uniform_dist)\n",
     "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
+    "model.settings = settings"
    ]
   },
   {
@@ -308,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -345,18 +350,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Instantiate a few different sections\n",
-    "total = mgxs.TotalXS(domain=cell, groups=groups)\n",
-    "absorption = mgxs.AbsorptionXS(domain=cell, groups=groups)\n",
-    "scattering = mgxs.ScatterXS(domain=cell, groups=groups)\n",
+    "total = mgxs.TotalXS(domain=cell, energy_groups=groups)\n",
+    "absorption = mgxs.AbsorptionXS(domain=cell, energy_groups=groups)\n",
+    "scattering = mgxs.ScatterXS(domain=cell, energy_groups=groups)\n",
     "\n",
     "# Note that if we wanted to incorporate neutron multiplication in the\n",
     "# scattering cross section we would write the previous line as:\n",
-    "# scattering = mgxs.ScatterXS(domain=cell, groups=groups, nu=True)"
+    "# scattering = mgxs.ScatterXS(domain=cell, energy_groups=groups, nu=True)"
    ]
   },
   {
@@ -368,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -392,7 +397,7 @@
        "              \tEstimator      =\ttracklength)])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -410,35 +415,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Instantiate an empty Tallies object\n",
-    "tallies_file = openmc.Tallies()\n",
+    "tallies = openmc.Tallies()\n",
     "\n",
     "# Add total tallies to the tallies file\n",
-    "tallies_file += total.tallies.values()\n",
+    "tallies += total.tallies.values()\n",
     "\n",
     "# Add absorption tallies to the tallies file\n",
-    "tallies_file += absorption.tallies.values()\n",
+    "tallies += absorption.tallies.values()\n",
     "\n",
     "# Add scattering tallies to the tallies file\n",
-    "tallies_file += scattering.tallies.values()\n",
+    "tallies += scattering.tallies.values()\n",
     "\n",
-    "# Export to \"tallies.xml\"\n",
-    "tallies_file.export_to_xml()"
+    "model.tallies = tallies"
    ]
   },
   {
@@ -450,9 +443,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=3.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=4.\n",
+      "  warn(msg, IDWarning)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -481,29 +484,30 @@
       "                          ########     %%%%%%%%%%%%%%\n",
       "                                      %%%%%%%%%%%\n",
       "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-28 08:44:52\n",
-      "    OpenMP Threads | 2\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 12:30:35\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 H1 from /opt/data/xs/nndc_hdf5/H1.h5\n",
-      " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n",
-      " Reading U235 from /opt/data/xs/nndc_hdf5/U235.h5\n",
-      " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n",
-      " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
+      " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n",
+      " Reading O16 from /home/pshriwise/data/xs/openmc/nndc_hdf5/O16.h5\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 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: 20000000.0 eV for H1\n",
+      " Maximum neutron transport energy: 20000000 eV for H1\n",
       " Initializing source particles...\n",
       "\n",
       " ====================>     K EIGENVALUE SIMULATION     <====================\n",
@@ -564,21 +568,21 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     = 2.9038e-01 seconds\n",
-      "   Reading cross sections          = 2.7861e-01 seconds\n",
-      " Total time in simulation          = 3.9987e+00 seconds\n",
-      "   Time in transport only          = 3.9829e+00 seconds\n",
-      "   Time in inactive batches        = 4.9461e-01 seconds\n",
-      "   Time in active batches          = 3.5041e+00 seconds\n",
-      "   Time synchronizing fission bank = 5.2042e-03 seconds\n",
-      "     Sampling source sites         = 4.4431e-03 seconds\n",
-      "     SEND/RECV source sites        = 7.3804e-04 seconds\n",
-      "   Time accumulating tallies       = 3.6977e-04 seconds\n",
-      "   Time writing statepoints        = 6.6301e-03 seconds\n",
-      " Total time for finalization       = 1.9945e-04 seconds\n",
-      " Total time elapsed                = 4.2948e+00 seconds\n",
-      " Calculation Rate (inactive)       = 50544.8 particles/second\n",
-      " Calculation Rate (active)         = 28537.8 particles/second\n",
+      " Total time for initialization     = 7.5024e-01 seconds\n",
+      "   Reading cross sections          = 7.4182e-01 seconds\n",
+      " Total time in simulation          = 1.9857e+01 seconds\n",
+      "   Time in transport only          = 1.9835e+01 seconds\n",
+      "   Time in inactive batches        = 2.4766e+00 seconds\n",
+      "   Time in active batches          = 1.7380e+01 seconds\n",
+      "   Time synchronizing fission bank = 1.1568e-02 seconds\n",
+      "     Sampling source sites         = 1.0777e-02 seconds\n",
+      "     SEND/RECV source sites        = 7.5224e-04 seconds\n",
+      "   Time accumulating tallies       = 3.8921e-04 seconds\n",
+      "   Time writing statepoints        = 3.0584e-03 seconds\n",
+      " Total time for finalization       = 1.9732e-04 seconds\n",
+      " Total time elapsed                = 2.0619e+01 seconds\n",
+      " Calculation Rate (inactive)       = 10094.3 particles/second\n",
+      " Calculation Rate (active)         = 5753.6 particles/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -593,7 +597,7 @@
    ],
    "source": [
     "# Run OpenMC\n",
-    "openmc.run()"
+    "statepoint_filename = model.run()"
    ]
   },
   {
@@ -612,12 +616,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Load the last statepoint file\n",
-    "sp = openmc.StatePoint('statepoint.50.h5')"
+    "sp = openmc.StatePoint(statepoint_filename)"
    ]
   },
   {
@@ -636,7 +640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -671,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -704,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -762,7 +766,7 @@
        "0     1         2   total  1.292060   0.007737"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -781,9 +785,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mgxs/mgxs.py:2007: FutureWarning: As the xlwt package is no longer maintained, the xlwt engine will be removed in a future version of pandas. This is the only engine in pandas that supports writing in the xls format. Install openpyxl and write to an xlsx file instead. You can set the option io.excel.xls.writer to 'xlwt' to silence this warning. While this option is deprecated and will also raise a warning, it can be globally set and the warning suppressed.\n",
+      "  df.to_excel(filename + '.xls', index=False)\n"
+     ]
+    }
+   ],
    "source": [
     "absorption.export_xs_data(filename='absorption-xs', format='excel')"
    ]
@@ -797,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -822,7 +835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -863,7 +876,7 @@
        "      <td>6.250000e-01</td>\n",
        "      <td>total</td>\n",
        "      <td>(((total / flux) - (absorption / flux)) - (sca...</td>\n",
-       "      <td>4.440892e-16</td>\n",
+       "      <td>1.110223e-15</td>\n",
        "      <td>0.011435</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -873,7 +886,7 @@
        "      <td>2.000000e+07</td>\n",
        "      <td>total</td>\n",
        "      <td>(((total / flux) - (absorption / flux)) - (sca...</td>\n",
-       "      <td>-2.553513e-15</td>\n",
+       "      <td>-2.997602e-15</td>\n",
        "      <td>0.002567</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -886,11 +899,11 @@
        "1     1         6.25e-01          2.00e+07   total   \n",
        "\n",
        "                                               score      mean  std. dev.  \n",
-       "0  (((total / flux) - (absorption / flux)) - (sca...  4.44e-16   1.14e-02  \n",
-       "1  (((total / flux) - (absorption / flux)) - (sca... -2.55e-15   2.57e-03  "
+       "0  (((total / flux) - (absorption / flux)) - (sca...  1.11e-15   1.14e-02  \n",
+       "1  (((total / flux) - (absorption / flux)) - (sca... -3.00e-15   2.57e-03  "
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -912,7 +925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -980,7 +993,7 @@
        "1  ((absorption / flux) / (total / flux)) 1.94e-02   9.26e-05  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -995,7 +1008,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1063,7 +1076,7 @@
        "1  ((scatter / flux) / (total / flux)) 9.81e-01   3.73e-03  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1085,7 +1098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1153,7 +1166,7 @@
        "1  (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00   3.73e-03  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1169,7 +1182,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1183,9 +1196,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/mgxs-part-ii.ipynb b/mgxs-part-ii.ipynb
index 495f012..a32106c 100644
--- a/mgxs-part-ii.ipynb
+++ b/mgxs-part-ii.ipynb
@@ -44,6 +44,16 @@
     "%matplotlib inline"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a model object to tie together geometry, materials, settings, and tallies\n",
+    "model = openmc.Model()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -53,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,15 +95,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Instantiate a Materials collection\n",
-    "materials_file = openmc.Materials([fuel, water, zircaloy])\n",
-    "\n",
-    "# Export to \"materials.xml\"\n",
-    "materials_file.export_to_xml()"
+    "model.materials = openmc.Materials([fuel, water, zircaloy])"
    ]
   },
   {
@@ -105,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -161,15 +168,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Create Geometry and set root Universe\n",
-    "openmc_geometry = openmc.Geometry(pin_cell_universe)\n",
-    "\n",
-    "# Export to \"geometry.xml\"\n",
-    "openmc_geometry.export_to_xml()"
+    "model.geometry = openmc.Geometry(pin_cell_universe)"
    ]
   },
   {
@@ -181,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,23 +195,22 @@
     "particles = 10000\n",
     "\n",
     "# Instantiate a Settings object\n",
-    "settings_file = openmc.Settings()\n",
-    "settings_file.batches = batches\n",
-    "settings_file.inactive = inactive\n",
-    "settings_file.particles = particles\n",
-    "settings_file.output = {'tallies': True}\n",
+    "settings = openmc.Settings()\n",
+    "settings.batches = batches\n",
+    "settings.inactive = inactive\n",
+    "settings.particles = particles\n",
+    "settings.output = {'tallies': True}\n",
     "\n",
     "# Create an initial uniform spatial source distribution over fissionable zones\n",
     "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
     "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings_file.source = openmc.Source(space=uniform_dist)\n",
+    "settings.source = openmc.Source(space=uniform_dist)\n",
     "\n",
     "# Activate tally precision triggers\n",
-    "settings_file.trigger_active = True\n",
-    "settings_file.trigger_max_batches = settings_file.batches * 4\n",
+    "settings.trigger_active = True\n",
+    "settings.trigger_max_batches = settings.batches * 4\n",
     "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
+    "model.settings = settings"
    ]
   },
   {
@@ -219,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,12 +243,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Extract all Cells filled by Materials\n",
-    "openmc_cells = openmc_geometry.get_all_material_cells().values()\n",
+    "openmc_cells = model.geometry.get_all_material_cells().values()\n",
     "\n",
     "# Create dictionary to store multi-group cross sections for all cells\n",
     "xs_library = {}\n",
@@ -253,11 +256,11 @@
     "# Instantiate 8-group cross sections for each cell\n",
     "for cell in openmc_cells:\n",
     "    xs_library[cell.id] = {}\n",
-    "    xs_library[cell.id]['transport']  = mgxs.TransportXS(groups=fine_groups)\n",
-    "    xs_library[cell.id]['fission'] = mgxs.FissionXS(groups=fine_groups)\n",
-    "    xs_library[cell.id]['nu-fission'] = mgxs.FissionXS(groups=fine_groups, nu=True)\n",
-    "    xs_library[cell.id]['nu-scatter'] = mgxs.ScatterMatrixXS(groups=fine_groups, nu=True)\n",
-    "    xs_library[cell.id]['chi'] = mgxs.Chi(groups=fine_groups)"
+    "    xs_library[cell.id]['transport']  = mgxs.TransportXS(energy_groups=fine_groups)\n",
+    "    xs_library[cell.id]['fission'] = mgxs.FissionXS(energy_groups=fine_groups)\n",
+    "    xs_library[cell.id]['nu-fission'] = mgxs.FissionXS(energy_groups=fine_groups, nu=True)\n",
+    "    xs_library[cell.id]['nu-scatter'] = mgxs.ScatterMatrixXS(energy_groups=fine_groups, nu=True)\n",
+    "    xs_library[cell.id]['chi'] = mgxs.Chi(energy_groups=fine_groups)"
    ]
   },
   {
@@ -269,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -291,33 +294,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=53.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=21.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=3.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=4.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=41.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=15.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Instantiate an empty Tallies object\n",
-    "tallies_file = openmc.Tallies()\n",
+    "tallies = openmc.Tallies()\n",
     "\n",
     "# Iterate over all cells and cross section types\n",
     "for cell in openmc_cells:\n",
@@ -331,10 +313,9 @@
     "                \n",
     "        # Add OpenMC tallies to the tallies file for XML generation\n",
     "        for tally in xs_library[cell.id][rxn_type].tallies.values():\n",
-    "            tallies_file.append(tally, merge=True)\n",
-    "\n",
-    "# Export to \"tallies.xml\"\n",
-    "tallies_file.export_to_xml()"
+    "            tallies.append(tally, merge=True)\n",
+    "            \n",
+    "model.tallies = tallies"
    ]
   },
   {
@@ -346,9 +327,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=53.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=21.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=2.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=3.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=4.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=41.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=15.\n",
+      "  warn(msg, IDWarning)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -377,211 +378,211 @@
       "                          ########     %%%%%%%%%%%%%%\n",
       "                                      %%%%%%%%%%%\n",
       "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2019 MIT and OpenMC contributors\n",
-      "           License | http://openmc.readthedocs.io/en/latest/license.html\n",
-      "           Version | 0.11.0-dev\n",
-      "          Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n",
-      "         Date/Time | 2019-07-19 07:08:16\n",
-      "    OpenMP Threads | 4\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 | 2022-10-05 23:55:59\n",
+      "   MPI Processes | 1\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 /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n",
-      " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n",
-      " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n",
-      " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n",
-      " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n",
-      " Maximum neutron transport energy: 20000000.000000 eV for U235\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 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    1.20332\n",
-      "        2/1    1.22209\n",
-      "        3/1    1.24322\n",
-      "        4/1    1.21622\n",
-      "        5/1    1.25850\n",
-      "        6/1    1.22581\n",
-      "        7/1    1.21118\n",
-      "        8/1    1.23377\n",
-      "        9/1    1.24254\n",
-      "       10/1    1.21241\n",
-      "       11/1    1.21042\n",
-      "       12/1    1.23539    1.22290 +/- 0.01249\n",
-      "       13/1    1.22436    1.22339 +/- 0.00723\n",
-      "       14/1    1.22888    1.22476 +/- 0.00529\n",
-      "       15/1    1.22553    1.22491 +/- 0.00410\n",
-      "       16/1    1.24194    1.22775 +/- 0.00439\n",
-      "       17/1    1.24755    1.23058 +/- 0.00466\n",
-      "       18/1    1.21117    1.22815 +/- 0.00471\n",
-      "       19/1    1.22530    1.22784 +/- 0.00417\n",
-      "       20/1    1.20762    1.22582 +/- 0.00424\n",
-      "       21/1    1.20377    1.22381 +/- 0.00433\n",
-      "       22/1    1.24305    1.22541 +/- 0.00426\n",
-      "       23/1    1.22434    1.22533 +/- 0.00392\n",
-      "       24/1    1.22937    1.22562 +/- 0.00364\n",
-      "       25/1    1.22458    1.22555 +/- 0.00339\n",
-      "       26/1    1.18978    1.22332 +/- 0.00388\n",
-      "       27/1    1.20582    1.22229 +/- 0.00379\n",
-      "       28/1    1.22719    1.22256 +/- 0.00358\n",
-      "       29/1    1.21307    1.22206 +/- 0.00343\n",
-      "       30/1    1.20915    1.22141 +/- 0.00331\n",
-      "       31/1    1.22799    1.22173 +/- 0.00317\n",
-      "       32/1    1.21251    1.22131 +/- 0.00305\n",
-      "       33/1    1.20540    1.22062 +/- 0.00299\n",
-      "       34/1    1.20052    1.21978 +/- 0.00299\n",
-      "       35/1    1.24552    1.22081 +/- 0.00304\n",
-      "       36/1    1.21685    1.22066 +/- 0.00293\n",
-      "       37/1    1.22395    1.22078 +/- 0.00282\n",
-      "       38/1    1.22379    1.22089 +/- 0.00272\n",
-      "       39/1    1.20951    1.22049 +/- 0.00265\n",
-      "       40/1    1.25199    1.22154 +/- 0.00277\n",
-      "       41/1    1.23243    1.22190 +/- 0.00270\n",
-      "       42/1    1.20973    1.22152 +/- 0.00264\n",
-      "       43/1    1.24682    1.22228 +/- 0.00268\n",
-      "       44/1    1.20694    1.22183 +/- 0.00263\n",
-      "       45/1    1.22196    1.22183 +/- 0.00256\n",
-      "       46/1    1.20687    1.22142 +/- 0.00252\n",
-      "       47/1    1.22023    1.22139 +/- 0.00245\n",
-      "       48/1    1.22204    1.22140 +/- 0.00239\n",
-      "       49/1    1.22077    1.22139 +/- 0.00232\n",
-      "       50/1    1.23166    1.22164 +/- 0.00228\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.17623 for flux in tally 53\n",
-      " The estimated number of batches is 66\n",
+      "        1/1    1.23300\n",
+      "        2/1    1.22712\n",
+      "        3/1    1.23403\n",
+      "        4/1    1.23023\n",
+      "        5/1    1.20250\n",
+      "        6/1    1.20565\n",
+      "        7/1    1.23034\n",
+      "        8/1    1.21931\n",
+      "        9/1    1.23826\n",
+      "       10/1    1.25090\n",
+      "       11/1    1.21111\n",
+      "       12/1    1.20786    1.20948 +/- 0.00162\n",
+      "       13/1    1.23784    1.21894 +/- 0.00950\n",
+      "       14/1    1.22692    1.22093 +/- 0.00701\n",
+      "       15/1    1.20622    1.21799 +/- 0.00617\n",
+      "       16/1    1.20024    1.21503 +/- 0.00584\n",
+      "       17/1    1.20624    1.21378 +/- 0.00510\n",
+      "       18/1    1.20491    1.21267 +/- 0.00455\n",
+      "       19/1    1.20434    1.21174 +/- 0.00412\n",
+      "       20/1    1.22590    1.21316 +/- 0.00395\n",
+      "       21/1    1.20102    1.21206 +/- 0.00374\n",
+      "       22/1    1.23117    1.21365 +/- 0.00376\n",
+      "       23/1    1.22446    1.21448 +/- 0.00356\n",
+      "       24/1    1.22469    1.21521 +/- 0.00338\n",
+      "       25/1    1.23859    1.21677 +/- 0.00351\n",
+      "       26/1    1.25129    1.21893 +/- 0.00393\n",
+      "       27/1    1.22524    1.21930 +/- 0.00371\n",
+      "       28/1    1.22082    1.21938 +/- 0.00350\n",
+      "       29/1    1.21838    1.21933 +/- 0.00331\n",
+      "       30/1    1.24252    1.22049 +/- 0.00335\n",
+      "       31/1    1.23912    1.22138 +/- 0.00330\n",
+      "       32/1    1.22704    1.22163 +/- 0.00316\n",
+      "       33/1    1.22330    1.22171 +/- 0.00302\n",
+      "       34/1    1.20966    1.22120 +/- 0.00294\n",
+      "       35/1    1.20998    1.22076 +/- 0.00285\n",
+      "       36/1    1.21069    1.22037 +/- 0.00277\n",
+      "       37/1    1.20250    1.21971 +/- 0.00274\n",
+      "       38/1    1.20573    1.21921 +/- 0.00269\n",
+      "       39/1    1.23647    1.21980 +/- 0.00266\n",
+      "       40/1    1.22719    1.22005 +/- 0.00258\n",
+      "       41/1    1.23410    1.22050 +/- 0.00254\n",
+      "       42/1    1.23220    1.22087 +/- 0.00249\n",
+      "       43/1    1.23399    1.22127 +/- 0.00244\n",
+      "       44/1    1.23143    1.22156 +/- 0.00239\n",
+      "       45/1    1.23144    1.22185 +/- 0.00234\n",
+      "       46/1    1.22653    1.22198 +/- 0.00227\n",
+      "       47/1    1.21239    1.22172 +/- 0.00223\n",
+      "       48/1    1.22858    1.22190 +/- 0.00218\n",
+      "       49/1    1.20952    1.22158 +/- 0.00214\n",
+      "       50/1    1.20497    1.22117 +/- 0.00213\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.2584438813928658 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 74\n",
       " Creating state point statepoint.050.h5...\n",
-      "       51/1    1.20071    1.22113 +/- 0.00228\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.26577 for flux in tally 53\n",
-      " The estimated number of batches is 76\n",
-      "       52/1    1.21423    1.22097 +/- 0.00223\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.24 for flux in tally 53\n",
-      " The estimated number of batches is 75\n",
-      "       53/1    1.25595    1.22178 +/- 0.00233\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.2112 for flux in tally 53\n",
-      " The estimated number of batches is 74\n",
-      "       54/1    1.21806    1.22170 +/- 0.00227\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.18484 for flux in tally 53\n",
+      "       51/1    1.22506    1.22126 +/- 0.00208\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.2360657756693463 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 73\n",
+      "       52/1    1.22892    1.22144 +/- 0.00204\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.206696013966578 for flux in tally\n",
+      " 53\n",
       " The estimated number of batches is 72\n",
-      "       55/1    1.22911    1.22186 +/- 0.00223\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.1596 for flux in tally 53\n",
-      " The estimated number of batches is 71\n",
-      "       56/1    1.23054    1.22205 +/- 0.00219\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.13453 for flux in tally 53\n",
+      "       53/1    1.20425    1.22104 +/- 0.00203\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.1794033562761703 for flux in tally\n",
+      " 53\n",
       " The estimated number of batches is 70\n",
-      "       57/1    1.19384    1.22145 +/- 0.00222\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.11914 for flux in tally 53\n",
+      "       54/1    1.18761    1.22028 +/- 0.00212\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.1745090363258377 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 71\n",
+      "       55/1    1.21624    1.22019 +/- 0.00208\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.1488291964971802 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 70\n",
+      "       56/1    1.22056    1.22020 +/- 0.00203\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.1253755542228503 for flux in tally\n",
+      " 53\n",
       " The estimated number of batches is 69\n",
-      "       58/1    1.20625    1.22114 +/- 0.00220\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.11471 for flux in tally 53\n",
+      "       57/1    1.22213    1.22024 +/- 0.00199\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.1013090664425782 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 68\n",
+      "       58/1    1.23192    1.22049 +/- 0.00196\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.0814142069475086 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 67\n",
+      "       59/1    1.23142    1.22071 +/- 0.00193\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.0688144272705478 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 66\n",
+      "       60/1    1.23744    1.22104 +/- 0.00192\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.0845694915740336 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 69\n",
+      "       61/1    1.24681    1.22155 +/- 0.00195\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.0650160561418593 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 68\n",
+      "       62/1    1.23159    1.22174 +/- 0.00192\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.0628835462503399 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 69\n",
+      "       63/1    1.21588    1.22163 +/- 0.00189\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.0440750211689802 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 68\n",
+      "       64/1    1.21867    1.22158 +/- 0.00186\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.0258844139280563 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 67\n",
+      "       65/1    1.22410    1.22162 +/- 0.00182\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.038412688234605 for flux in tally\n",
+      " 53\n",
       " The estimated number of batches is 70\n",
-      "       59/1    1.21977    1.22111 +/- 0.00216\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.10334 for flux in tally 53\n",
+      "       66/1    1.20786    1.22138 +/- 0.00181\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.0213633609643367 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 69\n",
+      "       67/1    1.21064    1.22119 +/- 0.00178\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.019182247265982 for flux in tally\n",
+      " 53\n",
       " The estimated number of batches is 70\n",
-      "       60/1    1.20813    1.22085 +/- 0.00213\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.09813 for flux in tally 53\n",
+      "       68/1    1.23857    1.22149 +/- 0.00178\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.0128609683027328 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 70\n",
+      "       69/1    1.21603    1.22139 +/- 0.00175\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.0113107304662599 for flux in tally\n",
+      " 53\n",
       " The estimated number of batches is 71\n",
-      "       61/1    1.22077    1.22085 +/- 0.00209\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.10221 for flux in tally 53\n",
-      " The estimated number of batches is 72\n",
-      "       62/1    1.21956    1.22082 +/- 0.00205\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.11395 for flux in tally 53\n",
-      " The estimated number of batches is 75\n",
-      "       63/1    1.22360    1.22087 +/- 0.00201\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.09283 for flux in tally 53\n",
+      "       70/1    1.19730    1.22099 +/- 0.00177\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.025023774532644 for flux in tally\n",
+      " 53\n",
       " The estimated number of batches is 74\n",
-      "       64/1    1.23955    1.22122 +/- 0.00200\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.07416 for flux in tally 53\n",
+      "       71/1    1.22459    1.22105 +/- 0.00174\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.0084011441757024 for flux in tally\n",
+      " 53\n",
       " The estimated number of batches is 73\n",
-      "       65/1    1.21143    1.22104 +/- 0.00197\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.06461 for flux in tally 53\n",
-      " The estimated number of batches is 73\n",
-      "       66/1    1.21791    1.22099 +/- 0.00194\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.13207 for flux in tally 53\n",
-      " The estimated number of batches is 82\n",
-      "       67/1    1.24897    1.22148 +/- 0.00196\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.11277 for flux in tally 53\n",
-      " The estimated number of batches is 81\n",
-      "       68/1    1.22221    1.22149 +/- 0.00193\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.09514 for flux in tally 53\n",
-      " The estimated number of batches is 80\n",
-      "       69/1    1.25627    1.22208 +/- 0.00199\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.07653 for flux in tally 53\n",
-      " The estimated number of batches is 79\n",
-      "       70/1    1.21493    1.22196 +/- 0.00196\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.12831 for flux in tally 53\n",
-      " The estimated number of batches is 87\n",
-      "       71/1    1.23406    1.22216 +/- 0.00193\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.11005 for flux in tally 53\n",
-      " The estimated number of batches is 86\n",
-      "       72/1    1.23842    1.22242 +/- 0.00192\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.09352 for flux in tally 53\n",
-      " The estimated number of batches is 85\n",
-      "       73/1    1.24542    1.22279 +/- 0.00193\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.08766 for flux in tally 53\n",
-      " The estimated number of batches is 85\n",
-      "       74/1    1.21314    1.22263 +/- 0.00190\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Triggers unsatisfied, max unc./thresh. is 1.07419 for flux in tally 53\n",
-      " The estimated number of batches is 84\n",
-      "       75/1    1.26484    1.22328 +/- 0.00198\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.06788 for flux in tally 53\n",
-      " The estimated number of batches is 85\n",
-      "       76/1    1.22243    1.22327 +/- 0.00195\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.05164 for flux in tally 53\n",
-      " The estimated number of batches is 83\n",
-      "       77/1    1.21865    1.22320 +/- 0.00192\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.04022 for flux in tally 53\n",
-      " The estimated number of batches is 83\n",
-      "       78/1    1.23500    1.22338 +/- 0.00190\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.0275 for flux in tally 53\n",
-      " The estimated number of batches is 82\n",
-      "       79/1    1.22125    1.22334 +/- 0.00187\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.0283 for flux in tally 53\n",
-      " The estimated number of batches is 83\n",
-      "       80/1    1.23793    1.22355 +/- 0.00186\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.01363 for flux in tally 53\n",
-      " The estimated number of batches is 82\n",
-      "       81/1    1.24238    1.22382 +/- 0.00185\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.01172 for flux in tally 53\n",
-      " The estimated number of batches is 83\n",
-      "       82/1    1.23493    1.22397 +/- 0.00183\n",
-      " Triggers satisfied for batch 82\n",
-      " Creating state point statepoint.082.h5...\n",
+      "       72/1    1.24564    1.22145 +/- 0.00176\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.0083223109528934 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 74\n",
+      "       73/1    1.21948    1.22142 +/- 0.00173\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.0051824390021815 for flux in tally\n",
+      " 53\n",
+      " The estimated number of batches is 74\n",
+      "       74/1    1.20923    1.22123 +/- 0.00171\n",
+      " Triggers satisfied for batch 74\n",
+      " Creating state point statepoint.074.h5...\n",
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     = 9.5644e-01 seconds\n",
-      "   Reading cross sections          = 9.0579e-01 seconds\n",
-      " Total time in simulation          = 9.9887e+01 seconds\n",
-      "   Time in transport only          = 9.9333e+01 seconds\n",
-      "   Time in inactive batches        = 5.4841e+00 seconds\n",
-      "   Time in active batches          = 9.4403e+01 seconds\n",
-      "   Time synchronizing fission bank = 7.3998e-02 seconds\n",
-      "     Sampling source sites         = 5.9021e-02 seconds\n",
-      "     SEND/RECV source sites        = 1.4787e-02 seconds\n",
-      "   Time accumulating tallies       = 1.2234e-03 seconds\n",
-      " Total time for finalization       = 2.8416e-02 seconds\n",
-      " Total time elapsed                = 1.0094e+02 seconds\n",
-      " Calculation Rate (inactive)       = 18234.5 particles/second\n",
-      " Calculation Rate (active)         = 7626.89 particles/second\n",
+      " Total time for initialization     = 1.0770e-01 seconds\n",
+      "   Reading cross sections          = 1.0300e-01 seconds\n",
+      " Total time in simulation          = 2.9592e+01 seconds\n",
+      "   Time in transport only          = 2.9528e+01 seconds\n",
+      "   Time in inactive batches        = 1.8603e+00 seconds\n",
+      "   Time in active batches          = 2.7732e+01 seconds\n",
+      "   Time synchronizing fission bank = 4.2199e-02 seconds\n",
+      "     Sampling source sites         = 2.5148e-02 seconds\n",
+      "     SEND/RECV source sites        = 1.6948e-02 seconds\n",
+      "   Time accumulating tallies       = 1.1797e-03 seconds\n",
+      "   Time writing statepoints        = 8.0299e-03 seconds\n",
+      " Total time for finalization       = 1.1328e-03 seconds\n",
+      " Total time elapsed                = 2.9709e+01 seconds\n",
+      " Calculation Rate (inactive)       = 53753.6 particles/second\n",
+      " Calculation Rate (active)         = 23078.2 particles/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
-      " k-effective (Collision)     = 1.22348 +/- 0.00169\n",
-      " k-effective (Track-length)  = 1.22397 +/- 0.00183\n",
-      " k-effective (Absorption)    = 1.22467 +/- 0.00117\n",
-      " Combined k-effective        = 1.22448 +/- 0.00108\n",
+      " k-effective (Collision)     = 1.22070 +/- 0.00153\n",
+      " k-effective (Track-length)  = 1.22123 +/- 0.00171\n",
+      " k-effective (Absorption)    = 1.22336 +/- 0.00157\n",
+      " Combined k-effective        = 1.22204 +/- 0.00135\n",
       " Leakage Fraction            = 0.00000 +/- 0.00000\n",
       "\n"
      ]
@@ -589,7 +590,7 @@
    ],
    "source": [
     "# Run OpenMC\n",
-    "openmc.run()"
+    "sp_file = model.run()"
    ]
   },
   {
@@ -608,12 +609,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Load the last statepoint file\n",
-    "sp = openmc.StatePoint('statepoint.082.h5')"
+    "sp = openmc.StatePoint(sp_file)"
    ]
   },
   {
@@ -625,7 +626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -658,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -671,25 +672,25 @@
       "\tDomain ID      =\t1\n",
       "\tNuclide        =\tU235\n",
       "\tCross Sections [barns]:\n",
-      "            Group 1 [821000.0   - 20000000.0eV]:\t3.30e+00 +/- 2.14e-01%\n",
-      "            Group 2 [5530.0     - 821000.0  eV]:\t3.96e+00 +/- 1.33e-01%\n",
-      "            Group 3 [4.0        - 5530.0    eV]:\t5.50e+01 +/- 2.29e-01%\n",
-      "            Group 4 [0.625      - 4.0       eV]:\t8.85e+01 +/- 3.10e-01%\n",
-      "            Group 5 [0.28       - 0.625     eV]:\t2.90e+02 +/- 3.94e-01%\n",
-      "            Group 6 [0.14       - 0.28      eV]:\t4.49e+02 +/- 4.12e-01%\n",
-      "            Group 7 [0.058      - 0.14      eV]:\t6.87e+02 +/- 3.01e-01%\n",
-      "            Group 8 [0.0        - 0.058     eV]:\t1.44e+03 +/- 2.79e-01%\n",
+      "            Group 1 [821000.0   - 20000000.0eV]:\t3.30e+00 +/- 2.34e-01%\n",
+      "            Group 2 [5530.0     - 821000.0  eV]:\t3.96e+00 +/- 1.53e-01%\n",
+      "            Group 3 [4.0        - 5530.0    eV]:\t5.52e+01 +/- 2.15e-01%\n",
+      "            Group 4 [0.625      - 4.0       eV]:\t8.83e+01 +/- 3.51e-01%\n",
+      "            Group 5 [0.28       - 0.625     eV]:\t2.90e+02 +/- 5.28e-01%\n",
+      "            Group 6 [0.14       - 0.28      eV]:\t4.49e+02 +/- 4.05e-01%\n",
+      "            Group 7 [0.058      - 0.14      eV]:\t6.87e+02 +/- 3.36e-01%\n",
+      "            Group 8 [0.0        - 0.058     eV]:\t1.44e+03 +/- 2.54e-01%\n",
       "\n",
       "\tNuclide        =\tU238\n",
       "\tCross Sections [barns]:\n",
-      "            Group 1 [821000.0   - 20000000.0eV]:\t1.06e+00 +/- 2.53e-01%\n",
-      "            Group 2 [5530.0     - 821000.0  eV]:\t1.21e-03 +/- 2.60e-01%\n",
-      "            Group 3 [4.0        - 5530.0    eV]:\t5.73e-04 +/- 2.93e+00%\n",
-      "            Group 4 [0.625      - 4.0       eV]:\t6.54e-06 +/- 2.72e-01%\n",
-      "            Group 5 [0.28       - 0.625     eV]:\t1.07e-05 +/- 3.83e-01%\n",
-      "            Group 6 [0.14       - 0.28      eV]:\t1.55e-05 +/- 4.13e-01%\n",
-      "            Group 7 [0.058      - 0.14      eV]:\t2.30e-05 +/- 3.01e-01%\n",
-      "            Group 8 [0.0        - 0.058     eV]:\t4.24e-05 +/- 2.79e-01%\n",
+      "            Group 1 [821000.0   - 20000000.0eV]:\t1.06e+00 +/- 2.64e-01%\n",
+      "            Group 2 [5530.0     - 821000.0  eV]:\t1.21e-03 +/- 2.83e-01%\n",
+      "            Group 3 [4.0        - 5530.0    eV]:\t5.51e-04 +/- 2.99e+00%\n",
+      "            Group 4 [0.625      - 4.0       eV]:\t6.54e-06 +/- 3.28e-01%\n",
+      "            Group 5 [0.28       - 0.625     eV]:\t1.07e-05 +/- 5.12e-01%\n",
+      "            Group 6 [0.14       - 0.28      eV]:\t1.55e-05 +/- 4.08e-01%\n",
+      "            Group 7 [0.058      - 0.14      eV]:\t2.30e-05 +/- 3.35e-01%\n",
+      "            Group 8 [0.0        - 0.058     eV]:\t4.24e-05 +/- 2.53e-01%\n",
       "\n",
       "\n",
       "\n"
@@ -710,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -722,14 +723,14 @@
       "\tDomain Type    =\tcell\n",
       "\tDomain ID      =\t1\n",
       "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [821000.0   - 20000000.0eV]:\t2.52e-02 +/- 2.41e-01%\n",
-      "            Group 2 [5530.0     - 821000.0  eV]:\t1.51e-03 +/- 1.31e-01%\n",
-      "            Group 3 [4.0        - 5530.0    eV]:\t2.07e-02 +/- 2.29e-01%\n",
-      "            Group 4 [0.625      - 4.0       eV]:\t3.32e-02 +/- 3.10e-01%\n",
-      "            Group 5 [0.28       - 0.625     eV]:\t1.09e-01 +/- 3.94e-01%\n",
-      "            Group 6 [0.14       - 0.28      eV]:\t1.69e-01 +/- 4.12e-01%\n",
-      "            Group 7 [0.058      - 0.14      eV]:\t2.58e-01 +/- 3.01e-01%\n",
-      "            Group 8 [0.0        - 0.058     eV]:\t5.40e-01 +/- 2.79e-01%\n",
+      "            Group 1 [821000.0   - 20000000.0eV]:\t2.52e-02 +/- 2.51e-01%\n",
+      "            Group 2 [5530.0     - 821000.0  eV]:\t1.51e-03 +/- 1.50e-01%\n",
+      "            Group 3 [4.0        - 5530.0    eV]:\t2.07e-02 +/- 2.14e-01%\n",
+      "            Group 4 [0.625      - 4.0       eV]:\t3.31e-02 +/- 3.51e-01%\n",
+      "            Group 5 [0.28       - 0.625     eV]:\t1.09e-01 +/- 5.28e-01%\n",
+      "            Group 6 [0.14       - 0.28      eV]:\t1.69e-01 +/- 4.05e-01%\n",
+      "            Group 7 [0.058      - 0.14      eV]:\t2.58e-01 +/- 3.36e-01%\n",
+      "            Group 8 [0.0        - 0.058     eV]:\t5.40e-01 +/- 2.54e-01%\n",
       "\n",
       "\n",
       "\n"
@@ -750,7 +751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -789,8 +790,8 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>H1</td>\n",
-       "      <td>0.233991</td>\n",
-       "      <td>0.003752</td>\n",
+       "      <td>0.233979</td>\n",
+       "      <td>0.003921</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>127</th>\n",
@@ -798,8 +799,8 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>O16</td>\n",
-       "      <td>1.569288</td>\n",
-       "      <td>0.006360</td>\n",
+       "      <td>1.566955</td>\n",
+       "      <td>0.006599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>124</th>\n",
@@ -807,8 +808,8 @@
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>H1</td>\n",
-       "      <td>1.587279</td>\n",
-       "      <td>0.003098</td>\n",
+       "      <td>1.589223</td>\n",
+       "      <td>0.003224</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>125</th>\n",
@@ -816,8 +817,8 @@
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>O16</td>\n",
-       "      <td>0.285599</td>\n",
-       "      <td>0.001422</td>\n",
+       "      <td>0.286382</td>\n",
+       "      <td>0.001478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>122</th>\n",
@@ -825,8 +826,8 @@
        "      <td>1</td>\n",
        "      <td>3</td>\n",
        "      <td>H1</td>\n",
-       "      <td>0.010482</td>\n",
-       "      <td>0.000220</td>\n",
+       "      <td>0.011405</td>\n",
+       "      <td>0.000244</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>123</th>\n",
@@ -843,8 +844,8 @@
        "      <td>1</td>\n",
        "      <td>4</td>\n",
        "      <td>H1</td>\n",
+       "      <td>0.000015</td>\n",
        "      <td>0.000009</td>\n",
-       "      <td>0.000006</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>121</th>\n",
@@ -879,19 +880,19 @@
       ],
       "text/plain": [
        "     cell  group in  group out nuclide      mean  std. dev.\n",
-       "126     3         1          1      H1  0.233991   0.003752\n",
-       "127     3         1          1     O16  1.569288   0.006360\n",
-       "124     3         1          2      H1  1.587279   0.003098\n",
-       "125     3         1          2     O16  0.285599   0.001422\n",
-       "122     3         1          3      H1  0.010482   0.000220\n",
+       "126     3         1          1      H1  0.233979   0.003921\n",
+       "127     3         1          1     O16  1.566955   0.006599\n",
+       "124     3         1          2      H1  1.589223   0.003224\n",
+       "125     3         1          2     O16  0.286382   0.001478\n",
+       "122     3         1          3      H1  0.011405   0.000244\n",
        "123     3         1          3     O16  0.000000   0.000000\n",
-       "120     3         1          4      H1  0.000009   0.000006\n",
+       "120     3         1          4      H1  0.000015   0.000009\n",
        "121     3         1          4     O16  0.000000   0.000000\n",
        "118     3         1          5      H1  0.000005   0.000005\n",
        "119     3         1          5     O16  0.000000   0.000000"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -911,7 +912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -931,7 +932,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -944,18 +945,18 @@
       "\tDomain ID      =\t1\n",
       "\tNuclide        =\tU235\n",
       "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [0.625      - 20000000.0eV]:\t7.79e-03 +/- 2.12e-01%\n",
-      "            Group 2 [0.0        - 0.625     eV]:\t1.82e-01 +/- 1.92e-01%\n",
+      "            Group 1 [0.625      - 20000000.0eV]:\t7.77e-03 +/- 2.18e-01%\n",
+      "            Group 2 [0.0        - 0.625     eV]:\t1.82e-01 +/- 1.85e-01%\n",
       "\n",
       "\tNuclide        =\tU238\n",
       "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [0.625      - 20000000.0eV]:\t2.17e-01 +/- 1.12e-01%\n",
-      "            Group 2 [0.0        - 0.625     eV]:\t2.53e-01 +/- 1.89e-01%\n",
+      "            Group 1 [0.625      - 20000000.0eV]:\t2.17e-01 +/- 1.32e-01%\n",
+      "            Group 2 [0.0        - 0.625     eV]:\t2.53e-01 +/- 1.91e-01%\n",
       "\n",
       "\tNuclide        =\tO16\n",
       "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [0.625      - 20000000.0eV]:\t1.45e-01 +/- 1.12e-01%\n",
-      "            Group 2 [0.0        - 0.625     eV]:\t1.74e-01 +/- 2.03e-01%\n",
+      "            Group 1 [0.625      - 20000000.0eV]:\t1.45e-01 +/- 1.26e-01%\n",
+      "            Group 2 [0.0        - 0.625     eV]:\t1.74e-01 +/- 2.02e-01%\n",
       "\n",
       "\n",
       "\n"
@@ -968,7 +969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1005,48 +1006,48 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>20.763062</td>\n",
-       "      <td>0.044093</td>\n",
+       "      <td>20.715441</td>\n",
+       "      <td>0.045146</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>U238</td>\n",
-       "      <td>9.579086</td>\n",
-       "      <td>0.010757</td>\n",
+       "      <td>9.579757</td>\n",
+       "      <td>0.012606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>O16</td>\n",
-       "      <td>3.157274</td>\n",
-       "      <td>0.003531</td>\n",
+       "      <td>3.155966</td>\n",
+       "      <td>0.003977</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>U235</td>\n",
-       "      <td>485.349036</td>\n",
-       "      <td>0.930937</td>\n",
+       "      <td>485.656482</td>\n",
+       "      <td>0.899766</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>U238</td>\n",
-       "      <td>11.199167</td>\n",
-       "      <td>0.021167</td>\n",
+       "      <td>11.191961</td>\n",
+       "      <td>0.021372</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>O16</td>\n",
-       "      <td>3.788383</td>\n",
-       "      <td>0.007676</td>\n",
+       "      <td>3.790699</td>\n",
+       "      <td>0.007656</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1054,15 +1055,15 @@
       ],
       "text/plain": [
        "   cell  group in nuclide        mean  std. dev.\n",
-       "3     1         1    U235   20.763062   0.044093\n",
-       "4     1         1    U238    9.579086   0.010757\n",
-       "5     1         1     O16    3.157274   0.003531\n",
-       "0     1         2    U235  485.349036   0.930937\n",
-       "1     1         2    U238   11.199167   0.021167\n",
-       "2     1         2     O16    3.788383   0.007676"
+       "3     1         1    U235   20.715441   0.045146\n",
+       "4     1         1    U238    9.579757   0.012606\n",
+       "5     1         1     O16    3.155966   0.003977\n",
+       "0     1         2    U235  485.656482   0.899766\n",
+       "1     1         2    U238   11.191961   0.021372\n",
+       "2     1         2     O16    3.790699   0.007656"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1088,7 +1089,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1105,7 +1106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1148,7 +1149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1173,7 +1174,6 @@
       "[  NORMAL ]  Progress Segmenting 2D tracks: 100.00 %\n",
       "[  NORMAL ]  Initializing FSR lookup vectors\n",
       "[  NORMAL ]  Total number of FSRs 3\n",
-      "[  RESULT ]  Total Track Generation & Segmentation Time...........2.5566E-02 sec\n",
       "[  NORMAL ]  Initializing MOC eigenvalue solver...\n",
       "[  NORMAL ]  Initializing solver arrays...\n",
       "[  NORMAL ]  Centering segments around FSR centroid...\n",
@@ -1188,490 +1188,480 @@
       "[  NORMAL ]  CMFD acceleration: OFF\n",
       "[  NORMAL ]  Using 1 threads\n",
       "[  NORMAL ]  Computing the eigenvalue...\n",
-      "[  NORMAL ]  Iteration 0:  k_eff = 0.423133   res = 5.671E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -57686 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 1:  k_eff = 0.475953   res = 2.442E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  5282 D.R. = 4.31\n",
-      "[  NORMAL ]  Iteration 2:  k_eff = 0.491468   res = 4.764E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1551 D.R. = 1.95\n",
-      "[  NORMAL ]  Iteration 3:  k_eff = 0.487446   res = 2.253E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -402 D.R. = 0.47\n",
-      "[  NORMAL ]  Iteration 4:  k_eff = 0.483930   res = 6.957E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -351 D.R. = 0.31\n",
-      "[  NORMAL ]  Iteration 5:  k_eff = 0.477280   res = 3.902E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -665 D.R. = 5.61\n",
-      "[  NORMAL ]  Iteration 6:  k_eff = 0.468938   res = 3.161E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -834 D.R. = 0.81\n",
-      "[  NORMAL ]  Iteration 7:  k_eff = 0.460319   res = 2.480E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -861 D.R. = 0.78\n",
-      "[  NORMAL ]  Iteration 8:  k_eff = 0.450591   res = 9.377E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -972 D.R. = 0.38\n",
-      "[  NORMAL ]  Iteration 9:  k_eff = 0.441377   res = 3.085E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -921 D.R. = 3.29\n",
-      "[  NORMAL ]  Iteration 10:  k_eff = 0.431990   res = 1.028E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -938 D.R. = 0.33\n",
-      "[  NORMAL ]  Iteration 11:  k_eff = 0.422932   res = 1.180E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -905 D.R. = 1.15\n",
-      "[  NORMAL ]  Iteration 12:  k_eff = 0.414487   res = 1.633E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -844 D.R. = 1.38\n",
-      "[  NORMAL ]  Iteration 13:  k_eff = 0.406708   res = 1.754E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -777 D.R. = 1.07\n",
-      "[  NORMAL ]  Iteration 14:  k_eff = 0.399378   res = 5.021E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -732 D.R. = 2.86\n",
-      "[  NORMAL ]  Iteration 15:  k_eff = 0.393067   res = 9.074E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -631 D.R. = 0.18\n",
-      "[  NORMAL ]  Iteration 16:  k_eff = 0.387427   res = 4.840E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -564 D.R. = 0.53\n",
-      "[  NORMAL ]  Iteration 17:  k_eff = 0.382668   res = 2.299E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -475 D.R. = 4.75\n",
-      "[  NORMAL ]  Iteration 18:  k_eff = 0.378741   res = 1.573E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -392 D.R. = 0.68\n",
-      "[  NORMAL ]  Iteration 19:  k_eff = 0.375642   res = 7.017E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -309 D.R. = 4.46\n",
-      "[  NORMAL ]  Iteration 20:  k_eff = 0.373489   res = 4.053E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -215 D.R. = 0.58\n",
-      "[  NORMAL ]  Iteration 21:  k_eff = 0.372357   res = 4.235E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -113 D.R. = 1.04\n",
-      "[  NORMAL ]  Iteration 22:  k_eff = 0.371974   res = 6.352E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -38 D.R. = 1.50\n",
-      "[  NORMAL ]  Iteration 23:  k_eff = 0.372581   res = 3.267E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  60 D.R. = 0.51\n",
-      "[  NORMAL ]  Iteration 24:  k_eff = 0.374056   res = 1.573E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  147 D.R. = 0.48\n",
-      "[  NORMAL ]  Iteration 25:  k_eff = 0.376384   res = 3.630E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  232 D.R. = 2.31\n",
-      "[  NORMAL ]  Iteration 26:  k_eff = 0.379563   res = 2.420E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  317 D.R. = 0.07\n",
-      "[  NORMAL ]  Iteration 27:  k_eff = 0.383583   res = 3.146E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  401 D.R. = 13.00\n",
-      "[  NORMAL ]  Iteration 28:  k_eff = 0.388380   res = 1.089E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  479 D.R. = 0.35\n",
-      "[  NORMAL ]  Iteration 29:  k_eff = 0.393938   res = 6.049E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  555 D.R. = 5.56\n",
-      "[  NORMAL ]  Iteration 30:  k_eff = 0.400234   res = 3.267E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  629 D.R. = 0.54\n",
-      "[  NORMAL ]  Iteration 31:  k_eff = 0.407235   res = 2.420E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  700 D.R. = 0.74\n",
-      "[  NORMAL ]  Iteration 32:  k_eff = 0.414884   res = 1.815E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  764 D.R. = 0.75\n",
-      "[  NORMAL ]  Iteration 33:  k_eff = 0.423172   res = 7.259E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  828 D.R. = 0.40\n",
-      "[  NORMAL ]  Iteration 34:  k_eff = 0.432051   res = 6.049E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  887 D.R. = 8.33\n",
-      "[  NORMAL ]  Iteration 35:  k_eff = 0.441471   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  942 D.R. = 0.96\n",
-      "[  NORMAL ]  Iteration 36:  k_eff = 0.451430   res = 2.662E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  995 D.R. = 0.46\n",
-      "[  NORMAL ]  Iteration 37:  k_eff = 0.461853   res = 8.227E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1042 D.R. = 3.09\n",
-      "[  NORMAL ]  Iteration 38:  k_eff = 0.472730   res = 5.928E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1087 D.R. = 0.72\n",
-      "[  NORMAL ]  Iteration 39:  k_eff = 0.484006   res = 2.299E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1127 D.R. = 0.39\n",
-      "[  NORMAL ]  Iteration 40:  k_eff = 0.495653   res = 2.299E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1164 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 41:  k_eff = 0.507634   res = 7.017E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1198 D.R. = 3.05\n",
-      "[  NORMAL ]  Iteration 42:  k_eff = 0.519914   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1227 D.R. = 0.28\n",
-      "[  NORMAL ]  Iteration 43:  k_eff = 0.532458   res = 1.089E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1254 D.R. = 0.56\n",
-      "[  NORMAL ]  Iteration 44:  k_eff = 0.545234   res = 6.291E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1277 D.R. = 5.78\n",
-      "[  NORMAL ]  Iteration 45:  k_eff = 0.558210   res = 3.509E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1297 D.R. = 0.56\n",
-      "[  NORMAL ]  Iteration 46:  k_eff = 0.571353   res = 3.025E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1314 D.R. = 0.86\n",
-      "[  NORMAL ]  Iteration 47:  k_eff = 0.584635   res = 7.259E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1328 D.R. = 0.24\n",
-      "[  NORMAL ]  Iteration 48:  k_eff = 0.598027   res = 4.961E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1339 D.R. = 6.83\n",
-      "[  NORMAL ]  Iteration 49:  k_eff = 0.611500   res = 9.014E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1347 D.R. = 1.82\n",
-      "[  NORMAL ]  Iteration 50:  k_eff = 0.625029   res = 5.203E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1352 D.R. = 0.58\n",
-      "[  NORMAL ]  Iteration 51:  k_eff = 0.638590   res = 1.512E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1356 D.R. = 0.29\n",
-      "[  NORMAL ]  Iteration 52:  k_eff = 0.652158   res = 2.359E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1356 D.R. = 1.56\n",
-      "[  NORMAL ]  Iteration 53:  k_eff = 0.665710   res = 4.598E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1355 D.R. = 1.95\n",
-      "[  NORMAL ]  Iteration 54:  k_eff = 0.679228   res = 2.783E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1351 D.R. = 0.61\n",
-      "[  NORMAL ]  Iteration 55:  k_eff = 0.692689   res = 2.117E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1346 D.R. = 0.76\n",
-      "[  NORMAL ]  Iteration 56:  k_eff = 0.706075   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1338 D.R. = 0.91\n",
-      "[  NORMAL ]  Iteration 57:  k_eff = 0.719370   res = 5.505E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1329 D.R. = 2.84\n",
-      "[  NORMAL ]  Iteration 58:  k_eff = 0.732556   res = 2.238E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1318 D.R. = 0.41\n",
-      "[  NORMAL ]  Iteration 59:  k_eff = 0.745619   res = 7.864E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1306 D.R. = 0.35\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[  NORMAL ]  Iteration 60:  k_eff = 0.758546   res = 4.477E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1292 D.R. = 5.69\n",
-      "[  NORMAL ]  Iteration 61:  k_eff = 0.771323   res = 4.658E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1277 D.R. = 1.04\n",
-      "[  NORMAL ]  Iteration 62:  k_eff = 0.783939   res = 9.679E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1261 D.R. = 0.21\n",
-      "[  NORMAL ]  Iteration 63:  k_eff = 0.796383   res = 4.235E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1244 D.R. = 0.44\n",
-      "[  NORMAL ]  Iteration 64:  k_eff = 0.808646   res = 9.074E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1226 D.R. = 2.14\n",
-      "[  NORMAL ]  Iteration 65:  k_eff = 0.820719   res = 6.412E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1207 D.R. = 7.07\n",
-      "[  NORMAL ]  Iteration 66:  k_eff = 0.832594   res = 2.420E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1187 D.R. = 0.04\n",
-      "[  NORMAL ]  Iteration 67:  k_eff = 0.844264   res = 2.299E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1167 D.R. = 9.50\n",
-      "[  NORMAL ]  Iteration 68:  k_eff = 0.855724   res = 5.928E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1146 D.R. = 2.58\n",
-      "[  NORMAL ]  Iteration 69:  k_eff = 0.866968   res = 6.049E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1124 D.R. = 1.02\n",
-      "[  NORMAL ]  Iteration 70:  k_eff = 0.877992   res = 2.722E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1102 D.R. = 0.45\n",
-      "[  NORMAL ]  Iteration 71:  k_eff = 0.888792   res = 1.633E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1079 D.R. = 0.60\n",
-      "[  NORMAL ]  Iteration 72:  k_eff = 0.899364   res = 2.117E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1057 D.R. = 1.30\n",
-      "[  NORMAL ]  Iteration 73:  k_eff = 0.909708   res = 3.327E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1034 D.R. = 1.57\n",
-      "[  NORMAL ]  Iteration 74:  k_eff = 0.919819   res = 4.477E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1011 D.R. = 1.35\n",
-      "[  NORMAL ]  Iteration 75:  k_eff = 0.929699   res = 3.569E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  987 D.R. = 0.80\n",
-      "[  NORMAL ]  Iteration 76:  k_eff = 0.939346   res = 4.840E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  964 D.R. = 0.14\n",
-      "[  NORMAL ]  Iteration 77:  k_eff = 0.948758   res = 4.840E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  941 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 78:  k_eff = 0.957938   res = 6.049E-10  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  917 D.R. = 0.12\n",
-      "[  NORMAL ]  Iteration 79:  k_eff = 0.966885   res = 2.057E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  894 D.R. = 34.00\n",
-      "[  NORMAL ]  Iteration 80:  k_eff = 0.975601   res = 4.235E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  871 D.R. = 2.06\n",
-      "[  NORMAL ]  Iteration 81:  k_eff = 0.984087   res = 5.868E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  848 D.R. = 1.39\n",
-      "[  NORMAL ]  Iteration 82:  k_eff = 0.992344   res = 7.259E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  825 D.R. = 0.12\n",
-      "[  NORMAL ]  Iteration 83:  k_eff = 1.000375   res = 1.512E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  803 D.R. = 2.08\n",
-      "[  NORMAL ]  Iteration 84:  k_eff = 1.008182   res = 7.864E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  780 D.R. = 0.52\n",
-      "[  NORMAL ]  Iteration 85:  k_eff = 1.015768   res = 7.259E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  758 D.R. = 0.92\n",
-      "[  NORMAL ]  Iteration 86:  k_eff = 1.023136   res = 3.025E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  736 D.R. = 0.42\n",
-      "[  NORMAL ]  Iteration 87:  k_eff = 1.030288   res = 1.210E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  715 D.R. = 4.00\n",
-      "[  NORMAL ]  Iteration 88:  k_eff = 1.037228   res = 3.690E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  693 D.R. = 3.05\n",
-      "[  NORMAL ]  Iteration 89:  k_eff = 1.043960   res = 5.203E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  673 D.R. = 1.41\n",
-      "[  NORMAL ]  Iteration 90:  k_eff = 1.050486   res = 6.231E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  652 D.R. = 1.20\n",
-      "[  NORMAL ]  Iteration 91:  k_eff = 1.056812   res = 6.775E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  632 D.R. = 1.09\n",
-      "[  NORMAL ]  Iteration 92:  k_eff = 1.062939   res = 2.964E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  612 D.R. = 0.44\n",
-      "[  NORMAL ]  Iteration 93:  k_eff = 1.068872   res = 5.505E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  593 D.R. = 1.86\n",
-      "[  NORMAL ]  Iteration 94:  k_eff = 1.074616   res = 4.235E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  574 D.R. = 0.08\n",
-      "[  NORMAL ]  Iteration 95:  k_eff = 1.080173   res = 2.541E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  555 D.R. = 6.00\n",
-      "[  NORMAL ]  Iteration 96:  k_eff = 1.085550   res = 1.996E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  537 D.R. = 0.79\n",
-      "[  NORMAL ]  Iteration 97:  k_eff = 1.090748   res = 3.388E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  519 D.R. = 1.70\n",
-      "[  NORMAL ]  Iteration 98:  k_eff = 1.095774   res = 3.085E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  502 D.R. = 0.91\n",
-      "[  NORMAL ]  Iteration 99:  k_eff = 1.100629   res = 3.267E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  485 D.R. = 1.06\n",
-      "[  NORMAL ]  Iteration 100:  k_eff = 1.105320   res = 3.025E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 469 D.R. = 0.09\n",
-      "[  NORMAL ]  Iteration 101:  k_eff = 1.109851   res = 6.654E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 453 D.R. = 2.20\n",
-      "[  NORMAL ]  Iteration 102:  k_eff = 1.114224   res = 3.569E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 437 D.R. = 5.36\n",
-      "[  NORMAL ]  Iteration 103:  k_eff = 1.118444   res = 5.203E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 421 D.R. = 1.46\n",
-      "[  NORMAL ]  Iteration 104:  k_eff = 1.122516   res = 1.452E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 407 D.R. = 0.28\n",
-      "[  NORMAL ]  Iteration 105:  k_eff = 1.126445   res = 5.445E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 392 D.R. = 0.38\n",
-      "[  NORMAL ]  Iteration 106:  k_eff = 1.130232   res = 2.783E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 378 D.R. = 5.11\n",
-      "[  NORMAL ]  Iteration 107:  k_eff = 1.133884   res = 1.996E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 365 D.R. = 0.72\n",
-      "[  NORMAL ]  Iteration 108:  k_eff = 1.137403   res = 4.235E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 351 D.R. = 2.12\n",
-      "[  NORMAL ]  Iteration 109:  k_eff = 1.140793   res = 3.751E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 339 D.R. = 0.89\n",
-      "[  NORMAL ]  Iteration 110:  k_eff = 1.144059   res = 4.174E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 326 D.R. = 1.11\n",
-      "[  NORMAL ]  Iteration 111:  k_eff = 1.147203   res = 4.416E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 314 D.R. = 1.06\n",
-      "[  NORMAL ]  Iteration 112:  k_eff = 1.150231   res = 3.025E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 302 D.R. = 0.68\n",
-      "[  NORMAL ]  Iteration 113:  k_eff = 1.153146   res = 5.384E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 291 D.R. = 1.78\n",
-      "[  NORMAL ]  Iteration 114:  k_eff = 1.155950   res = 3.569E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 280 D.R. = 0.66\n",
-      "[  NORMAL ]  Iteration 115:  k_eff = 1.158649   res = 5.142E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 269 D.R. = 1.44\n",
-      "[  NORMAL ]  Iteration 116:  k_eff = 1.161244   res = 2.843E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 259 D.R. = 0.55\n",
-      "[  NORMAL ]  Iteration 117:  k_eff = 1.163739   res = 3.267E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 249 D.R. = 1.15\n",
-      "[  NORMAL ]  Iteration 118:  k_eff = 1.166139   res = 5.505E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 239 D.R. = 1.69\n",
-      "[  NORMAL ]  Iteration 119:  k_eff = 1.168445   res = 4.719E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 230 D.R. = 0.86\n",
-      "[  NORMAL ]  Iteration 120:  k_eff = 1.170662   res = 6.170E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 221 D.R. = 1.31\n",
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-      "[  NORMAL ]  Iteration 162:  k_eff = 1.213522   res = 1.996E-08  delta-k (pcm)\n",
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-      "[  NORMAL ]  ...  = 0 D.R. = 5.29\n"
+      "[  NORMAL ]  Iteration 0:  k_eff = 0.423006   res = 1.792E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -57699 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 1:  k_eff = 0.475905   res = 3.879E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  5289 D.R. = 2.1646\n",
+      "[  NORMAL ]  Iteration 2:  k_eff = 0.491392   res = 3.463E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1548 D.R. = 0.8928\n",
+      "[  NORMAL ]  Iteration 3:  k_eff = 0.487335   res = 3.478E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -405 D.R. = 0.1004\n",
+      "[  NORMAL ]  Iteration 4:  k_eff = 0.483788   res = 8.318E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -354 D.R. = 2.3913\n",
+      "[  NORMAL ]  Iteration 5:  k_eff = 0.477110   res = 1.437E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -667 D.R. = 1.7273\n",
+      "[  NORMAL ]  Iteration 6:  k_eff = 0.468745   res = 1.966E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -836 D.R. = 0.1368\n",
+      "[  NORMAL ]  Iteration 7:  k_eff = 0.460108   res = 5.656E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -863 D.R. = 28.7692\n",
+      "[  NORMAL ]  Iteration 8:  k_eff = 0.450367   res = 1.612E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -974 D.R. = 2.8503\n",
+      "[  NORMAL ]  Iteration 9:  k_eff = 0.441143   res = 6.685E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -922 D.R. = 0.4146\n",
+      "[  NORMAL ]  Iteration 10:  k_eff = 0.431749   res = 3.902E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -939 D.R. = 0.5837\n",
+      "[  NORMAL ]  Iteration 11:  k_eff = 0.422685   res = 2.208E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -906 D.R. = 0.5659\n",
+      "[  NORMAL ]  Iteration 12:  k_eff = 0.414237   res = 2.964E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -844 D.R. = 1.3425\n",
+      "[  NORMAL ]  Iteration 13:  k_eff = 0.406456   res = 2.238E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -778 D.R. = 0.7551\n",
+      "[  NORMAL ]  Iteration 14:  k_eff = 0.399125   res = 2.117E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -733 D.R. = 0.9459\n",
+      "[  NORMAL ]  Iteration 15:  k_eff = 0.392814   res = 3.569E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -631 D.R. = 1.6857\n",
+      "[  NORMAL ]  Iteration 16:  k_eff = 0.387174   res = 3.206E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -564 D.R. = 0.8983\n",
+      "[  NORMAL ]  Iteration 17:  k_eff = 0.382416   res = 5.263E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -475 D.R. = 1.6415\n",
+      "[  NORMAL ]  Iteration 18:  k_eff = 0.378489   res = 4.537E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -392 D.R. = 0.8621\n",
+      "[  NORMAL ]  Iteration 19:  k_eff = 0.375392   res = 3.085E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -309 D.R. = 0.6800\n",
+      "[  NORMAL ]  Iteration 20:  k_eff = 0.373239   res = 9.679E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -215 D.R. = 0.3137\n",
+      "[  NORMAL ]  Iteration 21:  k_eff = 0.372107   res = 1.210E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -113 D.R. = 0.1250\n",
+      "[  NORMAL ]  Iteration 22:  k_eff = 0.371724   res = 4.235E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -38 D.R. = 3.5000\n",
+      "[  NORMAL ]  Iteration 23:  k_eff = 0.372330   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  60 D.R. = 4.5714\n",
+      "[  NORMAL ]  Iteration 24:  k_eff = 0.373804   res = 5.686E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  147 D.R. = 2.9375\n",
+      "[  NORMAL ]  Iteration 25:  k_eff = 0.376130   res = 4.840E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  232 D.R. = 0.8511\n",
+      "[  NORMAL ]  Iteration 26:  k_eff = 0.379307   res = 3.509E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  317 D.R. = 0.7250\n",
+      "[  NORMAL ]  Iteration 27:  k_eff = 0.383324   res = 3.993E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  401 D.R. = 1.1379\n",
+      "[  NORMAL ]  Iteration 28:  k_eff = 0.388118   res = 8.469E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  479 D.R. = 0.2121\n",
+      "[  NORMAL ]  Iteration 29:  k_eff = 0.393671   res = 1.452E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  555 D.R. = 1.7143\n",
+      "[  NORMAL ]  Iteration 30:  k_eff = 0.399962   res = 2.904E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  629 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 31:  k_eff = 0.406957   res = 3.146E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  699 D.R. = 1.0833\n",
+      "[  NORMAL ]  Iteration 32:  k_eff = 0.414600   res = 6.896E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  764 D.R. = 2.1923\n",
+      "[  NORMAL ]  Iteration 33:  k_eff = 0.422881   res = 1.815E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  828 D.R. = 0.2632\n",
+      "[  NORMAL ]  Iteration 34:  k_eff = 0.431752   res = 9.679E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  887 D.R. = 0.5333\n",
+      "[  NORMAL ]  Iteration 35:  k_eff = 0.441164   res = 2.904E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  941 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 36:  k_eff = 0.451113   res = 8.469E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  994 D.R. = 0.2917\n",
+      "[  NORMAL ]  Iteration 37:  k_eff = 0.461526   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1041 D.R. = 4.5714\n",
+      "[  NORMAL ]  Iteration 38:  k_eff = 0.472392   res = 7.501E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1086 D.R. = 1.9375\n",
+      "[  NORMAL ]  Iteration 39:  k_eff = 0.483657   res = 6.533E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1126 D.R. = 0.8710\n",
+      "[  NORMAL ]  Iteration 40:  k_eff = 0.495292   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1163 D.R. = 0.5926\n",
+      "[  NORMAL ]  Iteration 41:  k_eff = 0.507260   res = 3.388E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1196 D.R. = 0.8750\n",
+      "[  NORMAL ]  Iteration 42:  k_eff = 0.519528   res = 4.598E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1226 D.R. = 1.3571\n",
+      "[  NORMAL ]  Iteration 43:  k_eff = 0.532058   res = 4.598E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1253 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 44:  k_eff = 0.544820   res = 8.469E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1276 D.R. = 0.1842\n",
+      "[  NORMAL ]  Iteration 45:  k_eff = 0.557781   res = 1.089E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1296 D.R. = 1.2857\n",
+      "[  NORMAL ]  Iteration 46:  k_eff = 0.570908   res = 6.775E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1312 D.R. = 6.2222\n",
+      "[  NORMAL ]  Iteration 47:  k_eff = 0.584174   res = 7.864E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1326 D.R. = 1.1607\n",
+      "[  NORMAL ]  Iteration 48:  k_eff = 0.597549   res = 9.074E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1337 D.R. = 0.1154\n",
+      "[  NORMAL ]  Iteration 49:  k_eff = 0.611006   res = 2.662E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1345 D.R. = 2.9333\n",
+      "[  NORMAL ]  Iteration 50:  k_eff = 0.624518   res = 3.085E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1351 D.R. = 1.1591\n",
+      "[  NORMAL ]  Iteration 51:  k_eff = 0.638062   res = 7.501E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1354 D.R. = 2.4314\n",
+      "[  NORMAL ]  Iteration 52:  k_eff = 0.651612   res = 6.654E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1355 D.R. = 0.8871\n",
+      "[  NORMAL ]  Iteration 53:  k_eff = 0.665146   res = 2.057E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1353 D.R. = 0.3091\n",
+      "[  NORMAL ]  Iteration 54:  k_eff = 0.678645   res = 1.512E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1349 D.R. = 0.7353\n",
+      "[  NORMAL ]  Iteration 55:  k_eff = 0.692087   res = 1.210E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1344 D.R. = 0.0800\n",
+      "[  NORMAL ]  Iteration 56:  k_eff = 0.705453   res = 1.028E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1336 D.R. = 8.5000\n",
+      "[  NORMAL ]  Iteration 57:  k_eff = 0.718729   res = 9.074E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1327 D.R. = 0.8824\n",
+      "[  NORMAL ]  Iteration 58:  k_eff = 0.731896   res = 6.049E-10  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1316 D.R. = 0.0667\n",
+      "[  NORMAL ]  Iteration 59:  k_eff = 0.744940   res = 1.149E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1304 D.R. = 19.0000\n",
+      "[  NORMAL ]  Iteration 60:  k_eff = 0.757847   res = 2.057E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1290 D.R. = 1.7895\n",
+      "[  NORMAL ]  Iteration 61:  k_eff = 0.770604   res = 1.149E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1275 D.R. = 0.5588\n",
+      "[  NORMAL ]  Iteration 62:  k_eff = 0.783200   res = 3.146E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1259 D.R. = 2.7368\n",
+      "[  NORMAL ]  Iteration 63:  k_eff = 0.795624   res = 3.448E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1242 D.R. = 1.0962\n",
+      "[  NORMAL ]  Iteration 64:  k_eff = 0.807866   res = 2.964E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1224 D.R. = 0.8596\n",
+      "[  NORMAL ]  Iteration 65:  k_eff = 0.819919   res = 6.715E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1205 D.R. = 2.2653\n",
+      "[  NORMAL ]  Iteration 66:  k_eff = 0.831774   res = 2.238E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1185 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 67:  k_eff = 0.843424   res = 1.633E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1165 D.R. = 0.7297\n",
+      "[  NORMAL ]  Iteration 68:  k_eff = 0.854864   res = 1.633E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1143 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 69:  k_eff = 0.866087   res = 2.359E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1122 D.R. = 1.4444\n",
+      "[  NORMAL ]  Iteration 70:  k_eff = 0.877091   res = 2.299E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1100 D.R. = 0.9744\n",
+      "[  NORMAL ]  Iteration 71:  k_eff = 0.887870   res = 1.875E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1077 D.R. = 0.8158\n",
+      "[  NORMAL ]  Iteration 72:  k_eff = 0.898423   res = 2.420E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1055 D.R. = 0.1290\n",
+      "[  NORMAL ]  Iteration 73:  k_eff = 0.908747   res = 7.864E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1032 D.R. = 3.2500\n",
+      "[  NORMAL ]  Iteration 74:  k_eff = 0.918839   res = 1.633E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1009 D.R. = 2.0769\n",
+      "[  NORMAL ]  Iteration 75:  k_eff = 0.928699   res = 4.174E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  986 D.R. = 2.5556\n",
+      "[  NORMAL ]  Iteration 76:  k_eff = 0.938326   res = 4.779E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  962 D.R. = 1.1449\n",
+      "[  NORMAL ]  Iteration 77:  k_eff = 0.947719   res = 1.210E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  939 D.R. = 0.0253\n",
+      "[  NORMAL ]  Iteration 78:  k_eff = 0.956880   res = 6.049E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  916 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 79:  k_eff = 0.965808   res = 1.512E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  892 D.R. = 2.5000\n",
+      "[  NORMAL ]  Iteration 80:  k_eff = 0.974505   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  869 D.R. = 1.2800\n",
+      "[  NORMAL ]  Iteration 81:  k_eff = 0.982972   res = 1.452E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  846 D.R. = 0.7500\n",
+      "[  NORMAL ]  Iteration 82:  k_eff = 0.991211   res = 5.445E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  823 D.R. = 0.3750\n",
+      "[  NORMAL ]  Iteration 83:  k_eff = 0.999224   res = 3.630E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  801 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 84:  k_eff = 1.007014   res = 5.445E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  778 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 85:  k_eff = 1.014581   res = 4.537E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  756 D.R. = 8.3333\n",
+      "[  NORMAL ]  Iteration 86:  k_eff = 1.021932   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  735 D.R. = 1.2800\n",
+      "[  NORMAL ]  Iteration 87:  k_eff = 1.029067   res = 6.049E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  713 D.R. = 1.0417\n",
+      "[  NORMAL ]  Iteration 88:  k_eff = 1.035990   res = 7.078E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  692 D.R. = 1.1700\n",
+      "[  NORMAL ]  Iteration 89:  k_eff = 1.042705   res = 2.662E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  671 D.R. = 0.3761\n",
+      "[  NORMAL ]  Iteration 90:  k_eff = 1.049215   res = 3.569E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  650 D.R. = 1.3409\n",
+      "[  NORMAL ]  Iteration 91:  k_eff = 1.055524   res = 1.149E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  630 D.R. = 0.3220\n",
+      "[  NORMAL ]  Iteration 92:  k_eff = 1.061635   res = 3.327E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  611 D.R. = 2.8947\n",
+      "[  NORMAL ]  Iteration 93:  k_eff = 1.067553   res = 1.149E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  591 D.R. = 0.3455\n",
+      "[  NORMAL ]  Iteration 94:  k_eff = 1.073282   res = 1.815E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  572 D.R. = 0.1579\n",
+      "[  NORMAL ]  Iteration 95:  k_eff = 1.078825   res = 2.904E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  554 D.R. = 16.0000\n",
+      "[  NORMAL ]  Iteration 96:  k_eff = 1.084186   res = 4.235E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  536 D.R. = 0.1458\n",
+      "[  NORMAL ]  Iteration 97:  k_eff = 1.089370   res = 1.694E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  518 D.R. = 4.0000\n",
+      "[  NORMAL ]  Iteration 98:  k_eff = 1.094381   res = 2.299E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  501 D.R. = 1.3571\n",
+      "[  NORMAL ]  Iteration 99:  k_eff = 1.099224   res = 3.569E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  484 D.R. = 1.5526\n",
+      "[  NORMAL ]  Iteration 100:  k_eff = 1.103901   res = 2.299E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 467 D.R. = 0.6441\n",
+      "[  NORMAL ]  Iteration 101:  k_eff = 1.108418   res = 4.779E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 451 D.R. = 2.0789\n",
+      "[  NORMAL ]  Iteration 102:  k_eff = 1.112777   res = 4.235E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 435 D.R. = 0.0886\n",
+      "[  NORMAL ]  Iteration 103:  k_eff = 1.116986   res = 2.480E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 420 D.R. = 5.8571\n",
+      "[  NORMAL ]  Iteration 104:  k_eff = 1.121046   res = 1.573E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 405 D.R. = 0.6341\n",
+      "[  NORMAL ]  Iteration 105:  k_eff = 1.124962   res = 7.320E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 391 D.R. = 4.6538\n",
+      "[  NORMAL ]  Iteration 106:  k_eff = 1.128738   res = 5.082E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 377 D.R. = 0.6942\n",
+      "[  NORMAL ]  Iteration 107:  k_eff = 1.132377   res = 6.049E-10  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 363 D.R. = 0.0119\n",
+      "[  NORMAL ]  Iteration 108:  k_eff = 1.135884   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 350 D.R. = 48.0000\n",
+      "[  NORMAL ]  Iteration 109:  k_eff = 1.139264   res = 3.509E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 337 D.R. = 1.2083\n",
+      "[  NORMAL ]  Iteration 110:  k_eff = 1.142519   res = 1.633E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 325 D.R. = 0.4655\n",
+      "[  NORMAL ]  Iteration 111:  k_eff = 1.145654   res = 5.505E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 313 D.R. = 3.3704\n",
+      "[  NORMAL ]  Iteration 112:  k_eff = 1.148671   res = 2.178E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 301 D.R. = 0.3956\n",
+      "[  NORMAL ]  Iteration 113:  k_eff = 1.151575   res = 9.074E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 290 D.R. = 0.4167\n",
+      "[  NORMAL ]  Iteration 114:  k_eff = 1.154371   res = 4.235E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 279 D.R. = 0.4667\n",
+      "[  NORMAL ]  Iteration 115:  k_eff = 1.157059   res = 1.512E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 268 D.R. = 3.5714\n",
+      "[  NORMAL ]  Iteration 116:  k_eff = 1.159645   res = 2.843E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 258 D.R. = 1.8800\n",
+      "[  NORMAL ]  Iteration 117:  k_eff = 1.162132   res = 4.658E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 248 D.R. = 1.6383\n",
+      "[  NORMAL ]  Iteration 118:  k_eff = 1.164522   res = 3.025E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 239 D.R. = 0.0649\n",
+      "[  NORMAL ]  Iteration 119:  k_eff = 1.166820   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 229 D.R. = 16.0000\n",
+      "[  NORMAL ]  Iteration 120:  k_eff = 1.169029   res = 3.630E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 220 D.R. = 0.0750\n",
+      "[  NORMAL ]  Iteration 121:  k_eff = 1.171150   res = 7.864E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 212 D.R. = 2.1667\n",
+      "[  NORMAL ]  Iteration 122:  k_eff = 1.173188   res = 1.331E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 203 D.R. = 1.6923\n",
+      "[  NORMAL ]  Iteration 123:  k_eff = 1.175145   res = 2.057E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 195 D.R. = 1.5455\n",
+      "[  NORMAL ]  Iteration 124:  k_eff = 1.177024   res = 1.452E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 187 D.R. = 0.7059\n",
+      "[  NORMAL ]  Iteration 125:  k_eff = 1.178828   res = 1.149E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 180 D.R. = 0.7917\n",
+      "[  NORMAL ]  Iteration 126:  k_eff = 1.180560   res = 3.388E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 173 D.R. = 2.9474\n",
+      "[  NORMAL ]  Iteration 127:  k_eff = 1.182222   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 166 D.R. = 0.5714\n",
+      "[  NORMAL ]  Iteration 128:  k_eff = 1.183817   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 159 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 129:  k_eff = 1.185346   res = 1.875E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 152 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 130:  k_eff = 1.186813   res = 4.658E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 146 D.R. = 2.4839\n",
+      "[  NORMAL ]  Iteration 131:  k_eff = 1.188220   res = 1.270E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 140 D.R. = 0.2727\n",
+      "[  NORMAL ]  Iteration 132:  k_eff = 1.189569   res = 2.299E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 134 D.R. = 1.8095\n",
+      "[  NORMAL ]  Iteration 133:  k_eff = 1.190862   res = 2.843E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 129 D.R. = 1.2368\n",
+      "[  NORMAL ]  Iteration 134:  k_eff = 1.192102   res = 4.658E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 123 D.R. = 1.6383\n",
+      "[  NORMAL ]  Iteration 135:  k_eff = 1.193290   res = 2.178E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 118 D.R. = 0.4675\n",
+      "[  NORMAL ]  Iteration 136:  k_eff = 1.194428   res = 6.049E-10  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 113 D.R. = 0.0278\n",
+      "[  NORMAL ]  Iteration 137:  k_eff = 1.195519   res = 2.420E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 109 D.R. = 40.0000\n",
+      "[  NORMAL ]  Iteration 138:  k_eff = 1.196563   res = 3.932E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 104 D.R. = 1.6250\n",
+      "[  NORMAL ]  Iteration 139:  k_eff = 1.197564   res = 5.505E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 100 D.R. = 1.4000\n",
+      "[  NORMAL ]  Iteration 140:  k_eff = 1.198522   res = 2.420E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 95 D.R. = 0.0440\n",
+      "[  NORMAL ]  Iteration 141:  k_eff = 1.199440   res = 8.348E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 91 D.R. = 34.5000\n",
+      "[  NORMAL ]  Iteration 142:  k_eff = 1.200317   res = 3.993E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 87 D.R. = 0.4783\n",
+      "[  NORMAL ]  Iteration 143:  k_eff = 1.201159   res = 5.263E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 84 D.R. = 1.3182\n",
+      "[  NORMAL ]  Iteration 144:  k_eff = 1.201963   res = 6.352E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 80 D.R. = 1.2069\n",
+      "[  NORMAL ]  Iteration 145:  k_eff = 1.202733   res = 5.686E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 77 D.R. = 0.8952\n",
+      "[  NORMAL ]  Iteration 146:  k_eff = 1.203470   res = 6.654E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 73 D.R. = 0.1170\n",
+      "[  NORMAL ]  Iteration 147:  k_eff = 1.204175   res = 3.448E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 70 D.R. = 5.1818\n",
+      "[  NORMAL ]  Iteration 148:  k_eff = 1.204849   res = 2.541E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 67 D.R. = 0.7368\n",
+      "[  NORMAL ]  Iteration 149:  k_eff = 1.205494   res = 2.178E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 64 D.R. = 0.8571\n",
+      "[  NORMAL ]  Iteration 150:  k_eff = 1.206111   res = 3.993E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 61 D.R. = 1.8333\n",
+      "[  NORMAL ]  Iteration 151:  k_eff = 1.206700   res = 5.324E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 58 D.R. = 1.3333\n",
+      "[  NORMAL ]  Iteration 152:  k_eff = 1.207264   res = 1.391E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 56 D.R. = 0.2614\n",
+      "[  NORMAL ]  Iteration 153:  k_eff = 1.207804   res = 1.210E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 53 D.R. = 0.0870\n",
+      "[  NORMAL ]  Iteration 154:  k_eff = 1.208319   res = 5.021E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 51 D.R. = 41.5000\n",
+      "[  NORMAL ]  Iteration 155:  k_eff = 1.208811   res = 6.654E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 49 D.R. = 0.1325\n",
+      "[  NORMAL ]  Iteration 156:  k_eff = 1.209282   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 47 D.R. = 1.4545\n",
+      "[  NORMAL ]  Iteration 157:  k_eff = 1.209732   res = 6.049E-10  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 44 D.R. = 0.0625\n",
+      "[  NORMAL ]  Iteration 158:  k_eff = 1.210162   res = 3.690E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 42 D.R. = 61.0000\n",
+      "[  NORMAL ]  Iteration 159:  k_eff = 1.210573   res = 1.149E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 41 D.R. = 0.3115\n",
+      "[  NORMAL ]  Iteration 160:  k_eff = 1.210964   res = 7.259E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 39 D.R. = 6.3158\n",
+      "[  NORMAL ]  Iteration 161:  k_eff = 1.211339   res = 1.010E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 37 D.R. = 1.3917\n",
+      "[  NORMAL ]  Iteration 162:  k_eff = 1.211697   res = 5.263E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 35 D.R. = 0.5210\n",
+      "[  NORMAL ]  Iteration 163:  k_eff = 1.212039   res = 6.110E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 34 D.R. = 1.1609\n",
+      "[  NORMAL ]  Iteration 164:  k_eff = 1.212365   res = 4.356E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 32 D.R. = 0.7129\n",
+      "[  NORMAL ]  Iteration 165:  k_eff = 1.212676   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 31 D.R. = 0.4444\n",
+      "[  NORMAL ]  Iteration 166:  k_eff = 1.212973   res = 4.235E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 29 D.R. = 0.2187\n",
+      "[  NORMAL ]  Iteration 167:  k_eff = 1.213258   res = 1.815E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 28 D.R. = 4.2857\n",
+      "[  NORMAL ]  Iteration 168:  k_eff = 1.213529   res = 5.747E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 27 D.R. = 3.1667\n",
+      "[  NORMAL ]  Iteration 169:  k_eff = 1.213787   res = 8.530E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 25 D.R. = 1.4842\n",
+      "[  NORMAL ]  Iteration 170:  k_eff = 1.214034   res = 6.049E-10  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 24 D.R. = 0.0071\n",
+      "[  NORMAL ]  Iteration 171:  k_eff = 1.214270   res = 8.469E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 23 D.R. = 14.0000\n",
+      "[  NORMAL ]  Iteration 172:  k_eff = 1.214494   res = 3.206E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 22 D.R. = 3.7857\n",
+      "[  NORMAL ]  Iteration 173:  k_eff = 1.214709   res = 3.751E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 21 D.R. = 1.1698\n",
+      "[  NORMAL ]  Iteration 174:  k_eff = 1.214913   res = 1.089E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 20 D.R. = 0.2903\n",
+      "[  NORMAL ]  Iteration 175:  k_eff = 1.215108   res = 4.537E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 19 D.R. = 4.1667\n",
+      "[  NORMAL ]  Iteration 176:  k_eff = 1.215295   res = 3.267E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 18 D.R. = 0.7200\n",
+      "[  NORMAL ]  Iteration 177:  k_eff = 1.215472   res = 3.690E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 17 D.R. = 1.1296\n",
+      "[  NORMAL ]  Iteration 178:  k_eff = 1.215641   res = 3.085E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 16 D.R. = 0.8361\n",
+      "[  NORMAL ]  Iteration 179:  k_eff = 1.215803   res = 2.178E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 16 D.R. = 0.7059\n",
+      "[  NORMAL ]  Iteration 180:  k_eff = 1.215957   res = 3.932E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 15 D.R. = 1.8056\n",
+      "[  NORMAL ]  Iteration 181:  k_eff = 1.216103   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 14 D.R. = 0.2462\n",
+      "[  NORMAL ]  Iteration 182:  k_eff = 1.216243   res = 8.469E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 14 D.R. = 0.8750\n",
+      "[  NORMAL ]  Iteration 183:  k_eff = 1.216377   res = 6.049E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 13 D.R. = 0.7143\n",
+      "[  NORMAL ]  Iteration 184:  k_eff = 1.216504   res = 2.299E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 12 D.R. = 3.8000\n",
+      "[  NORMAL ]  Iteration 185:  k_eff = 1.216625   res = 4.053E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 12 D.R. = 1.7632\n",
+      "[  NORMAL ]  Iteration 186:  k_eff = 1.216741   res = 2.238E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 11 D.R. = 0.5522\n",
+      "[  NORMAL ]  Iteration 187:  k_eff = 1.216851   res = 5.565E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 11 D.R. = 2.4865\n",
+      "[  NORMAL ]  Iteration 188:  k_eff = 1.216955   res = 6.049E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 10 D.R. = 1.0870\n",
+      "[  NORMAL ]  Iteration 189:  k_eff = 1.217056   res = 5.445E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 10 D.R. = 0.0900\n",
+      "[  NORMAL ]  Iteration 190:  k_eff = 1.217151   res = 7.864E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 9 D.R. = 1.4444\n",
+      "[  NORMAL ]  Iteration 191:  k_eff = 1.217242   res = 2.299E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 9 D.R. = 2.9231\n",
+      "[  NORMAL ]  Iteration 192:  k_eff = 1.217328   res = 6.049E-10  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 8 D.R. = 0.0263\n",
+      "[  NORMAL ]  Iteration 193:  k_eff = 1.217410   res = 4.114E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 8 D.R. = 68.0000\n",
+      "[  NORMAL ]  Iteration 194:  k_eff = 1.217489   res = 2.420E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 0.5882\n",
+      "[  NORMAL ]  Iteration 195:  k_eff = 1.217563   res = 3.569E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 1.4750\n",
+      "[  NORMAL ]  Iteration 196:  k_eff = 1.217635   res = 1.210E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 0.0339\n",
+      "[  NORMAL ]  Iteration 197:  k_eff = 1.217703   res = 7.864E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 6.5000\n",
+      "[  NORMAL ]  Iteration 198:  k_eff = 1.217768   res = 1.149E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 1.4615\n",
+      "[  NORMAL ]  Iteration 199:  k_eff = 1.217829   res = 2.238E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 1.9474\n",
+      "[  NORMAL ]  Iteration 200:  k_eff = 1.217887   res = 4.235E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 0.1892\n",
+      "[  NORMAL ]  Iteration 201:  k_eff = 1.217943   res = 3.993E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 9.4286\n",
+      "[  NORMAL ]  Iteration 202:  k_eff = 1.217996   res = 2.843E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 0.7121\n",
+      "[  NORMAL ]  Iteration 203:  k_eff = 1.218047   res = 7.441E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 2.6170\n",
+      "[  NORMAL ]  Iteration 204:  k_eff = 1.218095   res = 1.089E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 0.1463\n",
+      "[  NORMAL ]  Iteration 205:  k_eff = 1.218141   res = 1.754E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 1.6111\n",
+      "[  NORMAL ]  Iteration 206:  k_eff = 1.218184   res = 4.356E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 2.4828\n",
+      "[  NORMAL ]  Iteration 207:  k_eff = 1.218225   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 0.2222\n",
+      "[  NORMAL ]  Iteration 208:  k_eff = 1.218265   res = 1.210E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 1.2500\n",
+      "[  NORMAL ]  Iteration 209:  k_eff = 1.218302   res = 3.146E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 2.6000\n",
+      "[  NORMAL ]  Iteration 210:  k_eff = 1.218338   res = 5.626E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 1.7885\n",
+      "[  NORMAL ]  Iteration 211:  k_eff = 1.218372   res = 1.815E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 0.0323\n",
+      "[  NORMAL ]  Iteration 212:  k_eff = 1.218404   res = 6.291E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 34.6667\n",
+      "[  NORMAL ]  Iteration 213:  k_eff = 1.218435   res = 4.356E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 0.6923\n",
+      "[  NORMAL ]  Iteration 214:  k_eff = 1.218465   res = 6.836E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.5694\n",
+      "[  NORMAL ]  Iteration 215:  k_eff = 1.218493   res = 4.719E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.6903\n",
+      "[  NORMAL ]  Iteration 216:  k_eff = 1.218519   res = 6.654E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.1410\n",
+      "[  NORMAL ]  Iteration 217:  k_eff = 1.218544   res = 1.210E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.8182\n",
+      "[  NORMAL ]  Iteration 218:  k_eff = 1.218568   res = 1.573E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.3000\n",
+      "[  NORMAL ]  Iteration 219:  k_eff = 1.218591   res = 1.875E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.1923\n",
+      "[  NORMAL ]  Iteration 220:  k_eff = 1.218613   res = 3.388E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.8065\n",
+      "[  NORMAL ]  Iteration 221:  k_eff = 1.218634   res = 3.751E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.1071\n",
+      "[  NORMAL ]  Iteration 222:  k_eff = 1.218654   res = 1.815E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.0484\n",
+      "[  NORMAL ]  Iteration 223:  k_eff = 1.218672   res = 5.445E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 224:  k_eff = 1.218690   res = 1.815E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 225:  k_eff = 1.218707   res = 6.291E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 34.6667\n",
+      "[  NORMAL ]  Iteration 226:  k_eff = 1.218723   res = 7.864E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.1250\n",
+      "[  NORMAL ]  Iteration 227:  k_eff = 1.218738   res = 6.896E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 8.7692\n",
+      "[  NORMAL ]  Iteration 228:  k_eff = 1.218753   res = 6.715E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.9737\n",
+      "[  NORMAL ]  Iteration 229:  k_eff = 1.218766   res = 1.512E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.2252\n",
+      "[  NORMAL ]  Iteration 230:  k_eff = 1.218779   res = 1.573E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.0400\n",
+      "[  NORMAL ]  Iteration 231:  k_eff = 1.218792   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.2308\n",
+      "[  NORMAL ]  Iteration 232:  k_eff = 1.218804   res = 4.840E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 233:  k_eff = 1.218815   res = 4.174E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 8.6250\n",
+      "[  NORMAL ]  Iteration 234:  k_eff = 1.218826   res = 4.114E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.9855\n",
+      "[  NORMAL ]  Iteration 235:  k_eff = 1.218836   res = 3.630E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.8824\n",
+      "[  NORMAL ]  Iteration 236:  k_eff = 1.218846   res = 7.804E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 0 D.R. = 2.1500\n"
      ]
     }
    ],
@@ -1694,23 +1684,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "openmc keff = 1.224484\n",
-      "openmoc keff = 1.220711\n",
-      "bias [pcm]: -377.3\n"
+      "openmc keff = 1.222044\n",
+      "openmoc keff = 1.218846\n",
+      "bias [pcm]: -319.8\n"
      ]
     }
    ],
    "source": [
     "# Print report of keff and bias with OpenMC\n",
     "openmoc_keff = solver.getKeff()\n",
-    "openmc_keff = sp.k_combined.n\n",
+    "openmc_keff = sp.keff.n\n",
     "bias = (openmoc_keff - openmc_keff) * 1e5\n",
     "\n",
     "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n",
@@ -1718,6 +1708,13 @@
     "print('bias [pcm]: {0:1.1f}'.format(bias))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1727,7 +1724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1765,7 +1762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1790,7 +1787,6 @@
       "[  NORMAL ]  Progress Segmenting 2D tracks: 100.00 %\n",
       "[  NORMAL ]  Initializing FSR lookup vectors\n",
       "[  NORMAL ]  Total number of FSRs 3\n",
-      "[  RESULT ]  Total Track Generation & Segmentation Time...........3.9517E-02 sec\n",
       "[  NORMAL ]  Initializing MOC eigenvalue solver...\n",
       "[  NORMAL ]  Initializing solver arrays...\n",
       "[  NORMAL ]  Centering segments around FSR centroid...\n",
@@ -1805,720 +1801,706 @@
       "[  NORMAL ]  CMFD acceleration: OFF\n",
       "[  NORMAL ]  Using 1 threads\n",
       "[  NORMAL ]  Computing the eigenvalue...\n",
-      "[  NORMAL ]  Iteration 0:  k_eff = 0.366880   res = 4.840E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -63312 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 1:  k_eff = 0.391184   res = 9.679E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  2430 D.R. = 0.20\n",
-      "[  NORMAL ]  Iteration 2:  k_eff = 0.392990   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  180 D.R. = 6.00\n",
-      "[  NORMAL ]  Iteration 3:  k_eff = 0.381099   res = 9.195E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -1189 D.R. = 1.58\n",
-      "[  NORMAL ]  Iteration 4:  k_eff = 0.375018   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -608 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 5:  k_eff = 0.369593   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -542 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 6:  k_eff = 0.365543   res = 1.065E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -405 D.R. = 5.50\n",
-      "[  NORMAL ]  Iteration 7:  k_eff = 0.363054   res = 1.065E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -248 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 8:  k_eff = 0.361473   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -158 D.R. = 0.18\n",
-      "[  NORMAL ]  Iteration 9:  k_eff = 0.361280   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -19 D.R. = 3.00\n",
-      "[  NORMAL ]  Iteration 10:  k_eff = 0.362003   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  72 D.R. = 0.33\n",
-      "[  NORMAL ]  Iteration 11:  k_eff = 0.363718   res = 4.840E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  171 D.R. = 2.50\n",
-      "[  NORMAL ]  Iteration 12:  k_eff = 0.366338   res = 1.258E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  262 D.R. = 2.60\n",
-      "[  NORMAL ]  Iteration 13:  k_eff = 0.369804   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  346 D.R. = 0.77\n",
-      "[  NORMAL ]  Iteration 14:  k_eff = 0.373989   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  418 D.R. = 0.40\n",
-      "[  NORMAL ]  Iteration 15:  k_eff = 0.378923   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  493 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 16:  k_eff = 0.384479   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  555 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 17:  k_eff = 0.390637   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  615 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 18:  k_eff = 0.397338   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  670 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 19:  k_eff = 0.404533   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  719 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 20:  k_eff = 0.412184   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  765 D.R. = 1.67\n",
-      "[  NORMAL ]  Iteration 21:  k_eff = 0.420253   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  806 D.R. = 0.80\n",
-      "[  NORMAL ]  Iteration 22:  k_eff = 0.428686   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  843 D.R. = 0.75\n",
-      "[  NORMAL ]  Iteration 23:  k_eff = 0.437462   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  877 D.R. = 1.67\n",
-      "[  NORMAL ]  Iteration 24:  k_eff = 0.446538   res = 1.161E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  907 D.R. = 1.20\n",
-      "[  NORMAL ]  Iteration 25:  k_eff = 0.455883   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  934 D.R. = 0.17\n",
-      "[  NORMAL ]  Iteration 26:  k_eff = 0.465469   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  958 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 27:  k_eff = 0.475265   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  979 D.R. = 2.00\n",
-      "[  NORMAL ]  Iteration 28:  k_eff = 0.485246   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  998 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 29:  k_eff = 0.495385   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1013 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 30:  k_eff = 0.505661   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1027 D.R. = 4.00\n",
-      "[  NORMAL ]  Iteration 31:  k_eff = 0.516051   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1038 D.R. = 1.25\n",
-      "[  NORMAL ]  Iteration 32:  k_eff = 0.526534   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1048 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 33:  k_eff = 0.537092   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1055 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 34:  k_eff = 0.547706   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1061 D.R. = 0.60\n",
-      "[  NORMAL ]  Iteration 35:  k_eff = 0.558361   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1065 D.R. = 0.33\n",
-      "[  NORMAL ]  Iteration 36:  k_eff = 0.569040   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1067 D.R. = 3.00\n",
-      "[  NORMAL ]  Iteration 37:  k_eff = 0.579730   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1068 D.R. = 1.67\n",
-      "[  NORMAL ]  Iteration 38:  k_eff = 0.590416   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1068 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 39:  k_eff = 0.601087   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  Iteration 0:  k_eff = 0.366644   res = 9.679E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -63335 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 1:  k_eff = 0.390955   res = 1.210E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  2431 D.R. = 12.5000\n",
+      "[  NORMAL ]  Iteration 2:  k_eff = 0.392706   res = 9.679E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  175 D.R. = 0.0800\n",
+      "[  NORMAL ]  Iteration 3:  k_eff = 0.380770   res = 8.711E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -1193 D.R. = 9.0000\n",
+      "[  NORMAL ]  Iteration 4:  k_eff = 0.374646   res = 5.324E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -612 D.R. = 0.6111\n",
+      "[  NORMAL ]  Iteration 5:  k_eff = 0.369186   res = 6.775E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -546 D.R. = 1.2727\n",
+      "[  NORMAL ]  Iteration 6:  k_eff = 0.365104   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -408 D.R. = 1.4286\n",
+      "[  NORMAL ]  Iteration 7:  k_eff = 0.362589   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -251 D.R. = 0.8000\n",
+      "[  NORMAL ]  Iteration 8:  k_eff = 0.360985   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -160 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 9:  k_eff = 0.360771   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -21 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 10:  k_eff = 0.361478   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  70 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 11:  k_eff = 0.363179   res = 9.679E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  170 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 12:  k_eff = 0.365787   res = 2.904E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  260 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 13:  k_eff = 0.369244   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  345 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 14:  k_eff = 0.373421   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  417 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 15:  k_eff = 0.378350   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  492 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 16:  k_eff = 0.383902   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  555 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 17:  k_eff = 0.390057   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  615 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 18:  k_eff = 0.396756   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  669 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 19:  k_eff = 0.403951   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  719 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 20:  k_eff = 0.411603   res = 1.355E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  765 D.R. = 7.0000\n",
+      "[  NORMAL ]  Iteration 21:  k_eff = 0.419673   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  807 D.R. = 0.1429\n",
+      "[  NORMAL ]  Iteration 22:  k_eff = 0.428109   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  843 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 23:  k_eff = 0.436887   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  877 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 24:  k_eff = 0.445966   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  907 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 25:  k_eff = 0.455315   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  934 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 26:  k_eff = 0.464905   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  958 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 27:  k_eff = 0.474705   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  980 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 28:  k_eff = 0.484690   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  998 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 29:  k_eff = 0.494835   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1014 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 30:  k_eff = 0.505115   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1028 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 31:  k_eff = 0.515510   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1039 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 32:  k_eff = 0.525998   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1048 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 33:  k_eff = 0.536561   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1056 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 34:  k_eff = 0.547180   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1061 D.R. = 1.2500\n",
+      "[  NORMAL ]  Iteration 35:  k_eff = 0.557840   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1065 D.R. = 0.4000\n",
+      "[  NORMAL ]  Iteration 36:  k_eff = 0.568524   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1068 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 37:  k_eff = 0.579219   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1069 D.R. = -nan\n",
+      "[  NORMAL ]  Iteration 38:  k_eff = 0.589911   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1069 D.R. = -nan\n",
+      "[  NORMAL ]  Iteration 39:  k_eff = 0.600586   res = 1.936E-08  delta-k (pcm) =\n",
       "[  NORMAL ]  ...  1067 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 40:  k_eff = 0.611731   res = 1.549E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1064 D.R. = 1.60\n",
-      "[  NORMAL ]  Iteration 41:  k_eff = 0.622338   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1060 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 42:  k_eff = 0.632897   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1055 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 43:  k_eff = 0.643400   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1050 D.R. = 0.75\n",
-      "[  NORMAL ]  Iteration 44:  k_eff = 0.653837   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1043 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 45:  k_eff = 0.664203   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1036 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 46:  k_eff = 0.674488   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1028 D.R. = 1.25\n",
-      "[  NORMAL ]  Iteration 47:  k_eff = 0.684688   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1020 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 48:  k_eff = 0.694796   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1010 D.R. = 0.40\n",
-      "[  NORMAL ]  Iteration 49:  k_eff = 0.704807   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1001 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 50:  k_eff = 0.714715   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  990 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 51:  k_eff = 0.724517   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  980 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 52:  k_eff = 0.734209   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  969 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 53:  k_eff = 0.743787   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  957 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 54:  k_eff = 0.753247   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  946 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 55:  k_eff = 0.762588   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  934 D.R. = 1.50\n",
-      "[  NORMAL ]  Iteration 56:  k_eff = 0.771806   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  921 D.R. = 0.33\n",
-      "[  NORMAL ]  Iteration 57:  k_eff = 0.780901   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  909 D.R. = 2.00\n",
-      "[  NORMAL ]  Iteration 58:  k_eff = 0.789868   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  896 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 59:  k_eff = 0.798708   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  884 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 60:  k_eff = 0.807419   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  871 D.R. = 4.00\n",
-      "[  NORMAL ]  Iteration 61:  k_eff = 0.816000   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  858 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 62:  k_eff = 0.824450   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  844 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 63:  k_eff = 0.832768   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  831 D.R. = 2.00\n",
-      "[  NORMAL ]  Iteration 64:  k_eff = 0.840954   res = 1.742E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  818 D.R. = 2.25\n",
-      "[  NORMAL ]  Iteration 65:  k_eff = 0.849008   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  805 D.R. = 0.44\n",
-      "[  NORMAL ]  Iteration 66:  k_eff = 0.856930   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  792 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 67:  k_eff = 0.864720   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  778 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 68:  k_eff = 0.872378   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  765 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 69:  k_eff = 0.879905   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  752 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 70:  k_eff = 0.887301   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  739 D.R. = 2.00\n",
-      "[  NORMAL ]  Iteration 71:  k_eff = 0.894566   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  726 D.R. = 1.50\n",
-      "[  NORMAL ]  Iteration 72:  k_eff = 0.901702   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  713 D.R. = 0.33\n",
-      "[  NORMAL ]  Iteration 73:  k_eff = 0.908710   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  700 D.R. = 2.00\n",
-      "[  NORMAL ]  Iteration 74:  k_eff = 0.915590   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  687 D.R. = 1.50\n",
-      "[  NORMAL ]  Iteration 75:  k_eff = 0.922342   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  675 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 76:  k_eff = 0.928971   res = 1.161E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  662 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 77:  k_eff = 0.935474   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  650 D.R. = 0.17\n",
-      "[  NORMAL ]  Iteration 78:  k_eff = 0.941853   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  637 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 79:  k_eff = 0.948112   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  625 D.R. = 5.00\n",
-      "[  NORMAL ]  Iteration 80:  k_eff = 0.954249   res = 1.065E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  613 D.R. = 1.10\n",
-      "[  NORMAL ]  Iteration 81:  k_eff = 0.960267   res = 9.679E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  601 D.R. = 0.09\n",
-      "[  NORMAL ]  Iteration 82:  k_eff = 0.966168   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  590 D.R. = 8.00\n",
-      "[  NORMAL ]  Iteration 83:  k_eff = 0.971953   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  578 D.R. = 0.75\n",
-      "[  NORMAL ]  Iteration 84:  k_eff = 0.977623   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  566 D.R. = 0.33\n",
-      "[  NORMAL ]  Iteration 85:  k_eff = 0.983179   res = 2.904E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  555 D.R. = 1.50\n",
-      "[  NORMAL ]  Iteration 86:  k_eff = 0.988624   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  544 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 87:  k_eff = 0.993958   res = 6.775E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  533 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 88:  k_eff = 0.999184   res = 9.679E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  522 D.R. = 0.14\n",
-      "[  NORMAL ]  Iteration 89:  k_eff = 1.004304   res = 4.840E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  511 D.R. = 5.00\n",
-      "[  NORMAL ]  Iteration 90:  k_eff = 1.009318   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  501 D.R. = 0.80\n",
-      "[  NORMAL ]  Iteration 91:  k_eff = 1.014228   res = 4.840E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  491 D.R. = 1.25\n",
-      "[  NORMAL ]  Iteration 92:  k_eff = 1.019037   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  480 D.R. = 1.20\n",
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-      "[  NORMAL ]  ...  470 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 94:  k_eff = 1.028353   res = 6.775E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  460 D.R. = 2.33\n",
-      "[  NORMAL ]  Iteration 95:  k_eff = 1.032865   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  451 D.R. = 1.14\n",
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-      "[  NORMAL ]  ...  441 D.R. = 1.75\n",
-      "[  NORMAL ]  Iteration 97:  k_eff = 1.041604   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  432 D.R. = 0.43\n",
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-      "[  NORMAL ]  ...  422 D.R. = 0.83\n"
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-      "[  NORMAL ]  Iteration 99:  k_eff = 1.049974   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  413 D.R. = 1.20\n",
-      "[  NORMAL ]  Iteration 100:  k_eff = 1.054024   res = 5.807E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 405 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 101:  k_eff = 1.057987   res = 4.840E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 396 D.R. = 0.83\n",
-      "[  NORMAL ]  Iteration 102:  k_eff = 1.061864   res = 9.679E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 387 D.R. = 0.20\n",
-      "[  NORMAL ]  Iteration 103:  k_eff = 1.065657   res = 2.904E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 379 D.R. = 3.00\n",
-      "[  NORMAL ]  Iteration 104:  k_eff = 1.069367   res = 3.872E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 370 D.R. = 1.33\n",
-      "[  NORMAL ]  Iteration 105:  k_eff = 1.072996   res = 3.872E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 362 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 106:  k_eff = 1.076545   res = 1.936E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 354 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 107:  k_eff = 1.080016   res = 6.775E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 347 D.R. = 3.50\n",
-      "[  NORMAL ]  Iteration 108:  k_eff = 1.083411   res = 1.161E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 339 D.R. = 1.71\n",
-      "[  NORMAL ]  Iteration 109:  k_eff = 1.086731   res = 5.807E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 331 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 110:  k_eff = 1.089976   res = 1.936E-08  delta-k (pcm)\n",
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-      "[  NORMAL ]  Iteration 111:  k_eff = 1.093150   res = 7.743E-08  delta-k (pcm)\n",
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-      "[  NORMAL ]  Iteration 112:  k_eff = 1.096253   res = 7.743E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 310 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 113:  k_eff = 1.099286   res = 3.872E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 303 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 114:  k_eff = 1.102251   res = 4.840E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 296 D.R. = 1.25\n",
-      "[  NORMAL ]  Iteration 115:  k_eff = 1.105150   res = 9.679E-09  delta-k (pcm)\n",
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-      "[  NORMAL ]  Iteration 116:  k_eff = 1.107983   res = 1.452E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 283 D.R. = 15.00\n",
-      "[  NORMAL ]  Iteration 117:  k_eff = 1.110753   res = 4.840E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 276 D.R. = 0.33\n",
-      "[  NORMAL ]  Iteration 118:  k_eff = 1.113459   res = 8.711E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 270 D.R. = 1.80\n",
-      "[  NORMAL ]  Iteration 119:  k_eff = 1.116105   res = 1.065E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 264 D.R. = 1.22\n",
-      "[  NORMAL ]  Iteration 120:  k_eff = 1.118690   res = 3.872E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 258 D.R. = 0.36\n",
-      "[  NORMAL ]  Iteration 121:  k_eff = 1.121216   res = 9.679E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 252 D.R. = 0.25\n",
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-      "[  NORMAL ]  ...  = 246 D.R. = 1.00\n",
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-      "[  NORMAL ]  Iteration 124:  k_eff = 1.128454   res = 0.000E+00  delta-k (pcm)\n",
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-      "[  NORMAL ]  Iteration 125:  k_eff = 1.130758   res = 1.936E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 230 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 126:  k_eff = 1.133008   res = 1.065E-07  delta-k (pcm)\n",
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-     "name": "stdout",
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-      "[  NORMAL ]  Iteration 212:  k_eff = 1.214337   res = 2.904E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 28 D.R. = 0.30\n",
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-      "[  NORMAL ]  ...  = 26 D.R. = 1.29\n",
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-      "[  NORMAL ]  Iteration 216:  k_eff = 1.215402   res = 1.936E-08  delta-k (pcm)\n",
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-      "[  NORMAL ]  ...  = 24 D.R. = 2.17\n",
-      "[  NORMAL ]  Iteration 219:  k_eff = 1.216136   res = 9.679E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 23 D.R. = 0.77\n",
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-      "[  NORMAL ]  ...  = 14 D.R. = -nan\n",
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-      "[  NORMAL ]  ...  = 2 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 319:  k_eff = 1.224893   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  Iteration 40:  k_eff = 0.611235   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1064 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 41:  k_eff = 0.621847   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1061 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 42:  k_eff = 0.632410   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1056 D.R. = 0.2000\n",
+      "[  NORMAL ]  Iteration 43:  k_eff = 0.642917   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1050 D.R. = 4.0000\n",
+      "[  NORMAL ]  Iteration 44:  k_eff = 0.653360   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1044 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 45:  k_eff = 0.663729   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1036 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 46:  k_eff = 0.674019   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1028 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 47:  k_eff = 0.684223   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1020 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 48:  k_eff = 0.694335   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1011 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 49:  k_eff = 0.704350   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1001 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 50:  k_eff = 0.714262   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  991 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 51:  k_eff = 0.724068   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  980 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 52:  k_eff = 0.733764   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  969 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 53:  k_eff = 0.743346   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  958 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 54:  k_eff = 0.752809   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  946 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 55:  k_eff = 0.762154   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  934 D.R. = 1.2500\n",
+      "[  NORMAL ]  Iteration 56:  k_eff = 0.771376   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  922 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 57:  k_eff = 0.780473   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  909 D.R. = 0.6000\n",
+      "[  NORMAL ]  Iteration 58:  k_eff = 0.789445   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  897 D.R. = 1.3333\n",
+      "[  NORMAL ]  Iteration 59:  k_eff = 0.798288   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  884 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 60:  k_eff = 0.807003   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  871 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 61:  k_eff = 0.815587   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  858 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 62:  k_eff = 0.824040   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  845 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 63:  k_eff = 0.832361   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  832 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 64:  k_eff = 0.840551   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  818 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 65:  k_eff = 0.848608   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  805 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 66:  k_eff = 0.856534   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  792 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 67:  k_eff = 0.864327   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  779 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 68:  k_eff = 0.871988   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  766 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 69:  k_eff = 0.879518   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  752 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 70:  k_eff = 0.886917   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  739 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 71:  k_eff = 0.894186   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  726 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 72:  k_eff = 0.901326   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  713 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 73:  k_eff = 0.908336   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  701 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 74:  k_eff = 0.915219   res = 1.161E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  688 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 75:  k_eff = 0.921976   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  675 D.R. = 0.1667\n",
+      "[  NORMAL ]  Iteration 76:  k_eff = 0.928607   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  663 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 77:  k_eff = 0.935114   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  650 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 78:  k_eff = 0.941498   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  638 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 79:  k_eff = 0.947759   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  626 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 80:  k_eff = 0.953900   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  614 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 81:  k_eff = 0.959922   res = 8.711E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  602 D.R. = 0.9000\n",
+      "[  NORMAL ]  Iteration 82:  k_eff = 0.965827   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  590 D.R. = 0.4444\n",
+      "[  NORMAL ]  Iteration 83:  k_eff = 0.971615   res = 4.840E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  578 D.R. = 1.2500\n",
+      "[  NORMAL ]  Iteration 84:  k_eff = 0.977288   res = 2.904E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  567 D.R. = 0.6000\n",
+      "[  NORMAL ]  Iteration 85:  k_eff = 0.982848   res = 9.679E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  555 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 86:  k_eff = 0.988297   res = 2.904E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  544 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 87:  k_eff = 0.993635   res = 4.840E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  533 D.R. = 1.6667\n",
+      "[  NORMAL ]  Iteration 88:  k_eff = 0.998865   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  523 D.R. = 0.8000\n",
+      "[  NORMAL ]  Iteration 89:  k_eff = 1.003988   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  512 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 90:  k_eff = 1.009006   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  501 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 91:  k_eff = 1.013921   res = 6.775E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  491 D.R. = 3.5000\n",
+      "[  NORMAL ]  Iteration 92:  k_eff = 1.018732   res = 8.711E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  481 D.R. = 1.2857\n",
+      "[  NORMAL ]  Iteration 93:  k_eff = 1.023444   res = 6.775E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  471 D.R. = 0.7778\n",
+      "[  NORMAL ]  Iteration 94:  k_eff = 1.028057   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  461 D.R. = 0.2857\n",
+      "[  NORMAL ]  Iteration 95:  k_eff = 1.032573   res = 6.775E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  451 D.R. = 3.5000\n",
+      "[  NORMAL ]  Iteration 96:  k_eff = 1.036994   res = 2.904E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  442 D.R. = 0.4286\n",
+      "[  NORMAL ]  Iteration 97:  k_eff = 1.041321   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  432 D.R. = 2.6667\n",
+      "[  NORMAL ]  Iteration 98:  k_eff = 1.045554   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  423 D.R. = 0.7500\n",
+      "[  NORMAL ]  Iteration 99:  k_eff = 1.049699   res = 9.679E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  414 D.R. = 0.1667\n",
+      "[  NORMAL ]  Iteration 100:  k_eff = 1.053753   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 405 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 101:  k_eff = 1.057721   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 396 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 102:  k_eff = 1.061602   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 388 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 103:  k_eff = 1.065399   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 379 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 104:  k_eff = 1.069113   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 371 D.R. = 0.8889\n",
+      "[  NORMAL ]  Iteration 105:  k_eff = 1.072747   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 363 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 106:  k_eff = 1.076301   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 355 D.R. = 4.5000\n",
+      "[  NORMAL ]  Iteration 107:  k_eff = 1.079776   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 347 D.R. = 0.1111\n",
+      "[  NORMAL ]  Iteration 108:  k_eff = 1.083176   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 339 D.R. = 6.0000\n",
+      "[  NORMAL ]  Iteration 109:  k_eff = 1.086499   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 332 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 110:  k_eff = 1.089750   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 325 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 111:  k_eff = 1.092928   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 317 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 112:  k_eff = 1.096035   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 310 D.R. = 2.2000\n",
+      "[  NORMAL ]  Iteration 113:  k_eff = 1.099073   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 303 D.R. = 0.2727\n",
+      "[  NORMAL ]  Iteration 114:  k_eff = 1.102043   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 297 D.R. = 2.6667\n",
+      "[  NORMAL ]  Iteration 115:  k_eff = 1.104946   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 290 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 116:  k_eff = 1.107783   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 283 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 117:  k_eff = 1.110557   res = 9.679E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 277 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 118:  k_eff = 1.113268   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 271 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 119:  k_eff = 1.115919   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 265 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 120:  k_eff = 1.118508   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 258 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 121:  k_eff = 1.121039   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 253 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 122:  k_eff = 1.123513   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 247 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 123:  k_eff = 1.125930   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 241 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 124:  k_eff = 1.128291   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 236 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 125:  k_eff = 1.130599   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 230 D.R. = 6.0000\n",
+      "[  NORMAL ]  Iteration 126:  k_eff = 1.132854   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 225 D.R. = 0.1667\n",
+      "[  NORMAL ]  Iteration 127:  k_eff = 1.135057   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 220 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 128:  k_eff = 1.137210   res = 9.679E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 215 D.R. = 3.3333\n",
+      "[  NORMAL ]  Iteration 129:  k_eff = 1.139313   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 210 D.R. = 0.4000\n",
+      "[  NORMAL ]  Iteration 130:  k_eff = 1.141367   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 205 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 131:  k_eff = 1.143374   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 200 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 132:  k_eff = 1.145334   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 195 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 133:  k_eff = 1.147250   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 191 D.R. = 0.4545\n",
+      "[  NORMAL ]  Iteration 134:  k_eff = 1.149120   res = 9.679E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 187 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 135:  k_eff = 1.150948   res = 1.549E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 182 D.R. = 1.6000\n",
+      "[  NORMAL ]  Iteration 136:  k_eff = 1.152733   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 178 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 137:  k_eff = 1.154476   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 174 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 138:  k_eff = 1.156179   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 170 D.R. = 1.1250\n",
+      "[  NORMAL ]  Iteration 139:  k_eff = 1.157842   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 166 D.R. = 0.4444\n",
+      "[  NORMAL ]  Iteration 140:  k_eff = 1.159466   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 162 D.R. = 1.7500\n",
+      "[  NORMAL ]  Iteration 141:  k_eff = 1.161053   res = 9.679E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 158 D.R. = 1.4286\n",
+      "[  NORMAL ]  Iteration 142:  k_eff = 1.162602   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 154 D.R. = 0.1000\n",
+      "[  NORMAL ]  Iteration 143:  k_eff = 1.164115   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 151 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 144:  k_eff = 1.165592   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 147 D.R. = 1.4000\n",
+      "[  NORMAL ]  Iteration 145:  k_eff = 1.167035   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 144 D.R. = 0.7143\n",
+      "[  NORMAL ]  Iteration 146:  k_eff = 1.168444   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 140 D.R. = 0.8000\n",
+      "[  NORMAL ]  Iteration 147:  k_eff = 1.169819   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 137 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 148:  k_eff = 1.171163   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 134 D.R. = 4.0000\n",
+      "[  NORMAL ]  Iteration 149:  k_eff = 1.172475   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 131 D.R. = 0.6250\n",
+      "[  NORMAL ]  Iteration 150:  k_eff = 1.173756   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 128 D.R. = 1.6000\n",
+      "[  NORMAL ]  Iteration 151:  k_eff = 1.175006   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 125 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 152:  k_eff = 1.176227   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 122 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 153:  k_eff = 1.177420   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 119 D.R. = 4.0000\n",
+      "[  NORMAL ]  Iteration 154:  k_eff = 1.178584   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 116 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 155:  k_eff = 1.179720   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 113 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 156:  k_eff = 1.180830   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 111 D.R. = 0.1250\n",
+      "[  NORMAL ]  Iteration 157:  k_eff = 1.181914   res = 1.258E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 108 D.R. = 13.0000\n",
+      "[  NORMAL ]  Iteration 158:  k_eff = 1.182971   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 105 D.R. = 0.4615\n",
+      "[  NORMAL ]  Iteration 159:  k_eff = 1.184004   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 103 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 160:  k_eff = 1.185012   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 100 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 161:  k_eff = 1.185997   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 98 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 162:  k_eff = 1.186957   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 96 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 163:  k_eff = 1.187895   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 93 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 164:  k_eff = 1.188811   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 91 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 165:  k_eff = 1.189705   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 89 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 166:  k_eff = 1.190578   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 87 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 167:  k_eff = 1.191430   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 85 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 168:  k_eff = 1.192261   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 83 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 169:  k_eff = 1.193073   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 81 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 170:  k_eff = 1.193865   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 79 D.R. = 4.5000\n",
+      "[  NORMAL ]  Iteration 171:  k_eff = 1.194638   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 77 D.R. = 0.8889\n",
+      "[  NORMAL ]  Iteration 172:  k_eff = 1.195394   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 75 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 173:  k_eff = 1.196130   res = 1.355E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 73 D.R. = 7.0000\n",
+      "[  NORMAL ]  Iteration 174:  k_eff = 1.196849   res = 1.452E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 71 D.R. = 1.0714\n",
+      "[  NORMAL ]  Iteration 175:  k_eff = 1.197551   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 70 D.R. = 0.1333\n",
+      "[  NORMAL ]  Iteration 176:  k_eff = 1.198236   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 68 D.R. = 2.5000\n",
+      "[  NORMAL ]  Iteration 177:  k_eff = 1.198905   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 66 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 178:  k_eff = 1.199558   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 65 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 179:  k_eff = 1.200195   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 63 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 180:  k_eff = 1.200818   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 62 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 181:  k_eff = 1.201424   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 60 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 182:  k_eff = 1.202017   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 59 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 183:  k_eff = 1.202595   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 57 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 184:  k_eff = 1.203159   res = 1.452E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 56 D.R. = 1.6667\n",
+      "[  NORMAL ]  Iteration 185:  k_eff = 1.203710   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 55 D.R. = 0.7333\n",
+      "[  NORMAL ]  Iteration 186:  k_eff = 1.204247   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 53 D.R. = 0.6364\n",
+      "[  NORMAL ]  Iteration 187:  k_eff = 1.204772   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 52 D.R. = 1.5714\n",
+      "[  NORMAL ]  Iteration 188:  k_eff = 1.205284   res = 1.161E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 51 D.R. = 1.0909\n",
+      "[  NORMAL ]  Iteration 189:  k_eff = 1.205784   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 50 D.R. = 0.1667\n",
+      "[  NORMAL ]  Iteration 190:  k_eff = 1.206273   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 48 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 191:  k_eff = 1.206748   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 47 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 192:  k_eff = 1.207212   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 46 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 193:  k_eff = 1.207666   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 45 D.R. = 1.6667\n",
+      "[  NORMAL ]  Iteration 194:  k_eff = 1.208109   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 44 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 195:  k_eff = 1.208540   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 43 D.R. = 0.4000\n",
+      "[  NORMAL ]  Iteration 196:  k_eff = 1.208962   res = 1.258E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 42 D.R. = 6.5000\n",
+      "[  NORMAL ]  Iteration 197:  k_eff = 1.209373   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 41 D.R. = 0.6923\n",
+      "[  NORMAL ]  Iteration 198:  k_eff = 1.209775   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 40 D.R. = 0.4444\n",
+      "[  NORMAL ]  Iteration 199:  k_eff = 1.210167   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 39 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 200:  k_eff = 1.210549   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 38 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 201:  k_eff = 1.210922   res = 1.161E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 37 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 202:  k_eff = 1.211286   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 36 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 203:  k_eff = 1.211641   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 35 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 204:  k_eff = 1.211988   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 34 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 205:  k_eff = 1.212326   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 33 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 206:  k_eff = 1.212656   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 33 D.R. = 2.3333\n",
+      "[  NORMAL ]  Iteration 207:  k_eff = 1.212979   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 32 D.R. = 1.5714\n",
+      "[  NORMAL ]  Iteration 208:  k_eff = 1.213293   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 31 D.R. = 0.4545\n",
+      "[  NORMAL ]  Iteration 209:  k_eff = 1.213600   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 30 D.R. = 0.6000\n",
+      "[  NORMAL ]  Iteration 210:  k_eff = 1.213899   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 29 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 211:  k_eff = 1.214192   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 29 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 212:  k_eff = 1.214477   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 28 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 213:  k_eff = 1.214755   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 27 D.R. = 2.6667\n",
+      "[  NORMAL ]  Iteration 214:  k_eff = 1.215027   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 27 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 215:  k_eff = 1.215292   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 26 D.R. = -nan\n",
+      "[  NORMAL ]  Iteration 216:  k_eff = 1.215551   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 25 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 217:  k_eff = 1.215803   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 25 D.R. = 1.1667\n",
+      "[  NORMAL ]  Iteration 218:  k_eff = 1.216049   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 24 D.R. = 0.1429\n",
+      "[  NORMAL ]  Iteration 219:  k_eff = 1.216289   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 23 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 220:  k_eff = 1.216524   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 23 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 221:  k_eff = 1.216753   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 22 D.R. = 0.2000\n",
+      "[  NORMAL ]  Iteration 222:  k_eff = 1.216976   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 22 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 223:  k_eff = 1.217193   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 21 D.R. = 0.8000\n",
+      "[  NORMAL ]  Iteration 224:  k_eff = 1.217406   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 21 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 225:  k_eff = 1.217613   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 20 D.R. = 4.0000\n",
+      "[  NORMAL ]  Iteration 226:  k_eff = 1.217816   res = 1.645E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 20 D.R. = 2.1250\n",
+      "[  NORMAL ]  Iteration 227:  k_eff = 1.218013   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 19 D.R. = 0.3529\n",
+      "[  NORMAL ]  Iteration 228:  k_eff = 1.218206   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 19 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 229:  k_eff = 1.218394   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 18 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 230:  k_eff = 1.218578   res = 1.258E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 18 D.R. = 2.1667\n",
+      "[  NORMAL ]  Iteration 231:  k_eff = 1.218758   res = 9.679E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 17 D.R. = 0.7692\n",
+      "[  NORMAL ]  Iteration 232:  k_eff = 1.218932   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 17 D.R. = 0.3000\n",
+      "[  NORMAL ]  Iteration 233:  k_eff = 1.219103   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 17 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 234:  k_eff = 1.219269   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 16 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 235:  k_eff = 1.219431   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 16 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 236:  k_eff = 1.219590   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 15 D.R. = 7.0000\n",
+      "[  NORMAL ]  Iteration 237:  k_eff = 1.219745   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 15 D.R. = 0.2857\n",
+      "[  NORMAL ]  Iteration 238:  k_eff = 1.219896   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 15 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 239:  k_eff = 1.220043   res = 9.679E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 14 D.R. = 3.3333\n",
+      "[  NORMAL ]  Iteration 240:  k_eff = 1.220187   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 14 D.R. = 0.2000\n",
+      "[  NORMAL ]  Iteration 241:  k_eff = 1.220327   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 13 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 242:  k_eff = 1.220464   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 13 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 243:  k_eff = 1.220597   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 13 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 244:  k_eff = 1.220727   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 13 D.R. = 4.5000\n",
+      "[  NORMAL ]  Iteration 245:  k_eff = 1.220854   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 12 D.R. = 0.4444\n",
+      "[  NORMAL ]  Iteration 246:  k_eff = 1.220978   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 12 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 247:  k_eff = 1.221099   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 12 D.R. = 1.7500\n",
+      "[  NORMAL ]  Iteration 248:  k_eff = 1.221217   res = 1.258E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 11 D.R. = 1.8571\n",
+      "[  NORMAL ]  Iteration 249:  k_eff = 1.221333   res = 1.161E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 11 D.R. = 0.9231\n",
+      "[  NORMAL ]  Iteration 250:  k_eff = 1.221445   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 11 D.R. = 0.1667\n",
+      "[  NORMAL ]  Iteration 251:  k_eff = 1.221555   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 10 D.R. = 3.5000\n",
+      "[  NORMAL ]  Iteration 252:  k_eff = 1.221662   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 10 D.R. = 0.2857\n",
+      "[  NORMAL ]  Iteration 253:  k_eff = 1.221766   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 10 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 254:  k_eff = 1.221868   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 10 D.R. = 0.8333\n",
+      "[  NORMAL ]  Iteration 255:  k_eff = 1.221968   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 9 D.R. = 0.4000\n",
+      "[  NORMAL ]  Iteration 256:  k_eff = 1.222064   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 9 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 257:  k_eff = 1.222159   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 9 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 258:  k_eff = 1.222251   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 9 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 259:  k_eff = 1.222342   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 9 D.R. = 2.2500\n",
+      "[  NORMAL ]  Iteration 260:  k_eff = 1.222429   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 8 D.R. = 0.5556\n",
+      "[  NORMAL ]  Iteration 261:  k_eff = 1.222515   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 8 D.R. = 1.2000\n",
+      "[  NORMAL ]  Iteration 262:  k_eff = 1.222599   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 8 D.R. = 0.1667\n",
+      "[  NORMAL ]  Iteration 263:  k_eff = 1.222681   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 8 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 264:  k_eff = 1.222760   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 265:  k_eff = 1.222838   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 266:  k_eff = 1.222914   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 267:  k_eff = 1.222988   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 9.0000\n",
+      "[  NORMAL ]  Iteration 268:  k_eff = 1.223060   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 0.7778\n",
+      "[  NORMAL ]  Iteration 269:  k_eff = 1.223130   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 0.1429\n",
+      "[  NORMAL ]  Iteration 270:  k_eff = 1.223199   res = 1.258E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 13.0000\n",
+      "[  NORMAL ]  Iteration 271:  k_eff = 1.223266   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 0.6923\n",
+      "[  NORMAL ]  Iteration 272:  k_eff = 1.223331   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 0.2222\n",
+      "[  NORMAL ]  Iteration 273:  k_eff = 1.223395   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 274:  k_eff = 1.223457   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 275:  k_eff = 1.223518   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 276:  k_eff = 1.223578   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 277:  k_eff = 1.223636   res = 1.161E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 278:  k_eff = 1.223692   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 0.5833\n",
+      "[  NORMAL ]  Iteration 279:  k_eff = 1.223747   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 0.1429\n",
+      "[  NORMAL ]  Iteration 280:  k_eff = 1.223801   res = 1.258E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 13.0000\n",
+      "[  NORMAL ]  Iteration 281:  k_eff = 1.223853   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 0.6923\n",
+      "[  NORMAL ]  Iteration 282:  k_eff = 1.223905   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 283:  k_eff = 1.223955   res = 1.258E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 4.3333\n",
+      "[  NORMAL ]  Iteration 284:  k_eff = 1.224003   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 0.6923\n",
+      "[  NORMAL ]  Iteration 285:  k_eff = 1.224050   res = 9.679E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 1.1111\n",
+      "[  NORMAL ]  Iteration 286:  k_eff = 1.224097   res = 1.355E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 1.4000\n",
+      "[  NORMAL ]  Iteration 287:  k_eff = 1.224142   res = 1.452E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 1.0714\n",
+      "[  NORMAL ]  Iteration 288:  k_eff = 1.224186   res = 1.258E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 0.8667\n",
+      "[  NORMAL ]  Iteration 289:  k_eff = 1.224229   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 0.6923\n",
+      "[  NORMAL ]  Iteration 290:  k_eff = 1.224272   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 0.4444\n",
+      "[  NORMAL ]  Iteration 291:  k_eff = 1.224313   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 1.2500\n",
+      "[  NORMAL ]  Iteration 292:  k_eff = 1.224352   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 1.8000\n",
+      "[  NORMAL ]  Iteration 293:  k_eff = 1.224392   res = 9.679E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 1.1111\n",
+      "[  NORMAL ]  Iteration 294:  k_eff = 1.224430   res = 1.355E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 1.4000\n",
+      "[  NORMAL ]  Iteration 295:  k_eff = 1.224467   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 0.2857\n",
+      "[  NORMAL ]  Iteration 296:  k_eff = 1.224503   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 297:  k_eff = 1.224538   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 298:  k_eff = 1.224573   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 299:  k_eff = 1.224607   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 300:  k_eff = 1.224640   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 1.1667\n",
+      "[  NORMAL ]  Iteration 301:  k_eff = 1.224672   res = 1.645E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 2.4286\n",
+      "[  NORMAL ]  Iteration 302:  k_eff = 1.224703   res = 1.355E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 0.8235\n",
+      "[  NORMAL ]  Iteration 303:  k_eff = 1.224734   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 304:  k_eff = 1.224763   res = 2.904E-08  delta-k (pcm)\n",
       "[  NORMAL ]  ...  = 2 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 320:  k_eff = 1.224913   res = 4.840E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.63\n",
-      "[  NORMAL ]  Iteration 321:  k_eff = 1.224932   res = 6.775E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 1.40\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[  NORMAL ]  Iteration 322:  k_eff = 1.224951   res = 8.711E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 1.29\n",
-      "[  NORMAL ]  Iteration 323:  k_eff = 1.224969   res = 9.679E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.11\n",
-      "[  NORMAL ]  Iteration 324:  k_eff = 1.224987   res = 7.743E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 8.00\n",
-      "[  NORMAL ]  Iteration 325:  k_eff = 1.225005   res = 1.936E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.25\n",
-      "[  NORMAL ]  Iteration 326:  k_eff = 1.225022   res = 9.679E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 327:  k_eff = 1.225039   res = 1.936E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 2.00\n",
-      "[  NORMAL ]  Iteration 328:  k_eff = 1.225055   res = 5.807E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 3.00\n",
-      "[  NORMAL ]  Iteration 329:  k_eff = 1.225071   res = 1.065E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 1.83\n",
-      "[  NORMAL ]  Iteration 330:  k_eff = 1.225086   res = 2.904E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.27\n",
-      "[  NORMAL ]  Iteration 331:  k_eff = 1.225102   res = 2.904E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 332:  k_eff = 1.225116   res = 2.904E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 333:  k_eff = 1.225131   res = 5.807E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 2.00\n",
-      "[  NORMAL ]  Iteration 334:  k_eff = 1.225145   res = 4.840E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.83\n",
-      "[  NORMAL ]  Iteration 335:  k_eff = 1.225159   res = 9.679E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.20\n",
-      "[  NORMAL ]  Iteration 336:  k_eff = 1.225172   res = 5.807E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 6.00\n",
-      "[  NORMAL ]  Iteration 337:  k_eff = 1.225185   res = 1.355E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 2.33\n",
-      "[  NORMAL ]  Iteration 338:  k_eff = 1.225198   res = 1.258E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.93\n",
-      "[  NORMAL ]  Iteration 339:  k_eff = 1.225210   res = 0.000E+00  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 340:  k_eff = 1.225222   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  Iteration 305:  k_eff = 1.224792   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 306:  k_eff = 1.224821   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 4.5000\n",
+      "[  NORMAL ]  Iteration 307:  k_eff = 1.224849   res = 1.549E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.7778\n",
+      "[  NORMAL ]  Iteration 308:  k_eff = 1.224876   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 309:  k_eff = 1.224902   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.1250\n",
+      "[  NORMAL ]  Iteration 310:  k_eff = 1.224928   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.2222\n",
+      "[  NORMAL ]  Iteration 311:  k_eff = 1.224953   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.5455\n",
+      "[  NORMAL ]  Iteration 312:  k_eff = 1.224977   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.1667\n",
+      "[  NORMAL ]  Iteration 313:  k_eff = 1.225001   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 314:  k_eff = 1.225025   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 315:  k_eff = 1.225047   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 316:  k_eff = 1.225069   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 317:  k_eff = 1.225091   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 318:  k_eff = 1.225112   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.3333\n",
+      "[  NORMAL ]  Iteration 319:  k_eff = 1.225133   res = 1.161E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 320:  k_eff = 1.225153   res = 1.161E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 321:  k_eff = 1.225173   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 322:  k_eff = 1.225192   res = 2.904E-08  delta-k (pcm)\n",
       "[  NORMAL ]  ...  = 1 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 341:  k_eff = 1.225233   res = 3.872E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 4.00\n",
-      "[  NORMAL ]  Iteration 342:  k_eff = 1.225245   res = 1.936E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 343:  k_eff = 1.225256   res = 0.000E+00  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 344:  k_eff = 1.225267   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  Iteration 323:  k_eff = 1.225210   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.3333\n",
+      "[  NORMAL ]  Iteration 324:  k_eff = 1.225228   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 2.2500\n",
+      "[  NORMAL ]  Iteration 325:  k_eff = 1.225246   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.5556\n",
+      "[  NORMAL ]  Iteration 326:  k_eff = 1.225264   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.2000\n",
+      "[  NORMAL ]  Iteration 327:  k_eff = 1.225280   res = 9.679E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.6667\n",
+      "[  NORMAL ]  Iteration 328:  k_eff = 1.225297   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 329:  k_eff = 1.225313   res = 8.711E-08  delta-k (pcm)\n",
       "[  NORMAL ]  ...  = 1 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 345:  k_eff = 1.225278   res = 2.904E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.38\n",
-      "[  NORMAL ]  Iteration 346:  k_eff = 1.225288   res = 3.872E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 1.33\n",
-      "[  NORMAL ]  Iteration 347:  k_eff = 1.225298   res = 1.936E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 0 D.R. = 0.50\n"
+      "[  NORMAL ]  Iteration 330:  k_eff = 1.225329   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.5556\n",
+      "[  NORMAL ]  Iteration 331:  k_eff = 1.225344   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 332:  k_eff = 1.225359   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 333:  k_eff = 1.225374   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 334:  k_eff = 1.225388   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 335:  k_eff = 1.225402   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 336:  k_eff = 1.225416   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 337:  k_eff = 1.225429   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 338:  k_eff = 1.225442   res = 1.452E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 2.5000\n",
+      "[  NORMAL ]  Iteration 339:  k_eff = 1.225455   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.4667\n",
+      "[  NORMAL ]  Iteration 340:  k_eff = 1.225467   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.2857\n",
+      "[  NORMAL ]  Iteration 341:  k_eff = 1.225479   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 342:  k_eff = 1.225491   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 343:  k_eff = 1.225502   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 344:  k_eff = 1.225513   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 9.0000\n",
+      "[  NORMAL ]  Iteration 345:  k_eff = 1.225524   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.2222\n",
+      "[  NORMAL ]  Iteration 346:  k_eff = 1.225535   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 347:  k_eff = 1.225545   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 348:  k_eff = 1.225555   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.3636\n",
+      "[  NORMAL ]  Iteration 349:  k_eff = 1.225565   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 0 D.R. = 0.2500\n"
      ]
     }
    ],
@@ -2534,23 +2516,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "openmc keff = 1.224484\n",
-      "openmoc keff = 1.225298\n",
-      "bias [pcm]: 81.4\n"
+      "openmc keff = 1.222044\n",
+      "openmoc keff = 1.225565\n",
+      "bias [pcm]: 352.2\n"
      ]
     }
    ],
    "source": [
     "# Print report of keff and bias with OpenMC\n",
     "openmoc_keff = solver.getKeff()\n",
-    "openmc_keff = sp.k_combined.n\n",
+    "openmc_keff = sp.keff.n\n",
     "bias = (openmoc_keff - openmc_keff) * 1e5\n",
     "\n",
     "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n",
@@ -2558,6 +2540,13 @@
     "print('bias [pcm]: {0:1.1f}'.format(bias))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2571,9 +2560,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "source": [
     "## Visualizing MGXS Data"
    ]
@@ -2591,7 +2578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -2600,13 +2587,13 @@
        "(1e-05, 20000000.0)"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2654,7 +2641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2684,12 +2671,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -2718,12 +2705,22 @@
     "# Show the plot on screen\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# close statepoint file to release HDF5 file handles\n",
+    "sp.close()"
+   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2737,9 +2734,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/mgxs-part-iii.ipynb b/mgxs-part-iii.ipynb
index 60f44ac..127ba26 100644
--- a/mgxs-part-iii.ipynb
+++ b/mgxs-part-iii.ipynb
@@ -45,6 +45,16 @@
     "%matplotlib inline"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a model object to tie together geometry, materials, settings, and tallies\n",
+    "model = openmc.Model()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -54,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,15 +97,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Instantiate a Materials object\n",
-    "materials_file = openmc.Materials([fuel, water, zircaloy])\n",
-    "\n",
-    "# Export to \"materials.xml\"\n",
-    "materials_file.export_to_xml()"
+    "model.materials = openmc.Materials([fuel, water, zircaloy])"
    ]
   },
   {
@@ -107,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -275,24 +282,14 @@
     "We now must create a geometry that is assigned a root universe and export it to XML."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Geometry and set root Universe\n",
-    "geometry = openmc.Geometry(root_universe)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Export to \"geometry.xml\"\n",
-    "geometry.export_to_xml()"
+    "# Create Geometry and set root Universe\n",
+    "model.geometry = openmc.Geometry(root_universe)"
    ]
   },
   {
@@ -314,19 +311,18 @@
     "particles = 10000\n",
     "\n",
     "# Instantiate a Settings object\n",
-    "settings_file = openmc.Settings()\n",
-    "settings_file.batches = batches\n",
-    "settings_file.inactive = inactive\n",
-    "settings_file.particles = particles\n",
-    "settings_file.output = {'tallies': False}\n",
+    "settings = openmc.Settings()\n",
+    "settings.batches = batches\n",
+    "settings.inactive = inactive\n",
+    "settings.particles = particles\n",
+    "settings.output = {'tallies': False}\n",
     "\n",
     "# Create an initial uniform spatial source distribution over fissionable zones\n",
     "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n",
     "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings_file.source = openmc.Source(space=uniform_dist)\n",
+    "settings.source = openmc.Source(space=uniform_dist)\n",
     "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
+    "model.settings = settings"
    ]
   },
   {
@@ -343,7 +339,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -355,7 +351,8 @@
    ],
    "source": [
     "# Instantiate a Plot\n",
-    "plot = openmc.Plot.from_geometry(geometry)\n",
+    "model.export_to_xml()\n",
+    "plot = openmc.Plot.from_geometry(model.geometry)\n",
     "plot.pixels = (250, 250)\n",
     "plot.color_by = 'material'\n",
     "plot.to_ipython_image()"
@@ -407,7 +404,7 @@
    "outputs": [],
    "source": [
     "# Initialize a 2-group MGXS Library for OpenMOC\n",
-    "mgxs_lib = openmc.mgxs.Library(geometry)\n",
+    "mgxs_lib = openmc.mgxs.Library(model.geometry)\n",
     "mgxs_lib.energy_groups = groups"
    ]
   },
@@ -467,7 +464,7 @@
     "mgxs_lib.domain_type = 'cell'\n",
     "\n",
     "# Specify the cell domains over which to compute multi-group cross sections\n",
-    "mgxs_lib.domains = geometry.get_all_material_cells().values()"
+    "mgxs_lib.domains = model.geometry.get_all_material_cells().values()"
    ]
   },
   {
@@ -520,8 +517,8 @@
    "outputs": [],
    "source": [
     "# Create a \"tallies.xml\" file for the MGXS Library\n",
-    "tallies_file = openmc.Tallies()\n",
-    "mgxs_lib.add_to_tallies_file(tallies_file, merge=True)"
+    "tallies = openmc.Tallies()\n",
+    "mgxs_lib.add_to_tallies_file(tallies, merge=True)"
    ]
   },
   {
@@ -552,40 +549,16 @@
     "tally.scores = ['fission', 'nu-fission']\n",
     "\n",
     "# Add tally to collection\n",
-    "tallies_file.append(tally)"
+    "tallies.append(tally)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=126.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=21.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=96.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=15.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=114.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Export all tallies to a \"tallies.xml\" file\n",
-    "tallies_file.export_to_xml()"
+    "model.tallies = tallies"
    ]
   },
   {
@@ -593,6 +566,28 @@
    "execution_count": 23,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=126.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=21.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=2.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=3.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=4.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=96.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=15.\n",
+      "  warn(msg, IDWarning)\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=114.\n",
+      "  warn(msg, IDWarning)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -621,30 +616,32 @@
       "                          ########     %%%%%%%%%%%%%%\n",
       "                                      %%%%%%%%%%%\n",
       "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-26 16:01:58\n",
-      "    OpenMP Threads | 2\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 | 2022-10-05 23:45:30\n",
+      "   MPI Processes | 1\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 /opt/data/xs/nndc_hdf5/U235.h5\n",
-      " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n",
-      " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n",
-      " Reading H1 from /opt/data/xs/nndc_hdf5/H1.h5\n",
-      " Reading B10 from /opt/data/xs/nndc_hdf5/B10.h5\n",
-      " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\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: 20000000.0 eV for U235\n",
+      " Maximum neutron transport energy: 20000000 eV for U235\n",
       " Initializing source particles...\n",
       "\n",
       " ====================>     K EIGENVALUE SIMULATION     <====================\n",
@@ -705,21 +702,21 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     = 2.4372e-01 seconds\n",
-      "   Reading cross sections          = 2.2745e-01 seconds\n",
-      " Total time in simulation          = 4.7933e+01 seconds\n",
-      "   Time in transport only          = 4.7887e+01 seconds\n",
-      "   Time in inactive batches        = 3.4521e+00 seconds\n",
-      "   Time in active batches          = 4.4481e+01 seconds\n",
-      "   Time synchronizing fission bank = 2.0174e-02 seconds\n",
-      "     Sampling source sites         = 1.7952e-02 seconds\n",
-      "     SEND/RECV source sites        = 2.1985e-03 seconds\n",
-      "   Time accumulating tallies       = 1.1888e-03 seconds\n",
-      "   Time writing statepoints        = 1.2929e-02 seconds\n",
-      " Total time for finalization       = 3.9800e-07 seconds\n",
-      " Total time elapsed                = 4.8193e+01 seconds\n",
-      " Calculation Rate (inactive)       = 28967.6 particles/second\n",
-      " Calculation Rate (active)         = 8992.65 particles/second\n",
+      " Total time for initialization     = 1.2070e-01 seconds\n",
+      "   Reading cross sections          = 1.1526e-01 seconds\n",
+      " Total time in simulation          = 2.9052e+01 seconds\n",
+      "   Time in transport only          = 2.9007e+01 seconds\n",
+      "   Time in inactive batches        = 2.2160e+00 seconds\n",
+      "   Time in active batches          = 2.6836e+01 seconds\n",
+      "   Time synchronizing fission bank = 3.0553e-02 seconds\n",
+      "     Sampling source sites         = 1.9077e-02 seconds\n",
+      "     SEND/RECV source sites        = 1.1409e-02 seconds\n",
+      "   Time accumulating tallies       = 1.0753e-03 seconds\n",
+      "   Time writing statepoints        = 6.1566e-03 seconds\n",
+      " Total time for finalization       = 1.0271e-05 seconds\n",
+      " Total time elapsed                = 2.9182e+01 seconds\n",
+      " Calculation Rate (inactive)       = 45126.1 particles/second\n",
+      " Calculation Rate (active)         = 14905.4 particles/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -734,7 +731,7 @@
    ],
    "source": [
     "# Run OpenMC\n",
-    "openmc.run()"
+    "statepoint_filename = model.run()"
    ]
   },
   {
@@ -758,7 +755,7 @@
    "outputs": [],
    "source": [
     "# Load the last statepoint file\n",
-    "sp = openmc.StatePoint('statepoint.50.h5')"
+    "sp = openmc.StatePoint(statepoint_filename)"
    ]
   },
   {
@@ -777,7 +774,7 @@
     "# Initialize MGXS Library with OpenMC statepoint data\n",
     "mgxs_lib.load_from_statepoint(sp)\n",
     "# Retrieve OpenMC's k-effective value\n",
-    "openmc_keff = sp.k_combined.nominal_value"
+    "openmc_keff = sp.keff.nominal_value"
    ]
   },
   {
@@ -970,7 +967,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/tallies.py:1216: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/tallies.py:1255: RuntimeWarning: invalid value encountered in true_divide\n",
       "  data = self.std_dev[indices] / self.mean[indices]\n"
      ]
     }
@@ -1344,13 +1341,7 @@
       "[  NORMAL ]  Iteration 56:  k_eff = 1.007012   res = 1.215E-04  delta-k (pcm) =\n",
       "[  NORMAL ]  ...  180 D.R. = 0.9374\n",
       "[  NORMAL ]  Iteration 57:  k_eff = 1.008689   res = 1.138E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  167 D.R. = 0.9369\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "[  NORMAL ]  ...  167 D.R. = 0.9369\n",
       "[  NORMAL ]  Iteration 58:  k_eff = 1.010251   res = 1.066E-04  delta-k (pcm) =\n",
       "[  NORMAL ]  ...  156 D.R. = 0.9369\n",
       "[  NORMAL ]  Iteration 59:  k_eff = 1.011706   res = 9.981E-05  delta-k (pcm) =\n",
@@ -1631,7 +1622,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -1657,12 +1648,22 @@
     "plt.imshow(openmoc_fission_rates, interpolation='none', cmap='jet')\n",
     "plt.title('OpenMOC Fission Rates')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# close statepoint file to release HDF5 file handles\n",
+    "sp.close()"
+   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1676,9 +1677,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/post-processing.ipynb b/post-processing.ipynb
index 8684cca..65d83bc 100644
--- a/post-processing.ipynb
+++ b/post-processing.ipynb
@@ -970,7 +970,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -984,9 +984,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }