diff --git a/docs/source/pythonapi/examples/mgxs-part-i.ipynb b/docs/source/pythonapi/examples/mgxs-part-i.ipynb index 01cd7cd7f..8db4cd4df 100644 --- a/docs/source/pythonapi/examples/mgxs-part-i.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-i.ipynb @@ -519,7 +519,7 @@ " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.1\n", " Git SHA1: 5f252e2df51930b9175fd41bafa8db01f3eaeb92\n", - " Date/Time: 2016-03-23 11:41:09\n", + " Date/Time: 2016-03-23 14:42:51\n", " MPI Processes: 1\n", " OpenMP Threads: 16\n", "\n", @@ -606,20 +606,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 5.7200E-01 seconds\n", - " Reading cross sections = 1.3300E-01 seconds\n", - " Total time in simulation = 2.7830E+00 seconds\n", - " Time in transport only = 2.1610E+00 seconds\n", - " Time in inactive batches = 4.1200E-01 seconds\n", - " Time in active batches = 2.3710E+00 seconds\n", - " Time synchronizing fission bank = 8.0000E-03 seconds\n", - " Sampling source sites = 5.0000E-03 seconds\n", - " SEND/RECV source sites = 2.0000E-03 seconds\n", + " Total time for initialization = 4.6200E-01 seconds\n", + " Reading cross sections = 1.3100E-01 seconds\n", + " Total time in simulation = 2.4000E+00 seconds\n", + " Time in transport only = 2.1340E+00 seconds\n", + " Time in inactive batches = 2.6400E-01 seconds\n", + " Time in active batches = 2.1360E+00 seconds\n", + " Time synchronizing fission bank = 2.0000E-03 seconds\n", + " Sampling source sites = 2.0000E-03 seconds\n", + " SEND/RECV source sites = 0.0000E+00 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", - " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 3.3710E+00 seconds\n", - " Calculation Rate (inactive) = 60679.6 neutrons/second\n", - " Calculation Rate (active) = 42176.3 neutrons/second\n", + " Total time for finalization = 1.0000E-03 seconds\n", + " Total time elapsed = 2.8800E+00 seconds\n", + " Calculation Rate (inactive) = 94697.0 neutrons/second\n", + " Calculation Rate (active) = 46816.5 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -914,7 +914,7 @@ " 6.250000e-07\n", " total\n", " (((total / flux) - (absorption / flux)) - (sca...\n", - " 0.000000e+00\n", + " 8.881784e-16\n", " 0.011292\n", " \n", " \n", @@ -924,7 +924,7 @@ " 2.000000e+01\n", " total\n", " (((total / flux) - (absorption / flux)) - (sca...\n", - " -3.330669e-16\n", + " -9.992007e-16\n", " 0.002570\n", " \n", " \n", @@ -937,8 +937,8 @@ "1 1 6.25e-07 2.00e+01 total \n", "\n", " score mean std. dev. \n", - "0 (((total / flux) - (absorption / flux)) - (sca... 0.00e+00 1.13e-02 \n", - "1 (((total / flux) - (absorption / flux)) - (sca... -3.33e-16 2.57e-03 " + "0 (((total / flux) - (absorption / flux)) - (sca... 8.88e-16 1.13e-02 \n", + "1 (((total / flux) - (absorption / flux)) - (sca... -9.99e-16 2.57e-03 " ] }, "execution_count": 23, diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb index 5d8da5da1..9378b1bd5 100644 --- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb @@ -41,17 +41,6 @@ "\n", " warnings.warn(_use_error_msg)\n" ] - }, - { - "ename": "ImportError", - "evalue": "No module named ace", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mopenmoc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mopenmoc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompatible\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mget_openmoc_geometry\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mpyne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mace\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'matplotlib inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mImportError\u001b[0m: No module named ace" - ] } ], "source": [ @@ -79,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -102,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -136,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -162,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -190,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -227,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -252,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -279,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -317,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -342,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -373,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -397,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -434,11 +423,185 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " .d88888b. 888b d888 .d8888b.\n", + " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", + " 888 888 88888b.d88888 888 888\n", + " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", + " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", + " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", + " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", + " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", + "__________________888______________________________________________________\n", + " 888\n", + " 888\n", + "\n", + " Copyright: 2011-2015 Massachusetts Institute of Technology\n", + " License: http://mit-crpg.github.io/openmc/license.html\n", + " Version: 0.7.1\n", + " Git SHA1: 5f252e2df51930b9175fd41bafa8db01f3eaeb92\n", + " Date/Time: 2016-03-23 14:41:04\n", + " MPI Processes: 1\n", + " OpenMP Threads: 16\n", + "\n", + " ===========================================================================\n", + " ========================> INITIALIZATION <=========================\n", + " ===========================================================================\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading geometry XML file...\n", + " Reading materials XML file...\n", + " Reading tallies XML file...\n", + " Building neighboring cells lists for each surface...\n", + " Loading ACE cross section table: 92235.71c\n", + " Loading ACE cross section table: 92238.71c\n", + " Loading ACE cross section table: 8016.71c\n", + " Loading ACE cross section table: 1001.71c\n", + " Loading ACE cross section table: 40090.71c\n", + " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n", + " Initializing source particles...\n", + "\n", + " ===========================================================================\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + " ===========================================================================\n", + "\n", + " Bat./Gen. k Average k \n", + " ========= ======== ==================== \n", + " 1/1 1.20332 \n", + " 2/1 1.22209 \n", + " 3/1 1.24309 \n", + " 4/1 1.22833 \n", + " 5/1 1.21786 \n", + " 6/1 1.22005 \n", + " 7/1 1.20894 \n", + " 8/1 1.22071 \n", + " 9/1 1.21279 \n", + " 10/1 1.22198 \n", + " 11/1 1.22287 \n", + " 12/1 1.25490 1.23888 +/- 0.01602\n", + " 13/1 1.20224 1.22667 +/- 0.01532\n", + " 14/1 1.23375 1.22844 +/- 0.01098\n", + " 15/1 1.23068 1.22889 +/- 0.00851\n", + " 16/1 1.23073 1.22920 +/- 0.00696\n", + " 17/1 1.25364 1.23269 +/- 0.00684\n", + " 18/1 1.20820 1.22963 +/- 0.00667\n", + " 19/1 1.23138 1.22982 +/- 0.00588\n", + " 20/1 1.20682 1.22752 +/- 0.00574\n", + " 21/1 1.23580 1.22827 +/- 0.00525\n", + " 22/1 1.24190 1.22941 +/- 0.00492\n", + " 23/1 1.23125 1.22955 +/- 0.00453\n", + " 24/1 1.21606 1.22859 +/- 0.00430\n", + " 25/1 1.23653 1.22912 +/- 0.00404\n", + " 26/1 1.23850 1.22970 +/- 0.00383\n", + " 27/1 1.20986 1.22853 +/- 0.00378\n", + " 28/1 1.25277 1.22988 +/- 0.00381\n", + " 29/1 1.23334 1.23006 +/- 0.00361\n", + " 30/1 1.24345 1.23073 +/- 0.00349\n", + " 31/1 1.21565 1.23001 +/- 0.00339\n", + " 32/1 1.20555 1.22890 +/- 0.00342\n", + " 33/1 1.22995 1.22895 +/- 0.00327\n", + " 34/1 1.19763 1.22764 +/- 0.00339\n", + " 35/1 1.22645 1.22760 +/- 0.00325\n", + " 36/1 1.23900 1.22803 +/- 0.00316\n", + " 37/1 1.24305 1.22859 +/- 0.00309\n", + " 38/1 1.22484 1.22846 +/- 0.00298\n", + " 39/1 1.20986 1.22782 +/- 0.00294\n", + " 40/1 1.23764 1.22814 +/- 0.00286\n", + " 41/1 1.20476 1.22739 +/- 0.00287\n", + " 42/1 1.21652 1.22705 +/- 0.00280\n", + " 43/1 1.21279 1.22662 +/- 0.00275\n", + " 44/1 1.20210 1.22590 +/- 0.00276\n", + " 45/1 1.22644 1.22591 +/- 0.00268\n", + " 46/1 1.22907 1.22600 +/- 0.00261\n", + " 47/1 1.24057 1.22639 +/- 0.00257\n", + " 48/1 1.21610 1.22612 +/- 0.00251\n", + " 49/1 1.22199 1.22602 +/- 0.00245\n", + " 50/1 1.20860 1.22558 +/- 0.00243\n", + " Triggers unsatisfied, max unc./thresh. is 1.25496 for flux in tally 10050\n", + " The estimated number of batches is 73\n", + " Creating state point statepoint.050.h5...\n", + " 51/1 1.21850 1.22541 +/- 0.00237\n", + " 52/1 1.22833 1.22548 +/- 0.00232\n", + " 53/1 1.20239 1.22494 +/- 0.00233\n", + " 54/1 1.24876 1.22548 +/- 0.00234\n", + " 55/1 1.20670 1.22506 +/- 0.00232\n", + " 56/1 1.24260 1.22545 +/- 0.00230\n", + " 57/1 1.21039 1.22512 +/- 0.00228\n", + " 58/1 1.23929 1.22542 +/- 0.00225\n", + " 59/1 1.21357 1.22518 +/- 0.00221\n", + " 60/1 1.23456 1.22537 +/- 0.00218\n", + " 61/1 1.23963 1.22565 +/- 0.00215\n", + " 62/1 1.24020 1.22593 +/- 0.00213\n", + " 63/1 1.22325 1.22587 +/- 0.00209\n", + " 64/1 1.22070 1.22578 +/- 0.00205\n", + " 65/1 1.22423 1.22575 +/- 0.00201\n", + " 66/1 1.22973 1.22582 +/- 0.00198\n", + " 67/1 1.21842 1.22569 +/- 0.00195\n", + " 68/1 1.19552 1.22517 +/- 0.00198\n", + " 69/1 1.21475 1.22500 +/- 0.00196\n", + " 70/1 1.21888 1.22489 +/- 0.00193\n", + " 71/1 1.19720 1.22444 +/- 0.00195\n", + " 72/1 1.23770 1.22465 +/- 0.00193\n", + " 73/1 1.23894 1.22488 +/- 0.00191\n", + " Triggers unsatisfied, max unc./thresh. is 1.00243 for flux in tally 10050\n", + " The estimated number of batches is 74\n", + " 74/1 1.22437 1.22487 +/- 0.00188\n", + " Triggers satisfied for batch 74\n", + " Creating state point statepoint.074.h5...\n", + "\n", + " ===========================================================================\n", + " ======================> SIMULATION FINISHED <======================\n", + " ===========================================================================\n", + "\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 4.5200E-01 seconds\n", + " Reading cross sections = 1.2500E-01 seconds\n", + " Total time in simulation = 2.6407E+01 seconds\n", + " Time in transport only = 2.5427E+01 seconds\n", + " Time in inactive batches = 1.7110E+00 seconds\n", + " Time in active batches = 2.4696E+01 seconds\n", + " Time synchronizing fission bank = 2.7000E-02 seconds\n", + " Sampling source sites = 1.9000E-02 seconds\n", + " SEND/RECV source sites = 5.0000E-03 seconds\n", + " Time accumulating tallies = 5.0000E-03 seconds\n", + " Total time for finalization = 2.0000E-02 seconds\n", + " Total time elapsed = 2.6954E+01 seconds\n", + " Calculation Rate (inactive) = 58445.4 neutrons/second\n", + " Calculation Rate (active) = 16197.0 neutrons/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.22358 +/- 0.00179\n", + " k-effective (Track-length) = 1.22487 +/- 0.00188\n", + " k-effective (Absorption) = 1.22300 +/- 0.00114\n", + " Combined k-effective = 1.22347 +/- 0.00106\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Run OpenMC\n", "executor = openmc.Executor()\n", @@ -461,14 +624,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load the last statepoint file\n", - "sp = openmc.StatePoint('statepoint.080.h5')" + "sp = openmc.StatePoint('statepoint.074.h5')" ] }, { @@ -480,7 +643,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -500,7 +663,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -535,11 +698,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\tnu-fission\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t10000\n", + "\tNuclide =\tU-235\n", + "\tCross Sections [barns]:\n", + " Group 1 [0.821 - 20.0 MeV]:\t3.30e+00 +/- 2.19e-01%\n", + " Group 2 [0.00553 - 0.821 MeV]:\t3.96e+00 +/- 1.32e-01%\n", + " Group 3 [4e-06 - 0.00553 MeV]:\t5.52e+01 +/- 2.31e-01%\n", + " Group 4 [6.25e-07 - 4e-06 MeV]:\t8.83e+01 +/- 2.96e-01%\n", + " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t2.90e+02 +/- 4.64e-01%\n", + " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t4.49e+02 +/- 4.22e-01%\n", + " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t6.87e+02 +/- 2.97e-01%\n", + " Group 8 [0.0 - 5.8e-08 MeV]:\t1.44e+03 +/- 2.91e-01%\n", + "\n", + "\tNuclide =\tU-238\n", + "\tCross Sections [barns]:\n", + " Group 1 [0.821 - 20.0 MeV]:\t1.06e+00 +/- 2.56e-01%\n", + " Group 2 [0.00553 - 0.821 MeV]:\t1.21e-03 +/- 2.55e-01%\n", + " Group 3 [4e-06 - 0.00553 MeV]:\t5.77e-04 +/- 3.67e+00%\n", + " Group 4 [6.25e-07 - 4e-06 MeV]:\t6.54e-06 +/- 2.74e-01%\n", + " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t1.07e-05 +/- 4.55e-01%\n", + " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t1.55e-05 +/- 4.25e-01%\n", + " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t2.30e-05 +/- 2.97e-01%\n", + " Group 8 [0.0 - 5.8e-08 MeV]:\t4.24e-05 +/- 2.90e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], "source": [ "nufission = xs_library[fuel_cell.id]['nu-fission']\n", "nufission.print_xs(xs_type='micro', nuclides=['U-235', 'U-238'])" @@ -554,11 +752,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\tnu-fission\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t10000\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [0.821 - 20.0 MeV]:\t2.52e-02 +/- 2.44e-01%\n", + " Group 2 [0.00553 - 0.821 MeV]:\t1.51e-03 +/- 1.30e-01%\n", + " Group 3 [4e-06 - 0.00553 MeV]:\t2.07e-02 +/- 2.31e-01%\n", + " Group 4 [6.25e-07 - 4e-06 MeV]:\t3.31e-02 +/- 2.96e-01%\n", + " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t1.09e-01 +/- 4.64e-01%\n", + " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t1.69e-01 +/- 4.22e-01%\n", + " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t2.58e-01 +/- 2.97e-01%\n", + " Group 8 [0.0 - 5.8e-08 MeV]:\t5.40e-01 +/- 2.91e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], "source": [ "nufission = xs_library[fuel_cell.id]['nu-fission']\n", "nufission.print_xs(xs_type='macro', nuclides='sum')" @@ -573,11 +794,141 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + 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" + ], + "text/plain": [ + " cell group in group out nuclide mean std. dev.\n", + "126 10002 1 1 H-1 0.234115 0.003568\n", + "127 10002 1 1 O-16 1.563707 0.005953\n", + "124 10002 1 2 H-1 1.594129 0.002369\n", + "125 10002 1 2 O-16 0.285761 0.001676\n", + "122 10002 1 3 H-1 0.011089 0.000248\n", + "123 10002 1 3 O-16 0.000000 0.000000\n", + "120 10002 1 4 H-1 0.000000 0.000000\n", + "121 10002 1 4 O-16 0.000000 0.000000\n", + "118 10002 1 5 H-1 0.000000 0.000000\n", + "119 10002 1 5 O-16 0.000000 0.000000" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n", "df = nuscatter.get_pandas_dataframe(xs_type='micro')\n", @@ -593,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "collapsed": true }, @@ -615,22 +966,133 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\ttransport\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t10000\n", + "\tNuclide =\tU-235\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t7.73e-03 +/- 5.06e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t1.82e-01 +/- 2.05e-01%\n", + "\n", + "\tNuclide =\tU-238\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t2.17e-01 +/- 1.44e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t2.53e-01 +/- 2.57e-01%\n", + "\n", + "\tNuclide =\tO-16\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t1.46e-01 +/- 1.60e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t1.75e-01 +/- 2.94e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], "source": [ "condensed_xs.print_xs()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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cellgroup innuclidemeanstd. dev.
3100001U-23520.6116920.104237
4100001U-2389.5853580.013808
5100001O-163.1641900.005049
0100002U-235485.4134260.996410
1100002U-23811.1903860.028731
2100002O-163.7948590.011139
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" + ], + "text/plain": [ + " cell group in nuclide mean std. dev.\n", + "3 10000 1 U-235 20.611692 0.104237\n", + "4 10000 1 U-238 9.585358 0.013808\n", + "5 10000 1 O-16 3.164190 0.005049\n", + "0 10000 2 U-235 485.413426 0.996410\n", + "1 10000 2 U-238 11.190386 0.028731\n", + "2 10000 2 O-16 3.794859 0.011139" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = condensed_xs.get_pandas_dataframe(xs_type='micro')\n", "df" @@ -652,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -671,7 +1133,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -716,11 +1178,183 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Importing ray tracing data from file...\n", + "[ NORMAL ] Computing the eigenvalue...\n", + "[ NORMAL ] Iteration 0:\tk_eff = 0.574672\tres = 0.000E+00\n", + "[ NORMAL ] Iteration 1:\tk_eff = 0.679815\tres = 4.253E-01\n", + "[ NORMAL ] Iteration 2:\tk_eff = 0.660826\tres = 1.830E-01\n", + "[ NORMAL ] Iteration 3:\tk_eff = 0.658940\tres = 2.793E-02\n", + "[ NORMAL ] Iteration 4:\tk_eff = 0.643012\tres = 2.853E-03\n", + "[ NORMAL ] Iteration 5:\tk_eff = 0.625810\tres = 2.417E-02\n", + "[ NORMAL ] Iteration 6:\tk_eff = 0.606678\tres = 2.675E-02\n", + "[ NORMAL ] Iteration 7:\tk_eff = 0.587485\tres = 3.057E-02\n", + "[ NORMAL ] Iteration 8:\tk_eff = 0.569029\tres = 3.164E-02\n", + "[ NORMAL ] Iteration 9:\tk_eff = 0.551707\tres = 3.142E-02\n", + "[ NORMAL ] Iteration 10:\tk_eff = 0.536035\tres = 3.044E-02\n", + "[ NORMAL ] Iteration 11:\tk_eff = 0.522275\tres = 2.841E-02\n", + "[ NORMAL ] Iteration 12:\tk_eff = 0.510610\tres = 2.567E-02\n", + "[ NORMAL ] Iteration 13:\tk_eff = 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NORMAL ] Iteration 161:\tk_eff = 1.220912\tres = 1.077E-05\n", + "[ NORMAL ] Iteration 162:\tk_eff = 1.220923\tres = 1.002E-05\n" + ] + } + ], "source": [ "# Generate tracks for OpenMOC\n", "track_generator = openmoc.TrackGenerator(openmoc_geometry, num_azim=128, spacing=0.1)\n", @@ -740,11 +1374,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "openmc keff = 1.223474\n", + "openmoc keff = 1.220923\n", + "bias [pcm]: -255.0\n" + ] + } + ], "source": [ "# Print report of keff and bias with OpenMC\n", "openmoc_keff = solver.getKeff()\n", @@ -765,7 +1409,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -805,11 +1449,251 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Importing ray tracing data from file...\n", + "[ NORMAL ] Computing the eigenvalue...\n", + "[ NORMAL ] Iteration 0:\tk_eff = 0.495816\tres = 0.000E+00\n", + "[ NORMAL ] Iteration 1:\tk_eff = 0.557477\tres = 5.042E-01\n", + "[ NORMAL ] Iteration 2:\tk_eff = 0.518301\tres = 1.244E-01\n", + "[ NORMAL ] Iteration 3:\tk_eff = 0.509212\tres = 7.027E-02\n", + "[ NORMAL ] Iteration 4:\tk_eff = 0.496489\tres = 1.754E-02\n", + "[ NORMAL ] Iteration 5:\tk_eff = 0.488581\tres = 2.498E-02\n", + "[ NORMAL ] Iteration 6:\tk_eff = 0.482897\tres = 1.593E-02\n", + "[ NORMAL ] Iteration 7:\tk_eff = 0.479775\tres = 1.163E-02\n", + "[ NORMAL ] Iteration 8:\tk_eff = 0.478835\tres = 6.464E-03\n", + "[ NORMAL ] Iteration 9:\tk_eff = 0.479872\tres = 1.960E-03\n", + "[ NORMAL ] Iteration 10:\tk_eff = 0.482685\tres = 2.166E-03\n", + "[ NORMAL ] Iteration 11:\tk_eff = 0.487085\tres = 5.861E-03\n", + "[ NORMAL ] Iteration 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1.616E-05\n", + "[ NORMAL ] Iteration 219:\tk_eff = 1.223098\tres = 1.554E-05\n", + "[ NORMAL ] Iteration 220:\tk_eff = 1.223116\tres = 1.495E-05\n", + "[ NORMAL ] Iteration 221:\tk_eff = 1.223132\tres = 1.437E-05\n", + "[ NORMAL ] Iteration 222:\tk_eff = 1.223149\tres = 1.382E-05\n", + "[ NORMAL ] Iteration 223:\tk_eff = 1.223164\tres = 1.330E-05\n", + "[ NORMAL ] Iteration 224:\tk_eff = 1.223179\tres = 1.279E-05\n", + "[ NORMAL ] Iteration 225:\tk_eff = 1.223194\tres = 1.230E-05\n", + "[ NORMAL ] Iteration 226:\tk_eff = 1.223208\tres = 1.183E-05\n", + "[ NORMAL ] Iteration 227:\tk_eff = 1.223221\tres = 1.138E-05\n", + "[ NORMAL ] Iteration 228:\tk_eff = 1.223234\tres = 1.094E-05\n", + "[ NORMAL ] Iteration 229:\tk_eff = 1.223246\tres = 1.052E-05\n", + "[ NORMAL ] Iteration 230:\tk_eff = 1.223258\tres = 1.012E-05\n" + ] + } + ], "source": [ "# Generate tracks for OpenMOC\n", "track_generator = openmoc.TrackGenerator(openmoc_geometry, num_azim=128, spacing=0.1)\n", @@ -822,11 +1706,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "openmc keff = 1.223474\n", + "openmoc keff = 1.223258\n", + "bias [pcm]: -21.5\n" + ] + } + ], "source": [ "# Print report of keff and bias with OpenMC\n", "openmoc_keff = solver.getKeff()\n", @@ -869,11 +1763,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'pyne' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Instantiate a PyNE ACE continuous-energy cross sections library\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mpyne_lib\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpyne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLibrary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../../../../data/nndc/293.6K/U_235_293.6K.ace'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mpyne_lib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'92235.71c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# Extract the U-235 data from the library\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'pyne' is not defined" + ] + } + ], "source": [ "# Instantiate a PyNE ACE continuous-energy cross sections library\n", "pyne_lib = pyne.ace.Library('../../../../data/nndc/293.6K/U_235_293.6K.ace')\n", diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb index 1ccff330d..625ddeb53 100644 --- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb +++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb @@ -382,7 +382,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -568,7 +568,7 @@ " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.1\n", " Git SHA1: 5f252e2df51930b9175fd41bafa8db01f3eaeb92\n", - " Date/Time: 2016-03-23 12:21:14\n", + " Date/Time: 2016-03-23 14:24:52\n", " MPI Processes: 1\n", " OpenMP Threads: 16\n", "\n", @@ -645,20 +645,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.7000E-01 seconds\n", - " Reading cross sections = 1.3500E-01 seconds\n", - " Total time in simulation = 2.1470E+00 seconds\n", - " Time in transport only = 1.8480E+00 seconds\n", - " Time in inactive batches = 2.1900E-01 seconds\n", - " Time in active batches = 1.9280E+00 seconds\n", - " Time synchronizing fission bank = 7.0000E-03 seconds\n", - " Sampling source sites = 4.0000E-03 seconds\n", - " SEND/RECV source sites = 3.0000E-03 seconds\n", - " Time accumulating tallies = 2.0000E-03 seconds\n", - " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 2.6360E+00 seconds\n", - " Calculation Rate (inactive) = 57077.6 neutrons/second\n", - " Calculation Rate (active) = 19450.2 neutrons/second\n", + " Total time for initialization = 4.7500E-01 seconds\n", + " Reading cross sections = 1.3300E-01 seconds\n", + " Total time in simulation = 2.3630E+00 seconds\n", + " Time in transport only = 1.9260E+00 seconds\n", + " Time in inactive batches = 2.6400E-01 seconds\n", + " Time in active batches = 2.0990E+00 seconds\n", + " Time synchronizing fission bank = 4.0000E-03 seconds\n", + " Sampling source sites = 3.0000E-03 seconds\n", + " SEND/RECV source sites = 1.0000E-03 seconds\n", + " Time accumulating tallies = 1.0000E-03 seconds\n", + " Total time for finalization = 1.0000E-03 seconds\n", + " Total time elapsed = 2.8570E+00 seconds\n", + " Calculation Rate (inactive) = 47348.5 neutrons/second\n", + " Calculation Rate (active) = 17865.7 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1137,7 +1137,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1761,27 +1761,407 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "(('level 1', 'lat', 'x'), array([], dtype=float64), Filter\n", - "\tType =\tdistribcell\n", - "\tBins =\t[10002]\n", - ")\n" - ] - }, - { - "ename": "ZeroDivisionError", - "evalue": "integer division or modulo by zero", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Get a pandas dataframe for the distribcell tally data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtally\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_pandas_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnuclides\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# Print the last twenty rows in the dataframe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m/home/wboyd/Documents/NSE-CRPG-Codes/openmc/openmc/tallies.pyc\u001b[0m in \u001b[0;36mget_pandas_dataframe\u001b[1;34m(self, filters, nuclides, scores, summary, float_format)\u001b[0m\n\u001b[0;32m 1609\u001b[0m \u001b[1;31m# Append each Filter's DataFrame to the overall DataFrame\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1610\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mself_filter\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilters\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1611\u001b[1;33m \u001b[0mfilter_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself_filter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_pandas_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msummary\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1612\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilter_df\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1613\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m/home/wboyd/Documents/NSE-CRPG-Codes/openmc/openmc/filter.py\u001b[0m in \u001b[0;36mget_pandas_dataframe\u001b[1;34m(self, data_size, summary)\u001b[0m\n\u001b[0;32m 739\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlevel_key\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel_bins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 740\u001b[0m \u001b[0mlevel_bins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlevel_bins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 741\u001b[1;33m 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level 1level 2level 3distribcellscoremeanstd. dev.
cellunivlatcelluniv
idididxyzidid
010003010001016010002100000absorption1.30e-048.67e-06
110003010001016010002100000scatter1.98e-026.50e-04
210003010001015010002100001absorption2.24e-041.44e-05
310003010001015010002100001scatter3.00e-028.80e-04
410003010001014010002100002absorption3.16e-042.15e-05
510003010001014010002100002scatter3.90e-021.25e-03
610003010001013010002100003absorption3.78e-041.45e-05
710003010001013010002100003scatter4.86e-021.24e-03
810003010001012010002100004absorption4.21e-042.14e-05
910003010001012010002100004scatter5.52e-029.85e-04
1010003010001011010002100005absorption4.86e-042.62e-05
1110003010001011010002100005scatter6.30e-021.35e-03
1210003010001010010002100006absorption5.30e-041.92e-05
1310003010001010010002100006scatter6.93e-021.30e-03
141000301000109010002100007absorption5.86e-042.02e-05
151000301000109010002100007scatter7.57e-021.40e-03
161000301000108010002100008absorption6.30e-042.35e-05
171000301000108010002100008scatter8.09e-021.49e-03
181000301000107010002100009absorption7.10e-042.23e-05
191000301000107010002100009scatter8.94e-021.37e-03
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" + ], + "text/plain": [ + " level 1 level 2 level 3 distribcell score \\\n", + " cell univ lat cell univ \n", + " id id id x y z id id \n", + "0 10003 0 10001 0 16 0 10002 10000 0 absorption \n", + "1 10003 0 10001 0 16 0 10002 10000 0 scatter \n", + "2 10003 0 10001 0 15 0 10002 10000 1 absorption \n", + "3 10003 0 10001 0 15 0 10002 10000 1 scatter \n", + "4 10003 0 10001 0 14 0 10002 10000 2 absorption \n", + "5 10003 0 10001 0 14 0 10002 10000 2 scatter \n", + "6 10003 0 10001 0 13 0 10002 10000 3 absorption \n", + "7 10003 0 10001 0 13 0 10002 10000 3 scatter \n", + "8 10003 0 10001 0 12 0 10002 10000 4 absorption \n", + "9 10003 0 10001 0 12 0 10002 10000 4 scatter \n", + "10 10003 0 10001 0 11 0 10002 10000 5 absorption \n", + "11 10003 0 10001 0 11 0 10002 10000 5 scatter \n", + "12 10003 0 10001 0 10 0 10002 10000 6 absorption \n", + "13 10003 0 10001 0 10 0 10002 10000 6 scatter \n", + "14 10003 0 10001 0 9 0 10002 10000 7 absorption \n", + "15 10003 0 10001 0 9 0 10002 10000 7 scatter \n", + "16 10003 0 10001 0 8 0 10002 10000 8 absorption \n", + "17 10003 0 10001 0 8 0 10002 10000 8 scatter \n", + "18 10003 0 10001 0 7 0 10002 10000 9 absorption \n", + "19 10003 0 10001 0 7 0 10002 10000 9 scatter \n", + "\n", + " mean std. dev. \n", + " \n", + " \n", + "0 1.30e-04 8.67e-06 \n", + "1 1.98e-02 6.50e-04 \n", + "2 2.24e-04 1.44e-05 \n", + "3 3.00e-02 8.80e-04 \n", + "4 3.16e-04 2.15e-05 \n", + "5 3.90e-02 1.25e-03 \n", + "6 3.78e-04 1.45e-05 \n", + "7 4.86e-02 1.24e-03 \n", + "8 4.21e-04 2.14e-05 \n", + "9 5.52e-02 9.85e-04 \n", + "10 4.86e-04 2.62e-05 \n", + "11 6.30e-02 1.35e-03 \n", + "12 5.30e-04 1.92e-05 \n", + "13 6.93e-02 1.30e-03 \n", + "14 5.86e-04 2.02e-05 \n", + "15 7.57e-02 1.40e-03 \n", + "16 6.30e-04 2.35e-05 \n", + "17 8.09e-02 1.49e-03 \n", + "18 7.10e-04 2.23e-05 \n", + "19 8.94e-02 1.37e-03 " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -1794,11 +2174,97 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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meanstd. dev.
count2.89e+022.89e+02
mean4.15e-041.71e-05
std2.41e-046.82e-06
min1.78e-052.81e-06
25%2.06e-041.16e-05
50%4.03e-041.71e-05
75%6.05e-042.19e-05
max9.35e-044.54e-05
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" + ], + "text/plain": [ + " mean std. dev.\n", + " \n", + " \n", + "count 2.89e+02 2.89e+02\n", + "mean 4.15e-04 1.71e-05\n", + "std 2.41e-04 6.82e-06\n", + "min 1.78e-05 2.81e-06\n", + "25% 2.06e-04 1.16e-05\n", + "50% 4.03e-04 1.71e-05\n", + "75% 6.05e-04 2.19e-05\n", + "max 9.35e-04 4.54e-05" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Show summary statistics for absorption distribcell tally data\n", "absorption = df[df['score'] == 'absorption']\n", @@ -1817,11 +2283,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mann-Whitney Test p-value: 1.39844745394e-41\n" + ] + } + ], "source": [ "# Extract tally data from pins in the pins divided along y=x diagonal \n", "multi_index = ('level 2', 'lat',)\n", @@ -1847,11 +2321,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mann-Whitney Test p-value: 0.902458041178\n" + ] + } + ], "source": [ "# Extract tally data from pins in the pins divided along y=-x diagonal\n", "multi_index = ('level 2', 'lat',)\n", @@ -1875,11 +2357,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wboyd/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Extract the scatter tally data from pandas\n", "scatter = df[df['score'] == 'scatter']\n", @@ -1892,11 +2406,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot a histogram and kernel density estimate for the scattering rates\n", "scatter['mean'].plot(kind='hist', bins=25)\n", diff --git a/openmc/filter.py b/openmc/filter.py index 5c62c1b6b..2536c3607 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -646,18 +646,10 @@ class Filter(object): # offsets to OpenCG LocalCoords linked lists offsets_to_coords = {} - # Use OpenCG to compute LocalCoords linked list for - # each region and store in dictionary - for region in range(num_regions): + for offset, path in enumerate(self.distribcell_paths): + region = opencg_geometry.get_region_from_path(path) coords = opencg_geometry.find_region(region) - path = opencg.get_path(coords) - cell_id = path[-1] - - # If this region is in Cell corresponding to the - # distribcell filter bin, store it in dictionary - if cell_id == self.bins[0]: - offset = openmc_geometry.get_cell_instance(path) - offsets_to_coords[offset] = coords + offsets_to_coords[offset] = coords # Each distribcell offset is a DataFrame bin # Unravel the paths into DataFrame columns