{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## MCNP output files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{eval-rst}\n", "The complete API can be found at :py:class:`f4enix.output.MCNPoutput.Output`\n", "```\n", "\n", "Examining an MCNP output file can be useful to extract data on any\n", "of the tables contained within it, controlling statistical checks\n", "results or debugging for lost particles." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1.30E+06'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import the related module and parse the MCNP output file\n", "from f4enix.output.MCNPoutput import Output\n", "\n", "# Parse the output file\n", "file = 'outp'\n", "outp = Output(file)\n", "# Check how many histories were run\n", "'%.2E' % outp.get_NPS()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'6.2'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get the MCNP/D1SUNED version used\n", "outp.get_code_version()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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celltracks enteringpopulation collisions collisions * weight (per historynumber weighted ) energyflux weighted energyaverage track weigh (relative)average t track mfp (cm)
31.0110882920500.0000E+001.7602E+001.7602E+001.0106E+000.0000E+00
42.0224955508721460681.3517E-081.7179E+001.7179E+001.0095E+002.7019E+00
53.0330828745941820891.6853E-081.7222E+001.7222E+001.0095E+002.7033E+00
64.0422084547461322812.4477E-081.6916E+001.6916E+001.0093E+002.6817E+00
75.0533313830061999863.7012E-081.7182E+001.7182E+001.0091E+002.6976E+00
.................................
103101.010130600765621835943.3959E-081.7152E+001.7152E+001.0085E+002.7015E+00
104102.010219463483791166202.1554E-081.7004E+001.7004E+001.0074E+002.6866E+00
105103.010323502581451404061.2984E-081.7072E+001.7072E+001.0088E+002.6981E+00
106104.01041362033617802447.4088E-091.7158E+001.7158E+001.0075E+002.7085E+00
107105.01051314431180740643.4224E-091.6804E+001.6804E+001.0088E+002.7045E+00
\n", "

105 rows × 10 columns

\n", "
" ], "text/plain": [ " cell tracks entering populatio n collision \\\n", "3 1.0 1 10882 9205 0 \n", "4 2.0 2 24955 50872 146068 \n", "5 3.0 3 30828 74594 182089 \n", "6 4.0 4 22084 54746 132281 \n", "7 5.0 5 33313 83006 199986 \n", ".. ... ... ... ... ... \n", "103 101.0 101 30600 76562 183594 \n", "104 102.0 102 19463 48379 116620 \n", "105 103.0 103 23502 58145 140406 \n", "106 104.0 104 13620 33617 80244 \n", "107 105.0 105 13144 31180 74064 \n", "\n", " s collisions * weight (per history number weighted ) energy \\\n", "3 0.0000E+00 1.7602E+00 \n", "4 1.3517E-08 1.7179E+00 \n", "5 1.6853E-08 1.7222E+00 \n", "6 2.4477E-08 1.6916E+00 \n", "7 3.7012E-08 1.7182E+00 \n", ".. ... ... \n", "103 3.3959E-08 1.7152E+00 \n", "104 2.1554E-08 1.7004E+00 \n", "105 1.2984E-08 1.7072E+00 \n", "106 7.4088E-09 1.7158E+00 \n", "107 3.4224E-09 1.6804E+00 \n", "\n", " flux weighted energy average track weigh (relative) \\\n", "3 1.7602E+00 1.0106E+00 \n", "4 1.7179E+00 1.0095E+00 \n", "5 1.7222E+00 1.0095E+00 \n", "6 1.6916E+00 1.0093E+00 \n", "7 1.7182E+00 1.0091E+00 \n", ".. ... ... \n", "103 1.7152E+00 1.0085E+00 \n", "104 1.7004E+00 1.0074E+00 \n", "105 1.7072E+00 1.0088E+00 \n", "106 1.7158E+00 1.0075E+00 \n", "107 1.6804E+00 1.0088E+00 \n", "\n", " average t track mfp (cm) \n", "3 0.0000E+00 \n", "4 2.7019E+00 \n", "5 2.7033E+00 \n", "6 2.6817E+00 \n", "7 2.6976E+00 \n", ".. ... \n", "103 2.7015E+00 \n", "104 2.6866E+00 \n", "105 2.6981E+00 \n", "106 2.7085E+00 \n", "107 2.7045E+00 \n", "\n", "[105 rows x 10 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Unpopulated cells fraction: 0.0 %\n" ] } ], "source": [ "# It is possible to read any printed table in the output\n", "# Some issues still in the header names due to uncorrect format in MCNP FWF\n", "# but the data is good.\n", "\n", "# We specify that we want to read the last instance of the table 126. The output file\n", "# may contain multiple instances of the same table due to multiple dumps and/or particle\n", "# types. \n", "table_126 = outp.get_table(126, instance_idx=-1)\n", "display(table_126)\n", "\n", "# get for instance a percentage of unpopulated cells (i.e. 0 tracks entering)\n", "unpopulated = len(table_126[table_126['populatio '].astype(int) == 0])\n", "print('Unpopulated cells fraction: {} %'.format(unpopulated/len(table_126)*100))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mean behaviourrel error valuerel error decreaserel error decrease rateVoV valueVoV decreaseVoV decrease rateFoM valueFoM behaviourPDF slopeOther TFC bins
Tally
4yesyesyesyesyesyesyesyesyesyesPassed
6yesyesyesyesyesyesyesyesyesyesPassed
14yesyesyesyesyesyesyesyesyesyesPassed
16yesyesyesyesyesyesyesyesyesyesPassed
24yesyesyesyesyesyesyesyesyesyesPassed
26yesyesyesyesyesyesyesyesyesyesPassed
34yesyesyesyesyesyesyesyesyesyesPassed
44yesyesyesyesyesyesyesyesyesyesPassed
54yesyesyesyesyesyesyesyesyesyesPassed
64yesyesyesyesyesyesyesyesyesyesPassed
74yesyesyesyesyesyesyesyesnoyesMissed
84yesyesyesyesyesyesyesyesnoyesMissed
94yesyesyesyesyesyesyesyesnoyesMissed
104yesyesyesyesyesyesyesyesyesyesPassed
114yesyesyesyesyesyesyesyesyesyesPassed
124yesyesyesyesyesyesyesyesyesyesPassed
134yesyesyesyesyesyesyesyesyesyesPassed
144yesyesyesyesyesyesyesyesyesyesPassed
154yesyesyesyesyesyesyesyesyesyesPassed
164yesyesyesyesyesyesyesyesyesyesPassed
174yesyesyesyesyesyesyesyesyesyesPassed
204yesyesyesyesyesyesyesyesyesyesPassed
214yesyesyesyesyesyesyesyesyesyesPassed
\n", "
" ], "text/plain": [ " mean behaviour rel error value rel error decrease \\\n", "Tally \n", "4 yes yes yes \n", "6 yes yes yes \n", "14 yes yes yes \n", "16 yes yes yes \n", "24 yes yes yes \n", "26 yes yes yes \n", "34 yes yes yes \n", "44 yes yes yes \n", "54 yes yes yes \n", "64 yes yes yes \n", "74 yes yes yes \n", "84 yes yes yes \n", "94 yes yes yes \n", "104 yes yes yes \n", "114 yes yes yes \n", "124 yes yes yes \n", "134 yes yes yes \n", "144 yes yes yes \n", "154 yes yes yes \n", "164 yes yes yes \n", "174 yes yes yes \n", "204 yes yes yes \n", "214 yes yes yes \n", "\n", " rel error decrease rate VoV value VoV decrease VoV decrease rate \\\n", "Tally \n", "4 yes yes yes yes \n", "6 yes yes yes yes \n", "14 yes yes yes yes \n", "16 yes yes yes yes \n", "24 yes yes yes yes \n", "26 yes yes yes yes \n", "34 yes yes yes yes \n", "44 yes yes yes yes \n", "54 yes yes yes yes \n", "64 yes yes yes yes \n", "74 yes yes yes yes \n", "84 yes yes yes yes \n", "94 yes yes yes yes \n", "104 yes yes yes yes \n", "114 yes yes yes yes \n", "124 yes yes yes yes \n", "134 yes yes yes yes \n", "144 yes yes yes yes \n", "154 yes yes yes yes \n", "164 yes yes yes yes \n", "174 yes yes yes yes \n", "204 yes yes yes yes \n", "214 yes yes yes yes \n", "\n", " FoM value FoM behaviour PDF slope Other TFC bins \n", "Tally \n", "4 yes yes yes Passed \n", "6 yes yes yes Passed \n", "14 yes yes yes Passed \n", "16 yes yes yes Passed \n", "24 yes yes yes Passed \n", "26 yes yes yes Passed \n", "34 yes yes yes Passed \n", "44 yes yes yes Passed \n", "54 yes yes yes Passed \n", "64 yes yes yes Passed \n", "74 yes no yes Missed \n", "84 yes no yes Missed \n", "94 yes no yes Missed \n", "104 yes yes yes Passed \n", "114 yes yes yes Passed \n", "124 yes yes yes Passed \n", "134 yes yes yes Passed \n", "144 yes yes yes Passed \n", "154 yes yes yes Passed \n", "164 yes yes yes Passed \n", "174 yes yes yes Passed \n", "204 yes yes yes Passed \n", "214 yes yes yes Passed " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check the MCNP 10 statistical checks\n", "outp.get_stat_checks_table().sort_index() # sort them by cell index" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mean behaviourrel error valuerel error decreaserel error decrease rateVoV valueVoV decreaseVoV decrease rateFoM valueFoM behaviourPDF slope
TFC bin behaviour
desiredrandom<0.10yes1/sqrt(nps)<0.10yes1/npsconstantrandom>3.00
observedrandom0.00yesyes0.00yesyesconstantdecrease10.00
passed?yesyesyesyesyesyesyesyesnoyes
\n", "
" ], "text/plain": [ " mean behaviour rel error value rel error decrease \\\n", "TFC bin behaviour \n", "desired random <0.10 yes \n", "observed random 0.00 yes \n", "passed? yes yes yes \n", "\n", " rel error decrease rate VoV value VoV decrease \\\n", "TFC bin behaviour \n", "desired 1/sqrt(nps) <0.10 yes \n", "observed yes 0.00 yes \n", "passed? yes yes yes \n", "\n", " VoV decrease rate FoM value FoM behaviour PDF slope \n", "TFC bin behaviour \n", "desired 1/nps constant random >3.00 \n", "observed yes constant decrease 10.00 \n", "passed? yes yes no yes " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get more info on the checks on a specific tally\n", "outp.get_tally_stat_checks(74)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Warning\n", "1 coincident energy grid points in 5011.31c 1\n", "1 coincident energy grid points in 42095.31c 1\n", "1000. p and 1000.84p are both called for. 1\n", "12000. p and 12000.84p are both called for. 1\n", "13000. p and 13000.84p are both called for. 1\n", "14000. p and 14000.84p are both called for. 1\n", "15000. p and 15000.84p are both called for. 1\n", "16000. p and 16000.84p are both called for. 1\n", "19000. p and 19000.84p are both called for. 1\n", "194 photons from neutron collisions were created below a local photon energy cutoff and were not followed. 1\n", "dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get a pd.Series counting all the warnings encountered in the simulation\n", "outp.get_warnings()[:10] # show only 10" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['no m card for material no. 1',\n", " 'surface 5 of tally 22 not found.',\n", " 'FILES card is missing.',\n", " 'photon material 1 is not defined.',\n", " '1 tally volumes or areas were not input nor calculated.']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get all fatal errors encountered in the simulation\n", "# Parse the output file\n", "outp = Output(\"outp_fatal\")\n", "outp.get_fatal_errors()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Lost particles debugging" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "677" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the total number of particles lost (if applicable)\n", "outp = Output('test_lp_u.o')\n", "outp.get_tot_lp()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.06763236763236763" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get the Lost Particle Rate\n", "outp.get_LPR()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "import tempfile # To have a scratch directory for the example\n", "outpath = tempfile.gettempdir()\n", "\n", "# This will output excel file, csv and vtp file containing data for\n", "# lost particles debugging in different formats.\n", "outp.print_lp_debug(outpath) #, print_video=True) <- get interactive plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Surface Cell x y z u v w\n", "0 6 6 8.51597 7.66724 5.83396 0.662279 0.596273 0.453702\n", "1 6 6 7.75842 9.13777 5.61458 0.768164 0.178897 0.614752\n", "2 6 6 7.36287 8.51361 13.97950 0.013925 0.389456 -0.920940\n", "3 6 6 12.50940 7.94754 6.19337 0.532819 0.704170 0.469306\n", "4 6 6 5.19721 8.90472 9.14354 0.907545 -0.184714 0.377151\n", ".. ... ... ... ... ... ... ... ...\n", "672 6 6 8.89572 6.45090 6.65568 0.489383 0.117032 0.864180\n", "673 6 6 8.49142 12.65140 6.03840 -0.273160 -0.408824 0.870774\n", "674 6 6 5.15440 8.84265 9.57497 0.367758 0.630910 0.683159\n", "675 6 6 9.96136 6.10580 6.86413 0.735109 0.450584 0.506546\n", "676 6 6 11.65190 8.34418 5.58079 0.539463 0.611278 0.579067\n", "\n", "[677 rows x 8 columns]\n" ] } ], "source": [ "# Get all the raw information as a pandas DataFrame\n", "df = outp.get_lp_debug_df()\n", "print(df)" ] } ], "metadata": { "kernelspec": { "display_name": "py312", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.9" } }, "nbformat": 4, "nbformat_minor": 2 }