MCNP output files#
The complete API can be found at f4enix.output.MCNPoutput.Output
Examining an MCNP output file can be useful to extract data on any of the tables contained within it, controlling statistical checks results or debugging for lost particles.
# Import the related module and parse the MCNP output file
from f4enix.output.MCNPoutput import Output
# Parse the output file
file = 'outp'
outp = Output(file)
# Check how many histories were run
'%.2E' % outp.get_NPS()
/home/docs/checkouts/readthedocs.org/user_builds/f4enix/envs/developing/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
'1.30E+06'
# get the MCNP/D1SUNED version used
outp.get_code_version()
'6.2'
# It is possible to read any printed table in the output
# Some issues still in the header names due to uncorrect format in MCNP FWF
# but the data is good.
# We specify that we want to read the last instance of the table 126. The output file
# may contain multiple instances of the same table due to multiple dumps and/or particle
# types.
table_126 = outp.get_table(126, instance_idx=-1)
display(table_126)
# get for instance a percentage of unpopulated cells (i.e. 0 tracks entering)
unpopulated = len(table_126[table_126['populatio '].astype(int) == 0])
print('Unpopulated cells fraction: {} %'.format(unpopulated/len(table_126)*100))
| cell | tracks entering | populatio | n collision | s collisions * weight (per history | number weighted ) energy | flux weighted energy | average track weigh (relative) | average t track mfp (cm) | ||
|---|---|---|---|---|---|---|---|---|---|---|
| 3 | 1.0 | 1 | 10882 | 9205 | 0 | 0.0000E+00 | 1.7602E+00 | 1.7602E+00 | 1.0106E+00 | 0.0000E+00 |
| 4 | 2.0 | 2 | 24955 | 50872 | 146068 | 1.3517E-08 | 1.7179E+00 | 1.7179E+00 | 1.0095E+00 | 2.7019E+00 |
| 5 | 3.0 | 3 | 30828 | 74594 | 182089 | 1.6853E-08 | 1.7222E+00 | 1.7222E+00 | 1.0095E+00 | 2.7033E+00 |
| 6 | 4.0 | 4 | 22084 | 54746 | 132281 | 2.4477E-08 | 1.6916E+00 | 1.6916E+00 | 1.0093E+00 | 2.6817E+00 |
| 7 | 5.0 | 5 | 33313 | 83006 | 199986 | 3.7012E-08 | 1.7182E+00 | 1.7182E+00 | 1.0091E+00 | 2.6976E+00 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 103 | 101.0 | 101 | 30600 | 76562 | 183594 | 3.3959E-08 | 1.7152E+00 | 1.7152E+00 | 1.0085E+00 | 2.7015E+00 |
| 104 | 102.0 | 102 | 19463 | 48379 | 116620 | 2.1554E-08 | 1.7004E+00 | 1.7004E+00 | 1.0074E+00 | 2.6866E+00 |
| 105 | 103.0 | 103 | 23502 | 58145 | 140406 | 1.2984E-08 | 1.7072E+00 | 1.7072E+00 | 1.0088E+00 | 2.6981E+00 |
| 106 | 104.0 | 104 | 13620 | 33617 | 80244 | 7.4088E-09 | 1.7158E+00 | 1.7158E+00 | 1.0075E+00 | 2.7085E+00 |
| 107 | 105.0 | 105 | 13144 | 31180 | 74064 | 3.4224E-09 | 1.6804E+00 | 1.6804E+00 | 1.0088E+00 | 2.7045E+00 |
105 rows × 10 columns
Unpopulated cells fraction: 0.0 %
# Check the MCNP 10 statistical checks
outp.get_stat_checks_table().sort_index() # sort them by cell index
| mean behaviour | rel error value | rel error decrease | rel error decrease rate | VoV value | VoV decrease | VoV decrease rate | FoM value | FoM behaviour | PDF slope | Other TFC bins | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Tally | |||||||||||
| 4 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 6 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 14 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 16 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 24 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 26 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 34 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 44 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 54 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 64 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 74 | yes | yes | yes | yes | yes | yes | yes | yes | no | yes | Missed |
| 84 | yes | yes | yes | yes | yes | yes | yes | yes | no | yes | Missed |
| 94 | yes | yes | yes | yes | yes | yes | yes | yes | no | yes | Missed |
| 104 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 114 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 124 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 134 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 144 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 154 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 164 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 174 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 204 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
| 214 | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | Passed |
# Get more info on the checks on a specific tally
outp.get_tally_stat_checks(74)
| mean behaviour | rel error value | rel error decrease | rel error decrease rate | VoV value | VoV decrease | VoV decrease rate | FoM value | FoM behaviour | PDF slope | |
|---|---|---|---|---|---|---|---|---|---|---|
| TFC bin behaviour | ||||||||||
| desired | random | <0.10 | yes | 1/sqrt(nps) | <0.10 | yes | 1/nps | constant | random | >3.00 |
| observed | random | 0.00 | yes | yes | 0.00 | yes | yes | constant | decrease | 10.00 |
| passed? | yes | yes | yes | yes | yes | yes | yes | yes | no | yes |
# get a pd.Series counting all the warnings encountered in the simulation
outp.get_warnings()[:10] # show only 10
Warning
1 coincident energy grid points in 5011.31c 1
1 coincident energy grid points in 42095.31c 1
1000. p and 1000.84p are both called for. 1
12000. p and 12000.84p are both called for. 1
13000. p and 13000.84p are both called for. 1
14000. p and 14000.84p are both called for. 1
15000. p and 15000.84p are both called for. 1
16000. p and 16000.84p are both called for. 1
19000. p and 19000.84p are both called for. 1
194 photons from neutron collisions were created below a local photon energy cutoff and were not followed. 1
dtype: int64
# get all fatal errors encountered in the simulation
# Parse the output file
outp = Output("outp_fatal")
outp.get_fatal_errors()
['no m card for material no. 1',
'surface 5 of tally 22 not found.',
'FILES card is missing.',
'photon material 1 is not defined.',
'1 tally volumes or areas were not input nor calculated.']
Lost particles debugging#
# Get the total number of particles lost (if applicable)
outp = Output('test_lp_u.o')
outp.get_tot_lp()
677
# get the Lost Particle Rate
outp.get_LPR()
0.06763236763236763
import tempfile # To have a scratch directory for the example
outpath = tempfile.gettempdir()
# This will output excel file, csv and vtp file containing data for
# lost particles debugging in different formats.
outp.print_lp_debug(outpath) #, print_video=True) <- get interactive plot
# Get all the raw information as a pandas DataFrame
df = outp.get_lp_debug_df()
print(df)
Surface Cell x y z u v w
0 6 6 8.51597 7.66724 5.83396 0.662279 0.596273 0.453702
1 6 6 7.75842 9.13777 5.61458 0.768164 0.178897 0.614752
2 6 6 7.36287 8.51361 13.97950 0.013925 0.389456 -0.920940
3 6 6 12.50940 7.94754 6.19337 0.532819 0.704170 0.469306
4 6 6 5.19721 8.90472 9.14354 0.907545 -0.184714 0.377151
.. ... ... ... ... ... ... ... ...
672 6 6 8.89572 6.45090 6.65568 0.489383 0.117032 0.864180
673 6 6 8.49142 12.65140 6.03840 -0.273160 -0.408824 0.870774
674 6 6 5.15440 8.84265 9.57497 0.367758 0.630910 0.683159
675 6 6 9.96136 6.10580 6.86413 0.735109 0.450584 0.506546
676 6 6 11.65190 8.34418 5.58079 0.539463 0.611278 0.579067
[677 rows x 8 columns]