Difference between revisions of "MC Routines"
From magneticfields
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The script [[Media:mc.py]] generates 10000 synthetic samples with the same number of events in the real data. The events are uniformly distributed in the spherical caps and do not fall close to a Fermi source. The samples are saved to disk in numpy array format. Their statistics (average and standard deviation is computed using this script [[Media:qstat.py]]. For b>80deg we obtain the following MC samples [[Media:mc80.tar.gz]]. | The script [[Media:mc.py]] generates 10000 synthetic samples with the same number of events in the real data. The events are uniformly distributed in the spherical caps and do not fall close to a Fermi source. The samples are saved to disk in numpy array format. Their statistics (average and standard deviation is computed using this script [[Media:qstat.py]]. For b>80deg we obtain the following MC samples [[Media:mc80.tar.gz]]. | ||
| − | == Tashiro's MC routines in | + | == Tashiro's MC routines in Fortran90 == |
| − | Tashiro's MC routines in | + | Tashiro's MC routines in F90 are included in [[File:Gamma codes.tar]]. |
Revision as of 11:38, 20 May 2014
To generate synthetic data, we adopt the fiducial model that the diffuse gamma ray background is isotropic.
Ferrer's MC Python routines
Here are Ferrer's MC routines in Python:
The script Media:mc.py generates 10000 synthetic samples with the same number of events in the real data. The events are uniformly distributed in the spherical caps and do not fall close to a Fermi source. The samples are saved to disk in numpy array format. Their statistics (average and standard deviation is computed using this script Media:qstat.py. For b>80deg we obtain the following MC samples Media:mc80.tar.gz.
Tashiro's MC routines in Fortran90
Tashiro's MC routines in F90 are included in File:Gamma codes.tar.
