Difference between revisions of "MC Routines"
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Tashiro's MC routines in F90 are included in [[File:Gamma codes.tar]]. | Tashiro's MC routines in F90 are included in [[File:Gamma codes.tar]]. | ||
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+ | "MC_code" contains the code for Monte Carlo simulations. There is a parameter "north_south" which is for choice of north and south. After the code, "generate_SQT.nb" generates the file for plotting the MC result. In the file, we can find R degree, average Q and the standard deviation. |
Revision as of 12:51, 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.
"MC_code" contains the code for Monte Carlo simulations. There is a parameter "north_south" which is for choice of north and south. After the code, "generate_SQT.nb" generates the file for plotting the MC result. In the file, we can find R degree, average Q and the standard deviation.