Difference between revisions of "MC Routines"
| (5 intermediate revisions by one user not shown) | |||
| Line 1: | Line 1: | ||
To generate synthetic data, we adopt the fiducial model that the diffuse gamma ray background is isotropic. | To generate synthetic data, we adopt the fiducial model that the diffuse gamma ray background is isotropic. | ||
| − | |||
| − | |||
| − | |||
| − | |||
| Line 10: | Line 6: | ||
Here are Ferrer's MC routines in Python: | 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]]. | + | 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. | ||
Latest revision as of 17:34, 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.
