Commit 35fceede88bafd6b65cde46aedbed7d256acae6f

Authored by Jürgen Knödlseder
1 parent ed19ef6c

Adapt test scripts to new GCaldb interface.

inst/cta/test/example_binned_ml_fit.py
@@ -2,7 +2,7 @@ @@ -2,7 +2,7 @@
2 # ========================================================================== 2 # ==========================================================================
3 # This script performs a binned maximum likelihood analysis of CTA data. 3 # This script performs a binned maximum likelihood analysis of CTA data.
4 # 4 #
5 -# Copyright (C) 2012 Juergen Knoedlseder 5 +# Copyright (C) 2012-2014 Juergen Knoedlseder
6 # 6 #
7 # This program is free software: you can redistribute it and/or modify 7 # This program is free software: you can redistribute it and/or modify
8 # it under the terms of the GNU General Public License as published by 8 # it under the terms of the GNU General Public License as published by
@@ -109,13 +109,13 @@ if __name__ == '__main__': @@ -109,13 +109,13 @@ if __name__ == '__main__':
109 print("... please wait for a few seconds") 109 print("... please wait for a few seconds")
110 110
111 # Set parameters 111 # Set parameters
112 - irf = "kb_E_50h_v3" 112 + irf = "cta_dummy_irf"
113 caldb = "../caldb" 113 caldb = "../caldb"
114 model = "data/crab.xml" 114 model = "data/crab.xml"
115 cntmap = "data/crab_cntmap.fits" 115 cntmap = "data/crab_cntmap.fits"
116 116
117 # Perform binned analysis 117 # Perform binned analysis
118 - result = binned_analysis(model, cntmap, irf, caldb) 118 + result = binned_analysis(model, cntmap, irf, GCaldb(caldb))
119 119
120 # Print model results 120 # Print model results
121 print(result) 121 print(result)
inst/cta/test/example_sim_psf.py
1 #! /usr/bin/env python 1 #! /usr/bin/env python
2 # ========================================================================== 2 # ==========================================================================
3 -# This script simulated the PSF distribution. 3 +# This script simulates the PSF distribution.
4 # 4 #
5 -# Based on the MAGIC spectrum of the Crab nebula, and by assuming a powerlaw,  
6 -# it will create a Monte Carlo sample of photons.  
7 -#  
8 -# If matplotlib is installed, the spectrum will be displayed on the screen. 5 +# If matplotlib is installed, the PSF will be displayed on the screen.
9 # 6 #
10 # -------------------------------------------------------------------------- 7 # --------------------------------------------------------------------------
11 # 8 #
12 -# Copyright (C) 2013 Juergen Knoedlseder 9 +# Copyright (C) 2013-2014 Juergen Knoedlseder
13 # 10 #
14 # This program is free software: you can redistribute it and/or modify 11 # This program is free software: you can redistribute it and/or modify
15 # it under the terms of the GNU General Public License as published by 12 # it under the terms of the GNU General Public License as published by
@@ -29,47 +26,9 @@ from gammalib import * @@ -29,47 +26,9 @@ from gammalib import *
29 from math import * 26 from math import *
30 27
31 28
32 -# ======================== #  
33 -# Simulate CTA observation #  
34 -# ======================== #  
35 -def simulate(xmlname, e_min, e_max, area, duration):  
36 - """  
37 - Simulate CTA observation.  
38 - """  
39 - # Allocate MC parameters  
40 - dir = GSkyDir()  
41 - emin = GEnergy()  
42 - emax = GEnergy()  
43 - tmin = GTime(0.0)  
44 - tmax = GTime(duration)  
45 -  
46 - # Define MC parameters  
47 - dir.radec_deg(83.6331, 22.0145)  
48 - radius = 10.0  
49 - emin.TeV(e_min)  
50 - emax.TeV(e_max)  
51 -  
52 - # Allocate random number generator  
53 - ran = GRan()  
54 -  
55 - # Load models and extract first model  
56 - models = GModels(xmlname)  
57 - model = models[0]  
58 - print(model)  
59 -  
60 - # Simulate photons  
61 - photons = model.mc(area, dir, radius, emin, emax, tmin, tmax, ran)  
62 -  
63 - # Print photons  
64 - print(str(len(photons)) + " photons simulated.")  
65 -  
66 - # Return photons  
67 - return photons  
68 -  
69 -  
70 -# ======== #  
71 -# Show PSF #  
72 -# ======== # 29 +# ============ #
  30 +# Simulate PSF #
  31 +# ============ #
73 def sim_psf(response, energy, r_max=0.8, rbins=1000, nmc=1000000): 32 def sim_psf(response, energy, r_max=0.8, rbins=1000, nmc=1000000):
74 """ 33 """
75 Simulate PSF and show results using matplotlib (if available). 34 Simulate PSF and show results using matplotlib (if available).
@@ -160,8 +119,8 @@ if __name__ == '__main__': @@ -160,8 +119,8 @@ if __name__ == '__main__':
160 print("****************") 119 print("****************")
161 120
162 # Load response 121 # Load response
163 - rsp = GCTAResponse("irf_test.fits",  
164 - "../caldb/data/cta/e/bcf/000002") 122 + rsp = GCTAResponse("irf_file.fits",
  123 + GCaldb("../caldb/data/cta/e/bcf/IFAE20120510_50h_King"))
165 124
166 # Simulate PSF 125 # Simulate PSF
167 sim_psf(rsp, 1.0) 126 sim_psf(rsp, 1.0)
inst/cta/test/test_model.py
@@ -2,7 +2,7 @@ @@ -2,7 +2,7 @@
2 # ========================================================================== 2 # ==========================================================================
3 # This script tests the model computations using the CTA response function. 3 # This script tests the model computations using the CTA response function.
4 # 4 #
5 -# Copyright (C) 2011-2013 Jurgen Knodlseder 5 +# Copyright (C) 2011-2014 Jurgen Knodlseder
6 # 6 #
7 # This program is free software: you can redistribute it and/or modify 7 # This program is free software: you can redistribute it and/or modify
8 # it under the terms of the GNU General Public License as published by 8 # it under the terms of the GNU General Public License as published by
@@ -150,7 +150,7 @@ def observation(ra=0.0, dec=0.0, emin=0.1, emax=100.0, @@ -150,7 +150,7 @@ def observation(ra=0.0, dec=0.0, emin=0.1, emax=100.0,
150 obs = GCTAObservation() 150 obs = GCTAObservation()
151 151
152 # Set response 152 # Set response
153 - obs.response("kb_E_50h_v3", "../caldb") 153 + obs.response("cta_dummy_irf", GCaldb("../caldb"))
154 154
155 # Set pointing 155 # Set pointing
156 dir = GSkyDir() 156 dir = GSkyDir()