Konstancja Satalecka / ctools

Commit 320f0d7db00c2935f5077ac5e71ddb644c08472e

Authored by Jürgen Knödlseder
1 parent 2e5b7227

Adapt to new GCaldb interface.

Inline Side-by-side
Showing 2 changed files with 298 additions and 298 deletions
scripts/obsutils.py
  View file @320f0d7
... ... @@ -266,7 +266,7 @@ def modmap(obs, eref=0.1, proj="TAN", coord="GAL", xval=0.0, yval=0.0, \
266 266 # ======================= #
267 267 def set(pntdir, tstart=0.0, duration=1800.0, deadc=0.95, \
268 268 emin=0.1, emax=100.0, rad=5.0, \
269   - irf="cta_dummy_irf", caldb=""):
  269 + irf="cta_dummy_irf", caldb="$CTOOLS/share/caldb/data/cta/dummy"):
270 270 """
271 271 Returns a single CTA observation. By looping over this function we can
272 272 add CTA observations to the observation container.
... ...
test/example_survey.py
  View file @320f0d7
... ... @@ -3,7 +3,7 @@
3 3 # This script simulates CTA surveys. It can be considered as the master
4 4 # script for CTA science simulations. Please modify as needed.
5 5 #
6   -# Copyright (C) 2011-2013 Juergen Knoedlseder
  6 +# Copyright (C) 2011-2014 Juergen Knoedlseder
7 7 #
8 8 # This program is free software: you can redistribute it and/or modify
9 9 # it under the terms of the GNU General Public License as published by
... ... @@ -32,331 +32,331 @@ import obsutils
32 32 # Plot counts #
33 33 # =========== #
34 34 def plot_counts(obs):
35   - """
36   - Plot counts.
37   - """
38   - # Only proceed if matplotlib is available
39   - try:
40   - # Import matplotlib
41   - import matplotlib.pyplot as plt
42   -
43   - # Set legend fontsize
44   - params = {'legend.fontsize': 10}
45   - plt.rcParams.update(params)
46   -
47   - # Set plot styles
48   - styles = ['b-', 'g-', 'y-', 'n-']
49   -
50   - # Dump header
51   - print("")
52   - print("Make plots (using matplotlib):")
53   - print("==============================")
54   -
55   - # Create figure 1
56   - plt.figure(1,figsize=(12,6))
57   - plt.subplots_adjust(hspace=.7)
58   -
59   - # Create subplot 1
60   - plt.subplot(121)
61   - plt.title("Spectrum (summed over all pixels)")
62   -
63   - # Loop over observations
64   - for run in obs:
65   -
66   - # Get event list
67   - list = run.events()
68   -
69   - # Create energy axis
70   - ebounds = GEbounds()
71   - emin = GEnergy()
72   - emax = GEnergy()
73   - emin.TeV(0.1)
74   - emax.TeV(100.0)
75   - ebounds.setlog(emin, emax, 10)
76   - energy = [ebounds.elogmean(i).TeV() for i in range(ebounds.size())]
77   -
78   - # Create spectrum
79   - print("Extract data:")
80   - counts = [0.0 for i in range(ebounds.size())]
81   - print(list.size())
82   - for atom in list:
83   - index = ebounds.index(atom.energy())
84   - counts[index] = counts[index] + 1.0
85   -
86   - # Create error bars
87   - error = [sqrt(c) for c in counts]
88   -
89   - # Plot spectrum
90   - plt.loglog(energy, counts, 'ro', label='data')
91   - #plt.errorbar(energy, counts, error, fmt=None, ecolor='r')
92   -
93   - # Extract models
94   - print("Extract models:")
95   - sum_model = [0.0 for i in range(ebounds.size())]
96   - for k, m in enumerate(obs.models()):
97   - print("- "+m.name())
98   - model = [0.0 for i in range(ebounds.size())]
99   - for atom in list:
100   - index = ebounds.index(atom.energy())
101   - prob = m.eval(atom, run)
102   - model[index] = model[index] + prob * atom.size()
103   - for i in range(ebounds.size()):
104   - sum_model[i] = sum_model[i] + model[i]
105   - #plt.loglog(energy, model, styles[k], label=m.name())
106   - #plt.loglog(energy, sum_model, 'r-', label='total')
107   -
108   - # Put labels
109   - plt.xlabel("Energy (TeV)")
110   - plt.ylabel("Counts")
111   - plt.legend(loc="lower left")
112   -
113   - # Create subplot 2
114   - plt.subplot(122)
115   - plt.title("Offset (summed over all energies)")
116   -
117   - # Set Crab direction
118   - crab = GSkyDir()
119   - crab.radec_deg(83.63, 22.01)
120   -
121   - # Loop over observations
122   - for run in obs:
123   -
124   - # Get event list
125   - list = run.events()
126   -
127   - # Create offset histogram
128   - print("Extract data:")
129   - nx = 30
130   - doffset2 = 0.01
131   - offset2 = [(i+0.5)*doffset2 for i in range(nx)]
132   - counts = [0.0 for i in range(nx)]
133   - for atom in list:
134   - off = atom.dir().dist_deg(crab)
135   - off2 = off*off
136   - index = int(off2/doffset2)
137   - if index < nx:
138   - counts[index] = counts[index] + 1.0
139   -
140   - # Create error bars
141   - error = [sqrt(c) for c in counts]
142   -
143   - # Plot distribution
144   - #plt.semilogy(offset2, counts, 'ro', label='data')
145   -
146   - # Extract models
147   - print("Extract models:")
148   - sum_model = [0.0 for i in range(nx)]
149   - for k, m in enumerate(obs.models()):
150   - print("- "+m.name())
151   - model = [0.0 for i in range(nx)]
152   - for atom in list:
153   - off = atom.dir().dist_deg(crab)
154   - off2 = off*off
155   - index = int(off2/doffset2)
156   - if index < nx:
157   - prob = m.eval(atom, run)
158   - model[index] = model[index] + prob * atom.size()
159   - for i in range(nx):
160   - sum_model[i] = sum_model[i] + model[i]
161   - #plt.plot(offset2, model, styles[k], label=m.name())
162   - #plt.plot(offset2, sum_model, 'r-', label='total')
163   - #plt.ylim(ymin=0.1)
164   -
165   - # Put labels
166   - plt.xlabel("Offset (deg^2)")
167   - plt.ylabel("Counts")
168   - #plt.legend(loc="upper right")
169   -
170   - # Show counts spectra
171   - plt.show()
172   -
173   - except ImportError:
174   - print("Matplotlib is not (correctly) installed on your system.")
  35 + """
  36 + Plot counts.
  37 + """
  38 + # Only proceed if matplotlib is available
  39 + try:
  40 + # Import matplotlib
  41 + import matplotlib.pyplot as plt
  42 +
  43 + # Set legend fontsize
  44 + params = {'legend.fontsize': 10}
  45 + plt.rcParams.update(params)
  46 +
  47 + # Set plot styles
  48 + styles = ['b-', 'g-', 'y-', 'n-']
  49 +
  50 + # Dump header
  51 + print("")
  52 + print("Make plots (using matplotlib):")
  53 + print("==============================")
  54 +
  55 + # Create figure 1
  56 + plt.figure(1,figsize=(12,6))
  57 + plt.subplots_adjust(hspace=.7)
  58 +
  59 + # Create subplot 1
  60 + plt.subplot(121)
  61 + plt.title("Spectrum (summed over all pixels)")
  62 +
  63 + # Loop over observations
  64 + for run in obs:
  65 +
  66 + # Get event list
  67 + list = run.events()
  68 +
  69 + # Create energy axis
  70 + ebounds = GEbounds()
  71 + emin = GEnergy()
  72 + emax = GEnergy()
  73 + emin.TeV(0.1)
  74 + emax.TeV(100.0)
  75 + ebounds.setlog(emin, emax, 10)
  76 + energy = [ebounds.elogmean(i).TeV() for i in range(ebounds.size())]
  77 +
  78 + # Create spectrum
  79 + print("Extract data:")
  80 + counts = [0.0 for i in range(ebounds.size())]
  81 + print(list.size())
  82 + for atom in list:
  83 + index = ebounds.index(atom.energy())
  84 + counts[index] = counts[index] + 1.0
  85 +
  86 + # Create error bars
  87 + error = [sqrt(c) for c in counts]
  88 +
  89 + # Plot spectrum
  90 + plt.loglog(energy, counts, 'ro', label='data')
  91 + #plt.errorbar(energy, counts, error, fmt=None, ecolor='r')
  92 +
  93 + # Extract models
  94 + print("Extract models:")
  95 + sum_model = [0.0 for i in range(ebounds.size())]
  96 + for k, m in enumerate(obs.models()):
  97 + print("- "+m.name())
  98 + model = [0.0 for i in range(ebounds.size())]
  99 + for atom in list:
  100 + index = ebounds.index(atom.energy())
  101 + prob = m.eval(atom, run)
  102 + model[index] = model[index] + prob * atom.size()
  103 + for i in range(ebounds.size()):
  104 + sum_model[i] = sum_model[i] + model[i]
  105 + #plt.loglog(energy, model, styles[k], label=m.name())
  106 + #plt.loglog(energy, sum_model, 'r-', label='total')
  107 +
  108 + # Put labels
  109 + plt.xlabel("Energy (TeV)")
  110 + plt.ylabel("Counts")
  111 + plt.legend(loc="lower left")
  112 +
  113 + # Create subplot 2
  114 + plt.subplot(122)
  115 + plt.title("Offset (summed over all energies)")
  116 +
  117 + # Set Crab direction
  118 + crab = GSkyDir()
  119 + crab.radec_deg(83.63, 22.01)
  120 +
  121 + # Loop over observations
  122 + for run in obs:
  123 +
  124 + # Get event list
  125 + list = run.events()
  126 +
  127 + # Create offset histogram
  128 + print("Extract data:")
  129 + nx = 30
  130 + doffset2 = 0.01
  131 + offset2 = [(i+0.5)*doffset2 for i in range(nx)]
  132 + counts = [0.0 for i in range(nx)]
  133 + for atom in list:
  134 + off = atom.dir().dist_deg(crab)
  135 + off2 = off*off
  136 + index = int(off2/doffset2)
  137 + if index < nx:
  138 + counts[index] = counts[index] + 1.0
  139 +
  140 + # Create error bars
  141 + error = [sqrt(c) for c in counts]
  142 +
  143 + # Plot distribution
  144 + #plt.semilogy(offset2, counts, 'ro', label='data')
  145 +
  146 + # Extract models
  147 + print("Extract models:")
  148 + sum_model = [0.0 for i in range(nx)]
  149 + for k, m in enumerate(obs.models()):
  150 + print("- "+m.name())
  151 + model = [0.0 for i in range(nx)]
  152 + for atom in list:
  153 + off = atom.dir().dist_deg(crab)
  154 + off2 = off*off
  155 + index = int(off2/doffset2)
  156 + if index < nx:
  157 + prob = m.eval(atom, run)
  158 + model[index] = model[index] + prob * atom.size()
  159 + for i in range(nx):
  160 + sum_model[i] = sum_model[i] + model[i]
  161 + #plt.plot(offset2, model, styles[k], label=m.name())
  162 + #plt.plot(offset2, sum_model, 'r-', label='total')
  163 + #plt.ylim(ymin=0.1)
  164 +
  165 + # Put labels
  166 + plt.xlabel("Offset (deg^2)")
  167 + plt.ylabel("Counts")
  168 + #plt.legend(loc="upper right")
  169 +
  170 + # Show counts spectra
  171 + plt.show()
  172 +
  173 + except ImportError:
  174 + print("Matplotlib is not (correctly) installed on your system.")
175 175  
176   - # Return
177   - return
  176 + # Return
  177 + return
178 178  
179 179  
180 180 # ======================================== #
181 181 # Add CTA background model to observations #
182 182 # ======================================== #
183 183 def add_background_model(obs):
184   - """
185   - Add standard CTA background model to observations container.
186   -
187   - We use a simple power law here, scaled to Konrad's E configuration
188   - performance table. The model needs still to be validated.
189   - """
190   - # Recover models from observation
191   - models = obs.models()
192   -
193   - # Define background model
194   - bgd_radial = GCTAModelRadialGauss(3.0)
195   - bgd_spectrum = GModelSpectralPlaw(61.8e-6, -1.85, GEnergy(1.0, "TeV"))
196   - bgd_model = GCTAModelRadialAcceptance(bgd_radial, bgd_spectrum)
197   - bgd_model.name("Background")
198   - bgd_model.instruments("CTA")
199   -
200   - # Add background model to container
201   - models.append(bgd_model)
202   -
203   - # Put container back in observation container
204   - obs.models(models)
205   -
206   - # Return observation container
207   - return obs
  184 + """
  185 + Add standard CTA background model to observations container.
  186 +
  187 + We use a simple power law here, scaled to Konrad's E configuration
  188 + performance table. The model needs still to be validated.
  189 + """
  190 + # Recover models from observation
  191 + models = obs.models()
  192 +
  193 + # Define background model
  194 + bgd_radial = GCTAModelRadialGauss(3.0)
  195 + bgd_spectrum = GModelSpectralPlaw(61.8e-6, -1.85, GEnergy(1.0, "TeV"))
  196 + bgd_model = GCTAModelRadialAcceptance(bgd_radial, bgd_spectrum)
  197 + bgd_model.name("Background")
  198 + bgd_model.instruments("CTA")
  199 +
  200 + # Add background model to container
  201 + models.append(bgd_model)
  202 +
  203 + # Put container back in observation container
  204 + obs.models(models)
  205 +
  206 + # Return observation container
  207 + return obs
208 208  
209 209  
210 210 # ================= #
211 211 # Set Crab spectrum #
212 212 # ================= #
213 213 def crab_spec():
214   - """
215   - Set Crab spectrum based on MAGIC observations
216   - (Albert et al. 2008, ApJ, 674, 1037)
217   - """
218   - # Set parameters
219   - spectrum = GModelSpectralPlaw(5.7e-16, -2.48, GEnergy(0.3, "TeV"))
220   -
221   - # Return spectrum
222   - return spectrum
  214 + """
  215 + Set Crab spectrum based on MAGIC observations
  216 + (Albert et al. 2008, ApJ, 674, 1037)
  217 + """
  218 + # Set parameters
  219 + spectrum = GModelSpectralPlaw(5.7e-16, -2.48, GEnergy(0.3, "TeV"))
  220 +
  221 + # Return spectrum
  222 + return spectrum
223 223  
224 224  
225 225 # =============================== #
226 226 # Setup single observation survey #
227 227 # =============================== #
228 228 def survey_single():
229   - """
230   - Creates a single observation survey for test purposes.
231   - """
232   - # Allocate observation container
233   - obs = GObservations()
234   -
235   - # Set single pointing at galactic centre
236   - pntdir = GSkyDir()
237   - pntdir.lb_deg(0.0, 0.0)
238   - #run = set_one_obs(pntdir)
239   - run = obsutils.set(pntdir)
240   - obs.append(run)
241   -
242   - # Define single point source with Crab flux at galactic centre
243   - center = GSkyDir()
244   - center.lb_deg(0.0, 0.0)
245   - point_spatial = GModelSpatialPointSource(center)
246   - point_spectrum = crab_spec()
247   - point = GModelSky(point_spatial, point_spectrum)
248   - point.name('GC source')
249   -
250   - # Create model container
251   - models = GModels()
252   - models.append(point)
253   - obs.models(models)
254   -
255   - # Return observation container
256   - return obs
  229 + """
  230 + Creates a single observation survey for test purposes.
  231 + """
  232 + # Allocate observation container
  233 + obs = GObservations()
  234 +
  235 + # Set single pointing at galactic centre
  236 + pntdir = GSkyDir()
  237 + pntdir.lb_deg(0.0, 0.0)
  238 + #run = set_one_obs(pntdir)
  239 + run = obsutils.set(pntdir)
  240 + obs.append(run)
  241 +
  242 + # Define single point source with Crab flux at galactic centre
  243 + center = GSkyDir()
  244 + center.lb_deg(0.0, 0.0)
  245 + point_spatial = GModelSpatialPointSource(center)
  246 + point_spectrum = crab_spec()
  247 + point = GModelSky(point_spatial, point_spectrum)
  248 + point.name('GC source')
  249 +
  250 + # Create model container
  251 + models = GModels()
  252 + models.append(point)
  253 + obs.models(models)
  254 +
  255 + # Return observation container
  256 + return obs
257 257  
258 258  
259 259 # =========================== #
260 260 # Setup Galactic plane survey #
261 261 # =========================== #
262 262 def survey_gplane(lrange=10, lstep=2):
263   - """
264   - Creates a single observation survey for test purposes.
265   -
266   - Keywords:
267   - lrange - Longitude range (integer deg)
268   - lstep - Longitude step size (integer deg)
269   - """
270   - # Allocate observation container
271   - obs = GObservations()
272   -
273   - # Loop over longitudes
274   - for l in range(-lrange,lrange+lstep,lstep):
275   -
276   - # Set pointing
277   - pntdir = GSkyDir()
278   - pntdir.lb_deg(l, 0.0)
279   - #run = set_one_obs(pntdir)
280   - run = obsutils.set(pntdir)
281   - run.id(str(l))
282   - obs.append(run)
283   -
284   - # Define single point source with Crab flux at galactic centre
285   - center = GSkyDir()
286   - center.lb_deg(0.0, 0.0)
287   - point_spatial = GModelSpatialPointSource(center)
288   - point_spectrum = crab_spec()
289   - point = GModelSky(point_spatial, point_spectrum)
290   - point.name('GC source')
291   -
292   - # Create model container
293   - models = GModels()
294   - models.append(point)
295   - obs.models(models)
296   -
297   - # Return observation container
298   - return obs
  263 + """
  264 + Creates a single observation survey for test purposes.
  265 +
  266 + Keywords:
  267 + lrange - Longitude range (integer deg)
  268 + lstep - Longitude step size (integer deg)
  269 + """
  270 + # Allocate observation container
  271 + obs = GObservations()
  272 +
  273 + # Loop over longitudes
  274 + for l in range(-lrange,lrange+lstep,lstep):
  275 +
  276 + # Set pointing
  277 + pntdir = GSkyDir()
  278 + pntdir.lb_deg(l, 0.0)
  279 + #run = set_one_obs(pntdir)
  280 + run = obsutils.set(pntdir)
  281 + run.id(str(l))
  282 + obs.append(run)
  283 +
  284 + # Define single point source with Crab flux at galactic centre
  285 + center = GSkyDir()
  286 + center.lb_deg(0.0, 0.0)
  287 + point_spatial = GModelSpatialPointSource(center)
  288 + point_spectrum = crab_spec()
  289 + point = GModelSky(point_spatial, point_spectrum)
  290 + point.name('GC source')
  291 +
  292 + # Create model container
  293 + models = GModels()
  294 + models.append(point)
  295 + obs.models(models)
  296 +
  297 + # Return observation container
  298 + return obs
299 299  
300 300  
301 301 #==========================#
302 302 # Main routine entry point #
303 303 #==========================#
304 304 if __name__ == '__main__':
305   - """
306   - CTA survey simulation and analysis script.
307   - """
308   - # Initialise flags
309   - need_help = False
310   -
311   - # Test for command line arguments
312   - print(sys.argv[0])
313   - if (len(sys.argv) > 1):
314   - if sys.argv[1] == "-h":
315   - need_help = True
316   - else:
317   - need_help = True
  305 + """
  306 + CTA survey simulation and analysis script.
  307 + """
  308 + # Initialise flags
  309 + need_help = False
  310 +
  311 + # Test for command line arguments
  312 + print(sys.argv[0])
  313 + if (len(sys.argv) > 1):
  314 + if sys.argv[1] == "-h":
  315 + need_help = True
  316 + else:
  317 + need_help = True
318 318  
319   - # Print help if needed and exit
320   - if need_help:
321   - print("Usage: example_survey.py [OPTIONS]")
322   - print(" -h Display this usage message")
323   - sys.exit()
324   -
325   - # Dump header
326   - print("***********************")
327   - print("* Simulate CTA survey *")
328   - print("***********************")
329   -
330   - # Remove any existing result files
331   - list = [glob.glob("*.fits"), glob.glob("*.log"), glob.glob("*.xml")]
332   - for files in list:
333   - for file in files:
334   - os.remove(file)
335   -
336   - # Setup single observation survey
337   - #obs = survey_single()
338   - obs = survey_gplane()
339   -
340   - # Add background model
341   - obs = add_background_model(obs)
342   -
343   - # Simulate events
344   - print("Simulate events")
345   - obs = obsutils.sim(obs)
346   -
347   - # Make counts map
348   - print("Make counts map")
349   - cntmap = obsutils.cntmap(obs)
350   -
351   - # Fit observations
352   - print("Fit observations")
353   - like = obsutils.fit(obs)
354   - #print like.opt()
355   - #print like.obs().models()
356   -
357   - # Make model map
358   - print("Make model map (this step will take some time)")
359   - modmap = obsutils.modmap(obs)
360   -
361   - # Show fit results
362   - #plot_counts(like.obs())
  319 + # Print help if needed and exit
  320 + if need_help:
  321 + print("Usage: example_survey.py [OPTIONS]")
  322 + print(" -h Display this usage message")
  323 + sys.exit()
  324 +
  325 + # Dump header
  326 + print("***********************")
  327 + print("* Simulate CTA survey *")
  328 + print("***********************")
  329 +
  330 + # Remove any existing result files
  331 + list = [glob.glob("*.fits"), glob.glob("*.log"), glob.glob("*.xml")]
  332 + for files in list:
  333 + for file in files:
  334 + os.remove(file)
  335 +
  336 + # Setup single observation survey
  337 + #obs = survey_single()
  338 + obs = survey_gplane()
  339 +
  340 + # Add background model
  341 + obs = add_background_model(obs)
  342 +
  343 + # Simulate events
  344 + print("Simulate events")
  345 + obs = obsutils.sim(obs)
  346 +
  347 + # Make counts map
  348 + print("Make counts map")
  349 + cntmap = obsutils.cntmap(obs)
  350 +
  351 + # Fit observations
  352 + print("Fit observations")
  353 + like = obsutils.fit(obs)
  354 + #print like.opt()
  355 + #print like.obs().models()
  356 +
  357 + # Make model map
  358 + print("Make model map (this step will take some time)")
  359 + modmap = obsutils.modmap(obs)
  360 +
  361 + # Show fit results
  362 + #plot_counts(like.obs())
... ...