Leonardo Di Venere / ctools

Commit 2b7d1365a6b41379316f63f16e1d46179fcf3abc

Authored by knodlseder
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test/example_survey.py 0 → 100755
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  1 +#! /usr/bin/env python
  2 +# ===========================================================================================#
  3 +# This script simulates CTA surveys.
  4 +#
  5 +# If matplotlib is installed, the spectrum will be displayed on the screen.
  6 +# ===========================================================================================#
  7 +from ctatools import *
  8 +from gammalib import *
  9 +from math import *
  10 +import os
  11 +import glob
  12 +import sys
  13 +
  14 +
  15 +# =========== #
  16 +# Plot counts #
  17 +# =========== #
  18 +def plot_counts(obs):
  19 + """
  20 + Plot counts.
  21 + """
  22 + # Only proceed if matplotlib is available
  23 + if 1 == 1:
  24 + #try:
  25 + # Import matplotlib
  26 + import matplotlib.pyplot as plt
  27 +
  28 + # Set legend fontsize
  29 + params = {'legend.fontsize': 10}
  30 + plt.rcParams.update(params)
  31 +
  32 + # Set plot styles
  33 + styles = ['b-', 'g-', 'y-', 'n-']
  34 +
  35 + # Dump header
  36 + print ""
  37 + print "Make plots (using matplotlib):"
  38 + print "=============================="
  39 +
  40 + # Create figure 1
  41 + plt.figure(1,figsize=(12,6))
  42 + plt.subplots_adjust(hspace=.7)
  43 +
  44 + # Create subplot 1
  45 + plt.subplot(121)
  46 + plt.title("Spectrum (summed over all pixels)")
  47 +
  48 + # Loop over observations
  49 + for run in obs:
  50 +
  51 + # Get event list
  52 + list = cast_GCTAEventList(run.events())
  53 +
  54 + # Create energy axis
  55 + ebounds = GEbounds()
  56 + emin = GEnergy()
  57 + emax = GEnergy()
  58 + emin.TeV(0.1)
  59 + emax.TeV(100.0)
  60 + ebounds.setlog(emin, emax, 10)
  61 + energy = [ebounds.elogmean(i).TeV() for i in range(ebounds.size())]
  62 +
  63 + # Create spectrum
  64 + print "Extract data:"
  65 + counts = [0.0 for i in range(ebounds.size())]
  66 + print list.size()
  67 + for atom in list:
  68 + index = ebounds.index(atom.energy())
  69 + counts[index] = counts[index] + 1.0
  70 +
  71 + # Create error bars
  72 + error = [sqrt(c) for c in counts]
  73 +
  74 + # Plot spectrum
  75 + plt.loglog(energy, counts, 'ro', label='data')
  76 + #plt.errorbar(energy, counts, error, fmt=None, ecolor='r')
  77 +
  78 + # Extract models
  79 + print "Extract models:"
  80 + sum_model = [0.0 for i in range(ebounds.size())]
  81 + for k, m in enumerate(obs.models()):
  82 + print "- "+m.name()
  83 + model = [0.0 for i in range(ebounds.size())]
  84 + for atom in list:
  85 + index = ebounds.index(atom.energy())
  86 + prob = m.eval(atom, run)
  87 + model[index] = model[index] + prob * atom.size()
  88 + for i in range(ebounds.size()):
  89 + sum_model[i] = sum_model[i] + model[i]
  90 + #plt.loglog(energy, model, styles[k], label=m.name())
  91 + #plt.loglog(energy, sum_model, 'r-', label='total')
  92 +
  93 + # Put labels
  94 + plt.xlabel("Energy (TeV)")
  95 + plt.ylabel("Counts")
  96 + plt.legend(loc="lower left")
  97 +
  98 + # Create subplot 2
  99 + plt.subplot(122)
  100 + plt.title("Offset (summed over all energies)")
  101 +
  102 + # Set Crab direction
  103 + crab = GSkyDir()
  104 + crab.radec_deg(83.63, 22.01)
  105 +
  106 + # Loop over observations
  107 + for run in obs:
  108 +
  109 + # Get event cube
  110 + list = cast_GCTAEventList(run.events())
  111 +
  112 + # Create offset histogram
  113 + print "Extract data:"
  114 + nx = 30
  115 + doffset2 = 0.01
  116 + offset2 = [(i+0.5)*doffset2 for i in range(nx)]
  117 + counts = [0.0 for i in range(nx)]
  118 + for atom in list:
  119 + off = atom.dir().dist_deg(crab)
  120 + off2 = off*off
  121 + index = int(off2/doffset2)
  122 + if index < nx:
  123 + counts[index] = counts[index] + 1.0
  124 +
  125 + # Create error bars
  126 + error = [sqrt(c) for c in counts]
  127 +
  128 + # Plot distribution
  129 + #plt.semilogy(offset2, counts, 'ro', label='data')
  130 +
  131 + # Extract models
  132 + print "Extract models:"
  133 + sum_model = [0.0 for i in range(nx)]
  134 + for k, m in enumerate(obs.models()):
  135 + print "- "+m.name()
  136 + model = [0.0 for i in range(nx)]
  137 + for atom in list:
  138 + off = atom.dir().dist_deg(crab)
  139 + off2 = off*off
  140 + index = int(off2/doffset2)
  141 + if index < nx:
  142 + prob = m.eval(atom, run)
  143 + model[index] = model[index] + prob * atom.size()
  144 + for i in range(nx):
  145 + sum_model[i] = sum_model[i] + model[i]
  146 + #plt.plot(offset2, model, styles[k], label=m.name())
  147 + #plt.plot(offset2, sum_model, 'r-', label='total')
  148 + #plt.ylim(ymin=0.1)
  149 +
  150 + # Put labels
  151 + plt.xlabel("Offset (deg^2)")
  152 + plt.ylabel("Counts")
  153 + #plt.legend(loc="upper right")
  154 +
  155 + # Show counts spectra
  156 + plt.show()
  157 +
  158 + #except:
  159 + # print "Matplotlib is not (correctly) installed on your system. No counts spectra are shown."
  160 +
  161 + # Return
  162 + return
  163 +
  164 +
  165 +# ===================== #
  166 +# Simulate observations #
  167 +# ===================== #
  168 +def sim_obs(obs):
  169 + """
  170 + Simulate events for all observations in the container.
  171 + """
  172 + # Simulate events
  173 + sim = ctobssim(obs)
  174 + sim.logFileOpen() # We need this to explicitely open the log file in Python mode
  175 + sim.run()
  176 +
  177 + # Extract copy of observations (we need a copy here as sim goes out of
  178 + # scope once we leave the function)
  179 + obs = sim.obs().copy()
  180 +
  181 + # Return observations
  182 + return obs
  183 +
  184 +
  185 +# ================ #
  186 +# Fit observations #
  187 +# ================ #
  188 +def fit_obs(obs):
  189 + """
  190 + Perform maximum likelihood fitting of observations.
  191 + """
  192 + # Perform maximum likelihood fitting
  193 + like = ctlike(obs)
  194 + like.logFileOpen() # We need this to explicitely open the log file in Python mode
  195 + like.run()
  196 +
  197 + # Extract copy of observations (we need a copy here as like goes out of
  198 + # scope once we leave the function)
  199 + #obs = like.obs().copy()
  200 +
  201 + #print like.opt()
  202 +
  203 + # Return observations
  204 + return like
  205 +
  206 +
  207 +# ================= #
  208 +# Create counts map #
  209 +# ================= #
  210 +def make_counts_map(obs):
  211 + """
  212 + Make counts map by combining the events of all observations.
  213 + """
  214 + # Allocate counts map
  215 + nxpix = 600
  216 + nypix = 200
  217 + cntmap = GSkymap("CAR", "GAL", 0.0, 0.0, -0.05, 0.05, nxpix, nypix, 1)
  218 +
  219 + # Fill all observations
  220 + for run in obs:
  221 +
  222 + # Loop over all events
  223 + for event in run.events():
  224 +
  225 + # Cast to CTA event
  226 + event = cast_GCTAEventAtom(event)
  227 +
  228 + # Determine sky pixel
  229 + skydir = event.dir().skydir()
  230 + pixel = cntmap.dir2pix(skydir)
  231 +
  232 + # Set pixel
  233 + cntmap[pixel] += 1.0
  234 +
  235 + # Save sky map
  236 + cntmap.save("survey_cntmap.fits", True)
  237 +
  238 + # Return counts map
  239 + return cntmap
  240 +
  241 +
  242 +# ================ #
  243 +# Create model map #
  244 +# ================ #
  245 +def make_model_map(obs):
  246 + """
  247 + Make model map by combining all observations.
  248 + """
  249 + # Allocate model map
  250 + nxpix = 600
  251 + nypix = 200
  252 + modmap = GSkymap("CAR", "GAL", 0.0, 0.0, -0.05, 0.05, nxpix, nypix, 1)
  253 +
  254 + # Set energy and time
  255 + energy = GEnergy()
  256 + time = GTime()
  257 + instdir = GCTAInstDir()
  258 + energy.TeV(0.1)
  259 +
  260 + # Loop over all map pixels
  261 + for pixel in range(modmap.npix()):
  262 +
  263 + # Get sky direction
  264 + skydir = modmap.pix2dir(pixel)
  265 + instdir.skydir(skydir)
  266 +
  267 + # Create event atom for map pixel
  268 + atom = GCTAEventAtom()
  269 + atom.dir(instdir)
  270 + atom.energy(energy)
  271 + atom.time(time)
  272 +
  273 + # Initialise model value
  274 + value = 0.0
  275 +
  276 + # Loop over all observations
  277 + for run in obs:
  278 + value += obs.models().eval(atom, run)
  279 +
  280 + # Set map value
  281 + modmap[pixel] = value
  282 +
  283 + # Save sky map
  284 + modmap.save("survey_modmap.fits", True)
  285 +
  286 + # Return model map
  287 + return modmap
  288 +
  289 +
  290 +# ======================= #
  291 +# Set one CTA observation #
  292 +# ======================= #
  293 +def set_one_obs(pntdir, tstart=0.0, duration=1800.0, emin=0.1, emax=100.0, rad=3.0, \
  294 + irf="kb_E_50h_v3", caldb="irf"):
  295 + """
  296 + Parameters:
  297 + pntdir - Pointing direction
  298 + Keywords:
  299 + tstart - Start time (seconds)
  300 + duration - Duration of observation (seconds)
  301 + emin - Minimum event energy (TeV)
  302 + emax - Maximum event energy (TeV)
  303 + rad - ROI radius (deg)
  304 + irf - Instrument response function
  305 + caldb - Calibration database path
  306 + """
  307 + # Allocate CTA observation
  308 + obs = GCTAObservation()
  309 +
  310 + # Set pointing direction
  311 + pnt = GCTAPointing()
  312 + pnt.dir(pntdir)
  313 + obs.pointing(pnt)
  314 +
  315 + # Set ROI
  316 + roi = GCTARoi()
  317 + instdir = GCTAInstDir()
  318 + instdir.skydir(pntdir)
  319 + roi.centre(instdir)
  320 + roi.radius(rad)
  321 +
  322 + # Set GTI
  323 + gti = GGti()
  324 + start = GTime()
  325 + stop = GTime()
  326 + start.met(tstart)
  327 + stop.met(tstart+duration)
  328 + gti.append(start, stop)
  329 +
  330 + # Set energy boundaries
  331 + ebounds = GEbounds()
  332 + e_min = GEnergy()
  333 + e_max = GEnergy()
  334 + e_min.TeV(emin)
  335 + e_max.TeV(emax)
  336 + ebounds.append(e_min, e_max)
  337 +
  338 + # Allocate event list
  339 + events = GCTAEventList()
  340 + events.roi(roi)
  341 + events.gti(gti)
  342 + events.ebounds(ebounds)
  343 + obs.events(events)
  344 +
  345 + # Set instrument response
  346 + obs.response(irf, caldb)
  347 +
  348 + # Return observation
  349 + return obs
  350 +
  351 +
  352 +# ======================================== #
  353 +# Add CTA background model to observations #
  354 +# ======================================== #
  355 +def add_background_model(obs):
  356 + """
  357 + Add standard CTA background model to observations container.
  358 +
  359 + We use a simple power law here, scaled to Konrad's E configuration
  360 + performance table. The model needs still to be validated.
  361 + """
  362 + # Recover models from observation
  363 + models = obs.models()
  364 +
  365 + # Define background model
  366 + bgd_radial = GCTAModelRadialGauss(3.0)
  367 + bgd_spectrum = GModelSpectralPlaw(61.8, -1.85)
  368 + bgd_spectrum["Prefactor"].scale(1.0e-6)
  369 + bgd_spectrum["PivotEnergy"].value(1.0)
  370 + bgd_spectrum["PivotEnergy"].scale(1.0e6)
  371 + bgd_model = GCTAModelRadialAcceptance(bgd_radial, bgd_spectrum)
  372 + bgd_model.name("Background")
  373 + bgd_model.instruments("CTA")
  374 +
  375 + # Add background model to container
  376 + models.append(bgd_model)
  377 +
  378 + # Put container back in observation container
  379 + obs.models(models)
  380 +
  381 + # Return observation container
  382 + return obs
  383 +
  384 +
  385 +# ================= #
  386 +# Set Crab spectrum #
  387 +# ================= #
  388 +def crab_spec():
  389 + """
  390 + Set Crab spectrum based on MAGIC observations
  391 + (Albert et al. 2008, ApJ, 674, 1037)
  392 + """
  393 + # Set parameters
  394 + spectrum = GModelSpectralPlaw(5.7, -2.48)
  395 + spectrum["Prefactor"].scale(1.0e-16)
  396 + spectrum["PivotEnergy"].value(0.3)
  397 + spectrum["PivotEnergy"].scale(1.0e6)
  398 +
  399 + # Return spectrum
  400 + return spectrum
  401 +
  402 +
  403 +# =============================== #
  404 +# Setup single observation survey #
  405 +# =============================== #
  406 +def survey_single():
  407 + """
  408 + Creates a single observation survey for test purposes.
  409 + """
  410 + # Allocate observation container
  411 + obs = GObservations()
  412 +
  413 + # Set single pointing at galactic centre
  414 + pntdir = GSkyDir()
  415 + pntdir.lb_deg(0.0, 0.0)
  416 + run = set_one_obs(pntdir)
  417 + obs.append(run)
  418 +
  419 + # Define single point source with Crab flux at galactic centre
  420 + center = GSkyDir()
  421 + center.lb_deg(0.0, 0.0)
  422 + point_spatial = GModelSpatialPtsrc(center)
  423 + point_spectrum = crab_spec()
  424 + point = GModelPointSource(point_spatial, point_spectrum)
  425 + point.name('GC source')
  426 +
  427 + # Create model container
  428 + models = GModels()
  429 + models.append(point)
  430 + obs.models(models)
  431 +
  432 + # Return observation container
  433 + return obs
  434 +
  435 +
  436 +# =========================== #
  437 +# Setup Galactic plane survey #
  438 +# =========================== #
  439 +def survey_gplane(lrange=10, lstep=2):
  440 + """
  441 + Creates a single observation survey for test purposes.
  442 +
  443 + Keywords:
  444 + lrange - Longitude range (integer deg)
  445 + lstep - Longitude step size (integer deg)
  446 + """
  447 + # Allocate observation container
  448 + obs = GObservations()
  449 +
  450 + # Loop over longitudes
  451 + for l in range(-lrange,lrange+lstep,lstep):
  452 +
  453 + # Set pointing
  454 + pntdir = GSkyDir()
  455 + pntdir.lb_deg(l, 0.0)
  456 + run = set_one_obs(pntdir)
  457 + obs.append(run)
  458 +
  459 + # Define single point source with Crab flux at galactic centre
  460 + center = GSkyDir()
  461 + center.lb_deg(0.0, 0.0)
  462 + point_spatial = GModelSpatialPtsrc(center)
  463 + point_spectrum = crab_spec()
  464 + point = GModelPointSource(point_spatial, point_spectrum)
  465 + point.name('GC source')
  466 +
  467 + # Create model container
  468 + models = GModels()
  469 + models.append(point)
  470 + obs.models(models)
  471 +
  472 + # Return observation container
  473 + return obs
  474 +
  475 +
  476 +#==========================#
  477 +# Main routine entry point #
  478 +#==========================#
  479 +if __name__ == '__main__':
  480 + """
  481 + CTA survey simulation and analysis script.
  482 + """
  483 + # Initialise flags
  484 + need_help = False
  485 +
  486 + # Test for command line arguments
  487 + print sys.argv[0]
  488 + if (len(sys.argv) > 1):
  489 + if sys.argv[1] == "-h":
  490 + need_help = True
  491 + else:
  492 + need_help = True
  493 +
  494 + # Print help if needed and exit
  495 + if need_help:
  496 + print "Usage: example_survey.py [OPTIONS]"
  497 + print " -h Display this usage message"
  498 + sys.exit()
  499 +
  500 + # Dump header
  501 + print "**********************"
  502 + print "* Simulate CTA survey *"
  503 + print "**********************"
  504 +
  505 + # Remove any existing result files
  506 + list = [glob.glob("*.fits"), glob.glob("*.log"), glob.glob("*.xml")]
  507 + for files in list:
  508 + for file in files:
  509 + os.remove(file)
  510 +
  511 + # Setup single observation survey
  512 + obs = survey_single()
  513 + obs = survey_gplane()
  514 +
  515 + # Add background model
  516 + obs = add_background_model(obs)
  517 +
  518 + # Simulate events
  519 + print "Simulate events"
  520 + obs = sim_obs(obs)
  521 +
  522 + # Make counts map
  523 + print "Make counts map"
  524 + cntmap = make_counts_map(obs)
  525 +
  526 + # Fit observations
  527 + print "Fit observatins"
  528 + like = fit_obs(obs)
  529 + #print like.opt()
  530 + #print like.obs().models()
  531 +
  532 + # Make model map
  533 + print "Make model map (this step will take some time)"
  534 + modmap = make_model_map(obs)
  535 +
  536 + # Show fit results
  537 + #plot_counts(like.obs())
... ...