diff --git a/ChangeLog b/ChangeLog index 2974224..f55c1b8 100644 --- a/ChangeLog +++ b/ChangeLog @@ -6,7 +6,7 @@ Support energy dispersion in ctobssim, ctlike and ctmodel -2014-01-30 Juergen Knoedlseder +2014-02-05 Juergen Knoedlseder * Bug fix Version 0.7.1 released ================================ diff --git a/NEWS b/NEWS index a4d8286..cf3c5c1 100644 --- a/NEWS +++ b/NEWS @@ -2,7 +2,7 @@ New Features and Important Changes in ctools 00-08-00 Author(s) : Juergen Knoedlseder -1 February 2014 +5 February 2014 1. Introduction diff --git a/README b/README index 7490233..ea2bc23 100644 --- a/README +++ b/README @@ -1,8 +1,8 @@ ctools information ================== -Version: 00-07-00 (18 January 2014) +Version: 00-07-01 (5 February 2014) Author: Juergen Knodlseder (jurgen.knodlseder@irap.omp.eu) -GammaLib dependency: 00-08-00 +GammaLib dependency: 00-08-01 License information diff --git a/doc/source/_templates/download.html b/doc/source/_templates/download.html index dc69d40..3dfc919 100644 --- a/doc/source/_templates/download.html +++ b/doc/source/_templates/download.html @@ -29,6 +29,11 @@ ctools and gammalib. {%endtrans%}

Mac OS X package +00-07-01 +00-08-01 +ctools-00-07-01-macosx10.3.dmg + + 00-07-00 00-08-00 ctools-00-07-00-macosx10.3.dmg diff --git a/doc/source/conf.py b/doc/source/conf.py index 9739d8a..c7c62bd 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -51,7 +51,7 @@ copyright = u'2014, ctools developers' version = '0.7' # The full version, including alpha/beta/rc tags. -release = '0.7' +release = '0.7.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/doc/source/user_manual/getting.rst b/doc/source/user_manual/getting.rst index f0d1a81..2a2507e 100644 --- a/doc/source/user_manual/getting.rst +++ b/doc/source/user_manual/getting.rst @@ -265,16 +265,12 @@ can be given, for example: Setting up the ctools environment --------------------------------- -If you installed ctools in the same directory as GammaLib (which -is the default) you are done. - -If you decided to install ctools in a different directory, you have to -configure ctools by setting up some environment variables. This will be -done automatically by an initialisation script that is found in the ``bin`` -directory of the directory into which ctools has been installed. Assuming -that you have installed ctools into ``/usr/local/ctools`` you need to add -the following to your ``$HOME/.bashrc`` or ``$HOME/.profile`` script on a -Linux machine: +You have to configure ctools by setting up some environment variables. This +will be done automatically by an initialisation script that is found in the +``bin`` directory of the directory into which ctools has been installed. +Assuming that you have installed ctools into ``/usr/local/ctools`` you need +to add the following to your ``$HOME/.bashrc`` or ``$HOME/.profile`` script +on a Linux machine: .. code-block:: bash diff --git a/test/example_survey.py b/test/example_survey.py index b66eff2..9eafce9 100755 --- a/test/example_survey.py +++ b/test/example_survey.py @@ -3,7 +3,7 @@ # This script simulates CTA surveys. It can be considered as the master # script for CTA science simulations. Please modify as needed. # -# Copyright (C) 2011-2013 Juergen Knoedlseder +# Copyright (C) 2011-2014 Juergen Knoedlseder # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -32,331 +32,331 @@ import obsutils # Plot counts # # =========== # def plot_counts(obs): - """ - Plot counts. - """ - # Only proceed if matplotlib is available - try: - # Import matplotlib - import matplotlib.pyplot as plt - - # Set legend fontsize - params = {'legend.fontsize': 10} - plt.rcParams.update(params) - - # Set plot styles - styles = ['b-', 'g-', 'y-', 'n-'] - - # Dump header - print("") - print("Make plots (using matplotlib):") - print("==============================") - - # Create figure 1 - plt.figure(1,figsize=(12,6)) - plt.subplots_adjust(hspace=.7) - - # Create subplot 1 - plt.subplot(121) - plt.title("Spectrum (summed over all pixels)") - - # Loop over observations - for run in obs: - - # Get event list - list = run.events() - - # Create energy axis - ebounds = GEbounds() - emin = GEnergy() - emax = GEnergy() - emin.TeV(0.1) - emax.TeV(100.0) - ebounds.setlog(emin, emax, 10) - energy = [ebounds.elogmean(i).TeV() for i in range(ebounds.size())] - - # Create spectrum - print("Extract data:") - counts = [0.0 for i in range(ebounds.size())] - print(list.size()) - for atom in list: - index = ebounds.index(atom.energy()) - counts[index] = counts[index] + 1.0 - - # Create error bars - error = [sqrt(c) for c in counts] - - # Plot spectrum - plt.loglog(energy, counts, 'ro', label='data') - #plt.errorbar(energy, counts, error, fmt=None, ecolor='r') - - # Extract models - print("Extract models:") - sum_model = [0.0 for i in range(ebounds.size())] - for k, m in enumerate(obs.models()): - print("- "+m.name()) - model = [0.0 for i in range(ebounds.size())] - for atom in list: - index = ebounds.index(atom.energy()) - prob = m.eval(atom, run) - model[index] = model[index] + prob * atom.size() - for i in range(ebounds.size()): - sum_model[i] = sum_model[i] + model[i] - #plt.loglog(energy, model, styles[k], label=m.name()) - #plt.loglog(energy, sum_model, 'r-', label='total') - - # Put labels - plt.xlabel("Energy (TeV)") - plt.ylabel("Counts") - plt.legend(loc="lower left") - - # Create subplot 2 - plt.subplot(122) - plt.title("Offset (summed over all energies)") - - # Set Crab direction - crab = GSkyDir() - crab.radec_deg(83.63, 22.01) - - # Loop over observations - for run in obs: - - # Get event list - list = run.events() - - # Create offset histogram - print("Extract data:") - nx = 30 - doffset2 = 0.01 - offset2 = [(i+0.5)*doffset2 for i in range(nx)] - counts = [0.0 for i in range(nx)] - for atom in list: - off = atom.dir().dist_deg(crab) - off2 = off*off - index = int(off2/doffset2) - if index < nx: - counts[index] = counts[index] + 1.0 - - # Create error bars - error = [sqrt(c) for c in counts] - - # Plot distribution - #plt.semilogy(offset2, counts, 'ro', label='data') - - # Extract models - print("Extract models:") - sum_model = [0.0 for i in range(nx)] - for k, m in enumerate(obs.models()): - print("- "+m.name()) - model = [0.0 for i in range(nx)] - for atom in list: - off = atom.dir().dist_deg(crab) - off2 = off*off - index = int(off2/doffset2) - if index < nx: - prob = m.eval(atom, run) - model[index] = model[index] + prob * atom.size() - for i in range(nx): - sum_model[i] = sum_model[i] + model[i] - #plt.plot(offset2, model, styles[k], label=m.name()) - #plt.plot(offset2, sum_model, 'r-', label='total') - #plt.ylim(ymin=0.1) - - # Put labels - plt.xlabel("Offset (deg^2)") - plt.ylabel("Counts") - #plt.legend(loc="upper right") - - # Show counts spectra - plt.show() - - except ImportError: - print("Matplotlib is not (correctly) installed on your system.") + """ + Plot counts. + """ + # Only proceed if matplotlib is available + try: + # Import matplotlib + import matplotlib.pyplot as plt + + # Set legend fontsize + params = {'legend.fontsize': 10} + plt.rcParams.update(params) + + # Set plot styles + styles = ['b-', 'g-', 'y-', 'n-'] + + # Dump header + print("") + print("Make plots (using matplotlib):") + print("==============================") + + # Create figure 1 + plt.figure(1,figsize=(12,6)) + plt.subplots_adjust(hspace=.7) + + # Create subplot 1 + plt.subplot(121) + plt.title("Spectrum (summed over all pixels)") + + # Loop over observations + for run in obs: + + # Get event list + list = run.events() + + # Create energy axis + ebounds = GEbounds() + emin = GEnergy() + emax = GEnergy() + emin.TeV(0.1) + emax.TeV(100.0) + ebounds.setlog(emin, emax, 10) + energy = [ebounds.elogmean(i).TeV() for i in range(ebounds.size())] + + # Create spectrum + print("Extract data:") + counts = [0.0 for i in range(ebounds.size())] + print(list.size()) + for atom in list: + index = ebounds.index(atom.energy()) + counts[index] = counts[index] + 1.0 + + # Create error bars + error = [sqrt(c) for c in counts] + + # Plot spectrum + plt.loglog(energy, counts, 'ro', label='data') + #plt.errorbar(energy, counts, error, fmt=None, ecolor='r') + + # Extract models + print("Extract models:") + sum_model = [0.0 for i in range(ebounds.size())] + for k, m in enumerate(obs.models()): + print("- "+m.name()) + model = [0.0 for i in range(ebounds.size())] + for atom in list: + index = ebounds.index(atom.energy()) + prob = m.eval(atom, run) + model[index] = model[index] + prob * atom.size() + for i in range(ebounds.size()): + sum_model[i] = sum_model[i] + model[i] + #plt.loglog(energy, model, styles[k], label=m.name()) + #plt.loglog(energy, sum_model, 'r-', label='total') + + # Put labels + plt.xlabel("Energy (TeV)") + plt.ylabel("Counts") + plt.legend(loc="lower left") + + # Create subplot 2 + plt.subplot(122) + plt.title("Offset (summed over all energies)") + + # Set Crab direction + crab = GSkyDir() + crab.radec_deg(83.63, 22.01) + + # Loop over observations + for run in obs: + + # Get event list + list = run.events() + + # Create offset histogram + print("Extract data:") + nx = 30 + doffset2 = 0.01 + offset2 = [(i+0.5)*doffset2 for i in range(nx)] + counts = [0.0 for i in range(nx)] + for atom in list: + off = atom.dir().dist_deg(crab) + off2 = off*off + index = int(off2/doffset2) + if index < nx: + counts[index] = counts[index] + 1.0 + + # Create error bars + error = [sqrt(c) for c in counts] + + # Plot distribution + #plt.semilogy(offset2, counts, 'ro', label='data') + + # Extract models + print("Extract models:") + sum_model = [0.0 for i in range(nx)] + for k, m in enumerate(obs.models()): + print("- "+m.name()) + model = [0.0 for i in range(nx)] + for atom in list: + off = atom.dir().dist_deg(crab) + off2 = off*off + index = int(off2/doffset2) + if index < nx: + prob = m.eval(atom, run) + model[index] = model[index] + prob * atom.size() + for i in range(nx): + sum_model[i] = sum_model[i] + model[i] + #plt.plot(offset2, model, styles[k], label=m.name()) + #plt.plot(offset2, sum_model, 'r-', label='total') + #plt.ylim(ymin=0.1) + + # Put labels + plt.xlabel("Offset (deg^2)") + plt.ylabel("Counts") + #plt.legend(loc="upper right") + + # Show counts spectra + plt.show() + + except ImportError: + print("Matplotlib is not (correctly) installed on your system.") - # Return - return + # Return + return # ======================================== # # Add CTA background model to observations # # ======================================== # def add_background_model(obs): - """ - Add standard CTA background model to observations container. - - We use a simple power law here, scaled to Konrad's E configuration - performance table. The model needs still to be validated. - """ - # Recover models from observation - models = obs.models() - - # Define background model - bgd_radial = GCTAModelRadialGauss(3.0) - bgd_spectrum = GModelSpectralPlaw(61.8e-6, -1.85, GEnergy(1.0, "TeV")) - bgd_model = GCTAModelRadialAcceptance(bgd_radial, bgd_spectrum) - bgd_model.name("Background") - bgd_model.instruments("CTA") - - # Add background model to container - models.append(bgd_model) - - # Put container back in observation container - obs.models(models) - - # Return observation container - return obs + """ + Add standard CTA background model to observations container. + + We use a simple power law here, scaled to Konrad's E configuration + performance table. The model needs still to be validated. + """ + # Recover models from observation + models = obs.models() + + # Define background model + bgd_radial = GCTAModelRadialGauss(3.0) + bgd_spectrum = GModelSpectralPlaw(61.8e-6, -1.85, GEnergy(1.0, "TeV")) + bgd_model = GCTAModelRadialAcceptance(bgd_radial, bgd_spectrum) + bgd_model.name("Background") + bgd_model.instruments("CTA") + + # Add background model to container + models.append(bgd_model) + + # Put container back in observation container + obs.models(models) + + # Return observation container + return obs # ================= # # Set Crab spectrum # # ================= # def crab_spec(): - """ - Set Crab spectrum based on MAGIC observations - (Albert et al. 2008, ApJ, 674, 1037) - """ - # Set parameters - spectrum = GModelSpectralPlaw(5.7e-16, -2.48, GEnergy(0.3, "TeV")) - - # Return spectrum - return spectrum + """ + Set Crab spectrum based on MAGIC observations + (Albert et al. 2008, ApJ, 674, 1037) + """ + # Set parameters + spectrum = GModelSpectralPlaw(5.7e-16, -2.48, GEnergy(0.3, "TeV")) + + # Return spectrum + return spectrum # =============================== # # Setup single observation survey # # =============================== # def survey_single(): - """ - Creates a single observation survey for test purposes. - """ - # Allocate observation container - obs = GObservations() - - # Set single pointing at galactic centre - pntdir = GSkyDir() - pntdir.lb_deg(0.0, 0.0) - #run = set_one_obs(pntdir) - run = obsutils.set(pntdir) - obs.append(run) - - # Define single point source with Crab flux at galactic centre - center = GSkyDir() - center.lb_deg(0.0, 0.0) - point_spatial = GModelSpatialPointSource(center) - point_spectrum = crab_spec() - point = GModelSky(point_spatial, point_spectrum) - point.name('GC source') - - # Create model container - models = GModels() - models.append(point) - obs.models(models) - - # Return observation container - return obs + """ + Creates a single observation survey for test purposes. + """ + # Allocate observation container + obs = GObservations() + + # Set single pointing at galactic centre + pntdir = GSkyDir() + pntdir.lb_deg(0.0, 0.0) + #run = set_one_obs(pntdir) + run = obsutils.set(pntdir) + obs.append(run) + + # Define single point source with Crab flux at galactic centre + center = GSkyDir() + center.lb_deg(0.0, 0.0) + point_spatial = GModelSpatialPointSource(center) + point_spectrum = crab_spec() + point = GModelSky(point_spatial, point_spectrum) + point.name('GC source') + + # Create model container + models = GModels() + models.append(point) + obs.models(models) + + # Return observation container + return obs # =========================== # # Setup Galactic plane survey # # =========================== # def survey_gplane(lrange=10, lstep=2): - """ - Creates a single observation survey for test purposes. - - Keywords: - lrange - Longitude range (integer deg) - lstep - Longitude step size (integer deg) - """ - # Allocate observation container - obs = GObservations() - - # Loop over longitudes - for l in range(-lrange,lrange+lstep,lstep): - - # Set pointing - pntdir = GSkyDir() - pntdir.lb_deg(l, 0.0) - #run = set_one_obs(pntdir) - run = obsutils.set(pntdir) - run.id(str(l)) - obs.append(run) - - # Define single point source with Crab flux at galactic centre - center = GSkyDir() - center.lb_deg(0.0, 0.0) - point_spatial = GModelSpatialPointSource(center) - point_spectrum = crab_spec() - point = GModelSky(point_spatial, point_spectrum) - point.name('GC source') - - # Create model container - models = GModels() - models.append(point) - obs.models(models) - - # Return observation container - return obs + """ + Creates a single observation survey for test purposes. + + Keywords: + lrange - Longitude range (integer deg) + lstep - Longitude step size (integer deg) + """ + # Allocate observation container + obs = GObservations() + + # Loop over longitudes + for l in range(-lrange,lrange+lstep,lstep): + + # Set pointing + pntdir = GSkyDir() + pntdir.lb_deg(l, 0.0) + #run = set_one_obs(pntdir) + run = obsutils.set(pntdir) + run.id(str(l)) + obs.append(run) + + # Define single point source with Crab flux at galactic centre + center = GSkyDir() + center.lb_deg(0.0, 0.0) + point_spatial = GModelSpatialPointSource(center) + point_spectrum = crab_spec() + point = GModelSky(point_spatial, point_spectrum) + point.name('GC source') + + # Create model container + models = GModels() + models.append(point) + obs.models(models) + + # Return observation container + return obs #==========================# # Main routine entry point # #==========================# if __name__ == '__main__': - """ - CTA survey simulation and analysis script. - """ - # Initialise flags - need_help = False - - # Test for command line arguments - print(sys.argv[0]) - if (len(sys.argv) > 1): - if sys.argv[1] == "-h": - need_help = True - else: - need_help = True + """ + CTA survey simulation and analysis script. + """ + # Initialise flags + need_help = False + + # Test for command line arguments + print(sys.argv[0]) + if (len(sys.argv) > 1): + if sys.argv[1] == "-h": + need_help = True + else: + need_help = True - # Print help if needed and exit - if need_help: - print("Usage: example_survey.py [OPTIONS]") - print(" -h Display this usage message") - sys.exit() - - # Dump header - print("***********************") - print("* Simulate CTA survey *") - print("***********************") - - # Remove any existing result files - list = [glob.glob("*.fits"), glob.glob("*.log"), glob.glob("*.xml")] - for files in list: - for file in files: - os.remove(file) - - # Setup single observation survey - #obs = survey_single() - obs = survey_gplane() - - # Add background model - obs = add_background_model(obs) - - # Simulate events - print("Simulate events") - obs = obsutils.sim(obs) - - # Make counts map - print("Make counts map") - cntmap = obsutils.cntmap(obs) - - # Fit observations - print("Fit observations") - like = obsutils.fit(obs) - #print like.opt() - #print like.obs().models() - - # Make model map - print("Make model map (this step will take some time)") - modmap = obsutils.modmap(obs) - - # Show fit results - #plot_counts(like.obs()) + # Print help if needed and exit + if need_help: + print("Usage: example_survey.py [OPTIONS]") + print(" -h Display this usage message") + sys.exit() + + # Dump header + print("***********************") + print("* Simulate CTA survey *") + print("***********************") + + # Remove any existing result files + list = [glob.glob("*.fits"), glob.glob("*.log"), glob.glob("*.xml")] + for files in list: + for file in files: + os.remove(file) + + # Setup single observation survey + #obs = survey_single() + obs = survey_gplane() + + # Add background model + obs = add_background_model(obs) + + # Simulate events + print("Simulate events") + obs = obsutils.sim(obs) + + # Make counts map + print("Make counts map") + cntmap = obsutils.cntmap(obs) + + # Fit observations + print("Fit observations") + like = obsutils.fit(obs) + #print like.opt() + #print like.obs().models() + + # Make model map + print("Make model map (this step will take some time)") + modmap = obsutils.modmap(obs) + + # Show fit results + #plot_counts(like.obs())