Complete adding of unit tests for all example scripts

Jürgen Knödlseder
1 parent ce0042b4
Showing 20 changed files with 611 additions and 237 deletions
ChangeLog
NEWS
README.md
examples/README.md
examples/make_ts_distributions.py
examples/show_butterfly.py
examples/show_model.py
examples/show_pha.py
examples/show_pull_evolution.py
examples/show_pull_histogram.py
examples/show_response.py
examples/show_sensitivity.py
examples/show_spectrum.py
examples/show_ts_distribution.py
test/README.md
test/data/sensitivity.dat
test/data/spectrum.fits
test/data/ts.dat
test/dev/README.md
test/test_examples.py
-2016-06-21  Juergen Knoedlseder  <jurgen.knodlseder@irap.omp.eu>
+2016-06-25  Juergen Knoedlseder  <jurgen.knodlseder@irap.omp.eu>
         * Version 1.1.0 released
         ========================
+        Add unit tests for all example scripts
         Handle zero parameter errors in ctulimit (#1795)
         Compute counts cube weights in ctbin (#1789)
         Adapt to new stacked cube interface (#1788)
 New Features and Important Changes in ctools 1.1.0
-21 June 2016
+25 June 2016
 Introduction
 ctools information
 ==================
-* Version:             1.1.0.dev1 (21 June 2016)
+* Version:             1.1.0.dev1 (25 June 2016)
 * Author:              Juergen Knoedlseder (jurgen.knodlseder@irap.omp.eu)
 * GammaLib dependency: 1.1.0.dev1
 ctools examples
 ===============
-This folder contains Python example scripts that illustrate the
-usage of the ctools.
-
-`benchmark_cta_analysis.py`
->  This script peforms a benchmark of the various CTA analysis types
->  (unbinned, binned, stacked), and if matplotlib is installed, creates
->  a plot of the benchmark results.
-
-`check_models.py`
->  Checks a variety of models using an unbinned analysis.
+This folder contains Python example scripts that illustrate the usage of
+the ctools.
 `generate_prod3_irfs/py`
->  Generate Prod3 Instrument Response Functions from FITS files.
+>  Generates Prod3 Instrument Response Functions from FITS files.
 `make_pointings.py`
->  This script creates pointings for CTA surveys. For the moment the
->  Galactic Plane Survey (gps), the Extragalactic Survey (extgal),
->  the Galactic Centre Survey (gc) and the LMC survey (lmc) are
->  implemented. The script outputs and ASCII file that can be used by
->  csobsdef.
-
-`make_pull_at_sensitivity_limit.py`
->  This script creates pull distributions as function of energy
->  for a test source that is at the 5 sigma sensitivity limit for
->  50h of effective observing time. It takes as input file the
->  output file generated using cspull.
->  If the Python processing module is installed the script can make
->  use of multiple cores and runs in parallel.
+>  Creates pointings for CTA surveys. For the moment the Galactic Plane
+>  Survey (gps), the Extragalactic Survey (extgal), the Galactic Centre
+>  Survey (gc) and the LMC survey (lmc) are implemented. The script outputs
+>  and ASCII file that can be used by csobsdef.
 `make_spectrum.py`
->  This script shows how to make a spectrum using obsutils.
-
-`make_survey.py`
->  This script simulates a CTA galactic plane survey.
+>  Simulates events and generates a spectrum from the using csspec.
 `make_ts_distributions.py`
->  This script creates TS distributions as function of energy for
->  an off observation of 50h effective observing time.
->  If the Python processing module is installed the script can make
->  use of multiple cores and runs in parallel.
+>  Creates TS distributions as function of energy for an OFF observaition
+>  of 50 hours effective observing time. If the Python processing module is
+>  installed the script can make use of multiple cores and runs in parallel.
 `pipeline_binned_disk.py`
->  Example pipeline for simulating events and performing a binned
->  analysis by saving intermediate results to disk.
+>  Example pipeline for simulating events and performing a binned analysis
+>  by saving intermediate results to disk.
 `pipeline_binned_mem.py`
->  Example pipeline for simulating events and performing a binned
->  analysis in memory. No files will be written to disk.
+>  Example pipeline for simulating events and performing a binned analysis
+>  in memory. No files will be written to disk.
 `pipeline_stacked_disk.py`
->  Example pipeline for simulating events and performing a stacked
->  analysis by saving intermediate results to disk.
+>  Example pipeline for simulating events and performing a stacked analysis
+>  by saving intermediate results to disk.
 `pipeline_stacked_mem.py`
->  Example pipeline for simulating events and performing a stacked
->  analysis in memory. No files will be written to disk.
+>  Example pipeline for simulating events and performing a stacked analysis
+>  in memory. No files will be written to disk.
 `pipeline_unbinned_disk.py`
->  Example pipeline for simulating events and performing an unbinned
->  analysis by saving intermediate results to disk.
+>  Example pipeline for simulating events and performing an unbinned analysis
+>  by saving intermediate results to disk.
 `pipeline_unbinned_mem.py`
->  Example pipeline for simulating events and performing an unbinned
->  analysis in memory. No files will be written to disk.
+>  Example pipeline for simulating events and performing an unbinned analysis
+>  in memory. No files will be written to disk.
 `show_butterfly.py`
 >  Displays a butterfly generated using the ctbutterfly tool.
@@ -86,14 +65,10 @@ usage of the ctools.
 >  Displays one or several CTA response functions.
 `show_sensitivity.py`
->  Displays sensitivity information generated by the cssens script.
+>  Displays the sensitivity generated by the cssens script.
 `show_spectrum.py`
 >  Displays spectrum generated by the csspec script.
 `show_ts_distribution.py`
->  Displays Test Statistic histograms generated using the cstsdist
->  script.
-
-`simulate_events.py`
->  Simulates events and compares the simulation to the model.
+>  Displays Test Statistic histograms generated using the cstsdist script.
 #! /usr/bin/env python
 # ==========================================================================
-# Generate TS distribution
+# Generate TS distributions as function of energy for an OFF observation
 #
 # Copyright (C) 2011-2016 Jurgen Knodlseder
 #
@@ -65,7 +65,7 @@ def create_ts(loge, emin, emax, ntrials=100, duration=180000.0,
     tsdist['outfile']  = outfile
     tsdist['ntrials']  = ntrials
     tsdist['caldb']    = 'prod2'
-    tsdist['irf']      = 'South_0.5h'
+    tsdist['irf']      = 'South_50h'
     tsdist['ra']       = 83.63
     tsdist['dec']      = 22.01
     tsdist['emin']     = emin
@@ -76,7 +76,6 @@ def create_ts(loge, emin, emax, ntrials=100, duration=180000.0,
     tsdist['rad']      = 5.0
     tsdist['npix']     = 200
     tsdist['binsz']    = 0.05
-    #tsdist['debug']    = True
     # Optionally open the log file
     if log:
@@ -95,7 +94,7 @@ def create_ts(loge, emin, emax, ntrials=100, duration=180000.0,
 if __name__ == '__main__':
     # Get input arguments
-    usage = 'make_ts_distributions [-n ntrials] [-e enumbins] [-m max_threads]'
+    usage = 'make_ts_distributions.py [-n ntrials] [-e enumbins] [-m max_threads]'
     if len(sys.argv) < 1:
         print(usage)
         sys.exit()
@@ -32,7 +32,7 @@ except:
 # ============ #
 # Script entry #
 # ============ #    
-if __name__ == "__main__":
+if __name__ == '__main__':
     # Check for given butterfly file
     if len(sys.argv) < 2:
@@ -83,7 +83,7 @@ if __name__ == &quot;__main__&quot;:
     plt.plot(line_x,line_y,color='black',ls='-')
     plt.fill(butterfly_x,butterfly_y,color='green',alpha=0.5)
     plt.xlabel('Energy (MeV)')
-    plt.ylabel('E dN/dE (MeV$^{-1}$ s$^{-1}$ cm$^{-2}$)')
+    plt.ylabel(r'E $\times$ dN/dE (MeV$^{-1}$ s$^{-1}$ cm$^{-2}$)')
     # Show spectrum or save it into file
     if len(plotfile) > 0:
@@ -103,7 +103,7 @@ def plot_spectrum(model, emin=0.01, emax=100.0, enumbins=100, plotfile=&#39;&#39;):
 # ============= #
 # Script entry  #
 # ============= #    
-if __name__ == "__main__":
+if __name__ == '__main__':
     # Check for model filename
     if len(sys.argv) < 2:
@@ -95,7 +95,7 @@ def show_pha(pha, plotfile=&#39;&#39;):
 if __name__ == '__main__':
     # Print usage information
-    usage = 'Usage: show_pha filename [file]'
+    usage = 'Usage: show_pha.py filename [file]'
     if len(sys.argv) < 2:
         print(usage)
         sys.exit()
@@ -89,7 +89,7 @@ def read_pull(filename, parname):
 if __name__ == '__main__':
     # Print usage information
-    usage = 'Usage: show_pull_evolution filename parname [file]'
+    usage = 'Usage: show_pull_evolution.py filename parname [file]'
     if len(sys.argv) < 3:
         print(usage)
         sys.exit()
@@ -90,7 +90,7 @@ def read_pull(filename, parname):
 if __name__ == '__main__':
     # Print usage information
-    usage = 'Usage: show_pull_histogram filename parname [bins] [file]'
+    usage = 'Usage: show_pull_histogram.py filename parname [bins] [file]'
     if len(sys.argv) < 3:
         print(usage)
         sys.exit()
 #! /usr/bin/env python
 # ==========================================================================
-# This script displays one or several CTA response functions.
+# Shows one or several CTA response functions.
 #
-# Required 3rd party modules:
-# - matplotlib
-# - numpy
-#
-# Copyright (C) 2014-2015 Juergen Knoedlseder
+# Copyright (C) 2014-2016 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
@@ -25,7 +21,13 @@
 import sys
 import math
 import gammalib
-import matplotlib.pyplot as plt
+try:
+    import matplotlib.pyplot as plt
+    plt.figure()
+    plt.close()
+except:
+    print('This script needs the "matplotlib" module')
+    sys.exit()
 # ================================== #
@@ -35,11 +37,19 @@ def sigma_lima(Non, Noff, alpha=0.2):
     """
     Compute Eq. (17) of Li & Ma (1983).
-    Parameters:
-     Non   - Number of on counts
-     Noff  - Number of off counts
-    Keywords:
-     alpha - Ratio of on-to-off exposure
+    Parameters
+    ----------
+    Non : float
+        Number of On counts
+    Noff : float
+        Number of Off counts
+    alpha : float, optional
+        Ratio of on-to-off exposure
+
+    Returns
+    -------
+    sigma : float
+        Detection significance in Gaussian sigma
     """
     # Compute sensitivity
     alpha1 = alpha + 1.0
@@ -61,11 +71,19 @@ def Non_lima(sigma, Noff, alpha=0.2):
     """
     Solve Eq. (17) of Li & Ma (1983) for Non
-    Parameters:
-     sigma - Required significance
-     Noff  - Number of off counts
-    Keywords:
-     alpha - Ratio of on-to-off exposure
+    Parameters
+    ----------
+    sigma : float
+        Required detection significance in Gaussian sigma
+    Noff : float
+        Number of Off counts
+    alpha : float, optional
+        Ratio of on-to-off exposure
+
+    Returns
+    -------
+    Non : float
+        Number of On counts
     """
     # Set initial guess for Non
     S   = 25.0
@@ -89,9 +107,18 @@ def Non_lima(sigma, Noff, alpha=0.2):
 # ======================= #
 # Show one effective area #
 # ======================= #
-def show_one_effective_area(rsp, name, color="r"):
+def show_one_effective_area(rsp, name, color='r'):
     """
-    Show one effective area.
+    Show one effective area
+
+    Parameters
+    ----------
+    rsp : `~gammalib.GCTAResponse`
+        Response function
+    name : str
+        Name of the response function
+    color : str, optional
+        Color for plot
     """
     # Set to figure 1
     plt.figure(1)
@@ -111,11 +138,11 @@ def show_one_effective_area(rsp, name, color=&quot;r&quot;):
     plt.loglog(E, aeff, color+'o')
     # Set axes
-    plt.xlabel("Energy [TeV]")
-    plt.ylabel("Effective area [cm2]")
+    plt.xlabel('Energy (TeV)')
+    plt.ylabel(r'Effective area (cm$^{2}$)')
     # Set legend
-    plt.legend(loc="lower right")
+    plt.legend(loc='lower right')
     # Return
     return
@@ -124,9 +151,18 @@ def show_one_effective_area(rsp, name, color=&quot;r&quot;):
 # ======================== #
 # Show one background rate #
 # ======================== #
-def show_one_background_rate(rsp, name, color="r"):
+def show_one_background_rate(rsp, name, color='r'):
     """
-    Show one background rate.
+    Show one background rate
+
+    Parameters
+    ----------
+    rsp : `~gammalib.GCTAResponse`
+        Response function
+    name : str
+        Name of the response function
+    color : str, optional
+        Color for plot
     """
     # Set to figure 2
     plt.figure(2)
@@ -148,11 +184,11 @@ def show_one_background_rate(rsp, name, color=&quot;r&quot;):
     plt.loglog(E, bgrate, color+'o')
     # Set axes
-    plt.xlabel("Energy [TeV]")
-    plt.ylabel("Background rate [events/s/MeV/sr]")
+    plt.xlabel('Energy (TeV)')
+    plt.ylabel(r'Background rate (counts s$^{-1}$ MeV$^{-1}$ sr$^{-1}$)')
     # Set legend
-    plt.legend(loc="lower left")
+    plt.legend(loc='lower left')
     # Return
     return
@@ -161,9 +197,25 @@ def show_one_background_rate(rsp, name, color=&quot;r&quot;):
 # ==================== #
 # Show one sensitivity #
 # ==================== #
-def show_one_sensitivity(rsp, name, color="r", duration=180000.0, alpha=0.2, sigma=5.0):
+def show_one_sensitivity(rsp, name, color='r', duration=180000.0, alpha=0.2,
+                         sigma=5.0):
     """
-    Show one sensitivity.
+    Show one sensitivity
+
+    Parameters
+    ----------
+    rsp : `~gammalib.GCTAResponse`
+        Response function
+    name : str
+        Name of the response function
+    color : str, optional
+        Color for plot
+    duration : float, optional
+        Duration of observation (s)
+    alpha : float, optional
+        Ratio of on-to-off exposure
+    sigma : float, optional
+        Required detection significance in Gaussian sigma
     """
     # Set constants
     r68_to_sigma = 0.6624305
@@ -194,9 +246,9 @@ def show_one_sensitivity(rsp, name, color=&quot;r&quot;, duration=180000.0, alpha=0.2, sig
                 src_counts = 0.05*bgd_counts
             if src_counts < 10:
                 src_counts = 10.0
-            emin    = gammalib.GEnergy(math.pow(10.0, logE[i]-0.1), "TeV")
-            emax    = gammalib.GEnergy(math.pow(10.0, logE[i]+0.1), "TeV")
-            epivot  = gammalib.GEnergy(math.pow(10.0, logE[i]), "TeV")
+            emin    = gammalib.GEnergy(math.pow(10.0, logE[i]-0.1), 'TeV')
+            emax    = gammalib.GEnergy(math.pow(10.0, logE[i]+0.1), 'TeV')
+            epivot  = gammalib.GEnergy(math.pow(10.0, logE[i]), 'TeV')
             plaw    = gammalib.GModelSpectralPlaw(1.0e-6, -2.6, epivot)
             conv    = TeV2erg*E[i]*E[i]/plaw.flux(emin, emax)
             flux[i] = conv * src_counts / (duration * aeff*0.68)
@@ -206,11 +258,11 @@ def show_one_sensitivity(rsp, name, color=&quot;r&quot;, duration=180000.0, alpha=0.2, sig
     plt.loglog(E, flux, color+'o')
     # Set axes
-    plt.xlabel("Energy [TeV]")
-    plt.ylabel("Sensitivity [erg/cm2/s]")
+    plt.xlabel('Energy (TeV)')
+    plt.ylabel(r'Sensitivity (erg cm$^{-2}$ s$^{-1}$)')
     # Set legend
-    plt.legend(loc="upper right")
+    plt.legend(loc='upper right')
     # Return
     return
@@ -219,9 +271,22 @@ def show_one_sensitivity(rsp, name, color=&quot;r&quot;, duration=180000.0, alpha=0.2, sig
 # ==================== #
 # Show one sensitivity #
 # ==================== #
-def show_one_response(rspname, dbname, name, rootdir=None, color="r"):
+def show_one_response(rspname, dbname, name, rootdir=None, color='r'):
     """
     Show one response.
+
+    Parameters
+    ----------
+    rspname : str
+        Response name
+    dbname : str
+        Database name
+    name : str
+        Name of the response function
+    rootdir : str, optional
+        Response root directory
+    color : str, optional
+        Color for plot
     """
     # Set-up calibration database
     caldb = gammalib.GCaldb()
@@ -230,7 +295,7 @@ def show_one_response(rspname, dbname, name, rootdir=None, color=&quot;r&quot;):
     if gammalib.dir_exists(dbname):
         caldb.rootdir(dbname)
     else:
-        caldb.open("cta", dbname)
+        caldb.open('cta', dbname)
     # Load response function
     rsp = gammalib.GCTAResponseIrf(rspname, caldb)
@@ -253,28 +318,35 @@ def show_one_response(rspname, dbname, name, rootdir=None, color=&quot;r&quot;):
 # ======================== #
 if __name__ == '__main__':
+    # Print usage information
+    usage = 'Usage: show_response.py [file]'
+    if len(sys.argv) < 1:
+        print(usage)
+        sys.exit()
+
+    # Check if plotting in file is requested
+    plotfile = ''
+    if len(sys.argv) == 2:
+        plotfile = sys.argv[1]
+
     # Create figures
     plt.figure(1)
-    plt.title("Effective area")
+    plt.title('Effective area')
     plt.figure(2)
-    plt.title("Background rate")
+    plt.title('Background rate')
     plt.figure(3)
-    plt.title("Sensitivity")
+    plt.title('Sensitivity')
     # Set response dictionary
     rsps = [
-            {'dbname':  "dummy",
-             'rspname': "cta_dummy_irf",
-             'name':    "Prod1 (Array E, MPIK)",
-             'color':   "b"},
-            {'dbname':  "e",
-             'rspname': "IFAE20120510_50h",
-             'name':    "Prod1 (Array E, IFAE)",
-             'color':   "g"},
-            {'dbname':  "aar",
-             'rspname': "DESY20140105_50h",
-             'name':    "Prod2 (Aar, DESY)",
-             'color':   "r"}
+            {'dbname':  'irf',
+             'rspname': 'cta_dummy_irf.dat',
+             'name':    'Prod1 (Array E, MPIK)',
+             'color':   'b'},
+            {'dbname':  'irf',
+             'rspname': 'irf_file.fits',
+             'name':    'Prod2 (Aar, IFAE)',
+             'color':   'r'}
             ]
     # Loop over all responses
@@ -293,5 +365,8 @@ if __name__ == &#39;__main__&#39;:
         # Show response
         show_one_response(rspname, dbname, name, rootdir=rootdir, color=color)
-    # Show plot
-    plt.show()
+    # Show figure
+    if len(plotfile) > 0:
+        plt.savefig(plotfile)
+    else:
+        plt.show()
 #! /usr/bin/env python
 # ==========================================================================
-# This script displays a differential sensitivity plot generated by cssens.
+# Display differential sensitivity plot generated by cssens
 #
-# Copyright (C) 2015 Juergen Knoedlseder
+# Copyright (C) 2015-2016 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
@@ -23,8 +23,11 @@ import math
 import gammalib
 try:
     import matplotlib.pyplot as plt
+    plt.figure()
+    plt.close()
 except:
-    sys.exit("This script needs matplotlib")
+    print('This script needs the "matplotlib" module')
+    sys.exit()
 # ===================== #
@@ -32,7 +35,17 @@ except:
 # ===================== #
 def read_sensitivity(filename):
     """
-    Read sensitivity information from CSV file.
+    Read sensitivity information from CSV file
+
+    Parameters
+    ----------
+    filename : str
+        Name of CSV file
+
+    Returns
+    -------
+    sensitivity : dict
+        Dictionary with sensitivity information
     """
     # Read filename
     csv = gammalib.GCsv(filename,',')
@@ -40,39 +53,39 @@ def read_sensitivity(filename):
     # Create dictionnary
     sensitivity = {}
     for column in range(csv.ncols()):
-        name   = csv[0,column]
+        name   = csv[0,column].rstrip('\r')
         values = []
         for row in range(csv.nrows()-1):
             values.append(float(csv[row+1,column]))
         sensitivity[name] = values
     # Check where file contains differential or integral sensitivity
-    mode = "Integral"
-    if sensitivity.has_key("emax"):
+    mode = 'Integral'
+    if sensitivity.has_key('emax'):
         emax_ref = -1.0
-        for value in sensitivity["emax"]:
+        for value in sensitivity['emax']:
             if emax_ref < 0.0:
                 emax_ref = value
             elif emax_ref != value:
-                mode = "Differential"
+                mode = 'Differential'
                 break
     # Add mode
-    sensitivity["mode"] = mode
+    sensitivity['mode'] = mode
     # Add linear energy values
-    if mode == "Differential":
-        if sensitivity.has_key("loge"):
+    if mode == 'Differential':
+        if sensitivity.has_key('loge'):
             name   = 'energy'
             values = []
-            for value in sensitivity["loge"]:
+            for value in sensitivity['loge']:
                 values.append(math.pow(10.0, value))
             sensitivity[name] = values
     else:
-        if sensitivity.has_key("emin"):
+        if sensitivity.has_key('emin'):
             name   = 'energy'
             values = []
-            for value in sensitivity["emin"]:
+            for value in sensitivity['emin']:
                 values.append(value)
             sensitivity[name] = values
@@ -83,9 +96,18 @@ def read_sensitivity(filename):
 # ===================== #
 # Show sensitivity data #
 # ===================== #
-def show_sensitivity(sensitivity, filename):
+def show_sensitivity(sensitivity, filename, plotfile=''):
     """
-    Plot sensitivity.
+    Plot sensitivity data
+
+    Parameters
+    ----------
+    sensitivity : dict
+        Dictionary with sensitivity information
+    filename : str
+        Name of CSV file
+    plotfile : str, optional
+        Plot file name
     """
     # Build title
     title = sensitivity['mode'] + ' sensitivity ('+filename+')'
@@ -102,14 +124,17 @@ def show_sensitivity(sensitivity, filename):
     plt.plot(sensitivity['energy'], sensitivity['sensitivity'], 'ro-')
     # Set labels
-    plt.xlabel("Energy (TeV)")
-    if sensitivity['mode'] == "Differential":
-        plt.ylabel(r"E$\times$ F(E) (erg cm$^{-2}$ s$^{-1}$)")
+    plt.xlabel('Energy (TeV)')
+    if sensitivity['mode'] == 'Differential':
+        plt.ylabel(r'E $\times$ F(E) (erg cm$^{-2}$ s$^{-1}$)')
     else:
-        plt.ylabel(r"E$\times$ F($>$E) (erg cm$^{-2}$ s$^{-1}$)")
+        plt.ylabel(r'E $\times$ F($>$E) (erg cm$^{-2}$ s$^{-1}$)')
-    # Show plot
-    plt.show()
+    # Show figure
+    if len(plotfile) > 0:
+        plt.savefig(plotfile)
+    else:
+        plt.show()
     # Return
     return
@@ -121,9 +146,9 @@ def show_sensitivity(sensitivity, filename):
 if __name__ == '__main__':
     # Print usage information
-    usage = "Usage: show_sensitivity [filename]"
+    usage = 'Usage: show_sensitivity.py [filename] [file]'
     if len(sys.argv) < 1:
-        sys.stdout.write(usage+"\n")
+        print(usage)
         sys.exit()
     # Extract parameters
@@ -131,9 +156,12 @@ if __name__ == &#39;__main__&#39;:
         filename = 'sensitivity.dat'
     else:
         filename = sys.argv[1]
+    plotfile = ''
+    if len(sys.argv) == 3:
+        plotfile = sys.argv[2]
     # Read sensitivity data
     sensitivity = read_sensitivity(filename)
     # Show sensitivity data
-    show_sensitivity(sensitivity, filename)
+    show_sensitivity(sensitivity, filename, plotfile=plotfile)
 #! /usr/bin/env python
 # ==========================================================================
-# This script shows a spectrum created with csspec
+# Display spectrum generated by csspec
 #
 # Copyright (C) 2015-2016 Juergen Knoedlseder
 #
@@ -18,35 +18,41 @@
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 #
 # ==========================================================================
-
-import gammalib
 import sys
+import gammalib
 try:
     import matplotlib.pyplot as plt
+    plt.figure()
+    plt.close()
 except:
-    sys.exit("This script needs matplotlib")
+    print('This script needs the "matplotlib" module')
+    sys.exit()
-# ============ #
-# Script entry #
-# ============ #    
-if __name__ == "__main__":
-
-    # Check for given spectrum file
-    if not len(sys.argv) == 2:
-        sys.exit("Usage: show_spectrum.py spectrum.fits")
+# ============= #
+# Show spectrum #
+# ============= #
+def show_spectrum(filename, plotfile=''):
+    """
+    Plot sensitivity data
+    Parameters
+    ----------
+    filename : str
+        Name of spectrum FITS file
+    plotfile : str, optional
+        Plot file name
+    """
     # Read spectrum file    
-    filename = sys.argv[1]
     fits     = gammalib.GFits(filename)
     table    = fits.table(1)
-    c_energy = table["Energy"]
-    c_ed     = table["ed_Energy"]
-    c_eu     = table["eu_Energy"]
-    c_flux   = table["Flux"]
-    c_eflux  = table["e_Flux"]
-    c_ts     = table["TS"]
-    c_upper  = table["UpperLimit"]
+    c_energy = table['Energy']
+    c_ed     = table['ed_Energy']
+    c_eu     = table['eu_Energy']
+    c_flux   = table['Flux']
+    c_eflux  = table['e_Flux']
+    c_ts     = table['TS']
+    c_upper  = table['UpperLimit']
     # Initialise arrays to be filled
     energies    = []
@@ -63,25 +69,22 @@ if __name__ == &quot;__main__&quot;:
     nrows = table.nrows()
     for row in range(nrows):
-        # Get TS
+        # Get Test Statistic, flux and flux error
         ts    = c_ts.real(row)
         flx   = c_flux.real(row)
         e_flx = c_eflux.real(row)
-        # Switch
+        # If Test Statistic is larger than 9 and flux error is smaller than
+        # flux then append flux plots ...
         if ts > 9.0 and e_flx < flx:
-
-            # Add information
             energies.append(c_energy.real(row))
             flux.append(c_flux.real(row))
             ed_engs.append(c_ed.real(row))
             eu_engs.append(c_eu.real(row))
             e_flux.append(c_eflux.real(row))
-        #
+        # ... otherwise append upper limit
         else:
-
-            # Add information
             ul_energies.append(c_energy.real(row))
             ul_flux.append(c_upper.real(row))
             ul_ed_engs.append(c_ed.real(row))
@@ -91,8 +94,39 @@ if __name__ == &quot;__main__&quot;:
     plt.figure()
     plt.loglog()
     plt.grid()
-    plt.errorbar(energies, flux, yerr=e_flux, xerr=[ed_engs, eu_engs], fmt='ro')
-    plt.errorbar(ul_energies, ul_flux, xerr=[ul_ed_engs, ul_eu_engs], yerr=1.0e-11, uplims=True, fmt='ro')
-    plt.xlabel("Energy (TeV)")
-    plt.ylabel(r"E dN/dE (erg cm$^{-2}$ s$^{-1}$)")    
-    plt.show()
+    plt.errorbar(energies, flux, yerr=e_flux, xerr=[ed_engs, eu_engs],
+                 fmt='ro')
+    plt.errorbar(ul_energies, ul_flux, xerr=[ul_ed_engs, ul_eu_engs],
+                 yerr=1.0e-11, uplims=True, fmt='ro')
+    plt.xlabel('Energy (TeV)')
+    plt.ylabel(r'E $\times$ dN/dE (erg cm$^{-2}$ s$^{-1}$)')
+
+    # Show figure
+    if len(plotfile) > 0:
+        plt.savefig(plotfile)
+    else:
+        plt.show()
+
+    # Return
+    return
+
+
+# ======================== #
+# Main routine entry point #
+# ======================== #
+if __name__ == '__main__':
+
+    # Print usage information
+    usage = 'Usage: show_spectrum.py filename [file]'
+    if len(sys.argv) < 2:
+        print(usage)
+        sys.exit()
+
+    # Extract parameters
+    filename = sys.argv[1]
+    plotfile = ''
+    if len(sys.argv) == 3:
+        plotfile = sys.argv[2]
+
+    # Show spectrum
+    show_spectrum(filename, plotfile=plotfile)
 #! /usr/bin/env python
 # ==========================================================================
-# This script displays the TS distribution values generated using cstsdist.
+# Display TS distribution generated by cstsdist
 #
-# Required 3rd party modules:
-# - matplotlib
-# - numpy
-#
-# Copyright (C) 2011 Jurgen Knodlseder
+# Copyright (C) 2011-2016 Jurgen Knodlseder
 #
 # 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
@@ -22,20 +18,42 @@
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 #
 # ==========================================================================
-import matplotlib.mlab as mlab
-import matplotlib.pyplot as plt
-import numpy as np
 import sys
 import csv
 import math
+try:
+    import matplotlib.pyplot as plt
+    import matplotlib.mlab as mlab
+    plt.figure()
+    plt.close()
+except:
+    print('This script needs the "matplotlib" module')
+    sys.exit()
+try:
+    import numpy as np
+except:
+    print('This script needs the "numpy" module')
+    sys.exit()
 # ==================== #
 # Read TS distribution #
 # ==================== #
-def read_ts(filename, tsname="TS"):
+def read_ts(filename, tsname='TS'):
     """
-    Read TS distribution values from CSV file.
+    Read TS distribution values from CSV file
+
+    Parameters
+    ----------
+    filename : str
+        Name of CSV file
+    tsname : str, optional
+        Column name of TS
+
+    Returns
+    -------
+    a : `numpy.array`
+        Array of TS values
     """
     # Initialise list
     values = []
@@ -53,9 +71,9 @@ def read_ts(filename, tsname=&quot;TS&quot;):
             try:
                 index = row.index(tsname)
             except:
-                sys.stdout.write('ERROR: Column "TS" not found in file\n')
-                sys.stdout.write(row+"\n")
-                raise NameError("TS")
+                print('ERROR: Column "'+tsname+'" not found in file')
+                print(row)
+                raise NameError(tsname)
         # Handle data rows
         else:
@@ -76,7 +94,17 @@ def read_ts(filename, tsname=&quot;TS&quot;):
 # =========== #
 def erf(x):
     """
-    Compute error function.
+    Compute error function
+
+    Parameters
+    ----------
+    x : float
+        Value
+
+    Returns
+    -------
+    erf(-x) : float
+        Error function
     """
     # save the sign of x
     sign = 1
@@ -103,9 +131,20 @@ def erf(x):
 # =============================== #
 # Display cumulative distribution #
 # =============================== #
-def dist_cdf(values, nbins, title):
+def dist_cdf(values, nbins, title, expect='', plotfile=''):
     """
-    Displays cumulative TS distribution and overplots expectation.
+    Display cumulative TS distribution and overplots expectation
+
+    Parameters
+    ----------
+    values : float
+        TS values
+    nbins : int
+        Number of bins
+    title : str
+        Plot title
+    plotfile : str, optional
+        Plot file name
     """
     # Set range and adapt number of bins. We make sure that we have a
     # histogram bin centred on 0 that will capture the TS=0 part.
@@ -142,19 +181,23 @@ def dist_cdf(values, nbins, title):
             hist_expect.append(norm)
     # Show expected distribution (half)
-    l = plt.semilogy(hist_x, hist_y,      'r-', linewidth=2, label="simulations")
-    l = plt.semilogy(hist_x, hist_expect, 'k-', linewidth=2, label="expectation")
-    l = plt.semilogy(hist_x, hist_y,      'r-', linewidth=2)
+    plt.semilogy(hist_x, hist_y,      'r-', linewidth=2, label='simulations')
+    if expect == 'show':
+        plt.semilogy(hist_x, hist_expect, 'k-', linewidth=2, label='expectation')
+        plt.semilogy(hist_x, hist_y,      'r-', linewidth=2)
     # Set plot
     plt.xlabel('TS')
     plt.ylabel('Number of trials > TS')
     plt.title(title)
     plt.grid(True)
-    plt.legend(loc="upper right")
+    plt.legend(loc='upper right')
-    # Show histogram
-    plt.show()
+    # Show figure
+    if len(plotfile) > 0:
+        plt.savefig(plotfile)
+    else:
+        plt.show()
     # Return
     return
@@ -163,10 +206,20 @@ def dist_cdf(values, nbins, title):
 # ======================================== #
 # Display probability density distribution #
 # ======================================== #
-def dist_pdf(values, nbins, title):
+def dist_pdf(values, nbins, title, expect='', plotfile=''):
     """
-    Display probability density TS distribution and overplots
-    expectation.
+    Display probability density TS distribution and overplots expectation
+
+    Parameters
+    ----------
+    values : float
+        TS values
+    nbins : int
+        Number of bins
+    title : str
+        Plot title
+    plotfile : str, optional
+        Plot file name
     """
     # Set range and adapt number of bins. We make sure that we have a
     # histogram bin centred on 0 that will capture the TS=0 part.
@@ -180,9 +233,9 @@ def dist_pdf(values, nbins, title):
     # Create histogram
     n, bins, patches = plt.hist(values, nbins, range=[min_value, max_value],
                                 align='mid', facecolor='green',
-                                                            log=True, label="simulations")
+                                log=True, label='simulations')
-    # Create expected distribution (onyl for positive TS). We compute the
+    # Create expected distribution (only for positive TS). We compute the
     # full and half of the distribution, as for positively constrained
     # quantities half of the values have TS=0
     x      = []
@@ -202,17 +255,24 @@ def dist_pdf(values, nbins, title):
             y_half.append(0.5*y)
     # Show expected distribution (half)
-    l = plt.semilogy(x, y_half, 'ro', linewidth=2, label="expectation")
+    if expect == 'show':
+        plt.semilogy(x, y_half, 'ro', linewidth=2, label='expectation')
     # Set plot
     plt.xlabel('TS')
     plt.ylabel('Number of trials')
     plt.title(title)
     plt.grid(True)
-    plt.legend(loc="upper right")
+    plt.legend(loc='upper right')
-    # Show histogram
-    plt.show()
+    # Show figure
+    if len(plotfile) > 0:
+        plt.savefig(plotfile)
+    else:
+        plt.show()
+
+    # Return
+    return
 # ======================== #
@@ -221,26 +281,22 @@ def dist_pdf(values, nbins, title):
 if __name__ == '__main__':
     # Print usage information
-    usage = "Usage: show_ts_distribution filename" \
-            " [-n bins] [-c column] [-t title] [-p plot]"
+    usage = 'Usage: show_ts_distribution.py filename' \
+            ' [-n bins] [-c column] [-t title] [-p plot] [-f file]'
     if len(sys.argv) < 2:
-        sys.stdout.write(usage+"\n")
+        print(usage)
         sys.exit()
     # Extract parameters
     filename = sys.argv[1]
-    # Set default parameters
-    nbins  = 30
-    tsname = "TS"
-    title  = "TS distribution"
-    plot   = "pdf"
-
     # Parameter dictionnary
-    pars = [{'option': '-n', 'value': nbins},
-            {'option': '-c', 'value': tsname},
-            {'option': '-t', 'value': title},
-                    {'option': '-p', 'value': plot}]
+    pars = [{'option': '-n', 'value': 30},
+            {'option': '-c', 'value': 'TS'},
+            {'option': '-t', 'value': 'TS distribution'},
+            {'option': '-p', 'value': 'pdf'},
+            {'option': '-e', 'value': ''},
+            {'option': '-f', 'value': ''}]
     # Gather parameters from command line
     i = 2
@@ -254,27 +310,29 @@ if __name__ == &#39;__main__&#39;:
                     try:
                         par['value'] = sys.argv[i]
                     except:
-                        sys.stdout.write(usage+"\n")
+                        print(usage)
                         sys.exit()
                 else:
-                    sys.stdout.write(usage+"\n")
+                    print(usage)
                     sys.exit()
         # Next item
         i += 1
     # Recover parameters
-    nbins  = int(pars[0]['value'])
-    tsname = pars[1]['value']
-    title  = pars[2]['value']
-    plot   = pars[3]['value']
+    nbins    = int(pars[0]['value'])
+    tsname   = pars[1]['value']
+    title    = pars[2]['value']
+    plot     = pars[3]['value']
+    expect   = pars[4]['value']
+    plotfile = pars[5]['value']
     # Read values from CSV file
     values = read_ts(filename, tsname=tsname)
-    sys.stdout.write(len(values), "values read.\n")
+    print('%d values read.' % len(values))
     # Show histogram
-    if plot == "pdf":
-        dist_pdf(values, nbins, title)
+    if plot == 'pdf':
+        dist_pdf(values, nbins, title, expect=expect, plotfile=plotfile)
     else:
-        dist_cdf(values, nbins, title)
+        dist_cdf(values, nbins, title, expect=expect, plotfile=plotfile)
-
 ctools unit test
 ================
 This folder contains files used for ctools unit testing.
-test_cscripts.sh
+loge,emin,emax,crab_flux,photon_flux,energy_flux,sensitivity
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+Developer test scripts
+======================
+
+This folder contains scripts that are used for various testing purposes
+during code development. None of the scripts actually needs to work, they
+may actually be outdated, but could still be useful for further testing
+if needed.
+
+`benchmark_cta_analysis.py`
+>  This script peforms a benchmark of the various CTA analysis types
+>  (unbinned, binned, stacked), and if matplotlib is installed, creates
+>  a plot of the benchmark results.
+
+`check_models.py`
+>  Checks a variety of models using an unbinned analysis.
+
+`make_pull_at_sensitivity_limit.py`
+>  This script creates pull distributions as function of energy
+>  for a test source that is at the 5 sigma sensitivity limit for
+>  50h of effective observing time. It takes as input file the
+>  output file generated using cspull.
+>  If the Python processing module is installed the script can make
+>  use of multiple cores and runs in parallel.
+
+`make_survey.py`
+>  This script simulates a CTA galactic plane survey.
+
+`simulate_events.py`
+>  Simulates events and compares the simulation to the model.
+
@@ -104,6 +104,10 @@ class Test(gammalib.GPythonTestSuite):
         self.append(self.test_show_pha, 'Test show_pha.py')
         self.append(self.test_show_pull_evolution, 'Test show_pull_evolution.py')
         self.append(self.test_show_pull_histogram, 'Test show_pull_histogram.py')
+        self.append(self.test_show_response, 'Test test_show_response.py')
+        self.append(self.test_show_sensitivity, 'Test test_show_sensitivity.py')
+        self.append(self.test_show_spectrum, 'Test test_show_spectrum.py')
+        self.append(self.test_show_ts_distribution, 'Test show_ts_distribution.py')
         # Return
         return
@@ -253,7 +257,8 @@ class Test(gammalib.GPythonTestSuite):
         """
         # Execute script
         self._execute_python('show_butterfly',
-                             args='data/butterfly.txt example_butterfly.eps')
+                             args='data/butterfly.txt '
+                                  'example_show_butterfly.eps')
         #TODO: Do any testing
@@ -267,7 +272,7 @@ class Test(gammalib.GPythonTestSuite):
         """
         # Execute script
         self._execute_python('show_model',
-                             args='data/crab.xml Crab example_model.eps')
+                             args='data/crab.xml Crab example_show_model.eps')
         #TODO: Do any testing
@@ -280,7 +285,8 @@ class Test(gammalib.GPythonTestSuite):
         Test show_pha
         """
         # Execute script
-        self._execute_python('show_pha', args='data/pha.fits example_pha.eps')
+        self._execute_python('show_pha',
+                             args='data/pha.fits example_show_pha.eps')
         #TODO: Do any testing
@@ -295,7 +301,7 @@ class Test(gammalib.GPythonTestSuite):
         # Execute script
         self._execute_python('show_pull_evolution',
                              args='data/pull.dat Pull_Crab_Prefactor '
-                                  'example_pull_evolution.eps')
+                                  'example_show_pull_evolution.eps')
         #TODO: Do any testing
@@ -310,7 +316,65 @@ class Test(gammalib.GPythonTestSuite):
         # Execute script
         self._execute_python('show_pull_histogram',
                              args='data/pull.dat Pull_Crab_Prefactor 50 '
-                                  'example_pull_histogram.eps')
+                                  'example_show_pull_histogram.eps')
+
+        #TODO: Do any testing
+
+        # Return
+        return
+
+    # Test show_response
+    def test_show_response(self):
+        """
+        Test show_response
+        """
+        # Execute script
+        self._execute_python('show_response', args='example_show_response.eps')
+
+        #TODO: Do any testing
+
+        # Return
+        return
+
+    # Test show_sensitivity
+    def test_show_sensitivity(self):
+        """
+        Test show_sensitivity
+        """
+        # Execute script
+        self._execute_python('show_sensitivity',
+                             args='data/sensitivity.dat '
+                                  'example_show_sensitivity.eps')
+
+        #TODO: Do any testing
+
+        # Return
+        return
+
+    # Test show_spectrum
+    def test_show_spectrum(self):
+        """
+        Test show_spectrum
+        """
+        # Execute script
+        self._execute_python('show_spectrum',
+                             args='data/spectrum.fits '
+                                  'example_show_spectrum.eps')
+
+        #TODO: Do any testing
+
+        # Return
+        return
+
+    # Test show_ts_distribution
+    def test_show_ts_distribution(self):
+        """
+        Test show_ts_distribution
+        """
+        # Execute script
+        self._execute_python('show_ts_distribution',
+                             args='data/ts.dat -n 100 '
+                                  '-f example_show_ts_distribution.eps')
         #TODO: Do any testing