diff --git a/test/example_binned.py b/test/example_binned.py
deleted file mode 100755
index 7f608b9..0000000
--- a/test/example_binned.py
+++ /dev/null
@@ -1,421 +0,0 @@
-#! /usr/bin/env python
-# ==========================================================================
-# This script illustrates how to build a binned analysis pipeline using the
-# ctatools. If matplotlib is installed, a counts spectrum and an offset
-# histogram will be displayed.
-#
-# Copyright (C) 2011 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
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-# ==========================================================================
-from ctatools import *
-from gammalib import *
-from math import *
-import os
-import glob
-
-
-# ============================= #
-# Analysis pipeline - version 1 #
-# ============================= #
-def pipeline_v1():
-	"""
-	Binned analysis pipeline - save intermediate results.
-    
-    This function implements an analysis pipeline that successively calls
-    ctobssim, ctbin and ctlike by saving the intermediate results as FITS
-    files on disk. This replicates the way how the analysis would be done
-    in a ftools-like approach.
-	"""
-	# Set script parameters
-	model_name  = "data/crab.xml"
-	events_name = "events.fits"
-	cntmap_name = "cntmap.fits"
-	result_name = "results.xml"
-	caldb       = "irf"
-	irf         = "kb_E_50h_v3"
-	ra          =   83.63
-	dec         =   22.01
-	rad_sim     =   10.0
-	tstart      =    0.0
-	tstop       = 1800.0
-	emin        =    0.1
-	emax        =  100.0
-	enumbins    =   20
-	nxpix       =  200
-	nypix       =  200
-	binsz       =    0.02
-	coordsys    = "CEL"
-	proj        = "CAR"
-
-	# Initialise timing
-	wall_seconds = 0.0
-	cpu_seconds  = 0.0
-
-	# Simulate events
-	sim = ctobssim()
-	sim.logFileOpen()  # We need this to explicitely open the log file in Python mode
-	sim["infile"].filename(model_name)
-	sim["outfile"].filename(events_name)
-	sim["caldb"].string(caldb)
-	sim["irf"].string(irf)
-	sim["ra"].real(ra)
-	sim["dec"].real(dec)
-	sim["rad"].real(rad_sim)
-	sim["tmin"].real(tstart)
-	sim["tmax"].real(tstop)
-	sim["emin"].real(emin)
-	sim["emax"].real(emax)
-	sim.execute()
-	print "Simulated events ("+str(sim.celapse())+" CPU seconds)"
-
-	# Update timing
-	wall_seconds += sim.telapse()
-	cpu_seconds  += sim.celapse()
-	
-	# Bin events into counts map
-	bin = ctbin()
-	bin.logFileOpen()  # We need this to explicitely open the log file in Python mode
-	bin["evfile"].filename(events_name)
-	bin["outfile"].filename(cntmap_name)
-	bin["emin"].real(emin)
-	bin["emax"].real(emax)
-	bin["enumbins"].integer(enumbins)
-	bin["nxpix"].integer(nxpix)
-	bin["nypix"].integer(nypix)
-	bin["binsz"].real(binsz)
-	bin["coordsys"].string(coordsys)
-	bin["xref"].real(ra)
-	bin["yref"].real(dec)
-	bin["proj"].string(proj)
-	bin.execute()
-	print "Binned events into counts map ("+str(bin.celapse())+" CPU seconds)"
-
-	# Update timing
-	wall_seconds += bin.telapse()
-	cpu_seconds  += bin.celapse()
-	
-	# Perform maximum likelihood fitting
-	like = ctlike()
-	like.logFileOpen()  # We need this to explicitely open the log file in Python mode
-	like["cntmap"].filename(cntmap_name)
-	like["srcmdl"].filename(model_name)
-	like["outmdl"].filename(result_name)
-	like["method"].string("BINNED")
-	like["caldb"].string(caldb)
-	like["irf"].string(irf)
-	like.execute()
-	print "Maximum likelihood fitting ("+str(like.celapse())+" CPU seconds)"
-
-	# Update timing
-	wall_seconds += like.telapse()
-	cpu_seconds  += like.celapse()
-	
-	# Show total times
-	print "Total wall time elapsed: "+str(wall_seconds)+" seconds"
-	print "Total CPU time used ...: "+str(cpu_seconds)+" seconds"
-
-	# Return
-	return
-
-
-# ============================= #
-# Analysis pipeline - version 2 #
-# ============================= #
-def pipeline_v2():
-	"""
-	Binned analysis pipeline - keep intermediate results in memory.
-    
-    This function implements an analysis pipeline that successively calls
-    ctobssim, ctbin and ctlike without saving the intermediate results as
-	FITS files on disk. All data are hold in memory.
-	
-	At the end, results are plotted (if matplotlib is installed)
-	"""
-	# Set script parameters
-	model_name  = "data/crab.xml"
-	caldb       = "irf"
-	irf         = "kb_E_50h_v3"
-	ra          =   83.63
-	dec         =   22.01
-	rad_sim     =   10.0
-	tstart      =    0.0
-	tstop       = 1800.0
-	emin        =    0.1
-	emax        =  100.0
-	enumbins    =   20
-	nxpix       =  200
-	nypix       =  200
-	binsz       =    0.02
-	coordsys    = "CEL"
-	proj        = "CAR"
-
-	# Initialise timing
-	wall_seconds = 0.0
-	cpu_seconds  = 0.0
-
-	# Simulate events
-	sim = ctobssim()
-	sim["infile"].filename(model_name)
-	sim["caldb"].string(caldb)
-	sim["irf"].string(irf)
-	sim["ra"].real(ra)
-	sim["dec"].real(dec)
-	sim["rad"].real(rad_sim)
-	sim["tmin"].real(tstart)
-	sim["tmax"].real(tstop)
-	sim["emin"].real(emin)
-	sim["emax"].real(emax)
-	sim.run()
-	print "Simulated events ("+str(sim.celapse())+" CPU seconds)"
-
-	# Update timing
-	wall_seconds += sim.telapse()
-	cpu_seconds  += sim.celapse()
-
-	# Bin events into counts map
-	bin = ctbin(sim.obs())
-	bin["emin"].real(emin)
-	bin["emax"].real(emax)
-	bin["enumbins"].integer(enumbins)
-	bin["nxpix"].integer(nxpix)
-	bin["nypix"].integer(nypix)
-	bin["binsz"].real(binsz)
-	bin["coordsys"].string(coordsys)
-	bin["xref"].real(ra)
-	bin["yref"].real(dec)
-	bin["proj"].string(proj)
-	bin.run()
-	print "Binned events into counts map ("+str(bin.celapse())+" CPU seconds)"
-
-	# Update timing
-	wall_seconds += bin.telapse()
-	cpu_seconds  += bin.celapse()
-
-	# Perform maximum likelihood fitting
-	like = ctlike(bin.obs())
-	like.run()
-	print "Maximum likelihood fitting ("+str(like.celapse())+" CPU seconds)"
-
-	# Update timing
-	wall_seconds += like.telapse()
-	cpu_seconds  += like.celapse()
-		
-	# Show total times
-	print "Total wall time elapsed: "+str(wall_seconds)+" seconds"
-	print "Total CPU time used ...: "+str(cpu_seconds)+" seconds"
-
-	# Show model fitting results
-	#print like.obs().models()
-
-	# Plot counts
-	plot_counts(bin.obs())
-	
-	# Return
-	return
-
-
-# =========== #
-# Plot counts #
-# =========== #
-def plot_counts(observations):
-	"""
-	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 obs in observations:
-		
-			# Get event cube
-			cube = cast_GCTAEventCube(obs.events())
-			
-			# Create energy axis
-			energy  = []
-			ebounds = cube.ebounds()
-			for i in range(ebounds.size()):
-				energy.append(ebounds.elogmean(i).TeV())
-
-			# Create spectrum
-			print "Extract data:"
-			counts = [0.0 for i in range(ebounds.size())]
-			for bin in cube:
-				index         = ebounds.index(bin.energy())
-				counts[index] = counts[index] + bin.counts()
-
-			# 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(observations.models()):
-				print "- "+m.name()
-				model = [0.0 for i in range(ebounds.size())]
-				for bin in cube:
-					index        = ebounds.index(bin.energy())
-					prob         = m.eval(bin, obs)
-					model[index] = model[index] + prob * bin.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 obs in observations:
-		
-			# Get event cube
-			cube = cast_GCTAEventCube(obs.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 bin in cube:
-				off   = bin.dir().dist_deg(crab)
-				off2  = off*off
-				index = int(off2/doffset2)
-				if index < nx:
-					counts[index] = counts[index] + bin.counts()
-
-			# 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(observations.models()):
-				print "- "+m.name()
-				model = [0.0 for i in range(nx)]
-				for bin in cube:
-					off   = bin.dir().dist_deg(crab)
-					off2  = off*off
-					index = int(off2/doffset2)
-					if index < nx:
-						prob         = m.eval(bin, obs)
-						model[index] = model[index] + prob * bin.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:
-		print "Matplotlib is not (correctly) installed on your system. No counts spectra are shown."
-
-	# Return
-	return
-
-
-#==========================#
-# Main routine entry point #
-#==========================#
-if __name__ == '__main__':
-	"""
-	CTA binned analysis script.
-	
-	This script implements the analysis steps to perform a binned analysis.
-	The steps include:
-	- Event simulation
-	- Event binning
-	- Binned maximum likelihood fitting
-	
-	Two variations of the script are implemented. One that calls the classes
-	like executables, storing the intermediate results on disk, and a second
-	that uses the results of the precedent class directly without storing
-	any data on disk.
-	
-	The second variation will also show a counts spectrum of the data after
-	fitting (only if matplotlib is available on your system). Note that the
-	counts spectrum is summed over the entire data space, i.e. no event selection
-	around the Crab has been performed.
-	"""
-	# Dump header
-	print "*************************************"
-	print "*    CTA binned analysis scripts    *"
-	print "*************************************"
-	print "... this script will take 1-2 minutes"
-
-	# 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)
-	
-	# Save intermediate results on disk
-	print ""
-	print "Executable analysis pipeline:"
-	print "============================="
-	pipeline_v1()
-
-	# Do analysis in memory
-	print ""
-	print "In memory analysis pipeline:"
-	print "============================"
-	pipeline_v2()
-	print ""
diff --git a/test/example_unbinned.py b/test/example_unbinned.py
deleted file mode 100755
index bb44529..0000000
--- a/test/example_unbinned.py
+++ /dev/null
@@ -1,424 +0,0 @@
-#! /usr/bin/env python
-# ==========================================================================
-# This script illustrates how to build an unbinned analysis pipeline with
-# the ctatools. If matplotlib is installed, the spectrum will be displayed
-# on the screen.
-#
-# Copyright (C) 2011 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
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-# ==========================================================================
-from ctatools import *
-from gammalib import *
-from math import *
-import os
-import glob
-import sys
-
-
-# ============================= #
-# Analysis pipeline - version 1 #
-# ============================= #
-def pipeline_v1():
-	"""
-	Unbinned analysis pipeline - save intermediate results.
-    
-    This function implements an analysis pipeline that successively calls
-    ctobssim, ctselect and ctlike by saving the intermediate results as FITS
-    files on disk. This replicates the way how the analysis would be done
-    in a ftools-like approach.
-	"""
-	# Set script parameters
-	model_name           = "data/crab.xml"
-	events_name          = "events.fits"
-	selected_events_name = "selected_events.fits"
-	result_name          = "results.xml"
-	caldb                = "irf"
-	irf                  = "kb_E_50h_v3"
-	ra                   =   83.63
-	dec                  =   22.01
-	rad_sim              =   10.0
-	tstart               =    0.0
-	tstop                = 1800.0
-	emin                 =    0.1
-	emax                 =  100.0
-	rad_select           =    3.0
-
-	# Initialise timing
-	wall_seconds = 0.0
-	cpu_seconds  = 0.0
-
-	# Simulate events
-	sim = ctobssim()
-	sim.logFileOpen()
-	sim["infile"].filename(model_name)
-	sim["outfile"].filename(events_name)
-	sim["caldb"].string(caldb)
-	sim["irf"].string(irf)
-	sim["ra"].real(ra)
-	sim["dec"].real(dec)
-	sim["rad"].real(rad_sim)
-	sim["tmin"].real(tstart)
-	sim["tmax"].real(tstop)
-	sim["emin"].real(emin)
-	sim["emax"].real(emax)
-	sim.execute()
-	print "Simulated events ("+str(sim.celapse())+" CPU seconds)"
-
-	# Update timing
-	wall_seconds += sim.telapse()
-	cpu_seconds  += sim.celapse()
-	
-	# Select events
-	select = ctselect()
-	select.logFileOpen()
-	select["infile"].filename(events_name)
-	select["outfile"].filename(selected_events_name)
-	select["ra"].real(ra)
-	select["dec"].real(dec)
-	select["rad"].real(rad_select)
-	select["tmin"].real(tstart)
-	select["tmax"].real(tstop)
-	select["emin"].real(emin)
-	select["emax"].real(emax)
-	select.execute()
-	print "Select event ("+str(select.celapse())+" CPU seconds)"
-
-	# Update timing
-	wall_seconds += select.telapse()
-	cpu_seconds  += select.celapse()
-	
-	# Perform maximum likelihood fitting
-	like = ctlike()
-	like.logFileOpen()
-	like["evfile"].filename(selected_events_name)
-	like["srcmdl"].filename(model_name)
-	like["outmdl"].filename(result_name)
-	like["method"].string("UNBINNED")
-	like["caldb"].string(caldb)
-	like["irf"].string(irf)
-	like.execute()
-	print "Maximum likelihood fitting ("+str(like.celapse())+" CPU seconds)"
-
-	# Update timing
-	wall_seconds += like.telapse()
-	cpu_seconds  += like.celapse()
-	
-	# Show total times
-	print "Total wall time elapsed: "+str(wall_seconds)+" seconds"
-	print "Total CPU time used ...: "+str(cpu_seconds)+" seconds"
-
-	# Return
-	return
-
-
-# ============================= #
-# Analysis pipeline - version 2 #
-# ============================= #
-def pipeline_v2(show_data):
-	"""
-	Unbinned analysis pipeline - keep intermediate results in memory.
-    
-    This function implements an analysis pipeline that successively calls
-    ctobssim, ctselect and ctlike without saving the intermediate results as
-	FITS files on disk. All data is only hold in memory.
-	"""
-	# Set script parameters
-	model_name  = "data/crab.xml"
-	caldb       = "irf"
-	irf         = "kb_E_50h_v3"
-	ra          =   83.63
-	dec         =   22.01
-	rad_sim     =   10.0
-	tstart      =    0.0
-	tstop       = 1800.0
-	emin        =    0.1
-	emax        =  100.0
-	rad_select  =    3.0
-
-	# Initialise timing
-	wall_seconds = 0.0
-	cpu_seconds  = 0.0
-
-	# Simulate events
-	sim = ctobssim()
-	sim["infile"].filename(model_name)
-	sim["caldb"].string(caldb)
-	sim["irf"].string(irf)
-	sim["ra"].real(ra)
-	sim["dec"].real(dec)
-	sim["rad"].real(rad_sim)
-	sim["tmin"].real(tstart)
-	sim["tmax"].real(tstop)
-	sim["emin"].real(emin)
-	sim["emax"].real(emax)
-	sim.run()
-	print "Simulated events ("+str(sim.celapse())+" CPU seconds)"
-
-	# Update timing
-	wall_seconds += sim.telapse()
-	cpu_seconds  += sim.celapse()
-
-	# Select events
-	select = ctselect(sim.obs())
-	select["ra"].real(ra)
-	select["dec"].real(dec)
-	select["rad"].real(rad_select)
-	select["tmin"].real(tstart)
-	select["tmax"].real(tstop)
-	select["emin"].real(emin)
-	select["emax"].real(emax)
-	select.run()
-	print "Select event ("+str(select.celapse())+" CPU seconds)"
-
-	# Update timing
-	wall_seconds += select.telapse()
-	cpu_seconds  += select.celapse()
-
-	# Perform maximum likelihood fitting
-	like = ctlike(select.obs())
-	like.run()
-	print "Maximum likelihood fitting ("+str(like.celapse())+" CPU seconds)"
-
-	# Update timing
-	wall_seconds += like.telapse()
-	cpu_seconds  += like.celapse()
-		
-	# Show total times
-	print "Total wall time elapsed: "+str(wall_seconds)+" seconds"
-	print "Total CPU time used ...: "+str(cpu_seconds)+" seconds"
-
-	# Show model fitting results
-	#print like.obs().models()
-	
-	# Optionally plot counts
-	if show_data:
-		plot_counts(like.obs())
-	
-	# Return
-	return
-
-
-# =========== #
-# Plot counts #
-# =========== #
-def plot_counts(observations):
-	"""
-	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 obs in observations:
-		
-			# Get event list
-			list = cast_GCTAEventList(obs.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(observations.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, obs)
-					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 obs in observations:
-		
-			# Get event cube
-			list = cast_GCTAEventList(obs.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(observations.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, obs)
-						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. No counts spectra are shown."
-
-	# Return
-	return
-
-
-#==========================#
-# Main routine entry point #
-#==========================#
-if __name__ == '__main__':
-	"""
-	CTA unbinned analysis script.
-	
-	This script implements the analysis steps to perform an unbinned analysis.
-	The steps include:
-	- Event simulation
-	- Event selection
-	- Binned maximum likelihood fitting
-	
-	Two variations of the script are implemented. One that calls the classes
-	like executables, storing the intermediate results on disk, and a second
-	that uses the results of the precedent class directly without storing
-	any data on disk.
-	"""
-	# Initialise flags
-	need_help = False
-	show_data = True
-	
-	# Test for command line arguments
-	print sys.argv[0]
-	if (len(sys.argv) > 1):
-		if sys.argv[1] == "-h":
-			need_help = True
-		elif sys.argv[1] == "-noshow":
-			show_data = False
-		else:
-			need_help = True			
-
-	# Print help if needed and exit
-	if need_help:
-		print "Usage: example_binned.py [OPTIONS]"
-		print "     -h       Display this usage message"
-		print "     -noshow  Do not show data using matplotlib"
-		sys.exit()
-
-	# Dump header
-	print "*********************************"
-	print "* CTA unbinned analysis scripts *"
-	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)
-	
-	# Save intermediate results on disk
-	print ""
-	print "Executable analysis pipeline:"
-	print "============================="
-	pipeline_v1()
-	
-	# Do analysis in memory
-	print ""
-	print "In memory analysis pipeline:"
-	print "============================"
-	pipeline_v2(show_data)
-	print ""