csspec.py

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#! /usr/bin/env python
# ==========================================================================
# Generates a spectrum.
#
# Copyright (C) 2014-2022 Michael Mayer
#
# 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/>.
#
# ==========================================================================
import sys
import math
import tempfile    # Kludge for file function generation
import gammalib
import ctools
from cscripts import mputils
 
 
# ============ #
# csspec class #
# ============ #
class csspec(ctools.csobservation):
    """
    Generates a spectrum
 
    This class implements the generation of a Spectral Energy Distribution
    (SED) from gamma-ray observations.
    """
 
    # Constructors and destructors
    def __init__(self, *argv):
        """
        Constructor
        """
        # Initialise application by calling the appropriate class constructor
        self._init_csobservation(self.__class__.__name__, ctools.__version__, argv)
 
        # Initialise data members
        self._ebounds     = gammalib.GEbounds()
        self._fits        = None
        self._binned_mode = False
        self._onoff_mode  = False
        self._method      = 'AUTO'
        self._nthreads    = 0
 
        # Return
        return
 
    def __del__(self):
        """
        Destructor
        """
        # Return
        return
 
    # State methods for pickling
    def __getstate__(self):
        """
        Extend ctools.csobservation getstate method to include some members
 
        Returns
        -------
        state : dict
            Pickled instance
        """
        # Set pickled dictionary
        state = {'base'         : ctools.csobservation.__getstate__(self),
                 'ebounds'      : self._ebounds,
                 'fits'         : self._fits,
                 'binned_mode'  : self._binned_mode,
                 'onoff_mode'   : self._onoff_mode,
                 'method'       : self._method,
                 'nthreads'     : self._nthreads}
 
        # Return pickled dictionary
        return state
 
    def __setstate__(self, state):
        """
        Extend ctools.csobservation setstate method to include some members
 
        Parameters
        ----------
        state : dict
            Pickled instance
        """
        ctools.csobservation.__setstate__(self, state['base'])
        self._ebounds       = state['ebounds']
        self._fits          = state['fits']
        self._binned_mode   = state['binned_mode']
        self._onoff_mode    = state['onoff_mode']
        self._method        = state['method']
        self._nthreads      = state['nthreads']
 
        # Return
        return
 
 
    # Private methods
    def _get_parameters(self):
        """
        Get parameters from parfile and setup the observation
        """
        # Set observation if not done before
        if self.obs().is_empty():
            self._require_inobs('csspec::get_parameters()')
            self.obs(self._get_observations())
 
        # Set observation statistic
        self._set_obs_statistic(gammalib.toupper(self['statistic'].string()))
 
        # Set models if we have none
        if self.obs().models().is_empty():
            self.obs().models(self['inmodel'].filename())
 
        # Query source name
        self['srcname'].string()
 
        # Get spectrum generation method
        self._method = self['method'].string()
 
        # Collect number of unbinned, binned and On/Off observations in
        # observation container
        n_unbinned = 0
        n_binned   = 0
        n_onoff    = 0
        for obs in self.obs():
            if obs.classname() == 'GCTAObservation':
                if obs.eventtype() == 'CountsCube':
                    n_binned += 1
                else:
                    n_unbinned += 1
            elif obs.classname() == 'GCTAOnOffObservation':
                n_onoff += 1
        n_cta   = n_unbinned + n_binned + n_onoff
        n_other = self.obs().size() - n_cta
 
        # If spectrum method is not "NODES" or 'BINS" then set spectrum method
        # and script mode according to type of observations
        if self._method != 'NODES' and self._method != 'BINS':
            if n_other > 0:
                self._method = 'NODES'
            else:
                if n_unbinned == 0 and n_binned != 0 and n_onoff == 0:
                    self._binned_mode = True
                    self._method      = 'SLICE'
                elif n_unbinned == 0 and n_binned == 0 and n_onoff != 0:
                    self._onoff_mode = True
                    self._method      = 'SLICE'
                elif n_unbinned == 0 and n_binned != 0 and n_onoff != 0:
                    msg = 'Mix of binned and On/Off CTA observations found ' \
                          'in observation container. csscript does not support ' \
                          'this mix.'
                    raise RuntimeError(msg)
                elif n_unbinned != 0 and (n_binned != 0 or n_onoff != 0):
                    msg = 'Mix of unbinned and binned or On/Off CTA observations ' \
                          'found in observation container. csscript does not ' \
                          'support this mix.'
                    raise RuntimeError(msg)
                elif n_unbinned != 0:
                    self._method = 'SLICE'
 
        # Set ebounds
        self._set_ebounds()
 
        # Query other parameeters
        self['edisp'].boolean()
        self['calc_ts'].boolean()
        self['calc_ulim'].boolean()
        self['confidence'].real()
        self['fix_bkg'].boolean()
        self['fix_srcs'].boolean()
        self['bingamma'].real()
 
        # Setup dlog-likelihood parameters
        self['dll_sigstep'].real()
        self['dll_sigmax'].real()
        self['dll_freenodes'].boolean()
 
        # Read ahead output parameters
        if self._read_ahead():
            self['outfile'].filename()
 
        #  Write input parameters into logger
        self._log_parameters(gammalib.TERSE)
 
        # Set number of processes for multiprocessing
        self._nthreads = mputils.nthreads(self)
 
        # Write spectrum method header and parameters
        self._log_header1(gammalib.TERSE, 'Spectrum method')
        self._log_value(gammalib.TERSE, 'Unbinned CTA observations', n_unbinned)
        self._log_value(gammalib.TERSE, 'Binned CTA observations', n_binned)
        self._log_value(gammalib.TERSE, 'On/off CTA observations', n_onoff)
        self._log_value(gammalib.TERSE, 'Other observations', n_other)
 
        # If there are a mix of CTA and non-CTA observations and the method
        # is 'SLICE' then log a warning that non-CTA observations will be
        # ignored
        warning = False
        if n_cta > 0 and n_other > 0 and self._method == 'SLICE':
            warning = True
 
        # If there are only non-CTA observations and the method is 'SLICE'
        # then stop now
        elif n_other > 0:
            if self._method == 'SLICE':
                msg = 'Selected "SLICE" method but none of the observations ' \
                      'is a CTA observation. Please select "AUTO", "NODES" ' \
                      'or "BINS" if no CTA observation is provided.'
                raise RuntimeError(msg)
            elif self._method == 'AUTO':
                self._method = 'NODES'
 
        # Log selected spectrum method
        self._log_value(gammalib.TERSE, 'Selected spectrum method', self._method)
 
        # Signal warning
        if warning:
            self._log_string(gammalib.TERSE, ' WARNING: Only CTA observation '
                             'can be handled with the "SLICE" method, all '
                             'non-CTA observation will be ignored.')
 
        # Return
        return
 
    def _set_ebounds(self):
        """
        Set energy boundaries
        """
        # If we are in binned or On/Off mode then align the energy boundaries
        # with the cube of RMF spectrum
        if self._binned_mode or self._onoff_mode:
 
            # Initialise energy boundaries for spectrum
            self._ebounds = gammalib.GEbounds()
 
            # Create energy boundaries according to user parameters
            ebounds = self._create_ebounds()
 
            # Extract binned energy boundaries from first observation in
            # container. This assumes that all observations have the same
            # energy binning. But even if this is not the case, the script
            # should work reasonably well since for each observation the
            # relevant energy bins will be selected.
            if self._binned_mode:
                cube_ebounds = self.obs()[0].events().ebounds()
            else:
                cube_ebounds = self.obs()[0].rmf().emeasured()
 
            # Loop over user energy boundaries and collect all energy bins
            # that overlap
            for i in range(ebounds.size()):
 
                # Extract minimum and maximum energy of user energy bin,
                # including some rounding tolerance
                emin = ebounds.emin(i).TeV() * 0.999 # Rounding tolerance
                emax = ebounds.emax(i).TeV() * 1.001 # Rounding tolerance
 
                # Set number of overlapping energy bins
                nbins = 0
 
                # Search first cube bin that is comprised within user energy
                # bin
                emin_value = -1.0
                for k in range(cube_ebounds.size()):
                    if cube_ebounds.emin(k).TeV() >= emin and \
                       cube_ebounds.emax(k).TeV() <= emax:
                        emin_value = cube_ebounds.emin(k).TeV()
                        break
                if emin_value < 0.0:
                    continue
 
                # Search last cube bin that is comprised within user energy bin
                for k in range(cube_ebounds.size()):
                    if cube_ebounds.emin(k).TeV() >= emin and \
                       cube_ebounds.emax(k).TeV() <= emax:
                        emax_value = cube_ebounds.emax(k).TeV()
                        nbins += 1
 
                # Append energy bin if there are overlapping bins in the
                # counts cube
                if nbins > 0:
                    self._ebounds.append(gammalib.GEnergy(emin_value, 'TeV'),
                                         gammalib.GEnergy(emax_value, 'TeV'))
 
            # Raise an exception if there are no overlapping layers
            if (len(self._ebounds) == 0):
                msg = 'Energy range ['+str(cube_ebounds.emin())+ \
                      ', '+str(cube_ebounds.emax())+'] of counts '+ \
                      'cube does not overlap with specified energy '+ \
                      'range ['+ \
                      str(ebounds.emin())+', '+str(ebounds.emax())+'].'+ \
                      ' Specify overlapping energy range.'
                raise RuntimeError(msg)
 
        # Unbinned mode
        else:
            self._ebounds = self._create_ebounds()
 
        # Return
        return
 
    def _log_spectral_binning(self):
        """
        Log spectral binning
        """
        # Write header
        self._log_header1(gammalib.TERSE, 'Spectral binning')
 
        # Log counts cube energy range for binned mode
        if self._binned_mode:
            cube_ebounds = self.obs()[0].events().ebounds()
            value = '%s - %s' % (str(cube_ebounds.emin()),
                                 str(cube_ebounds.emax()))
            self._log_value(gammalib.TERSE, 'Counts cube energy range', value)
 
        # Log RMF energy range for On/Off mode
        elif self._onoff_mode:
            etrue = self.obs()[0].rmf().etrue()
            ereco = self.obs()[0].rmf().emeasured()
            vtrue = '%s - %s' % (str(etrue.emin()), str(etrue.emax()))
            vreco = '%s - %s' % (str(ereco.emin()), str(ereco.emax()))
            self._log_value(gammalib.TERSE, 'RMF true energy range', vtrue)
            self._log_value(gammalib.TERSE, 'RMF measured energy range', vreco)
 
        # Log energy bins
        for i in range(self._ebounds.size()):
            value = '%s - %s' % (str(self._ebounds.emin(i)),
                                 str(self._ebounds.emax(i)))
            self._log_value(gammalib.TERSE, 'Bin %d' % (i+1), value)
 
        # Return
        return
 
    def _adjust_model_pars(self):
        """
        Adjust model parameters
        """
        # Write header
        self._log_header1(gammalib.TERSE, 'Adjust model parameters')
 
        # Adjust model parameters dependent on input user parameters
        for model in self.obs().models():
 
            # Initialise TS flag for all models to false
            model.tscalc(False)
 
            # Log model name
            self._log_header3(gammalib.NORMAL, model.name())
 
            # Deal with the source of interest
            if model.name() == self['srcname'].string():
 
                # Fix all model parameters
                for par in model:
                    if par.is_free():
                        self._log_string(gammalib.NORMAL,
                                         ' Fixing "'+par.name()+'"')
                    par.fix()
 
                # Convert spectral model into a file function. The file
                # function is logarithmically sampled in energy, with
                # 50 times the number of spectral bins. We do not use
                # the GModelSpectralFunc constructor to avoid non-positive
                # values that may occur in particular due to rounding
                # errors in energy if the model is a bin spectrum.
                self._log_string(gammalib.NORMAL, ' Converting spectral model '
                                 'into file function')
                nbins    = self._ebounds.size()
                num      = nbins * 50
                energies = gammalib.GEnergies(num, self._ebounds.emin(0),
                                                   self._ebounds.emax(nbins-1))
                #spectral = gammalib.GModelSpectralFunc(model.spectral(), energies)
                spectral = gammalib.GModelSpectralFunc()
                spectral.reserve(num)
                last_value = 0.0
                for energy in energies:
                    value = model.spectral().eval(energy)
                    if value <= 0.0:
                        if last_value != 0.0:
                            new_value = last_value
                        else:
                            new_value = 1.0e-50
                        self._log_string(gammalib.NORMAL,
                                         ' Spectrum('+str(energy)+')='+str(value)+'. '
                                         'Set value to '+str(new_value)+'.')
                        value = new_value
                    spectral.append(energy, value)
                    last_value = value
                model.spectral(spectral)
 
                # Free the normalisation parameter which is assumed to be
                # the first spectral parameter
                normpar = model.spectral()[0]
                if normpar.is_fixed():
                    self._log_string(gammalib.NORMAL,
                                     ' Freeing "'+normpar.name()+'"')
                normpar.free()
 
                # Optionally compute Test Statistic value
                if self['calc_ts'].boolean():
                    model.tscalc(True)
 
            # Deal with background models
            elif self['fix_bkg'].boolean() and \
                 not model.classname() == 'GModelSky':
                for par in model:
                    if par.is_free():
                        self._log_string(gammalib.NORMAL,
                                         ' Fixing "'+par.name()+'"')
                    par.fix()
 
            # Deal with source models
            elif self['fix_srcs'].boolean() and \
                 model.classname() == 'GModelSky':
                for par in model:
                    if par.is_free():
                        self._log_string(gammalib.NORMAL,
                                         ' Fixing "'+par.name()+'"')
                    par.fix()
 
        # Return
        return
 
    def _set_replace_src_spectrum_by_nodes(self):
        """
        Replace source spectrum by node function
        """
        # Initialise model container
        models = gammalib.GModels()
 
        # Loop over model containers
        for model in self.obs().models():
 
            # If we deal with source model then replace the spectral model
            # by a node function
            if model.name() == self['srcname'].string():
 
                # Setup energies at log mean energies of bins
                energies = gammalib.GEnergies()
                for i in range(self._ebounds.size()):
                    energies.append(self._ebounds.elogmean(i))
 
                # Setup spectral node function
                spectrum = gammalib.GModelSpectralNodes(model.spectral(), energies)
                spectrum.autoscale()
 
                # Make sure that all nodes are positive. Autoscale all
                # parameters so that their nominal value is unity.
                for i in range(spectrum.nodes()):
                    par     = spectrum[i*2+1]
                    par.autoscale()
                    value   = par.value()
                    minimum = 1.0e-20 * value
                    if minimum <= 0.0:
                        minimum = 1.0e-20
                        if minimum < value:
                            value = minimum
                    par.value(value)
                    par.min(minimum)
 
                # Set spectral component of source model
                model.spectral(spectrum)
 
                # Make sure that TS computation is disabled (makes computation
                # faster)
                model.tscalc(False)
 
                # Append model
                models.append(model)
 
            # ... otherwise just append model
            else:
                models.append(model)
 
        # Put new model in observation containers
        self.obs().models(models)
 
        # Return
        return
 
    def _set_replace_src_spectrum_by_bins(self):
        """
        Replace source spectrum by bin function
        """
        # Initialise model container
        models = gammalib.GModels()
 
        # Loop over model containers
        for model in self.obs().models():
 
            # If we deal with source model then replace the spectral model
            # by a bin function
            if model.name() == self['srcname'].string():
 
                # Get spectral bins index
                bingamma = self['bingamma'].real()
 
                # Setup spectral bin function
                spectrum = gammalib.GModelSpectralBins(model.spectral(),
                                                       self._ebounds,
                                                       bingamma)
                spectrum.autoscale()
 
                # Make sure that all bins are positive. Autoscale all
                # parameters so that their nominal value is unity.
                for i in range(spectrum.bins()):
                    name    = 'Intensity%d' % i
                    par     = spectrum[name]
                    par.autoscale()
                    value   = par.value()
                    minimum = 1.0e-20 * value
                    if minimum <= 0.0:
                        minimum = 1.0e-20
                        if minimum < value:
                            value = minimum
                    par.value(value)
                    par.min(minimum)
 
                # Set spectral component of source model
                model.spectral(spectrum)
 
                # Make sure that TS computation is disabled (makes computation
                # faster)
                model.tscalc(False)
 
                # Append model
                models.append(model)
 
            # ... otherwise just append model
            else:
                models.append(model)
 
        # Put new model in observation containers
        self.obs().models(models)
 
        # Return
        return
 
    def _select_onoff_obs(self, obs, emin, emax):
        """
        Select an energy interval from one CTA On/Off observation
 
        Parameters
        ----------
        obs : `~gammalib.GCTAOnOffObservation`
            Minimum energy
        emin : `~gammalib.GEnergy()`
            Minimum energy
        emax : `~gammalib.GEnergy()`
            Maximum energy
 
        Returns
        -------
        obs : `~gammalib.GCTAOnOffObservation`
            CTA On/Off observation
        """
        # Select energy bins in etrue and ereco. All etrue energy bins are
        # selected. A 0.1% margin is added for reconstructed energies to
        # accomodate for rounding errors.
        etrue     = obs.rmf().etrue()
        ereco     = gammalib.GEbounds()
        itrue     = [i for i in range(obs.rmf().etrue().size())]
        ireco     = []
        for i in range(obs.rmf().emeasured().size()):
            ereco_bin_min = obs.rmf().emeasured().emin(i)
            ereco_bin_max = obs.rmf().emeasured().emax(i)
            if ereco_bin_min * 1.001 >= emin and ereco_bin_max * 0.999 <= emax:
                ereco.append(ereco_bin_min, ereco_bin_max)
                ireco.append(i)
 
        # Extract PHA
        pha_on  = gammalib.GPha(ereco)
        pha_off = gammalib.GPha(ereco)
        pha_on.exposure(obs.on_spec().exposure())
        pha_off.exposure(obs.on_spec().exposure())
        for idst, isrc in enumerate(ireco):
            # On
            pha_on[idst] = obs.on_spec()[isrc]
            pha_on.areascal(idst, obs.on_spec().areascal(isrc))
            pha_on.backscal(idst, obs.on_spec().backscal(isrc))
            # Off
            pha_off[idst] = obs.off_spec()[isrc]
            pha_off.areascal(idst, obs.off_spec().areascal(isrc))
            pha_off.backscal(idst, obs.off_spec().backscal(isrc))
 
        # Extract BACKRESP
        pha_backresp = obs.off_spec()['BACKRESP']
        backresp     = []
        for idst, isrc in enumerate(ireco):
            backresp.append(pha_backresp[isrc])
        pha_off.append('BACKRESP', backresp)
 
        # Extract ARF
        arf = gammalib.GArf(etrue)
        for idst, isrc in enumerate(itrue):
            arf[idst] = obs.arf()[isrc]
 
        # Extract RMF
        rmf = gammalib.GRmf(etrue, ereco)
        for idst_true, isrc_true in enumerate(itrue):
            for idst_reco, isrc_reco in enumerate(ireco):
                rmf[idst_true, idst_reco] = obs.rmf()[isrc_true, isrc_reco]
 
        # Set On/Off observations
        obsid      = obs.id()
        statistic  = obs.statistic()
        instrument = obs.instrument()
        obs = gammalib.GCTAOnOffObservation(pha_on, pha_off, arf, rmf)
        obs.id(obsid)
        obs.statistic(statistic)
        obs.instrument(instrument)
 
        # Return observation
        return obs
 
    def _select_obs(self, emin, emax):
        """
        Select observations for energy interval
 
        Parameters
        ----------
        emin : `~gammalib.GEnergy()`
            Minimum energy
        emax : `~gammalib.GEnergy()`
            Maximum energy
 
        Returns
        -------
        obs : `~gammalib.GObservations`
            Observation container
        """
        # Use ctcubemask for binned analysis
        if self._binned_mode:
 
            # Write header
            self._log_header3(gammalib.EXPLICIT, 'Filtering cube')
 
            # Select layers
            cubemask            = ctools.ctcubemask(self.obs())
            cubemask['regfile'] = 'NONE'
            cubemask['ra']      = 'UNDEFINED'
            cubemask['dec']     = 'UNDEFINED'
            cubemask['rad']     = 'UNDEFINED'
            cubemask['emin']    = emin.TeV()
            cubemask['emax']    = emax.TeV()
 
            # If chatter level is verbose and debugging is requested then
            # switch also on the debug model in ctcubemask
            if self._logVerbose() and self._logDebug():
                cubemask['debug'] = True
 
            # Select layers
            cubemask.run() 
 
            # Set new binned observation
            obs = cubemask.obs().copy()
 
        # Use ...
        elif self._onoff_mode:
 
            # Write header
            self._log_header3(gammalib.EXPLICIT, 'Filtering PHA, ARF and RMF')
 
            # Initialise observation container
            obs = gammalib.GObservations()
 
            # Loop over all input observations and select energy bins for
            # all On/Off observations
            for run in self.obs():
                if run.classname() == 'GCTAOnOffObservation':
                    obs.append(self._select_onoff_obs(run, emin, emax))
 
            # Append models
            obs.models(self.obs().models())
 
        # Use ctselect for unbinned analysis
        else:
 
            # Write header
            self._log_header3(gammalib.EXPLICIT, 'Selecting events')
 
            # Select events
            select = ctools.ctselect(self.obs())
            select['ra']   = 'UNDEFINED'
            select['dec']  = 'UNDEFINED'
            select['rad']  = 'UNDEFINED'
            select['emin'] = emin.TeV()
            select['emax'] = emax.TeV()
            select['tmin'] = 'UNDEFINED'
            select['tmax'] = 'UNDEFINED'
 
            # If chatter level is verbose and debugging is requested then
            # switch also on the debug model in ctselect
            if self._logVerbose() and self._logDebug():
                select['debug'] = True
 
            # Run ctselect
            select.run()
 
            # Retrieve observation
            obs = select.obs().copy()
 
        # Return observation container
        return obs
 
    def _fit_energy_bin(self, i):
        """
        Fit data for one energy bin
 
        Parameters
        ----------
        i : int
            Energy bin index
 
        Returns
        -------
        result : dict
            Dictionary with fit results
        """
 
        # Write header for energy bin
        self._log_header2(gammalib.EXPLICIT, 'Energy bin ' + str(i + 1))
 
        # Get energy boundaries
        emin      = self._ebounds.emin(i)
        emax      = self._ebounds.emax(i)
        elogmean  = self._ebounds.elogmean(i)
        e_scale   = elogmean.MeV() * elogmean.MeV() * gammalib.MeV2erg
 
        # Select observations for energy bin
        obs = self._select_obs(emin, emax)
 
        # Initialise dictionary
        result = {'energy':      elogmean.TeV(),
                  'energy_low':  (elogmean - emin).TeV(),
                  'energy_high': (emax - elogmean).TeV(),
                  'flux':        0.0,
                  'e2dnde':      0.0,
                  'flux_err':    0.0,
                  'TS':          0.0,
                  'ulimit':      0.0,
                  'Npred':       0.0,
                  'logL':        0.0,
                  'dloglike':    [],
                  'norm_scan':   []}
 
        # Write header for fitting
        self._log_header3(gammalib.EXPLICIT, 'Performing fit in energy bin')
 
        # Setup maximum likelihood fit
        like = ctools.ctlike(obs)
        like['edisp']    = self['edisp'].boolean()
        like['nthreads'] = 1  # Avoids OpenMP conflict
 
        # If chatter level is verbose and debugging is requested then
        # switch also on the debug model in ctlike
        if self._logVerbose() and self._logDebug():
            like['debug'] = True
 
        # Perform maximum likelihood fit
        like.run()
 
        # Write model results for explicit chatter level
        self._log_string(gammalib.EXPLICIT, str(like.obs().models()))
 
        # Continue only if log-likelihood is non-zero
        if like.obs().logL() != 0.0:
 
            # Get results
            fitted_models = like.obs().models()
            source        = fitted_models[self['srcname'].string()]
 
            # Compute delta log-likelihood
            if self['dll_sigstep'].real() > 0.0:
 
                # Extract the normalization scan values
                parname = source.spectral()[0].name()
                (norm_scan, dlogL, loglike) = self._profile_logL(like.copy(), parname, elogmean)
                result['norm_scan'] = norm_scan
                result['dloglike']  = dlogL
                result['logL']      = loglike
 
            # Extract Test Statistic value
            if self['calc_ts'].boolean():
                result['TS'] = source.ts()
 
            # Compute Npred value (only works for unbinned analysis)
            if not self._binned_mode and not self._onoff_mode:
                for observation in like.obs():
                    result['Npred'] += observation.npred(source)
 
            # Compute upper flux limit
            ulimit_value = -1.0
            if self['calc_ulim'].boolean():
 
                # Logging information
                self._log_header3(gammalib.EXPLICIT,
                                  'Computing upper limit for energy bin')
 
                # Create upper limit object  
                ulimit = ctools.ctulimit(like.obs())
                ulimit['srcname']    = self['srcname'].string()
                ulimit['eref']       = elogmean.TeV()
                ulimit['confidence'] = self['confidence'].real()
 
                # If chatter level is verbose and debugging is requested
                # then switch also on the debug model in ctulimit
                if self._logVerbose() and self._logDebug():
                    ulimit['debug'] = True
 
                # Try to run upper limit and catch exceptions
                try:
                    ulimit.run()
                    ulimit_value = ulimit.diff_ulimit()
                except:
                    self._log_string(gammalib.EXPLICIT, 'Upper limit '
                                     'calculation failed.')
                    ulimit_value = -1.0
 
                # Compute upper limit
                if ulimit_value > 0.0:
                    result['ulimit'] = ulimit_value
 
            # Compute differential flux and flux error
            fitted_flux = source.spectral().eval(elogmean)
            parvalue    = source.spectral()[0].value()
            if parvalue != 0.0:
                rel_error = source.spectral()[0].error() / parvalue
                e_flux    = fitted_flux * rel_error
            else:
                e_flux = 0.0
 
            # If the source model is a cube then multiply-in the cube
            # spectrum
            if source.spatial().classname() == 'GModelSpatialDiffuseCube':
                region       = gammalib.GSkyRegionCircle(0.0, 0.0, 180.0)
                source.spatial().mc_cone(region)
                norm         = source.spatial().spectrum().eval(elogmean)
                fitted_flux *= norm
                e_flux      *= norm
 
            # Convert differential flux and flux error to nuFnu
            result['flux']     = fitted_flux
            result['e2dnde']   = fitted_flux * e_scale
            result['flux_err'] = e_flux
 
            # Log information
            value = '%e +/- %e' % (result['e2dnde'], result['flux_err']*e_scale)
            if self['calc_ulim'].boolean() and result['ulimit'] > 0.0:
                value += ' [< %e]' % (result['ulimit']*e_scale)
            value += ' erg/cm2/s'
            if self['calc_ts'].boolean() and result['TS'] > 0.0:
                value += ' (TS = %.3f)' % (result['TS'])
            self._log_value(gammalib.TERSE, 'Bin '+str(i+1), value)
 
        # ... otherwise if logL is zero then signal that bin is
        # skipped
        else:
            value = 'Likelihood is zero. Bin is skipped.'
            self._log_value(gammalib.TERSE, 'Bin '+str(i+1), value)
 
        # Return result
        return result
 
    def _fit_energy_bins(self):
        """
        Fit model to energy bins
 
        Returns
        -------
        results : list of dict
            List of dictionaries with fit results
        """
        # Write header
        self._log_header1(gammalib.TERSE, 'Generate spectrum')
        self._log_string(gammalib.TERSE, str(self._ebounds))
 
        # Initialise results
        results = []
 
        # If more than a single thread is requested then use multiprocessing
        if self._nthreads > 1:
 
            # Compute energy bins
            args        = [(self, '_fit_energy_bin', i)
                           for i in range(self._ebounds.size())]
            poolresults = mputils.process(self._nthreads, mputils.mpfunc, args)
 
            # Construct results
            for i in range(self._ebounds.size()):
                results.append(poolresults[i][0])
                self._log_string(gammalib.TERSE, poolresults[i][1]['log'], False)
 
        # Otherwise, loop over energy bins
        else:
            for i in range(self._ebounds.size()):
 
                # Fit energy bin
                result = self._fit_energy_bin(i)
 
                # Append results
                results.append(result)
 
        # Return results
        return results
 
    def _fit_model(self):
        """
        Fit model to observations
 
        Returns
        -------
        results : list of dict
            List of dictionaries with fit results
        """
        # Write header
        self._log_header1(gammalib.TERSE, 'Generate spectrum')
 
        # Write header for fitting
        self._log_header3(gammalib.EXPLICIT, 'Performing model fit')
 
        # Perform maximum likelihood fit
        like          = ctools.ctlike(self.obs())
        like['edisp'] = self['edisp'].boolean()
        like.run()
 
        # Initialise fit results
        results = []
 
        # Extract fit results
        model    = like.obs().models()[self['srcname'].string()]
        spectrum = model.spectral()
        logL0    = like.obs().logL()
 
        # Write model results for explicit chatter level
        self._log_string(gammalib.EXPLICIT, str(like.obs().models()))
 
        # Loop over all nodes or bins
        for i in range(self._ebounds.size()):
 
            # Get energy boundaries
            emin      = self._ebounds.emin(i)
            emax      = self._ebounds.emax(i)
            elogmean  = self._ebounds.elogmean(i)
 
            # Initialise dictionary
            result = {'energy':      elogmean.TeV(),
                      'energy_low':  (elogmean - emin).TeV(),
                      'energy_high': (emax - elogmean).TeV(),
                      'flux':        0.0,
                      'e2dnde':      0.0,
                      'flux_err':    0.0,
                      'TS':          0.0,
                      'ulimit':      0.0,
                      'Npred':       0.0,
                      'logL':        0.0,
                      'dloglike':    [],
                      'norm_scan':   []}
 
            # Convert differential flux and flux error to nuFnu
            norm               = elogmean.MeV() * elogmean.MeV()  * gammalib.MeV2erg
            result['flux']     = spectrum.intensity(i)
            result['e2dnde']   = spectrum.intensity(i) * norm
            result['flux_err'] = spectrum.error(i)
 
            # Compute upper flux limit
            ulimit_value = -1.0
            parname      = 'Intensity%d' % i
 
            # Compute delta log-likelihood
            if self['dll_sigstep'].real() > 0.0:
 
                # Extract the normalization scan values
                (norm_scan, dlogL, loglike) = self._profile_logL(like.copy(),
                                                                 parname,
                                                                 elogmean)
                result['norm_scan'] = norm_scan
                result['dloglike']  = dlogL
                result['logL']      = loglike
 
            # Compute upper flux limit
            if self['calc_ulim'].boolean():
 
                # Logging information
                self._log_header3(gammalib.EXPLICIT,
                                  'Computing upper limit for node energy %f TeV' %
                                  result['energy'])
 
                # Copy observation container
                obs = like.obs().copy()
 
                # Fix intensities of all nodes
                spectral = obs.models()[self['srcname'].string()].spectral()
                for par in spectral:
                    par.fix()
 
                # Create upper limit object  
                ulimit = ctools.ctulimit(obs)
                ulimit['srcname']    = self['srcname'].string()
                ulimit['parname']    = parname
                ulimit['eref']       = elogmean.TeV()
                ulimit['confidence'] = self['confidence'].real()
                ulimit['tol']        = 1.0e-3
 
                # Try to run upper limit and catch exceptions
                try:
                    ulimit.run()
                    ulimit_value = ulimit.diff_ulimit()
                except:
                    self._log_string(gammalib.EXPLICIT, 'Upper limit '
                                     'calculation failed.')
                    ulimit_value = -1.0
 
                # Compute upper limit
                if ulimit_value > 0.0:
                    result['ulimit'] = ulimit_value
 
            # Compute TS
            if self['calc_ts'].boolean():
 
                # Copy observation container
                obs = like.obs().copy()
 
                # Set intensity of node to tiny value by scaling the value
                # by a factor 1e-8.
                par = obs.models()[self['srcname'].string()].spectral()[parname]
                par.autoscale()
                par.factor_min(1.0e-8)
                par.factor_value(1.0e-8)
                par.autoscale()
                par.fix()
 
                # Perform maximum likelihood fit
                tslike          = ctools.ctlike(obs)
                tslike['edisp'] = self['edisp'].boolean()
                tslike.run()
 
                # Store Test Statistic
                model        = tslike.obs().models()[self['srcname'].string()]
                logL1        = tslike.obs().logL()
                result['TS'] = 2.0 * (logL1 - logL0)
 
            # Log information
            value = '%e +/- %e' % (result['e2dnde'], result['flux_err']*norm)
            if self['calc_ulim'].boolean() and result['ulimit'] > 0.0:
                value += ' [< %e]' % (result['ulimit']*norm)
            value += ' erg/cm2/s'
            if self['calc_ts'].boolean() and result['TS'] > 0.0:
                value += ' (TS = %.3f)' % (result['TS'])
            self._log_value(gammalib.TERSE, 'Bin '+str(i+1), value)
 
            # Append results
            results.append(result)
 
        # Return results
        return results
 
    def _profile_logL(self, like, parname, elogmean):
        """
        Computes the delta log-likelihood profile in a single energy bin
 
        Parameters
        ----------
        like : `~ctools.ctlike()`
            ctlike fitter containing prefit model
        parname : str
            Name of the spectral parameter to be fit
        elogmean : `~gammalib.GEnergy()`
            Energy at which the model is to be evaluated
 
        Returns
        -------
        norm_scan : list
            Normalization values 
        dloglike_scan : list
            Computed loglikelihood values
        loglike: float
            Computed reference log-likelihood for dloglike_scan
        """
        # Compute number of steps
        dll_sigmax  = self['dll_sigmax'].real()
        dll_sigstep = self['dll_sigstep'].real()
        sigsteps    = int(2 * (dll_sigmax/dll_sigstep) + 1)
 
        # Setup the source model for fitting
        source   = like.obs().models()[self['srcname'].string()]
        spectral = source.spectral()
        flux_par = spectral[parname]
        norm     = flux_par.value()
        norm_err = flux_par.error()
        source.tscalc(False)
 
        # Fix all parameters in the spectral model
        if (self._method != 'NODES' and self._method != 'BINS') or \
           (not self['dll_freenodes'].boolean()):
            for par in spectral:
                par.fix()
 
        # Re-compute the log-likelihood
        like.run()
        loglike = like.obs().logL()
 
        # Store the resulting log-likelihood
        norm_scan = []
        dloglike  = []
        ref_norm = norm
        if self._method == 'SLICE':
            ref_norm = spectral.eval(elogmean)
 
        # Compute the flux values to evaluate loglike at
        log_norm = math.log10(norm)
        if (norm_err > 0.0) and (norm > norm_err):
            log_step  = log_norm - math.log10(norm-norm_err)
            log_start = log_norm - (sigsteps/2) * log_step
        else:
            # For an upper limit bin use a broad range of steps in flux
            # from [10^-24, 10^-14] or [norm, 10^-14] whichever is broader
            log_start = log_norm
            if log_start > -24.0:
                log_start = -24.0
            log_step = (-14.0 - log_start) / (sigsteps-1)
        norm_vals = [10.0 ** (log_start + i*log_step) for i in range(sigsteps)]
 
        # Loop through normalizations
        flux_par.factor_min(0.0)
        flux_par.factor_max(1e30)
        for new_norm in norm_vals:
            flux_par.value(new_norm)
 
            # Re-run the fit
            like.run()
 
            # Store dlikelihood & norm
            dloglike.append(loglike - like.obs().logL())
            if self._method == 'SLICE':
                norm_scan.append(spectral.eval(elogmean))
            else:
                norm_scan.append(new_norm)
 
        # Return
        return (norm_scan, dloglike, -loglike)
 
    def _create_fits(self, results):
        """
        Create FITS file
 
        Parameters
        ----------
        results : list of dict
            List of result dictionaries
        """
        # Create FITS table columns
        nrows        = self._ebounds.size()
        ncols        = len(results[0]['norm_scan'])
        energy       = gammalib.GFitsTableDoubleCol('e_ref', nrows)
        energy_low   = gammalib.GFitsTableDoubleCol('e_min', nrows)
        energy_high  = gammalib.GFitsTableDoubleCol('e_max', nrows)
        norm         = gammalib.GFitsTableDoubleCol('norm', nrows)
        norm_err     = gammalib.GFitsTableDoubleCol('norm_err', nrows)
        norm_ul      = gammalib.GFitsTableDoubleCol('norm_ul', nrows)
        e2dnde       = gammalib.GFitsTableDoubleCol('ref_e2dnde', nrows)
        dnde         = gammalib.GFitsTableDoubleCol('ref_dnde', nrows)
        Npred_values = gammalib.GFitsTableDoubleCol('ref_npred', nrows)
        TSvalues     = gammalib.GFitsTableDoubleCol('ts', nrows)
        loglike      = gammalib.GFitsTableDoubleCol('loglike', nrows)
        norm_scan    = gammalib.GFitsTableDoubleCol('norm_scan', nrows, ncols)
        dloglike_scan= gammalib.GFitsTableDoubleCol('dloglike_scan', nrows, ncols)
        energy.unit('TeV')
        energy_low.unit('TeV')
        energy_high.unit('TeV')
        norm.unit('')
        norm_err.unit('')
        norm_ul.unit('')
        e2dnde.unit('erg/cm2/s')
        dnde.unit('counts/MeV/cm2/s')
        Npred_values.unit('counts')
        loglike.unit('')
        norm_scan.unit('')
        dloglike_scan.unit('')
 
        # File FITS table columns
        for i, result in enumerate(results):
            energy[i]       = result['energy']
            energy_low[i]   = result['energy_low']
            energy_high[i]  = result['energy_high']
            norm[i]         = 1.0
            if result['flux'] != 0.0:
                norm_err[i] = result['flux_err'] / result['flux']
                norm_ul[i]  = result['ulimit']   / result['flux']
            else:
                norm_err[i] = 0.0
                norm_ul[i]  = 0.0
            e2dnde[i]       = result['e2dnde']
            dnde[i]         = result['flux']
            Npred_values[i] = result['Npred']
            TSvalues[i]     = result['TS']
            loglike[i]      = result['logL']
 
            # Add likelihood scan values
            for fbin in range(ncols):
                dloglike_scan[i,fbin] = result['dloglike'][fbin]
                if result['flux'] != 0.0:
                    norm_scan[i,fbin] = result['norm_scan'][fbin] / result['flux']
                else:
                    norm_scan[i,fbin] = 0.0
 
        # Initialise FITS Table with extension "SPECTRUM"
        table = gammalib.GFitsBinTable(nrows)
        table.extname('SPECTRUM')
 
        # Add Header for compatibility with gammalib.GMWLSpectrum
        table.card('INSTRUME', 'CTA', 'Name of Instrument')
        table.card('TELESCOP', 'CTA', 'Name of Telescope')
 
        # Append filled columns to fits table    
        table.append(energy)
        table.append(energy_low)
        table.append(energy_high)
        table.append(norm)
        table.append(norm_err)
        table.append(norm_ul)
        table.append(e2dnde)
        table.append(dnde)
        table.append(TSvalues)
        table.append(Npred_values)
 
        # Set keywords
        table.card('SED_TYPE', 'norm,e2dnde,dnde,npred', 'SED type')
        table.card('UL_CONF', self['confidence'].real(), 'Confidence level of upper limits')
 
        # Add the likelihood data
        if ncols > 0:
            table.card('SED_TYPE').value('likelihood,e2dnde,dnde,npred')
            table.append(loglike)
            table.append(norm_scan)
            table.append(dloglike_scan)
 
        # Stamp table
        self._stamp(table)
 
        # Add metadata keywords
        table.card('OBJECT', self['srcname'].string(), 'Source name')
        table.card('METHOD', self._method, 'Spectrum generation method')
        if self._method == 'BINS':
            table.card('BINGAMMA', self['bingamma'].real(), 'Spectral index for BINS method')
        table.card('STAT',     self['statistic'].string(), 'Optimization statistic')
        table.card('EDISP',    self['edisp'].boolean(), 'Use energy dispersion?')
        table.card('CALCULIM', self['calc_ulim'].boolean(), 'Upper limits computation')
        table.card('CALCTS',   self['calc_ts'].boolean(), 'Test statistic computation')
        table.card('FIXBKG',   self['fix_bkg'].boolean(), 'Fix background parameters')
        table.card('FIXSRCS',  self['fix_srcs'].boolean(), 'Fix other source parameters')
 
        # Create the FITS file now
        self._fits = gammalib.GFits()
        self._fits.append(table)
 
        # Return
        return
 
 
    # Public methods
    def process(self):
        """
        Process the script
        """
        # Get parameters
        self._get_parameters()
 
        # Write input observation container into logger
        self._log_observations(gammalib.NORMAL, self.obs(), 'Input observation')
 
        # Write input models into logger
        self._log_models(gammalib.VERBOSE, self.obs().models(), 'Input model')
 
        # Write spectral binning into logger
        self._log_spectral_binning()
 
        # Adjust model parameters dependent on input user parameters
        self._adjust_model_pars()
 
        # Case A: "SLICE" method
        if self._method == 'SLICE':
 
            # Fit energy bins
            results = self._fit_energy_bins()
 
        # Case B: "NODES" method
        elif self._method == 'NODES':
 
            # Replace source spectrum by nodes function
            self._set_replace_src_spectrum_by_nodes()
 
            # Write replaced models into logger
            self._log_models(gammalib.VERBOSE, self.obs().models(),
                             '"NODES" spectrum generation model')
 
            # Fit model
            results = self._fit_model()
 
        # Case C: "BINS" method
        elif self._method == 'BINS':
 
            # Replace source spectrum by bins function
            self._set_replace_src_spectrum_by_bins()
 
            # Write replaced models into logger
            self._log_models(gammalib.VERBOSE, self.obs().models(),
                             '"BINS" spectrum generation model')
 
            # Fit model
            results = self._fit_model()
 
        # Create FITS file
        self._create_fits(results)
 
        # Optionally publish spectrum
        if self['publish'].boolean():
            self.publish()
 
        # Return
        return
 
    def save(self):
        """
        Save spectrum
        """
        # Write header
        self._log_header1(gammalib.TERSE, 'Save spectrum')
 
        # Continue only if FITS file is valid
        if self._fits != None:
 
            # Get outmap parameter
            outfile = self['outfile'].filename()
 
            # Log file name
            self._log_value(gammalib.NORMAL, 'Spectrum file', outfile.url())
 
            # Save spectrum
            self._fits.saveto(outfile, self['clobber'].boolean())
 
        # Return
        return
 
    def publish(self, name=''):
        """
        Publish spectrum
 
        Parameters
        ----------
        name : str, optional
            Name of spectrum
        """
        # Write header
        self._log_header1(gammalib.TERSE, 'Publish spectrum')
 
        # Continue only if FITS file is valid
        if self._fits != None:
 
            # Continue only if spectrum is valid
            if self._fits.contains('SPECTRUM'):
 
                # Set default name is user name is empty
                if not name:
                    user_name = self._name()
                else:
                    user_name = name
 
                # Log file name
                self._log_value(gammalib.NORMAL, 'Spectrum name', user_name)
 
                # Publish spectrum
                self._fits.publish('SPECTRUM', user_name)
 
        # Return
        return
 
    def spectrum(self):
        """
        Return spectrum FITS file
 
        Returns:
            FITS file containing spectrum
        """
        # Return
        return self._fits
 
    def models(self, models):
        """
        Set model
        """
        # Copy models
        self.obs().models(models.clone())
 
        # Return
        return
 
 
# ======================== #
# Main routine entry point #
# ======================== #
if __name__ == '__main__':
 
    # Create instance of application
    app = csspec(sys.argv)
 
    # Execute application
    app.execute()