cstsdist.py

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#! /usr/bin/env python
# ==========================================================================
# Generates the TS distribution for a particular model
#
# Copyright (C) 2011-2022 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
# 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 gammalib
import ctools
from cscripts import obsutils
from cscripts import modutils
from cscripts import ioutils
from cscripts import mputils
 
 
# ============== #
# cstsdist class #
# ============== #
class cstsdist(ctools.csobservation):
    """
    Generates Test Statistic distribution for a model
    """
 
    # Constructor
    def __init__(self, *argv):
        """
        Constructor
        """
        # Initialise application by calling the appropriate class constructor
        self._init_csobservation(self.__class__.__name__, ctools.__version__, argv)
 
        # Initialise some members
        self._srcname     = ''
        self._fits        = None
        self._log_clients = False
        self._model       = None
        self._nthreads    = 0
 
        # Return
        return
 
    # State methods por 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),
                 'srcname'     : self._srcname,
                 'fits'        : self._fits,
                 'log_clients' : self._log_clients,
                 'model'       : self._model,
                 '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._srcname     = state['srcname']
        self._fits        = state['fits']
        self._log_clients = state['log_clients']
        self._model       = state['model']
        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().size() == 0:
            self.obs(self._get_observations())
 
        # Set observation statistic
        self._set_obs_statistic(gammalib.toupper(self['statistic'].string()))
 
        # Get source name
        self._srcname = self['srcname'].string()
 
        # Set models if we have none
        if self.obs().models().size() == 0:
            self.obs().models(self['inmodel'].filename())
 
        # Query parameters
        self['edisp'].boolean()
        self['ntrials'].integer()
        self['debug'].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)
 
        # Return
        return
 
    def _sim(self, seed):
        """
        Return a simulated observation container
 
        Parameters
        ----------
        seed : int
            Random number generator seed
 
        Returns
        -------
        sim : `~gammalib.GObservations`
            Simulated observation container
        """
        # If observation is a counts cube then simulate events from the counts
        # cube model ...
        if self.obs().size() == 1 and self.obs()[0].eventtype() == 'CountsCube':
 
            # If no counts cube model exists then compute it now
            if self._model == None:
                model            = ctools.ctmodel(self.obs())
                model['debug']   = self['debug'].boolean()
                model['chatter'] = self['chatter'].integer()
                model.run()
                self._model = model.cube().copy() # Save copy for persistence
 
            # Allocate random number generator
            ran = gammalib.GRan()
 
            # Get copy of model map
            counts = self._model.counts().copy()
 
            # Randomize counts
            for i in range(counts.npix()):
                counts[i] = ran.poisson(counts[i])
 
            # Copy observations
            sim = self.obs().copy()
 
            # Set counts map
            sim[0].events().counts(counts)
 
        # ... otherwise simuate events from the observation container (works
        # only for event lists
        else:
            sim = obsutils.sim(self.obs(),
                               seed     = seed,
                               log      = self._log_clients,
                               debug    = self['debug'].boolean(),
                               nthreads = 1)
 
        # Return simulated observation
        return sim
 
    def _trial(self, seed):
        """
        Create the TS for a single trial
 
        Parameters
        ----------
        seed : int
            Random number generator seed
 
        Returns
        -------
        result : dict
            Result dictionary
        """
        # Write header
        self._log_header2(gammalib.EXPLICIT, 'Trial %d' % (seed+1))
 
        # Simulate events
        sim = self._sim(seed)
 
        # Determine number of events in simulation
        nevents = 0.0
        for run in sim:
            nevents += run.events().number()
 
        # Write simulation results
        self._log_header3(gammalib.EXPLICIT, 'Simulation')
        self._log_value(gammalib.EXPLICIT, 'Number of simulated events', nevents)
 
        # Fit model
        fit = ctools.ctlike(sim)
        fit['nthreads'] = 1  # Avoids OpenMP conflict
        fit['debug']    = self['debug'].boolean()
        fit['chatter']  = self['chatter'].integer()
        fit.run()
 
        # Get model fitting results
        logL   = fit.opt().value()
        npred  = fit.obs().npred()
        models = fit.obs().models()
        model  = models[self._srcname]
        ts     = model.ts()
 
        # Write fit results, either explicit or normal
        if self._logExplicit():
            self._log_header3(gammalib.EXPLICIT, 'Test source model fit')
            self._log_value(gammalib.EXPLICIT, 'Test statistics', ts)
            self._log_value(gammalib.EXPLICIT, 'log likelihood', logL)
            self._log_value(gammalib.EXPLICIT, 'Number of predicted events', npred)
            for model in models:
                self._log_value(gammalib.EXPLICIT, 'Model', model.name())
                for par in model:
                    self._log_string(gammalib.EXPLICIT, str(par))
        elif self._logNormal():
            prefactor = modutils.normalisation_parameter(model)
            name      = 'Trial %d' % seed
            value     = 'TS=%.3f  %s=%e +/- %e' % \
                        (ts, prefactor.name(), prefactor.value(), prefactor.error())
            self._log_value(gammalib.TERSE, name, value)
 
        # Initialise results
        colnames = []
        values   = {}
 
        # Set TS value
        colnames.append('TS')
        values['TS'] = ts
 
        # Set Nevents
        colnames.append('Nevents')
        values['Nevents'] = nevents
 
        # Set Npred
        colnames.append('Npred')
        values['Npred'] = npred
 
        # Gather free full fit parameters
        for model in models:
            model_name = model.name()
            for par in model:
                if par.is_free():
 
                    # Set parameter name
                    name = model_name + '_' + par.name()
 
                    # Append value
                    colnames.append(name)
                    values[name] = par.value()
 
                    # Append error
                    name = name+'_error'
                    colnames.append(name)
                    values[name] = par.error()
 
        # Bundle together results
        result = {'colnames': colnames, 'values': values}
 
        # Return
        return result
 
    def _create_fits(self, results):
        """
        Create FITS file from results
 
        Parameters
        ----------
        results : list of dict
            List of result dictionaries
        """
        # Gather headers for parameter columns
        headers = []
        for colname in results[0]['colnames']:
            if colname != 'TS' and colname != 'Nevents' and \
               colname != 'Npred':
                headers.append(colname)
 
        # Create FITS table columns
        nrows   = len(results)
        ts      = gammalib.GFitsTableDoubleCol('TS', nrows)
        nevents = gammalib.GFitsTableDoubleCol('NEVENTS', nrows)
        npred   = gammalib.GFitsTableDoubleCol('NPRED', nrows)
        ts.unit('')
        nevents.unit('counts')
        npred.unit('counts')
        columns = []
        for header in headers:
            name   = gammalib.toupper(header)
            column = gammalib.GFitsTableDoubleCol(name, nrows)
            column.unit('')
            columns.append(column)
 
        # Fill FITS table columns
        for i, result in enumerate(results):
            ts[i]      = result['values']['TS']
            nevents[i] = result['values']['Nevents']
            npred[i]   = result['values']['Npred']
            for k, column in enumerate(columns):
                column[i] = result['values'][headers[k]]
 
        # Initialise FITS Table with extension "TS_DISTRIBUTION"
        table = gammalib.GFitsBinTable(nrows)
        table.extname('TS_DISTRIBUTION')
 
        # Add keywors for compatibility with gammalib.GMWLSpectrum
        table.card('INSTRUME', 'CTA', 'Name of Instrument')
        table.card('TELESCOP', 'CTA', 'Name of Telescope')
 
        # Stamp header
        self._stamp(table)
 
        # Add script keywords
        table.card('NTRIALS', self['ntrials'].integer(),  'Number of trials')
        table.card('STAT',    self['statistic'].string(), 'Optimization statistic')
        table.card('EDISP',   self['edisp'].boolean(),    'Use energy dispersion?')
 
        # Append filled columns to fits table
        table.append(ts)
        table.append(nevents)
        table.append(npred)
        for column in columns:
            table.append(column)
 
        # 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()
 
        # Set test source model for this observation
        self.models(modutils.test_source(self.obs().models(), self._srcname))
 
        # Write observation into logger
        self._log_observations(gammalib.NORMAL, self.obs(), 'Input observation')
 
        # Write models into logger
        self._log_models(gammalib.NORMAL, self.obs().models(), 'Input model')
 
        # Write header
        self._log_header1(gammalib.TERSE, 'Generate TS distribution')
 
        # Get number of trials
        ntrials = self['ntrials'].integer()
 
        # Initialise results
        results  = []
 
        # If more than a single thread is requested then use multiprocessing
        if self._nthreads > 1:
            args        = [(self, '_trial', i) for i in range(ntrials)]
            poolresults = mputils.process(self._nthreads, mputils.mpfunc, args)
 
        # Continue with regular processing
        for i in range(ntrials):
 
            # If multiprocessing was used then recover results and put them
            # into the log file
            if self._nthreads > 1:
                results.append(poolresults[i][0])
                self._log_string(gammalib.TERSE, poolresults[i][1]['log'], False)
 
            # ... otherwise make a trial now
            else:
 
                # Run trial
                result = self._trial(i)
 
                # Append results
                results.append(result)
 
        # Create FITS file
        self._create_fits(results)
 
        # Return
        return
 
    def models(self, models):
        """
        Set model
 
        Parameters
        ----------
        models : `~gammalib.GModels`
            Model container
        """
        # Copy models
        self.obs().models(models)
 
        # Return
        return
 
    def save(self):
        """
        Save TS distribution FITS file
        """
        # Write header
        self._log_header1(gammalib.TERSE, 'Save TS distribution')
 
        # 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, 'TS distribution file', outfile.url())
 
            # Save TS distribution
            self._fits.saveto(outfile, self['clobber'].boolean())
 
        # Return
        return
 
    def ts_distribution(self):
        """
        Return TS distribution FITS file
 
        Returns:
            FITS file containing TS distribution
        """
        # Return
        return self._fits
 
 
# ======================== #
# Main routine entry point #
# ======================== #
if __name__ == '__main__':
 
    # Create instance of application
    app = cstsdist(sys.argv)
 
    # Execute application
    app.execute()