ctools

#! /usr/bin/env python

# ==========================================================================

# Display spectrum generated by csspec

#

# Copyright (C) 2015-2020 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

try:

    import matplotlib.pyplot as plt

    plt.figure()

    plt.close()

except (ImportError, RuntimeError):

    print('This script needs the "matplotlib" module')

    sys.exit()

try:

    import numpy as np

except ImportError:

    print('This script needs the "numpy" module')

    sys.exit()

import gammalib

import cscripts

# ===================================== #

# Extract the spectrum info from a file #

# ===================================== #

def get_spectrum_file(filename):

    """

    Extract the spectrum info from a file for plotting

    Parameters

    ----------

    filename : str

        Name of spectrum FITS file

    Returns

    -------

    spec : dict

        Python dictionary defining spectral plot parameters

    """

    # Read spectrum file    

    fits = gammalib.GFits(filename)

    # Return dictionary

    return get_spectrum_fits(fits)

# ============================================= #

# Extract the spectrum info from a GFits object #

# ============================================= #

def get_spectrum_fits(fits):

    """

    Extract the spectrum info from a GFits object

    Parameters

    ----------

    fits : `~gammalib.GFits`

        Spectral GFits object

    Returns

    -------

    spec : dict

        Python dictionary defining spectral plot parameters

    """

    # Read spectrum objects

    table    = fits.table(1)

    c_energy = table['e_ref']

    c_ed     = table['e_min']

    c_eu     = table['e_max']

    c_flux   = table['ref_e2dnde']

    c_norm   = table['norm']

    c_eflux  = table['norm_err']

    c_upper  = table['norm_ul']

    c_ts     = table['ts']

    # Initialise arrays to be filled

    spec = {

        'energies'    : [],

        'flux'        : [],

        'ed_engs'     : [],

        'eu_engs'     : [],

        'e_flux'      : [],

        'ul_energies' : [],

        'ul_ed_engs'  : [],

        'ul_eu_engs'  : [],

        'ul_flux'     : [],

        'yerr'        : [],

        'loglike'     : [],

        'engs_scan'   : [],

        'e2dnde_scan' : [],

        'dll_scan'    : []

    }

    # Determine if we can load the delta-log-likelihood profiles

    has_sedtype = table.has_card('SED_TYPE')

    load_dll    = False

    if has_sedtype:

        seds = table.card('SED_TYPE').string().split(',')

        # Load likelhood columns if present

        if seds[0] == 'likelihood':

            load_dll    = True

            c_loglike   = table['loglike']

            c_norm_scan = table['norm_scan']

            c_dll_scan  = table['dloglike_scan']

    # Loop over rows of the file

    nrows = table.nrows()

    for row in range(nrows):

        # Get Test Statistic, flux and flux error

        ts    = c_ts.real(row)

        norm  = c_norm.real(row)

        flx   = norm * c_flux.real(row)

        e_flx = flx * c_eflux.real(row)

        # 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:

            spec['energies'].append(c_energy.real(row))

            spec['flux'].append(flx)

            spec['ed_engs'].append(c_ed.real(row))

            spec['eu_engs'].append(c_eu.real(row))

            spec['e_flux'].append(e_flx)

        # ... otherwise append upper limit

        else:

            spec['ul_energies'].append(c_energy.real(row))

            spec['ul_flux'].append(flx*c_upper.real(row))

            spec['ul_ed_engs'].append(c_ed.real(row))

            spec['ul_eu_engs'].append(c_eu.real(row))

    # Load the delta log-likelihood values

    if load_dll:

        spec['e2dnde_scan'] = np.zeros((nrows, c_norm_scan.elements(0)))

        spec['dll_scan']    = np.zeros((nrows, c_norm_scan.elements(0)))

        for row in range(nrows):

            loglike = c_loglike.real(row)

            spec['loglike'].append(loglike)

            spec['engs_scan'].append(c_energy.real(row) - c_ed.real(row))

            for col in range(c_norm_scan.elements(row)):

                spec['e2dnde_scan'][row,col] = c_norm_scan.real(row,col) * c_flux.real(row)

                spec['dll_scan'][row,col] = c_dll_scan.real(row,col)

        spec['engs_scan'].append(c_energy.real(nrows-1)+c_eu.real(nrows-1))

    # Set upper limit errors

    spec['yerr'] = [0.6 * x for x in spec['ul_flux']]

    # Return dictionary

    return spec

# ========================= #

# Plot delta log-likelihood #

# ========================= #

def plot_dloglike(spec):

    """

    Plot delta log-likelihood

    Parameters

    ----------

    spec : dict

        Python dictionary defining spectral plot parameters

    """

    # Establish the bounds on the x,y plot

    ymin,ymax = plt.gca().get_ylim()

    ymin      = np.log10(ymin)

    ymax      = np.log10(ymax)

    steps     = 1000

    ebins     = len(spec['e2dnde_scan'])

    y_bounds  = np.linspace(ymin, ymax, steps+1)

    # Compute the logarithmic center of each bin

    fluxpnts = 10 ** ((y_bounds[1:]+y_bounds[:-1]) / 2.0)

    # Scale the boundaries on the grid

    y_bounds = 10 ** y_bounds

    # Interpolate the dlogLike values

    dll_hist = [[]]*ebins

    for ebin in range(ebins):

        dll_hist[ebin] = np.interp(fluxpnts, spec['e2dnde_scan'][ebin], 

                                             spec['dll_scan'][ebin])

    # Transpose the dll_hist array so x=energy, y=dnde

    dll_hist = [[row[i] for row in dll_hist] for i in range(steps)]

    # Plot the likelihood profiles

    plt.pcolormesh(np.array(spec['engs_scan']), y_bounds,

                   np.array(dll_hist), vmin=-10.0, vmax=0.0,

                   cmap='Reds', alpha=0.9)

    cbar = plt.colorbar()

    cbar.set_label(r'$\Delta$ log-likelihood')

    plt.grid()

    # Return

    return

# ============= #

# Plot spectrum #

# ============= #

def plot_spectrum(filename, plotfile):

    """

    Plot spectrum

    Parameters

    ----------

    filename : str

        Name of spectrum FITS file

    plotfile : str

        Plot file name

    """

    # Get spectrum parameters

    spec = get_spectrum_file(filename)

    # Create the plot

    plt.figure()

    plt.loglog()

    plt.grid()

    plt.xlabel('Energy (TeV)')

    plt.ylabel(r'E$^2$ $\times$ dN/dE (erg cm$^{-2}$ s$^{-1}$)')

    # Plot the spectrum

    plt.errorbar(spec['energies'], spec['flux'], 

                 yerr=spec['e_flux'], xerr=[spec['ed_engs'], spec['eu_engs']],

                 fmt='ro')

    # Plot upper limits

    if len(spec['ul_energies'])        > 0:

        plt.errorbar(spec['ul_energies'], spec['ul_flux'], yerr=spec['yerr'],

                     xerr=[spec['ul_ed_engs'], spec['ul_eu_engs']],

                     uplims=True, fmt='ro')

    # Plot log-likelihood profiles

    if len(spec['dll_scan']) > 0:

        plot_dloglike(spec)

    # Show figure

    if len(plotfile) > 0:

        plt.savefig(plotfile)

    else:

        plt.show()

    # Return

    return

# ============= #

# Show spectrum #

# ============= #

def show_spectrum():

    """

    Show spectrum

    """

    # Set usage string

    usage = 'show_spectrum.py [-p plotfile] [file]'

    # Set default options

    options = [{'option': '-p', 'value': ''}]

    # Get arguments and options from command line arguments

    args, options = cscripts.ioutils.get_args_options(options, usage)

    # Extract script parameters from options

    plotfile = options[0]['value']

    # Show spectrum

    plot_spectrum(args[0], plotfile)

    # Return

    return

# ======================== #

# Main routine entry point #

# ======================== #

if __name__ == '__main__':

    # Show spectrum

    show_spectrum()