#! /usr/bin/env python
# ==========================================================================
# Display butterfly generated by ctbutterfly and spectrum generated by csspec
#
# Copyright (C) 2018-2021 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 cscripts
try:
    import matplotlib.pyplot as plt
    plt.figure()
    plt.close()
except (ImportError, RuntimeError):
    print('This script needs the "matplotlib" module')
    sys.exit()


# =========================== #
# Plot spectrum and butterfly #
# =========================== #
def plot_spectrum_butterfly(specfile, butterfile, fmt='o', color='red'):
    """
    Plot sensitivity data

    Parameters
    ----------
    specfile : str
        Name of spectrum FITS file
    butterfile : str
        Name of butterfly FITS file
    fmt : str
        Symbol format
    color : str
        Symbol color
    """
    # Read spectrum file    
    fits     = gammalib.GFits(specfile)
    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']

    # Read butterfly file
    csv = gammalib.GCsv(butterfile)

    # Initialise arrays to be filled
    energies    = []
    flux        = []
    ed_engs     = []
    eu_engs     = []
    e_flux      = []
    ul_energies = []
    ul_ed_engs  = []
    ul_eu_engs  = []
    ul_flux     = []
    butterfly_x = []
    butterfly_y = []
    line_x      = []
    line_y      = []

    # 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:
            energies.append(c_energy.real(row))
            flux.append(flx)
            ed_engs.append(c_ed.real(row))
            eu_engs.append(c_eu.real(row))
            e_flux.append(e_flx)

        # ... otherwise append upper limit
        else:
            ul_energies.append(c_energy.real(row))
            ul_flux.append(flx*c_upper.real(row))
            ul_ed_engs.append(c_ed.real(row))
            ul_eu_engs.append(c_eu.real(row))

    # Set upper limit errors
    yerr = [0.6 * x for x in ul_flux]

    # Loop over rows of the file
    nrows = csv.nrows()
    for row in range(nrows):

        # Compute upper edge of confidence band
        butterfly_x.append(csv.real(row,0)/1.0e6) # TeV
        butterfly_y.append(csv.real(row,2)*csv.real(row,0)*csv.real(row,0)*gammalib.MeV2erg)

        # Set line values
        line_x.append(csv.real(row,0)/1.0e6) # TeV
        line_y.append(csv.real(row,1)*csv.real(row,0)*csv.real(row,0)*gammalib.MeV2erg)

    # Loop over the rows backwards to compute the lower edge of the
    # confidence band
    for row in range(nrows):
        index = nrows - 1 - row
        butterfly_x.append(csv.real(index,0)/1.0e6)
        low_error = max(csv.real(index,3)*csv.real(index,0)*csv.real(index,0)*gammalib.MeV2erg, 1e-26)
        butterfly_y.append(low_error)

    # Plot the spectrum 
    plt.errorbar(energies, flux, yerr=e_flux, xerr=[ed_engs, eu_engs],
                 fmt=fmt, color=color, markeredgecolor=color)
    plt.errorbar(ul_energies, ul_flux, xerr=[ul_ed_engs, ul_eu_engs],
                 yerr=yerr, uplims=True, fmt=fmt, color=color, markeredgecolor=color)

    # Plot the butterfly
    plt.plot(line_x, line_y, color='black', ls='-')
    plt.fill(butterfly_x, butterfly_y, color='green', alpha=0.5)

    # Return
    return


# =========================== #
# Show spectrum and butterfly #
# =========================== #
def show_spectrum_butterfly():
    """
    Show spectrum and butterfly
    """
    # Create figure
    plt.figure()
    plt.loglog()
    plt.grid()

    # Set usage string
    usage = 'show_onoff_spectrum.py [-p plotfile] [-t title] [specfile] [butterfile]'

    # Set default options
    options = [{'option': '-p', 'value': ''},
               {'option': '-t', 'value': 'Cassiopeia A'}]

    # 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']
    title    = options[1]['value']

    # Set filenames
    specfile   = 'spectrum.fits'
    butterfile = 'butterfly.txt'
    if len(args) > 0:
        specfile = args[0]
    if len(args) > 1:
        butterfile = args[1]

    # Show spectrum and butterfly
    plot_spectrum_butterfly(specfile, butterfile, fmt='ro', color='red')

    # Plot labels
    plt.xlabel('Energy (TeV)')
    plt.ylabel(r'E$^2$ $\times$ dN/dE (erg cm$^{-2}$ s$^{-1}$)')

    # Set title
    plt.title(title)

    # Show figure
    if len(plotfile) > 0:
        plt.savefig(plotfile)
    else:
        plt.show()

    # Return
    return


# ======================== #
# Main routine entry point #
# ======================== #
if __name__ == '__main__':

    # Show spectrum and butterfly
    show_spectrum_butterfly()