show_spectrum.py
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#! /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()