test_python.py
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#! /usr/bin/env python
from gammalib import *
from math import *
import os
import numpy as np
import matplotlib.pyplot as plt
# ===================== #
# Analyse unbinned data #
# ===================== #
def analyse_unbinned(xmlname):
"""
Analyse unbinned LAT data.
"""
# Allocate observations
obs = GObservations()
# Load LAT observation
lat = GLATObservation()
lat.load_unbinned("data/ft1.fits", "data/ft2.fits", "data/ltcube.fits");
lat.response("P6_v3_diff","irf");
# Setup ROI covered by data
instDir = GLATInstDir()
roi = GLATRoi()
instDir.radec_deg(83.6331, 22.0145)
roi.centre(instDir)
roi.radius(7.5)
lat.roi(roi)
# Setup energy range covered by data
emin = GEnergy()
emax = GEnergy()
emin.MeV(200.0)
emax.MeV(20000.0)
lat.ebounds().append(emin, emax)
# Append LAT observation to container
obs.append(lat)
# Load models from XML file
obs.models(xmlname)
# Perform optimization
opt = GOptimizerLM()
obs.optimize(opt)
print obs
# =================== #
# Analyse binned data #
# =================== #
def analyse_binned(xmlname):
"""
Analyse binned LAT data.
"""
# Allocate observations
obs = GObservations()
# Load LAT observation
lat = GLATObservation()
lat.load_binned("data/srcmap.fits", "data/binned_expmap.fits", "data/ltcube.fits");
lat.response("P6_v3_diff","irf");
# Append LAT observation to container
obs.append(lat)
# Load models from XML file
obs.models(xmlname)
# Perform optimization
log = GLog()
log.cout(True)
opt = GOptimizerLM(log)
#opt.lambda_inc(10.0)
#opt.lambda_dec(0.1)
obs.optimize(opt)
print obs
print opt
# Plot residuals
plot_residuals(obs)
# ============= #
# Plot residual #
# ============= #
def plot_residuals(obs):
"""
Plot residuals.
"""
# Set plot styles
styles = ['b-', 'g-', 'y-', 'n-']
# Open plot
plt.figure(1)
plt.title('Residuals')
# Loop over observations
for run in obs:
# Get event cube
cube = GLATEventCube(run.events())
# Create energy axis
energy = []
ebds = cube.ebds()
for i in range(ebds.size()):
energy.append(ebds.elogmean(i).MeV())
# Create spectrum
counts = [0.0 for i in range(ebds.size())]
for bin in cube:
index = ebds.index(bin.energy())
counts[index] = counts[index] + bin.counts()
# Create error bars
error = [sqrt(c) for c in counts]
# Plot spectrum
plt.loglog(energy, counts, 'ro', label='data')
#plt.errorbar(energy, counts, error, fmt=None, ecolor='r')
# Extract models
sum_model = [0.0 for i in range(ebds.size())]
for k, m in enumerate(obs.models()):
model = [0.0 for i in range(ebds.size())]
for i in range(cube.size()):
bin = cube.pointer(i)
index = ebds.index(bin.energy())
prob = m.eval_gradients(bin, run)
model[index] = model[index] + prob * bin.size()
for i in range(ebds.size()):
sum_model[i] = sum_model[i] + model[i]
plt.loglog(energy, model, styles[k], label=m.name())
plt.loglog(energy, sum_model, 'r-', label='total')
# Put labels
plt.xlabel("Energy (MeV)")
plt.ylabel("Counts")
plt.legend(loc="lower right")
# Show residuals
plt.show()
#==========================#
# Main routine entry point #
#==========================#
if __name__ == '__main__':
"""
Perform testing.
"""
# Dump result
print
print "****************************"
print "* Python interface testing *"
print "****************************"
# Initialise success counter
tests = 0
success = 0
# Extract data from FITS file
#extract_data("data/source_model.xml")
# Analyse data
#analyse_unbinned("data/source_model.xml")
analyse_binned("data/source_model.xml") # Original
#analyse_binned("data/source_model2.xml") # Powerlaw for extragal. diffuse
#analyse_binned("data/source_model3.xml") # No Crab, Powerlaw for extragal. diffuse
#analyse_binned("data/source_model4.xml") # No Crab, no extragal. diffuse
#analyse_binned("data/source_model5.xml") # No Crab, no galactic diffuse