示例#1
0
if eMin<100000:

	E = (like.energies[:-1] + like.energies[1:])/2.
	# The 'energies' array are the endpoints so we take the midpoint of the bins.

	plt.figure(figsize=(9,9))
	plt.ylim((0.4,1e4))
	plt.xlim((200,300000))
	sum_model = np.zeros_like(like._srcCnts(like.sourceNames()[0]))

	for sourceName in like.sourceNames():
	   sum_model = sum_model + like._srcCnts(sourceName)
	   plt.loglog(E,like._srcCnts(sourceName),label=sourceName[1:])

	plt.loglog(E,sum_model,label='Total Model')
	plt.errorbar(E,like._Nobs(),yerr=np.sqrt(like._Nobs()), fmt='o',label='Counts')
	plt.legend(bbox_to_anchor=(1.05, 1), loc=2)
	plt.savefig('results/1.eps',format='eps', bbox_inches='tight')                       # Save figure!


	# Plot residuals
	sum_counts=sum_model                                        # Is this right? Probably not :/
	resid = (like._Nobs() - sum_counts)/sum_counts
	resid_err = (np.sqrt(like._Nobs())/sum_counts)
	plt.figure(figsize=(9,9))
	plt.xscale('log')
	plt.errorbar(E,resid,yerr=resid_err,fmt='o')
	plt.axhline(0.0,ls=':')
	plt.savefig('results/2.eps',format='eps', bbox_inches='tight')

# Get indexes and stuff
示例#2
0
E = (like.energies[:-1] + like.energies[1:]) / 2.
# The 'energies' array are the endpoints so we take the midpoint of the bins.

plt.figure(figsize=(9, 9))
plt.ylim((0.4, 1e4))
plt.xlim((200, 300000))
sum_model = np.zeros_like(like._srcCnts(like.sourceNames()[0]))

for sourceName in like.sourceNames():
    sum_model = sum_model + like._srcCnts(sourceName)
    plt.loglog(E, like._srcCnts(sourceName), label=sourceName[1:])

plt.loglog(E, sum_model, label='Total Model')
plt.errorbar(E,
             like._Nobs(),
             yerr=np.sqrt(like._Nobs()),
             fmt='o',
             label='Counts')
plt.legend(bbox_to_anchor=(1.05, 1), loc=2)
plt.savefig('results/1.eps', format='eps', bbox_inches='tight')  # Save figure!

# Plot residuals
sum_counts = sum_model  # Is this right? Probably not :/
resid = (like._Nobs() - sum_counts) / sum_counts
resid_err = (np.sqrt(like._Nobs()) / sum_counts)
plt.figure(figsize=(9, 9))
plt.xscale('log')
plt.errorbar(E, resid, yerr=resid_err, fmt='o')
plt.axhline(0.0, ls=':')
plt.savefig('results/2.eps', format='eps', bbox_inches='tight')