printDictionaryToFile(sourceDetails,'TSValues.txt')
########################################
########### Make some plots! ###########
########################################
import matplotlib.pyplot as plt
import numpy as np
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)
