plt.plot(delta_ell, func_c, linestyle='solid',
color='black', linewidth=3.0)
plt.plot(delta_ell[10:], func_r, linestyle='dashed',
color='red', linewidth=3.0)
ax.set_xticks(ll)
plt.tick_params(labelsize=18)
plt.xlabel('$l$', fontsize=20)
plt.ylabel('$C_l/4\pi$', fontsize=20)
plt.show()
###################################
# Make a posterior-averaged skymap
###################################
bu.makeSkyMap(clm, lmax=LMAX)
plt.show()
################################
################################
'''
fig, ax = plt.subplots()
plt.contourf(mxx, mxy, H/np.sum(H), 50, cmap='Greys')
plt.colorbar()
plt.plot(delta_ell, func_c, linestyle='solid', color='black', linewidth=3.0)
#plt.plot(delta_ell[10:], func_l, linestyle='dashed', color='red', linewidth=3.0)
plt.plot(delta_ell[10:], func_r, linestyle='dashed', color='red', linewidth=3.0)
#fil = open('global_lmax4_upperlimits_prior.dat','w')
#for ii in range(len(func_r)):
# print>>fil, delta_ell[10:][ii], func_r[ii]
plt.xlabel('$l$', fontsize=20)
if not args.strainAnis:
plt.ylabel('$C_l/4\pi$', fontsize=20)
elif args.strainAnis:
plt.ylabel('$(A_h^4 C_l/4\pi)^{1/4}$', fontsize=20)
plt.show()
###################################
# Make a posterior-averaged skymap
###################################
if args.strainAnis:
#strainClm = np.array([clm[:,ii]*(10**(2*Agwb)) for ii in range(clm.shape[1])]).T
bu.makeSkyMap(clm,
lmax=LMAX,
cmap=newcmaps.viridis,
strain=Agwb,
psrs=positions)
else:
bu.makeSkyMap(clm, lmax=LMAX, cmap=newcmaps.viridis, psrs=positions)
plt.show()
################################
################################
'''
fig, ax = plt.subplots()
plt.contourf(mxx, mxy, H/np.sum(H), 50, cmap='Greys')
plt.colorbar()
plt.plot(delta_ell, func_c, linestyle='solid', color='black', linewidth=3.0)
#plt.plot(delta_ell[10:], func_l, linestyle='dashed', color='red', linewidth=3.0)
print "\n The ML coefficients of an l={0} search are {1}\n".format(args.LMAX,anisOptStat[0]/np.sqrt(4.0*np.pi))
print "\n The error-bars from the inverse Fisher matrix are {0}\n".format(np.sqrt(np.diag(anisOptStat[1]))/np.sqrt(4.0*np.pi))
print "\n The Fisher information is {0}\n".format(anisOptStat[2])
print "\n The ML coefficients of an l={0} search are {1}\n".format(args.LMAX,anisOptStat[0])
print "\n The full covariance matrix is {0}\n".format(anisOptStat[1])
np.save('mlcoeff_lmax{0}'.format(args.LMAX),anisOptStat[0])
np.save('invfisher_lmax{0}'.format(args.LMAX),anisOptStat[1])
psrlocs = np.loadtxt('PsrPos_SNR_{0}.txt'.format(snr_tag_ext),usecols=[1,2])
Asqr = anisOptStat[0][0]/np.sqrt(4.0*np.pi)
final_clm = np.array(anisOptStat[0]) / Asqr
bu.makeSkyMap(final_clm, lmax=args.LMAX, psrs=psrlocs)
plt.show()
'''
print "Fisher matrix singular values are {0}".format(anisOptStat[2])
plt.plot(anisOptStat[2])
plt.yscale('log')
plt.ylabel("Fisher matrix singular value",fontsize=15)
plt.show()
'''
plt.plot(anisOptStat[0]/np.sqrt(np.diag(anisOptStat[1])))
plt.xlabel("lm mode",fontsize=15)
plt.ylabel("ML value / error",fontsize=15)
plt.show()
ax.set_xticks(ll)
plt.tick_params(labelsize=18)
plt.xlabel('$l$', fontsize=20)
if not args.strainAnis:
plt.ylabel('$C_l/4\pi$', fontsize=20)
elif args.strainAnis:
plt.ylabel('$(A_h^4 C_l/4\pi)^{1/4}$', fontsize=20)
plt.show()
###################################
# Make a posterior-averaged skymap
###################################
if args.strainAnis:
#strainClm = np.array([clm[:,ii]*(10**(2*Agwb)) for ii in range(clm.shape[1])]).T
bu.makeSkyMap(clm, lmax=LMAX, cmap=newcmaps.viridis, strain=Agwb, psrs=positions)
else:
bu.makeSkyMap(clm, lmax=LMAX, cmap=newcmaps.viridis, psrs=positions)
plt.show()
################################
################################
'''
fig, ax = plt.subplots()
plt.contourf(mxx, mxy, H/np.sum(H), 50, cmap='Greys')
plt.colorbar()
plt.plot(delta_ell, func_c, linestyle='solid', color='black', linewidth=3.0)
#plt.plot(delta_ell[10:], func_l, linestyle='dashed', color='red', linewidth=3.0)
plt.plot(delta_ell[10:], func_r, linestyle='dashed', color='red', linewidth=3.0)
#fil = open('global_lmax4_upperlimits_prior.dat','w')
np.sqrt(np.diag(anisOptStat[1])) / np.sqrt(4.0 * np.pi))
print "\n The Fisher information is {0}\n".format(anisOptStat[2])
print "\n The ML coefficients of an l={0} search are {1}\n".format(
args.LMAX, anisOptStat[0])
print "\n The full covariance matrix is {0}\n".format(anisOptStat[1])
np.save('mlcoeff_lmax{0}'.format(args.LMAX), anisOptStat[0])
np.save('invfisher_lmax{0}'.format(args.LMAX), anisOptStat[1])
psrlocs = np.loadtxt('PsrPos_SNR_{0}.txt'.format(snr_tag_ext),
usecols=[1, 2])
Asqr = anisOptStat[0][0] / np.sqrt(4.0 * np.pi)
final_clm = np.array(anisOptStat[0]) / Asqr
bu.makeSkyMap(final_clm, lmax=args.LMAX, psrs=psrlocs)
plt.show()
'''
print "Fisher matrix singular values are {0}".format(anisOptStat[2])
plt.plot(anisOptStat[2])
plt.yscale('log')
plt.ylabel("Fisher matrix singular value",fontsize=15)
plt.show()
'''
plt.plot(anisOptStat[0] / np.sqrt(np.diag(anisOptStat[1])))
plt.xlabel("lm mode", fontsize=15)
plt.ylabel("ML value / error", fontsize=15)
plt.show()
