N_aq_array_fr=np.array(N_aq_array_fr).swapaxes(0,1).tolist() ##Add Noies sky Corrections #N_dq_2band_array*=1./fsky*np.mean(1./bls) #N_du_2band_array*=1./fsky*np.mean(1./bls) # #N_au_array*=1./fsky*np.mean(1./bls) #N_aq_array*=1./fsky*np.mean(1./bls) plot_l=[] #factor accounts of l(l+1), fksy fraction and beam fact=ll/fsky/bls fact_n=ll/fsky for m in xrange(len(cross_2band_array)): cross_2band_array[m]=bin_llcl.bin_llcl(fact*cross_2band_array[m],bins)['llcl'] cross_dq_2band_array[m]=bin_llcl.bin_llcl(fact*cross_dq_2band_array[m],bins)['llcl'] cross_du_2band_array[m]=bin_llcl.bin_llcl(fact*cross_du_2band_array[m],bins)['llcl'] cross_2band_array_fr[m]=bin_llcl.bin_llcl(fact*cross_2band_array_fr[m],bins)['llcl'] cross_dq_2band_array_fr[m]=bin_llcl.bin_llcl(fact*cross_dq_2band_array_fr[m],bins)['llcl'] cross_du_2band_array_fr[m]=bin_llcl.bin_llcl(fact*cross_du_2band_array_fr[m],bins)['llcl'] theory_2band_array[m]=bin_llcl.bin_llcl(fact*theory_2band_array[m],bins)['llcl'] theory_dq_2band_array[m]=bin_llcl.bin_llcl(fact*theory_dq_2band_array[m],bins)['llcl'] N_au_array[m]=bin_llcl.bin_llcl(fact_n*N_au_array[m],bins)['llcl'] N_aq_array[m]=bin_llcl.bin_llcl(fact_n*N_aq_array[m],bins)['llcl']
cmb = hp.read_map("CMB_SMICA_mask.fits")
# cmbd = hp.ud_grade(cmb, nside_out=1024)
cmbd = cmb
hp.mollview(cmbd)
plt.show()
# chiminusfore = hp.read_map('total_masked_map.fits')
lemon = 1024
cl = hp.anafast(cmbd, lmax=lemon)
l = np.arange(len(cl))
ll = l * (l + 1) / (2 * np.pi)
plt.figure()
bin_cl = bin_llcl.bin_llcl(ll * cl, 25)
plt.plot(bin_cl["l_out"], bin_cl["llcl"], ".b")
plt.errorbar(bin_cl["l_out"], bin_cl["llcl"], bin_cl["std_llcl"] / 5.0, fmt=".r")
plt.show()
mult[b,bins*b +2:bins*b+bins -1 +2] = 1. #add two to account for binning operator a la Hizon 2002
q_mult[bins*b +2:bins*b+bins -1 +2,b] = 1. #add two to account for binning operator a la Hizon 2002
Pbl *= mult
Qlb *= q_mult
norm = np.dot(Pbl,Qlb)[0,0]
Pbl /= np.sqrt(norm)
Qlb /= np.sqrt(norm)
l_out = bin_llcl.bin_llcl(ll,bins)['l_out']
bcross_cls= bin_llcl.bin_llcl(ll*cross_cls,bins)
bradio = bin_llcl.bin_llcl(ll*radio_cls,bins)
bcmb_cls = bin_llcl.bin_llcl(ll*cmb_cls,bins)
#bwls = bin_llcl.bin_llcl(ll*wls,bins)
Mll = MLL.Mll(wls,l)
#Mll = np.array(Mll)
np.savez('mll_mwa.npz',mll=Mll)
#Mll = np.load('mll_mwa.npz')['mll']
#Mll = Mll[2:beam_lmax,2:beam_lmax]
#Mll = Mll.reshape(lmax,lmax)
q_mult[bins*b:bins*b+bins -1 ,b] = 1. #add two to account for binning operator a la Hizon 2002
Pbl *= mult
Qlb *= q_mult ## divide by .9 so np.dot(Pbl,Qlb) = identity
#norm = np.dot(Pbl,Qlb).diagonal()
#norm.shape = (1,norm.shape[0])
#Pbl /= np.sqrt(norm)
#Qlb /= np.sqrt(norm)
#Pbl = Pbl[:, 2:beam_lmax]
#Qlb = Pbl[2:beam_lmax, :]
l_out = bin_llcl.bin_llcl(ll,bins)['l_out']
#bcross_cls= bin_llcl.bin_llcl(ll*cross_cls,bins)
#bcmb_cls = bin_llcl.bin_llcl(ll*cmb_cls,bins)
#bwls = bin_llcl.bin_llcl(ll*wls[2:],bins)
#Mll = MLL.Mll(wls,l)
#Mll = np.array(Mll)
#np.savez('mll_chipass.npz',mll=Mll)
Mll = np.load('mll_chipass.npz')['mll']
#Mll = Mll[2:beam_lmax,2:beam_lmax]
#Mll = Mll.reshape(lmax,lmax)
#compute TOD transfer function.. Maybe
def plot_mc():
bins=[1,5,10,20,25,50]
l=np.arange(3*nside_out)
ll=l*(l+1)/(2.*np.pi)
bls=hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),3*nside_out-1)**2
for num, mask_file in enumerate(mask_array):
f=np.load('prism_simul_'+mask_name[num]+'.npz')
theory1_array_in=f['the1_in']
theory2_array_in=f['the2_in']
cross1_array_in=f['c1_in']
cross2_array_in=f['c2_in']
noise1_array_in=f['n1_in']
noise2_array_in=f['n2_in']
Ndq_array_in=f['ndq_in']
Ndu_array_in=f['ndu_in']
Nau_array_in=f['nau_in']
Naq_array_in=f['naq_in']
mask_hdu=fits.open(mask_file)
mask=mask_hdu[1].data.field(0)
mask_hdu.close()
mask=hp.reorder(mask,n2r=1)
mask=hp.ud_grade(mask,nside_out=128)
mask_bool=~mask.astype(bool)
fsky= 1. - np.sum(mask)/float(len(mask))
L=np.sqrt(fsky*4*np.pi)
dl_eff=2*np.pi/L
theory1_array_in=np.array(theory1_array_in)/fsky
theory2_array_in=np.array(theory2_array_in)/fsky
cross1_array_in=np.array(cross1_array_in)/fsky
cross2_array_in=np.array(cross2_array_in)/fsky
Ndq_array_in=np.array(Ndq_array_in)/fsky
Ndu_array_in=np.array(Ndu_array_in)/fsky
Nau_array_in=np.array(Nau_array_in)/fsky
Naq_array_in=np.array(Naq_array_in)/fsky
noise1_array_in=np.array(noise1_array_in)/fsky
noise2_array_in=np.array(noise2_array_in)/fsky
for b in bins:
N_dq=np.mean(Ndq_array_in,axis=1)
N_au=np.mean(Nau_array_in,axis=1)
delta1_in=np.sqrt(2.*abs((np.mean(cross1_array_in,axis=1).T-np.mean(noise1_array_in,axis=1).T)**2+(np.mean(cross1_array_in,axis=1).T-np.mean(noise1_array_in,axis=1).T)/2.*(N_dq+N_au)+N_dq*N_au/2.).T/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
cosmic1_in=np.sqrt(2./((2.*l+1)*np.sqrt(b**2+dl_eff**2)*fsky)*np.mean(theory1_array_in,axis=1)**2)
N_du=np.mean(Ndu_array_in,axis=1)
N_aq=np.mean(Naq_array_in,axis=1)
delta2_in=np.sqrt(2.*abs((np.mean(cross2_array_in,axis=1).T-np.mean(noise2_array_in,axis=1).T)**2+(np.mean(cross2_array_in,axis=1).T-np.mean(noise2_array_in,axis=1).T)/2.*(N_dq+N_au)+N_dq*N_au/2.).T/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
cosmic2_in=np.sqrt(2./((2*l+1)*np.sqrt(b**2+dl_eff**2)*fsky)*np.mean(theory2_array_in,axis=1)**2)
cross1_array=[[],[],[]]
cross2_array=[[],[],[]]
Ndq_array=[[],[],[]]
Ndu_array=[[],[],[]]
Nau_array=[[],[],[]]
Naq_array=[[],[],[]]
noise1_array=[[],[],[]]
noise2_array=[[],[],[]]
theory1_array=[[],[],[]]
theory2_array=[[],[],[]]
cosmic1=[[],[],[]]
cosmic2=[[],[],[]]
delta1=[[],[],[]]
delta2=[[],[],[]]
plot_l=[]
if( b != 1):
for m in xrange(len(cross1_array_in)):
for n in xrange(len(cross1_array_in[0])):
tmp_t1=bin_llcl.bin_llcl(ll*theory1_array_in[m][n]/bls,b)
tmp_t2=bin_llcl.bin_llcl(ll*theory2_array_in[m][n]/bls,b)
tmp_c1=bin_llcl.bin_llcl(ll*cross1_array_in[m][n]/bls,b)
tmp_c2=bin_llcl.bin_llcl(ll*cross2_array_in[m][n]/bls,b)
tmp_n1=bin_llcl.bin_llcl(ll*noise1_array_in[m][n]/bls,b)
tmp_n2=bin_llcl.bin_llcl(ll*noise2_array_in[m][n]/bls,b)
theory1_array[m].append(tmp_t1['llcl'])
theory2_array[m].append(tmp_t2['llcl'])
cross1_array[m].append(tmp_c1['llcl'])
cross2_array[m].append(tmp_c2['llcl'])
noise1_array[m].append(tmp_n1['llcl'])
noise2_array[m].append(tmp_n2['llcl'])
if n == len(cross1_array_in[0])-1:
plot_l=tmp_c1['l_out']
tmp_c1=bin_llcl.bin_llcl(ll*cosmic1_in[m]/bls,b)
tmp_d1=bin_llcl.bin_llcl(ll*delta1_in[m]/bls,b)
cosmic1[m]=tmp_c1['llcl']
delta1[m]=tmp_d1['llcl']
tmp_c2=bin_llcl.bin_llcl(ll*cosmic2_in[m]/bls,b)
tmp_d2=bin_llcl.bin_llcl(ll*delta2_in[m]/bls,b)
cosmic2[m]=tmp_c2['llcl']
delta2[m]=tmp_d2['llcl']
else:
plot_l=l
theory1_array=np.multiply(ll/bls,theory1_array_in)
cross1_array=np.multiply(ll/bls,cross1_array_in)
noise1_array=np.multiply(ll/bls,noise1_array_in)
theory2_array=np.multiply(ll/bls,theory2_array_in)
cross2_array=np.multiply(ll/bls,cross2_array_in)
noise2_array=np.multiply(ll/bls,noise2_array_in)
cosmic1=cosmic1_in*ll/bls
cosmic2=cosmic2_in*ll/bls
delta1=delta1_in*ll/bls
delta2=delta2_in*ll/bls
#noise1=np.mean(noise1_array,axis=1)
#noise2=np.mean(noise2_array,axis=1)
theory_array = np.add(theory1_array,theory2_array)
theory=np.mean(theory_array,axis=1)
dtheory=np.std(theory_array,axis=1,ddof=1)
cross_array = np.add(np.subtract(cross1_array,noise1_array),np.subtract(cross2_array,noise2_array))
cross=np.mean(cross_array,axis=1)
dcross=np.std(cross_array,axis=1,ddof=1)
cosmic=np.sqrt(np.array(cosmic1)**2+np.array(cosmic2)**2)
delta=np.sqrt(np.array(delta1)**2+np.array(delta2)**2)
cross=np.average(cross,weights=1./dcross**2,axis=0)
theory=np.average(theory,weights=1./dcross**2,axis=0)
dtheory=np.average(dtheory,weights=1./dcross**2,axis=0)
cosmic=np.average(cosmic,weights=1./dcross**2,axis=0)
delta=np.average(delta,weights=1./dcross**2,axis=0)
dcross=np.sqrt(np.average(dcross**2,weights=1./dcross**2,axis=0))
#theory1=np.mean(theory1_array,axis=0)
#dtheory1=np.std(theory1_array,axis=0,ddof=1)
#cross1=np.mean(cross1_array,axis=0)
#dcross1=np.std(np.subtract(cross1_array,noise1),axis=0,ddof=1)
#ipdb.set_trace()
plot_binned.plotBinned((cross)*1e12,dcross*1e12,plot_l,b,'prism_FR_simulation',title='PRISM FR Correlator',theory=theory*1e12,dtheory=dtheory*1e12,delta=delta*1e12,cosmic=cosmic*1e12)
#theory2=np.mean(theory2_array,axis=0)
#dtheory2=np.std(theory2_array,axis=0,ddof=1)
#cross2=np.mean(cross2_array,axis=0)
##delta2=np.mean(delta2_array,axis=0)
#dcross2=np.std(np.subtract(cross2_array,noise2),axis=0,ddof=1)
##ipdb.set_trace()
#plot_binned.plotBinned((cross2-noise2)*1e12,dcross2*1e12,plot_l,b,'Cross_43x95_FR_UxaQ', title='Cross 43x95 FR UxaQ',theory=theory2*1e12,dtheory=dtheory2*1e12,delta=delta2*1e12,cosmic=cosmic2*1e12)
#ipdb.set_trace()
if b == 25 :
a_scales=np.linspace(-2,4,121)
chi_array=[]
for a in a_scales:
chi_array.append(np.sum( (cross - a*theory)**2/(dcross)**2))
ind = np.argmin(chi_array)
#likelihood=np.exp(np.multiply(-1./2.,chi_array))/np.sqrt(2*np.pi)
likelihood=np.exp(np.multiply(-1./2.,chi_array))/np.sum(np.exp(np.multiply(-1./2.,chi_array))*.05)
Sig=np.sum(cross/(dcross**2))/np.sum(1./dcross**2)
Noise=np.std(np.sum(cross_array/dcross**2,axis=1)/np.sum(1./dcross**2))
Sig1=np.sum(cross*(theory/dcross)**2)/np.sum((theory/dcross)**2)
Noise1=np.std(np.sum(cross_array*(theory/dcross)**2,axis=1)/np.sum((theory/dcross)**2))
SNR=Sig/Noise
SNR1=Sig1/Noise1
Sig2=np.sum(cross/(dcross**2))/np.sum(1./dcross**2)
Noise2=np.sqrt(1./np.sum(1./dcross**2))
Sig3=np.sum(cross*(theory/dcross)**2)/np.sum((theory/dcross)**2)
Noise3=np.sqrt(np.sum(theory**2)/np.sum(theory**2/dcross**2))
SNR2=Sig2/Noise2
SNR3=Sig3/Noise3
#ipdb.set_trace()
fig,ax1=plt.subplots(1,1)
ax1.plot(a_scales,likelihood,'k.')
ax1.set_title('Faraday Rotation Correlator')
ax1.set_xlabel('Likelihood scalar')
ax1.set_ylabel('Likelihood of Correlation')
fig.savefig('FR_Correlation_Likelihood.png',format='png')
fig.savefig('FR_Correlation_Likelihood.eps',format='eps')
#ipdb.set_trace()
f=open('Maximum_likelihood.txt','w')
f.write('Maximum Likelihood: {0:2.5f}% for scale factor {1:.2f} \n'.format(float(likelihood[ind]*100),float(a_scales[ind])))
f.write('Probability of scale factor =1: {0:2.5f}% \n \n'.format(float(likelihood[np.where(a_scales ==1)])*100))
f.write('Detection Levels using Standard Deviation \n')
f.write('Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n'.format(SNR,Sig, Noise))
f.write('Weighted Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n \n'.format(SNR1,Sig1,Noise))
f.write('Detection using Theoretical Noise \n')
f.write('Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n'.format(SNR2,Sig2, Noise2))
f.write('Weighted Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n'.format(SNR3,Sig3,Noise3))
f.close()
#if b == 1 :
# xbar= np.matrix(ll[1:]*(cross-np.mean(cross))[1:]).T
# vector=np.matrix(ll[1:]*cross[1:]).T
# mu=np.matrix(ll[1:]*theory[1:]).T
# fact=len(xbar)-1
# cov=(np.dot(xbar,xbar.T)/fact).squeeze()
# ipdb.set_trace()
# likelihood=np.exp(-np.dot(np.dot((vector-mu).T,lin.inv(cov)),(vector-mu))/2. )/(np.sqrt(2*np.pi*lin.det(cov)))
# print('Likelihood of fit is #{0:.5f}'.format(likelihood[0,0]))
# f=open('FR_likelihood.txt','w')
# f.write('Likelihood of fit is #{0:.5f}'.format(likelihood[0,0]))
# f.close()
#subprocess.call('mv Maximum_likelihood.txt gal_cut_{0:0>2d}/'.format(cut), shell=True)
subprocess.call('mv *01*.png bin_01/', shell=True)
subprocess.call('mv *05*.png bin_05/', shell=True)
subprocess.call('mv *10*.png bin_10/', shell=True)
subprocess.call('mv *20*.png bin_20/', shell=True)
subprocess.call('mv *25*.png bin_25/', shell=True)
subprocess.call('mv *50*.png bin_50/', shell=True)
subprocess.call('mv *.eps eps/', shell=True)
def main():
##Parameters for Binning, Number of Runs
## Beam correction
use_beam=0
N_runs=100
bins=[1,5,10,20,25,50]
gal_cut=[00,05,10,20,30]
bls=hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),3*nside_out-1)**2
l=np.arange(3*nside_out)
ll=l*(l+1)/(2*np.pi)
map_prefix='/home/matt/Planck/data/faraday/simul_maps/'
file_prefix=map_prefix+'prism_simulated_'
alpha_file='/data/wmap/faraday_MW_realdata.fits'
#wl=np.array([299792458./(band*1e9) for band in bands])
cross1_array_in=[[],[],[]]
cross2_array_in=[[],[],[]]
Ndq_array_in=[[],[],[]]
Ndu_array_in=[[],[],[]]
Nau_array_in=[[],[],[]]
Naq_array_in=[[],[],[]]
noise1_array_in=[[],[],[]]
noise2_array_in=[[],[],[]]
theory1_array_in=[[],[],[]]
theory2_array_in=[[],[],[]]
#simulate_fields.main()
for num, mask_file in enumerate(mask_array):
print(Fore.WHITE+Back.RED+Style.BRIGHT+'Mask: '+mask_name[num]+Back.RESET+Fore.RESET+Style.RESET_ALL)
count=0
for i in [0,1,2]:
for j in [3,4,5]:
#for n in xrange(N_runs):
for run in xrange(N_runs):
print(Fore.WHITE+Back.GREEN+Style.BRIGHT+'Correlation #{:03d}'.format(run+1)+Back.RESET+Fore.RESET+Style.RESET_ALL)
print('Bands: {0:0>3.0f} and {1:0>3.0f}'.format(bands[i],bands[j]))
ttmp1,ttmp2=correlate_theory(file_prefix+'{0:0>3.0f}.fits'.format(bands[i]),file_prefix+'{0:0>3.0f}.fits'.format(bands[j]),wl[i],wl[j],alpha_file,'{0:0>3.0f}x{1:0>3.0f}'.format(bands[i],bands[j]),beam=use_beam,mask_file=mask_file)
#f=open('cl_noise_FR_{0:0>3.0f}x{1:0>3.0f}_cut{2:0>2d}_UxaQ.json'.format(bands[i],bands[j],cut),'w')
theory1_array_in[count].append(ttmp1)
theory2_array_in[count].append(ttmp2)
tmp1,tmp2,n1,n2,n3,n4=correlate_signal(file_prefix+'{0:0>3.0f}.fits'.format(bands[i]),file_prefix+'{0:0>3.0f}.fits'.format(bands[j]),wl[i],wl[j],alpha_file,'{0:0>3.0f}x{1:0>3.0f}'.format(bands[i],bands[j]),beam=use_beam,mask_file=mask_file)
ntmp1,ntmp2=correlate_noise(file_prefix+'{0:0>3.0f}.fits'.format(bands[i]),file_prefix+'{0:0>3.0f}.fits'.format(bands[j]),wl[i],wl[j],alpha_file,'{0:0>3.0f}x{1:0>3.0f}'.format(bands[i],bands[j]),beam=use_beam,mask_file=mask_file)
cross1_array_in[count].append(tmp1)
cross2_array_in[count].append(tmp2)
Ndq_array_in[count].append(n1)
Ndu_array_in[count].append(n2)
Nau_array_in[count].append(n3)
Naq_array_in[count].append(n4)
noise1_array_in[count].append(ntmp1)
noise2_array_in[count].append(ntmp2)
count+=1
np.savez('prism_simul_'+mask_name[num]+'.npz',the1_in=theory1_array_in,the2_in=theory2_array_in,c1_in=cross1_array_in,c2_in=cross2_array_in,ndq_in=Ndq_array_in,ndu_in=Ndu_array_in,nau_in=Nau_array_in,naq_in=Naq_array_in,n1_in=noise1_array_in,n2_in=noise2_array_in)
#f=open('cl_theory_FR_{0:0>3.0f}x{1:0>3.0f}_cut{2:0>2d}_QxaU.json'.format(bands[i],bands[j],cut),'w')
#json.dump(np.array(theory1_array_in).tolist(),f)
#f.close()
#f=open('cl_theory_FR_{0:0>3.0f}x{1:0>3.0f}_cut{2:0>2d}_UxaQ.json'.format(bands[i],bands[j],cut),'w')
#json.dump(np.array(theory2_array_in).tolist(),f)
#f.close()
#f=open('cl_array_FR_{0:0>3.0f}x{1:0>3.0f}_cut{2:0>2d}_QxaU.json'.format(bands[i],bands[j],cut),'w')
#json.dump(np.array(cross1_array_in).tolist(),f)
#f.close()
#f=open('cl_array_FR_{0:0>3.0f}x{1:0>3.0f}_cut{2:0>2d}_UxaQ.json'.format(bands[i],bands[j],cut),'w')
#json.dump(np.array(cross2_array_in).tolist(),f)
#f.close()
#f=open('cl_noise_FR_{0:0>3.0f}x{1:0>3.0f}_cut{2:0>2d}_QxaU.json'.format(bands[i],bands[j],cut),'w')
#json.dump(np.array(noise1_array_in).tolist(),f)
#f.close()
#json.dump(np.array(noise2_array_in).tolist(),f)
#f.close()
#f=open('cl_Nau_FR_{0:0>3.0f}x{1:0>3.0f}_cut{2:0>2d}_QxaU.json'.format(bands[i],bands[j],cut),'w')
#json.dump(np.array(Nau_array_in).tolist(),f)
#f.close()
#f=open('cl_Ndq_FR_{0:0>3.0f}x{1:0>3.0f}_cut{2:0>2d}_QxaU.json'.format(bands[i],bands[j],cut),'w')
#json.dump(np.array(Ndq_array_in).tolist(),f)
#f.close()
#f=open('cl_Naq_FR_{0:0>3.0f}x{1:0>3.0f}_cut{2:0>2d}_UxaQ.json'.format(bands[i],bands[j],cut),'w')
#json.dump(np.array(Naq_array_in).tolist(),f)
#f.close()
#f=open('cl_Ndu_FR_{0:0>3.0f}x{1:0>3.0f}_cut{2:0>2d}_UxaQ.json'.format(bands[i],bands[j],cut),'w')
#json.dump(np.array(Ndu_array_in).tolist(),f)
#f.close()
#fsky= 1. - np.sin(cut*np.pi/180.)
#L=np.sqrt(fsky*4*np.pi)
#dl_eff=2*np.pi/L
mask_hdu=fits.open(mask_file)
mask=mask_hdu[1].data.field(0)
mask_hdu.close()
mask=hp.reorder(mask,n2r=1)
mask=hp.ud_grade(mask,nside_out=128)
mask_bool=~mask.astype(bool)
fsky= 1. - np.sum(mask)/float(len(mask))
L=np.sqrt(fsky*4*np.pi)
dl_eff=2*np.pi/L
theory1_array_in=np.array(theory1_array_in)/fsky
theory2_array_in=np.array(theory2_array_in)/fsky
cross1_array_in=np.array(cross1_array_in)/fsky
cross2_array_in=np.array(cross2_array_in)/fsky
Ndq_array_in=np.array(Ndq_array_in)/fsky
Ndu_array_in=np.array(Ndu_array_in)/fsky
Nau_array_in=np.array(Nau_array_in)/fsky
Naq_array_in=np.array(Naq_array_in)/fsky
noise1_array_in=np.array(noise1_array_in)/fsky
noise2_array_in=np.array(noise2_array_in)/fsky
for b in bins:
N_dq=np.mean(Ndq_array_in,axis=1)
N_au=np.mean(Nau_array_in,axis=1)
delta1_in=np.sqrt(2.*abs((np.mean(cross1_array_in,axis=1).T-np.mean(noise1_array_in,axis=1).T)**2+(np.mean(cross1_array_in,axis=1).T-np.mean(noise1_array_in,axis=1).T)/2.*(N_dq+N_au)+N_dq*N_au/2.).T/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
cosmic1_in=np.sqrt(2./((2.*l+1)*np.sqrt(b**2+dl_eff**2)*fsky)*np.mean(theory1_array_in,axis=1)**2)
N_du=np.mean(Ndu_array_in,axis=1)
N_aq=np.mean(Naq_array_in,axis=1)
delta2_in=np.sqrt(2.*abs((np.mean(cross2_array_in,axis=1).T-np.mean(noise2_array_in,axis=1).T)**2+(np.mean(cross2_array_in,axis=1).T-np.mean(noise2_array_in,axis=1).T)/2.*(N_dq+N_au)+N_dq*N_au/2.).T/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
cosmic2_in=np.sqrt(2./((2*l+1)*np.sqrt(b**2+dl_eff**2)*fsky)*np.mean(theory2_array_in,axis=1)**2)
cross1_array=[[],[],[]]
cross2_array=[[],[],[]]
Ndq_array=[[],[],[]]
Ndu_array=[[],[],[]]
Nau_array=[[],[],[]]
Naq_array=[[],[],[]]
noise1_array=[[],[],[]]
noise2_array=[[],[],[]]
theory1_array=[[],[],[]]
theory2_array=[[],[],[]]
cosmic1=[[],[],[]]
cosmic2=[[],[],[]]
delta1=[[],[],[]]
delta2=[[],[],[]]
plot_l=[]
if( b != 1):
for m in xrange(len(cross1_array_in)):
for n in xrange(len(cross1_array_in[0])):
tmp_t1=bin_llcl.bin_llcl(ll*theory1_array_in[m][n]/bls,b)
tmp_t2=bin_llcl.bin_llcl(ll*theory2_array_in[m][n]/bls,b)
tmp_c1=bin_llcl.bin_llcl(ll*cross1_array_in[m][n]/bls,b)
tmp_c2=bin_llcl.bin_llcl(ll*cross2_array_in[m][n]/bls,b)
tmp_n1=bin_llcl.bin_llcl(ll*noise1_array_in[m][n]/bls,b)
tmp_n2=bin_llcl.bin_llcl(ll*noise2_array_in[m][n]/bls,b)
theory1_array[m].append(tmp_t1['llcl'])
theory2_array[m].append(tmp_t2['llcl'])
cross1_array[m].append(tmp_c1['llcl'])
cross2_array[m].append(tmp_c2['llcl'])
noise1_array[m].append(tmp_n1['llcl'])
noise2_array[m].append(tmp_n2['llcl'])
if n == len(cross1_array_in[0])-1:
plot_l=tmp_c1['l_out']
tmp_c1=bin_llcl.bin_llcl(ll*cosmic1_in[m]/bls,b)
tmp_d1=bin_llcl.bin_llcl(ll*delta1_in[m]/bls,b)
cosmic1[m]=tmp_c1['llcl']
delta1[m]=tmp_d1['llcl']
tmp_c2=bin_llcl.bin_llcl(ll*cosmic2_in[m]/bls,b)
tmp_d2=bin_llcl.bin_llcl(ll*delta2_in[m]/bls,b)
cosmic2[m]=tmp_c2['llcl']
delta2[m]=tmp_d2['llcl']
else:
plot_l=l
theory1_array=np.multiply(ll/bls,theory1_array_in)
cross1_array=np.multiply(ll/bls,cross1_array_in)
noise1_array=np.multiply(ll/bls,noise1_array_in)
theory2_array=np.multiply(ll/bls,theory2_array_in)
cross2_array=np.multiply(ll/bls,cross2_array_in)
noise2_array=np.multiply(ll/bls,noise2_array_in)
cosmic1=cosmic1_in*ll/bls
cosmic2=cosmic2_in*ll/bls
delta1=delta1_in*ll/bls
delta2=delta2_in*ll/bls
#noise1=np.mean(noise1_array,axis=1)
#noise2=np.mean(noise2_array,axis=1)
theory_array = np.add(theory1_array,theory2_array)
theory=np.mean(theory_array,axis=1)
dtheory=np.std(theory_array,axis=1,ddof=1)
cross_array = np.add(np.subtract(cross1_array,noise1_array),np.subtract(cross2_array,noise2_array))
cross=np.mean(cross_array,axis=1)
dcross=np.std(cross_array,axis=1,ddof=1)
cosmic=np.sqrt(np.array(cosmic1)**2+np.array(cosmic2)**2)
delta=np.sqrt(np.array(delta1)**2+np.array(delta2)**2)
cross=np.average(cross,weights=1./dcross**2,axis=0)
theory=np.average(theory,weights=1./dcross**2,axis=0)
dtheory=np.average(dtheory,weights=1./dcross**2,axis=0)
cosmic=np.average(cosmic,weights=1./dcross**2,axis=0)
delta=np.average(delta,weights=1./dcross**2,axis=0)
dcross=np.sqrt(np.average(dcross**2,weights=1./dcross**2,axis=0))
#theory1=np.mean(theory1_array,axis=0)
#dtheory1=np.std(theory1_array,axis=0,ddof=1)
#cross1=np.mean(cross1_array,axis=0)
#dcross1=np.std(np.subtract(cross1_array,noise1),axis=0,ddof=1)
#ipdb.set_trace()
plot_binned.plotBinned((cross)*1e12,dcross*1e12,plot_l,b,'prism_FR_simulation',title='PRISM FR Correlator',theory=theory*1e12,dtheory=dtheory*1e12,delta=delta*1e12,cosmic=cosmic*1e12)
#theory2=np.mean(theory2_array,axis=0)
#dtheory2=np.std(theory2_array,axis=0,ddof=1)
#cross2=np.mean(cross2_array,axis=0)
##delta2=np.mean(delta2_array,axis=0)
#dcross2=np.std(np.subtract(cross2_array,noise2),axis=0,ddof=1)
##ipdb.set_trace()
#plot_binned.plotBinned((cross2-noise2)*1e12,dcross2*1e12,plot_l,b,'Cross_43x95_FR_UxaQ', title='Cross 43x95 FR UxaQ',theory=theory2*1e12,dtheory=dtheory2*1e12,delta=delta2*1e12,cosmic=cosmic2*1e12)
#ipdb.set_trace()
if b == 25 :
a_scales=np.linspace(-2,4,121)
chi_array=[]
for a in a_scales:
chi_array.append(np.sum( (cross - a*theory)**2/(dcross)**2))
ind = np.argmin(chi_array)
#likelihood=np.exp(np.multiply(-1./2.,chi_array))/np.sqrt(2*np.pi)
likelihood=np.exp(np.multiply(-1./2.,chi_array))/np.sum(np.exp(np.multiply(-1./2.,chi_array))*.05)
Sig=np.sum(cross/(dcross**2))/np.sum(1./dcross**2)
Noise=np.std(np.sum(cross_array/dcross**2,axis=1)/np.sum(1./dcross**2))
Sig1=np.sum(cross*(theory/dcross)**2)/np.sum((theory/dcross)**2)
Noise1=np.std(np.sum(cross_array*(theory/dcross)**2,axis=1)/np.sum((theory/dcross)**2))
SNR=Sig/Noise
SNR1=Sig1/Noise1
Sig2=np.sum(cross/(dcross**2))/np.sum(1./dcross**2)
Noise2=np.sqrt(1./np.sum(1./dcross**2))
Sig3=np.sum(cross*(theory/dcross)**2)/np.sum((theory/dcross)**2)
Noise3=np.sqrt(np.sum(theory**2)/np.sum(theory**2/dcross**2))
SNR2=Sig2/Noise2
SNR3=Sig3/Noise3
#ipdb.set_trace()
fig,ax1=plt.subplots(1,1)
ax1.plot(a_scales,likelihood,'k.')
ax1.set_title('Faraday Rotation Correlator')
ax1.set_xlabel('Likelihood scalar')
ax1.set_ylabel('Likelihood of Correlation')
fig.savefig('FR_Correlation_Likelihood.png',format='png')
fig.savefig('FR_Correlation_Likelihood.eps',format='eps')
#ipdb.set_trace()
f=open('Maximum_likelihood.txt','w')
f.write('Maximum Likelihood: {0:2.5f}% for scale factor {1:.2f} \n'.format(float(likelihood[ind]*100),float(a_scales[ind])))
f.write('Probability of scale factor =1: {0:2.5f}% \n \n'.format(float(likelihood[np.where(a_scales ==1)])*100))
f.write('Detection Levels using Standard Deviation \n')
f.write('Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n'.format(SNR,Sig, Noise))
f.write('Weighted Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n \n'.format(SNR1,Sig1,Noise))
f.write('Detection using Theoretical Noise \n')
f.write('Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n'.format(SNR2,Sig2, Noise2))
f.write('Weighted Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n'.format(SNR3,Sig3,Noise3))
f.close()
#if b == 1 :
# xbar= np.matrix(ll[1:]*(cross-np.mean(cross))[1:]).T
# vector=np.matrix(ll[1:]*cross[1:]).T
# mu=np.matrix(ll[1:]*theory[1:]).T
# fact=len(xbar)-1
# cov=(np.dot(xbar,xbar.T)/fact).squeeze()
# ipdb.set_trace()
# likelihood=np.exp(-np.dot(np.dot((vector-mu).T,lin.inv(cov)),(vector-mu))/2. )/(np.sqrt(2*np.pi*lin.det(cov)))
# print('Likelihood of fit is #{0:.5f}'.format(likelihood[0,0]))
# f=open('FR_likelihood.txt','w')
# f.write('Likelihood of fit is #{0:.5f}'.format(likelihood[0,0]))
# f.close()
#subprocess.call('mv Maximum_likelihood.txt gal_cut_{0:0>2d}/'.format(cut), shell=True)
subprocess.call('mv *01*.png bin_01/', shell=True)
subprocess.call('mv *05*.png bin_05/', shell=True)
subprocess.call('mv *10*.png bin_10/', shell=True)
subprocess.call('mv *20*.png bin_20/', shell=True)
subprocess.call('mv *25*.png bin_25/', shell=True)
subprocess.call('mv *50*.png bin_50/', shell=True)
subprocess.call('mv *.eps eps/', shell=True)
def main():
##Parameters for Binning, Number of Runs
## Beam correction
use_beam=0
# bls=hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),383)**2
#bls=hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),3*nside_out-1)**2
bls=(hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),3*nside_out-1)*hp.pixwin(nside_out)[:3*nside_out])**2
N_runs=500
bins=[1,5,10,20,50]
map_prefix='/home/matt/quiet/quiet_maps/'
i_file=map_prefix+'quiet_simulated_43.1'
j_file=map_prefix+'quiet_simulated_94.5'
alpha_file='/data/wmap/faraday_MW_realdata.fits'
bands=[43.1,94.5]
names=['43','95']
wl=np.array([299792458./(band*1e9) for band in bands])
cross1_array_in=[]
cross2_array_in=[]
Ndq_array_in=[]
Ndu_array_in=[]
Nau_array_in=[]
Naq_array_in=[]
noise1_array_in=[]
noise2_array_in=[]
theory1_array_in=[]
theory2_array_in=[]
#simulate_fields.main()
ttmp1,ttmp2=faraday_theory_quiet(i_file+'.fits',j_file+'.fits',wl[0],wl[1],alpha_file,names[0]+'x'+names[1],beam=use_beam)
theory1_array_in.append(ttmp1)
theory2_array_in.append(ttmp2)
#for n in xrange(N_runs):
for i in xrange(N_runs):
print(Fore.WHITE+Back.GREEN+Style.BRIGHT+'Correlation #{:03d}'.format(i+1)+Back.RESET+Fore.RESET+Style.RESET_ALL)
tmp1,tmp2,n1,n2,n3,n4=faraday_correlate_quiet(i_file+'.fits',j_file+'.fits',wl[0],wl[1],alpha_file,names[0]+'x'+names[1],beam=use_beam)
# ntmp1,ntmp2=faraday_noise_quiet(i_file+'.fits',j_file+'.fits',wl[0],wl[1],alpha_file,names[0]+'x'+names[1],beam=use_beam)
cross1_array_in.append(tmp1)
cross2_array_in.append(tmp2)
Ndq_array_in.append(n1)
Ndu_array_in.append(n2)
Nau_array_in.append(n3)
Naq_array_in.append(n4)
# noise1_array_in.append(ntmp1)
# noise2_array_in.append(ntmp2)
f=open('cl_theory_FR_QxaU.json','w')
json.dump(np.array(theory1_array_in).tolist(),f)
f.close()
f=open('cl_theory_FR_UxaQ.json','w')
json.dump(np.array(theory2_array_in).tolist(),f)
f.close()
theory1=np.mean(theory1_array_in,axis=0)
theory2=np.mean(theory2_array_in,axis=0)
hp.write_cl('cl_theory_FR_QxaU.fits',theory1)
hp.write_cl('cl_theory_FR_UxaQ.fits',theory2)
#f=open('cl_theory_FR_QxaU.json','r')
#theory1_array=json.load(f)
#f.close()
#f=open('cl_theory_FR_UxaQ.json','r')
#theory2_array=json.load(f)
#f.close()
f=open('cl_array_FR_QxaU.json','w')
json.dump(np.array(cross1_array_in).tolist(),f)
f.close()
f=open('cl_array_FR_UxaQ.json','w')
json.dump(np.array(cross2_array_in).tolist(),f)
f.close()
f=open('cl_Ndq_FR_QxaU.json','w')
json.dump(np.array(Ndq_array_in).tolist(),f)
f.close()
f=open('cl_Ndu_FR_UxaQ.json','w')
json.dump(np.array(Ndu_array_in).tolist(),f)
f.close()
f=open('cl_Nau_FR_QxaU.json','w')
json.dump(np.array(Nau_array_in).tolist(),f)
f.close()
f=open('cl_Naq_FR_UxaQ.json','w')
json.dump(np.array(Naq_array_in).tolist(),f)
f.close()
#f=open('cl_noise_FR_QxaU.json','w')
#json.dump(np.array(noise1_array_in).tolist(),f)
#f.close()
#f=open('cl_noise_FR_UxaQ.json','w')
#json.dump(np.array(noise2_array_in).tolist(),f)
#f.close()
bins=[1,5,10,20,25,50]
fsky=225.*(np.pi/180.)**2/(4*np.pi)
l=np.arange(len(cross1_array_in[0]))
ll=l*(l+1)/(2*np.pi)
L=np.sqrt(fsky*4*np.pi)
dl_eff=2*np.pi/L
theory1_array_in=np.array(theory1_array_in)/(fsky*bls)
theory2_array_in=np.array(theory2_array_in)/(fsky*bls)
cross1_array_in=np.array(cross1_array_in)/(fsky*bls)
cross2_array_in=np.array(cross2_array_in)/(fsky*bls)
Ndq_array_in=np.array(Ndq_array_in)/(fsky)
Ndu_array_in=np.array(Ndu_array_in)/(fsky)
Nau_array_in=np.array(Nau_array_in)/(fsky)
Naq_array_in=np.array(Naq_array_in)/(fsky)
#noise1_array_in=np.array(noise1_array_in)/(fsky*bls)
#noise2_array_in=np.array(noise2_array_in)/(fsky*bls)
Ndq_array_in.shape += (1,)
Ndu_array_in.shape += (1,)
Nau_array_in.shape += (1,)
Naq_array_in.shape += (1,)
for b in bins:
theory_cls=hp.read_cl('/home/matt/Planck/data/faraday/correlation/fr_theory_cl.fits')
# N_dq=np.mean(Ndq_array_in)
# N_au=np.mean(Nau_array_in)
# #delta1=np.sqrt(2.*abs((np.mean(cross1_array_in,axis=0)-np.mean(noise1_array_in,axis=0))**2+(np.mean(cross1_array_in,axis=0)-np.mean(noise1_array_in,axis=0))/2.*(N_dq+N_au)+N_dq*N_au/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
# delta1=np.sqrt(2.*((np.mean(theory1_array_in,axis=0))**2+(np.mean(theory1_array_in,axis=0))/2.*(N_dq+N_au)+N_dq*N_au/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
#
cosmic1=np.sqrt(2./((2.*l+1)*np.sqrt(b**2+dl_eff**2)*fsky)*np.mean(theory1_array_in,axis=0)**2)
# N_du=np.mean(Ndu_array_in)
# N_aq=np.mean(Naq_array_in)
# #delta2=np.sqrt(2.*abs((np.mean(cross2_array_in,axis=0)-np.mean(noise2_array_in,axis=0))**2+(np.mean(cross2_array_in,axis=0)-np.mean(noise2_array_in,axis=0))/2.*(N_dq+N_au)+N_dq*N_au/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
# delta2=np.sqrt(2.*((np.mean(theory2_array_in,axis=0))**2+(np.mean(theory2_array_in,axis=0))/2.*(N_du+N_aq)+N_du*N_aq/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
cosmic2=np.sqrt(2./((2*l+1)*np.sqrt(b**2+dl_eff**2)*fsky)*np.mean(theory2_array_in,axis=0)**2)
theory1_array=[]
theory2_array=[]
cross1_array=[]
cross2_array=[]
# noise1_array=[]
# noise2_array=[]
Ndq_array=[]
Ndu_array=[]
Nau_array=[]
Naq_array=[]
plot_l=[]
if( b != 1):
tmp_t1=bin_llcl.bin_llcl(ll*theory1_array_in,b)
tmp_t2=bin_llcl.bin_llcl(ll*theory2_array_in,b)
tmp_c1=bin_llcl.bin_llcl(ll*cross1_array_in,b)
tmp_c2=bin_llcl.bin_llcl(ll*cross2_array_in,b)
# tmp_n1=bin_llcl.bin_llcl(ll*noise1_array_in,b)
# tmp_n2=bin_llcl.bin_llcl(ll*noise2_array_in,b)
theory1_array=tmp_t1['llcl']
theory2_array=tmp_t2['llcl']
theory1_array.shape += (1,)
theory2_array.shape += (1,)
theory1_array=theory1_array.T
theory2_array=theory2_array.T
plot_l= tmp_t1['l_out']
cross1_array=tmp_c1['llcl']
cross2_array=tmp_c2['llcl']
# noise1_array=tmp_n1['llcl']
# noise2_array=tmp_n2['llcl']
Ndq_array=bin_llcl.bin_llcl(ll*Ndq_array_in,b)['llcl']
Ndu_array=bin_llcl.bin_llcl(ll*Ndu_array_in,b)['llcl']
Naq_array=bin_llcl.bin_llcl(ll*Naq_array_in,b)['llcl']
Nau_array=bin_llcl.bin_llcl(ll*Nau_array_in,b)['llcl']
tmp_c1=bin_llcl.bin_llcl((ll*cosmic1)**2,b)
#tmp_d1=bin_llcl.bin_llcl((ll*delta1)**2,b)
cosmic1=np.sqrt(tmp_c1['llcl'])
#delta1=np.sqrt(tmp_d1['llcl'])
tmp_c2=bin_llcl.bin_llcl((ll*cosmic2)**2,b)
#tmp_d2=bin_llcl.bin_llcl((ll*delta2)**2,b)
cosmic2=np.sqrt(tmp_c2['llcl'])
#delta2=np.sqrt(tmp_d2['llcl'])
t_tmp=bin_llcl.bin_llcl(ll*theory_cls,b)
theory_cls=t_tmp['llcl']
else:
plot_l=l
theory1_array=np.multiply(ll,theory1_array_in)
cross1_array=np.multiply(ll,cross1_array_in)
# noise1_array=np.multiply(ll,noise1_array_in)
theory2_array=np.multiply(ll,theory2_array_in)
cross2_array=np.multiply(ll,cross2_array_in)
# noise2_array=np.multiply(ll,noise2_array_in)
cosmic1*=ll
cosmic2*=ll
#delta1*=ll
#delta2*=ll
Ndq_array=np.multiply(ll,Ndq_array_in)
Ndu_array=np.multiply(ll,Ndu_array_in)
Naq_array=np.multiply(ll,Naq_array_in)
Nau_array=np.multiply(ll,Nau_array_in)
theory_cls*=ll
#ipdb.set_trace()
bad=np.where(plot_l < 24)
N_dq=np.mean(Ndq_array,axis=0)
N_du=np.mean(Ndu_array,axis=0)
N_aq=np.mean(Naq_array,axis=0)
N_au=np.mean(Nau_array,axis=0)
#noise1=np.mean(noise1_array,axis=0)
#noise2=np.mean(noise2_array,axis=0)
theory1=np.mean(theory1_array,axis=0)
theory2=np.mean(theory1_array,axis=0)
theory_array = np.add(theory1_array,theory2_array)
theory=np.mean(theory_array,axis=0)
#dtheory=np.sqrt(np.var(theory1_array,ddof=1) + np.var(theory2_array,ddof=1))
#cross_array = np.add(np.subtract(cross1_array,noise1),np.subtract(cross2_array,noise2))
cross_array = np.add(cross1_array,cross2_array)
cross=np.mean(cross_array,axis=0)
#dcross=np.std(cross_array,axis=0,ddof=1)
dcross=np.sqrt( ( np.var(cross1_array,axis=0,ddof=1) + np.var(cross2_array,axis=0,ddof=1)))
cosmic=np.sqrt(cosmic1**2+cosmic2**2)
delta1=np.sqrt(2./((2*plot_l+1)*fsky*np.sqrt(b**2+dl_eff**2))*(theory1**2 + theory1*(N_dq+N_au)/2. + N_dq*N_au/2.))
delta2=np.sqrt(2./((2*plot_l+1)*fsky*np.sqrt(b**2+dl_eff**2))*(theory2**2 + theory2*(N_du+N_aq)/2. + N_du*N_aq/2.))
delta=np.sqrt(delta1**2+delta2**2)
#cosmic=np.abs(theory_cls)*np.sqrt(2./((2*plot_l+1)*fsky*np.sqrt(dl_eff**2+b**2)))
#theory1=np.mean(theory1_array,axis=0)
#dtheory1=np.std(theory1_array,axis=0,ddof=1)
#cross1=np.mean(cross1_array,axis=0)
#dcross1=np.std(np.subtract(cross1_array,noise1),axis=0,ddof=1)
#ipdb.set_trace()
plot_binned.plotBinned((cross)*1e12,dcross*1e12,plot_l,b,'Cross_43x95_FR', title='QUIET FR Correlator',theory=theory*1e12,delta=delta*1e12,cosmic=cosmic*1e12)
#theory2=np.mean(theory2_array,axis=0)
#dtheory2=np.std(theory2_array,axis=0,ddof=1)
#cross2=np.mean(cross2_array,axis=0)
##delta2=np.mean(delta2_array,axis=0)
#dcross2=np.std(np.subtract(cross2_array,noise2),axis=0,ddof=1)
##ipdb.set_trace()
#plot_binned.plotBinned((cross2-noise2)*1e12,dcross2*1e12,plot_l,b,'Cross_43x95_FR_UxaQ', title='Cross 43x95 FR UxaQ',theory=theory2*1e12,dtheory=dtheory2*1e12,delta=delta2*1e12,cosmic=cosmic2*1e12)
#ipdb.set_trace()
if b == 25 :
good_l=np.logical_and(plot_l <= 200,plot_l >25)
likelihood(cross[good_l],delta[good_l],theory[good_l],'field1','c2bfr')
#if b == 1 :
# xbar= np.matrix(ll[1:]*(cross-np.mean(cross))[1:]).T
# vector=np.matrix(ll[1:]*cross[1:]).T
# mu=np.matrix(ll[1:]*theory[1:]).T
# fact=len(xbar)-1
# cov=(np.dot(xbar,xbar.T)/fact).squeeze()
## ipdb.set_trace()
# U,S,V =np.linalg.svd(cov)
# _cov= np.einsum('ij,j,jk', V.T,1./S,U.T)
# likelhd=np.exp(-np.dot(np.dot((vector-mu).T,_cov),(vector-mu))/2. )/(np.sqrt(2*np.pi*np.prod(S)))
## print('Likelihood of fit is #{0:.5f}'.format(likelihood[0,0]))
# f=open('FR_likelihood.txt','w')
# f.write('Likelihood of fit is #{0:.5f}'.format(likelhd[0,0]))
# f.close()
subprocess.call('mv *01*.png bin_01/', shell=True)
subprocess.call('mv *05*.png bin_05/', shell=True)
subprocess.call('mv *10*.png bin_10/', shell=True)
subprocess.call('mv *20*.png bin_20/', shell=True)
subprocess.call('mv *25*.png bin_25/', shell=True)
subprocess.call('mv *50*.png bin_50/', shell=True)
subprocess.call('mv *.eps eps/', shell=True)
def plot_mc():
f=open('cl_theory_FR_QxaU.json','r')
theory1_array_in=json.load(f)
f.close()
f=open('cl_theory_FR_UxaQ.json','r')
theory2_array_in=json.load(f)
f.close()
f=open('cl_array_FR_QxaU.json','r')
cross1_array_in=json.load(f)
f.close()
f=open('cl_array_FR_UxaQ.json','r')
cross2_array_in=json.load(f)
f.close()
# f=open('cl_noise_FR_QxaU.json','r')
# noise1_array_in=json.load(f)
# f.close()
# f=open('cl_noise_FR_UxaQ.json','r')
# noise2_array_in=json.load(f)
# f.close()
f=open('cl_Nau_FR_QxaU.json','r')
Nau_array_in=json.load(f)
f.close()
f=open('cl_Ndq_FR_QxaU.json','r')
Ndq_array_in=json.load(f)
f.close()
f=open('cl_Naq_FR_UxaQ.json','r')
Naq_array_in=json.load(f)
f.close()
f=open('cl_Ndu_FR_UxaQ.json','r')
Ndu_array_in=json.load(f)
f.close()
bins=[1,5,10,20,25,50]
N_runs=500
# bls=hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),383)**2
#bls=hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),3*nside_out-1)**2
bls=(hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),3*nside_out-1)*hp.pixwin(nside_out)[:3*nside_out])**2
#bls=np.repeat(1,3*nside_out)
fsky=225.*(np.pi/180.)**2/(4*np.pi)
l=np.arange(len(cross1_array_in[0]))
ll=l*(l+1)/(2*np.pi)
L=np.sqrt(fsky*4*np.pi)
dl_eff=2*np.pi/L
theory1_array_in=np.array(theory1_array_in)/(fsky*bls)
theory2_array_in=np.array(theory2_array_in)/(fsky*bls)
cross1_array_in=np.array(cross1_array_in)/(fsky*bls)
cross2_array_in=np.array(cross2_array_in)/(fsky*bls)
Ndq_array_in=np.array(Ndq_array_in)/(fsky)
Ndu_array_in=np.array(Ndu_array_in)/(fsky)
Nau_array_in=np.array(Nau_array_in)/(fsky)
Naq_array_in=np.array(Naq_array_in)/(fsky)
#noise1_array_in=np.array(noise1_array_in)/(fsky*bls)
#noise2_array_in=np.array(noise2_array_in)/(fsky*bls)
Ndq_array_in.shape += (1,)
Ndu_array_in.shape += (1,)
Nau_array_in.shape += (1,)
Naq_array_in.shape += (1,)
for b in bins:
theory_cls=hp.read_cl('/home/matt/Planck/data/faraday/correlation/fr_theory_cl.fits')
# N_dq=np.mean(Ndq_array_in)
# N_au=np.mean(Nau_array_in)
# #delta1=np.sqrt(2.*abs((np.mean(cross1_array_in,axis=0)-np.mean(noise1_array_in,axis=0))**2+(np.mean(cross1_array_in,axis=0)-np.mean(noise1_array_in,axis=0))/2.*(N_dq+N_au)+N_dq*N_au/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
# delta1=np.sqrt(2.*((np.mean(theory1_array_in,axis=0))**2+(np.mean(theory1_array_in,axis=0))/2.*(N_dq+N_au)+N_dq*N_au/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
#
cosmic1=np.sqrt(2./((2.*l+1)*np.sqrt(b**2+dl_eff**2)*fsky)*np.mean(theory1_array_in,axis=0)**2)
# N_du=np.mean(Ndu_array_in)
# N_aq=np.mean(Naq_array_in)
# #delta2=np.sqrt(2.*abs((np.mean(cross2_array_in,axis=0)-np.mean(noise2_array_in,axis=0))**2+(np.mean(cross2_array_in,axis=0)-np.mean(noise2_array_in,axis=0))/2.*(N_dq+N_au)+N_dq*N_au/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
# delta2=np.sqrt(2.*((np.mean(theory2_array_in,axis=0))**2+(np.mean(theory2_array_in,axis=0))/2.*(N_du+N_aq)+N_du*N_aq/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
cosmic2=np.sqrt(2./((2*l+1)*np.sqrt(b**2+dl_eff**2)*fsky)*np.mean(theory2_array_in,axis=0)**2)
theory1_array=[]
theory2_array=[]
cross1_array=[]
cross2_array=[]
# noise1_array=[]
# noise2_array=[]
Ndq_array=[]
Ndu_array=[]
Nau_array=[]
Naq_array=[]
plot_l=[]
if( b != 1):
tmp_t1=bin_llcl.bin_llcl(ll*theory1_array_in,b)
tmp_t2=bin_llcl.bin_llcl(ll*theory2_array_in,b)
tmp_c1=bin_llcl.bin_llcl(ll*cross1_array_in,b)
tmp_c2=bin_llcl.bin_llcl(ll*cross2_array_in,b)
# tmp_n1=bin_llcl.bin_llcl(ll*noise1_array_in,b)
# tmp_n2=bin_llcl.bin_llcl(ll*noise2_array_in,b)
theory1_array=tmp_t1['llcl']
theory2_array=tmp_t2['llcl']
theory1_array.shape += (1,)
theory2_array.shape += (1,)
theory1_array=theory1_array.T
theory2_array=theory2_array.T
plot_l= tmp_t1['l_out']
cross1_array=tmp_c1['llcl']
cross2_array=tmp_c2['llcl']
# noise1_array=tmp_n1['llcl']
# noise2_array=tmp_n2['llcl']
Ndq_array=bin_llcl.bin_llcl(ll*Ndq_array_in,b)['llcl']
Ndu_array=bin_llcl.bin_llcl(ll*Ndu_array_in,b)['llcl']
Naq_array=bin_llcl.bin_llcl(ll*Naq_array_in,b)['llcl']
Nau_array=bin_llcl.bin_llcl(ll*Nau_array_in,b)['llcl']
tmp_c1=bin_llcl.bin_llcl((ll*cosmic1)**2,b)
#tmp_d1=bin_llcl.bin_llcl((ll*delta1)**2,b)
cosmic1=np.sqrt(tmp_c1['llcl'])
#delta1=np.sqrt(tmp_d1['llcl'])
tmp_c2=bin_llcl.bin_llcl((ll*cosmic2)**2,b)
#tmp_d2=bin_llcl.bin_llcl((ll*delta2)**2,b)
cosmic2=np.sqrt(tmp_c2['llcl'])
#delta2=np.sqrt(tmp_d2['llcl'])
t_tmp=bin_llcl.bin_llcl(ll*theory_cls,b)
theory_cls=t_tmp['llcl']
else:
plot_l=l
theory1_array=np.multiply(ll,theory1_array_in)
cross1_array=np.multiply(ll,cross1_array_in)
# noise1_array=np.multiply(ll,noise1_array_in)
theory2_array=np.multiply(ll,theory2_array_in)
cross2_array=np.multiply(ll,cross2_array_in)
# noise2_array=np.multiply(ll,noise2_array_in)
cosmic1*=ll
cosmic2*=ll
#delta1*=ll
#delta2*=ll
Ndq_array=np.multiply(ll,Ndq_array_in)
Ndu_array=np.multiply(ll,Ndu_array_in)
Naq_array=np.multiply(ll,Naq_array_in)
Nau_array=np.multiply(ll,Nau_array_in)
theory_cls*=ll
#ipdb.set_trace()
bad=np.where(plot_l < 24)
N_dq=np.mean(Ndq_array,axis=0)
N_du=np.mean(Ndu_array,axis=0)
N_aq=np.mean(Naq_array,axis=0)
N_au=np.mean(Nau_array,axis=0)
#noise1=np.mean(noise1_array,axis=0)
#noise2=np.mean(noise2_array,axis=0)
theory1=np.mean(theory1_array,axis=0)
theory2=np.mean(theory1_array,axis=0)
theory_array = np.add(theory1_array,theory2_array)
theory=np.mean(theory_array,axis=0)
#dtheory=np.sqrt(np.var(theory1_array,ddof=1) + np.var(theory2_array,ddof=1))
#cross_array = np.add(np.subtract(cross1_array,noise1),np.subtract(cross2_array,noise2))
cross_array = np.add(cross1_array,cross2_array)
cross=np.mean(cross_array,axis=0)
#dcross=np.std(cross_array,axis=0,ddof=1)
dcross=np.sqrt( ( np.var(cross1_array,axis=0,ddof=1) + np.var(cross2_array,axis=0,ddof=1)))
cosmic=np.sqrt(cosmic1**2+cosmic2**2)
delta1=np.sqrt(2./((2*plot_l+1)*fsky*np.sqrt(b**2+dl_eff**2))*(theory1**2 + theory1*(N_dq+N_au)/2. + N_dq*N_au/2.))
delta2=np.sqrt(2./((2*plot_l+1)*fsky*np.sqrt(b**2+dl_eff**2))*(theory2**2 + theory2*(N_du+N_aq)/2. + N_du*N_aq/2.))
delta=np.sqrt(delta1**2+delta2**2)
#cosmic=np.abs(theory_cls)*np.sqrt(2./((2*plot_l+1)*fsky*np.sqrt(dl_eff**2+b**2)))
#theory1=np.mean(theory1_array,axis=0)
#dtheory1=np.std(theory1_array,axis=0,ddof=1)
#cross1=np.mean(cross1_array,axis=0)
#dcross1=np.std(np.subtract(cross1_array,noise1),axis=0,ddof=1)
#ipdb.set_trace()
good_l=np.logical_and(plot_l <= 250,plot_l >25)
plot_binned.plotBinned((cross)*1e12,dcross*1e12,plot_l,b,'Cross_43x95_FR', title='QUIET FR Correlator',theory=theory*1e12,delta=delta*1e12,cosmic=cosmic*1e12)
#theory2=np.mean(theory2_array,axis=0)
#dtheory2=np.std(theory2_array,axis=0,ddof=1)
#cross2=np.mean(cross2_array,axis=0)
##delta2=np.mean(delta2_array,axis=0)
#dcross2=np.std(np.subtract(cross2_array,noise2),axis=0,ddof=1)
##ipdb.set_trace()
#plot_binned.plotBinned((cross2-noise2)*1e12,dcross2*1e12,plot_l,b,'Cross_43x95_FR_UxaQ', title='Cross 43x95 FR UxaQ',theory=theory2*1e12,dtheory=dtheory2*1e12,delta=delta2*1e12,cosmic=cosmic2*1e12)
#ipdb.set_trace()
if b == 25 :
good_l=np.logical_and(plot_l <= 250,plot_l >25)
likelihood(cross[good_l],delta[good_l],theory[good_l],'field1','c2bfr')
#if b == 1 :
# xbar= np.matrix(ll[1:]*(cross-np.mean(cross))[1:]).T
# vector=np.matrix(ll[1:]*cross[1:]).T
# mu=np.matrix(ll[1:]*theory[1:]).T
# fact=len(xbar)-1
# cov=(np.dot(xbar,xbar.T)/fact).squeeze()
## ipdb.set_trace()
# U,S,V =np.linalg.svd(cov)
# _cov= np.einsum('ij,j,jk', V.T,1./S,U.T)
# likelhd=np.exp(-np.dot(np.dot((vector-mu).T,_cov),(vector-mu))/2. )/(np.sqrt(2*np.pi*np.prod(S)))
## print('Likelihood of fit is #{0:.5f}'.format(likelihood[0,0]))
# f=open('FR_likelihood.txt','w')
# f.write('Likelihood of fit is #{0:.5f}'.format(likelhd[0,0]))
# f.close()
subprocess.call('mv *01*.png bin_01/', shell=True)
subprocess.call('mv *05*.png bin_05/', shell=True)
subprocess.call('mv *10*.png bin_10/', shell=True)
subprocess.call('mv *20*.png bin_20/', shell=True)
subprocess.call('mv *25*.png bin_25/', shell=True)
subprocess.call('mv *50*.png bin_50/', shell=True)
subprocess.call('mv *.eps eps/', shell=True)
ispice(FitsDir+infile+'.fits',outCl,maskfile1=FitsDir+'MaskHard%s.fits' %GAL,\
weightfile1=FitsDir+'Mask%s.fits' %GAL,apodizesigma=sig_apodize,\
thetamax=sig_apodize)
# Now check for file to be created
start_time=time.time()
while not os.path.exists(outCl):
time.sleep(1)
if time.time()-start_time>10:
raise Exception('Failed to compute')
# Bin Cl files
Cl=hp.read_cl(outCl)
X,Y,_,E=bin_llcl(Cl[4:],5,flatten=True)
idx=np.where(Y>0.)
X=X[idx]
Y=Y[idx]
E=E[idx]
if infile=='A':
ClA.append([X,Y,E,Cl])
elif infile=='H2':
ClH2.append([X,Y,E,Cl])
else:
ClAng.append([X,Y,E,Cl])
# E is errors assuming full-sky coverage
sky_frac = np.sum(mask)/len(mask) # this is sky fraction used
E/=np.sqrt(sky_frac)
def main():
##Parameters for Binning, Number of Runs
## Beam correction
use_beam=0
N_runs=10
bins=[1,5,10,20,25,50]
gal_cut=[-20,-10,-05,00,05,10,20]
bls=hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),3*nside_out-1)**2
l=np.arange(3*nside_out)
ll=l*(l+1)/(2*np.pi)
#map_prefix='/home/matt/Planck/data/faraday/simul_maps/'
#file_prefix=map_prefix+'planck_simulated_'
alpha_file='/data/wmap/faraday_MW_realdata.fits'
#wl=np.array([299792458./(band*1e9) for band in bands])
#theory1_array_in=[]
#theory2_array_in=[]
dsets=glob.glob('/data/Planck/LFI*1024*R2.00*.fits')
dsets.sort()
#simulate_fields.main()
for cut in gal_cut:
cross1_array_in=[]
cross2_array_in=[]
dcross1_array_in=[]
dcross2_array_in=[]
Ndq_array_in=[]
Ndu_array_in=[]
Nau_array_in=[]
Naq_array_in=[]
noise1_array_in=[]
noise2_array_in=[]
print('Galactic cut: {:2d}'.format(cut))
for i in xrange(len(bands)-1):
for j in xrange(i+1,len(bands)):
print(' Bands: {0:0>3.0f} x {1:0>3.0f}'.format(bands[i],bands[j]))
tmp_cross1_array=[]
tmp_cross2_array=[]
tmp_noise1_array=[]
tmp_noise2_array=[]
##arrays used to average over noise realizations
tmp_Ndq_array=[]
tmp_Ndu_array=[]
tmp_Nau_array=[]
tmp_Naq_array=[]
tmp_c1,tmp_c2=correlate_signal(dsets[i],dsets[j],wl[i],wl[j],alpha_file,'{0:0>3.0f}x{1:0>3.0f}'.format(bands[i],bands[j]),beam=use_beam,gal_cut=cut)
cross1_array_in.append(tmp_c1)
cross2_array_in.append(tmp_c2)
for n in xrange(N_runs):
print('\tNoise Correlation #{:0>3d}'.format(n+1))
tmp_n1,tmp_n2,tmp_dq,tmp_du,tmp_au,tmp_aq=correlate_noise(dsets[i],dsets[j],wl[i],wl[j],alpha_file,'{0:0>3.0f}x{1:0>3.0f}'.format(bands[i],bands[j]),beam=use_beam,gal_cut=cut)
tmp_noise1_array.append(tmp_n1)
tmp_noise2_array.append(tmp_n2)
tmp_Ndq_array.append(tmp_dq)
tmp_Ndu_array.append(tmp_du)
tmp_Naq_array.append(tmp_aq)
tmp_Nau_array.append(tmp_au)
noise1_array_in.append(np.mean(tmp_noise1_array,axis=0))
noise2_array_in.append(np.mean(tmp_noise2_array,axis=0))
Ndq_array_in.append(np.mean(tmp_Ndq_array))
Ndu_array_in.append(np.mean(tmp_Ndu_array))
Nau_array_in.append(np.mean(tmp_Nau_array))
Naq_array_in.append(np.mean(tmp_Naq_array))
#print( str(np.shape(cross1_array_in) ) + ' line 587')
#read in theory_cls
theory_in=hp.read_cl('/home/matt/Planck/data/faraday/correlation/fr_theory_cl.fits')
#print( str(np.shape(cross1_array_in)) + ' line 592' )
#out_dic={'theory':theory_in,'c1':cross1_array_in,'c2':cross2_array_in,'n1':noise1_array_in,'n2':noise2_array_in,'ndq':Ndq_array_in,'nau':Nau_array_in,'ndu':Ndu_array_in,'naq':Naq_array_in}
np.savez('FR_planck_cut_{0:2>02d}.npz'.format(cut),theory=theory_in,c1=cross1_array_in,c2=cross2_array_in,n1=noise1_array_in,n2=noise2_array_in,ndq=Ndq_array_in,nau=Nau_array_in,ndu=Ndu_array_in,naq=Naq_array_in)
#print( str(np.shape(cross1_array_in)) + ' line 596')
if cut >= 0:
fsky= 1. - np.sin(cut*np.pi/180.)
else:
fsky= np.abs(np.sin(cut*np.pi/180.))
L=np.sqrt(fsky*4*np.pi)
dl_eff=2*np.pi/L
#print( str(np.shape(cross1_array_in)) + ' line 604')
#ipdb.set_trace()
cross1_array_in=np.array(cross1_array_in)/fsky
cross2_array_in=np.array(cross2_array_in)/fsky
noise1_array_in=np.array(noise1_array_in)/fsky
noise2_array_in=np.array(noise2_array_in)/fsky
Nau_array_in=np.array(Nau_array_in)/fsky
Naq_array_in=np.array(Naq_array_in)/fsky
Ndu_array_in=np.array(Ndu_array_in)/fsky
Ndq_array_in=np.array(Ndq_array_in)/fsky
#ipdb.set_trace()
for b in bins:
#N_dq=np.mean(Ndq_array_in)
#N_au=np.mean(Nau_array_in)
#Transpose arrays to match dimensions for operations
#cross1_array_in=cross1_array_in.T
#cross2_array_in=cross2_array_in.T
#noise1_array_in=noise1_array_in.T
#noise2_array_in=noise2_array_in.T
#ipdb.set_trace()
delta1=np.sqrt( np.divide(2.*abs( (cross1_array_in - noise1_array_in).T**2 + (cross1_array_in - noise1_array_in). T * (Ndq_array_in+Nau_array_in)/2. + Ndq_array_in*Nau_array_in/2.).T,((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky)))
#N_du=np.mean(Ndu_array_in)
#N_aq=np.mean(Naq_array_in)
delta2=np.sqrt( np.divide(2.*abs( (cross2_array_in - noise2_array_in).T**2 + (cross2_array_in - noise2_array_in).T * (Ndu_array_in+Naq_array_in)/2. + Ndu_array_in*Naq_array_in/2.).T,((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky)))
#Transpose arrays back to match for plotting
#cross1_array=cross1_array_in.T
#cross2_array=cross2_array_in.T
#noise1_array=noise1_array_in.T
#noise2_array=noise2_array_in.T
#ipdb.set_trace()
cosmic=np.sqrt(2./((2.*l+1)*np.sqrt(b**2+dl_eff**2)*fsky)*theory_in**2)
delta_array=np.sqrt(delta1**2+delta2**2)
cross_array = np.add(np.subtract(cross1_array_in,noise1_array_in),np.subtract(cross2_array_in,noise2_array_in))
cross=np.average(cross_array,weights=1./delta_array**2,axis=0)
dcross=np.average(delta_array,weights=1./delta_array**2,axis=0)
plot_l=[]
if( b != 1):
tmp_c1=bin_llcl.bin_llcl(ll*cross/bls,b)
cross=tmp_c1['llcl']
tmp_dc1=bin_llcl.bin_llcl(ll*dcross/bls,b)
dcross= tmp_dc1['llcl']
plot_l=tmp_c1['l_out']
tmp_t1=bin_llcl.bin_llcl(ll*theory_in/bls,b)
theory=tmp_t1['llcl']
tmp_c1=bin_llcl.bin_llcl(ll*cosmic/bls,b)
cosmic=tmp_c1['llcl']
else:
plot_l=l
theory=np.multiply(ll/bls,theory_in)
#cross1_array=np.multiply(ll/bls,cross1_array_in)
#noise1_array=np.multiply(ll/bls,noise1_array_in)
#cross2_array=np.multiply(ll/bls,cross2_array_in)
#noise2_array=np.multiply(ll/bls,noise2_array_in)
cross*=ll/bls
cosmic*=ll/bls
dcross*=ll/bls
#cosmic2*=ll/bls
#delta1*=ll/bls
#delta2*=ll/bls
#ipdb.set_trace()
bad=np.where(plot_l < dl_eff)
#noise1=np.mean(noise1_array,axis=0)
#noise2=np.mean(noise2_array,axis=0)
#theory_array = np.add(theory1_array,theory2_array)
#theory=np.mean(theory_array,axis=0)
#dtheory=np.std(theory_array,axis=0,ddof=1)
#cross_array = np.add(np.subtract(cross1_array,noise1_array),np.subtract(cross2_array,noise2_array))
#delta_array=np.sqrt(delta1**2+delta2**2)
##cross_array=np.add(cross1_array,cross2_array)
#ipdb.set_trace()
#cross=np.average(cross_array,weights=1./delta_array**2,axis=0)
#cosmic=np.sqrt(cosmic1**2+cosmic2**2)
#theory1=np.mean(theory1_array,axis=0)
#dtheory1=np.std(theory1_array,axis=0,ddof=1)
#cross1=np.mean(cross1_array,axis=0)
#dcross1=np.std(np.subtract(cross1_array,noise1),axis=0,ddof=1)
#dcross=np.average(delta_array,weights=1./delta_array**2,axis=0)
#ipdb.set_trace()
plot_binned.plotBinned((cross)*1e12,dcross*1e12,plot_l,b,'Cross_FR_cut_{0:0>2d}'.format(cut), title='Faraday Rotation Correlator',theory=theory*1e12,dtheory=cosmic*1e12)
#theory2=np.mean(theory2_array,axis=0)
#dtheory2=np.std(theory2_array,axis=0,ddof=1)
#cross2=np.mean(cross2_array,axis=0)
##delta2=np.mean(delta2_array,axis=0)
#dcross2=np.std(np.subtract(cross2_array,noise2),axis=0,ddof=1)
##ipdb.set_trace()
#plot_binned.plotBinned((cross2-noise2)*1e12,dcross2*1e12,plot_l,b,'Cross_43x95_FR_UxaQ', title='Cross 43x95 FR UxaQ',theory=theory2*1e12,dtheory=dtheory2*1e12,delta=delta2*1e12,cosmic=cosmic2*1e12)
#ipdb.set_trace()
if b == 25 :
a_scales=np.linspace(-10,10,1001)
chi_array=[]
for a in a_scales:
chi_array.append(np.sum( (cross - a*theory)**2/(dcross)**2))
ind = np.argmin(chi_array)
#likelihood=np.exp(np.multiply(-1./2.,chi_array))/np.sqrt(2*np.pi)
likelihood=np.exp(np.multiply(-1./2.,chi_array))/np.sum(np.exp(np.multiply(-1./2.,chi_array))*.05)
Sig=np.sum(cross/(dcross**2))/np.sum(1./dcross**2)
#Noise=np.std(np.sum(cross_array/dcross**2,axis=1)/np.sum(1./dcross**2)) \
Noise=np.sqrt(1./np.sum(1./dcross**2))
Sig1=np.sum(cross*(theory/dcross)**2)/np.sum((theory/dcross)**2)
Noise1=np.sum(dcross*(theory/dcross)**2)/np.sum((theory/dcross)**2)
SNR=Sig/Noise
SNR1=Sig1/Noise1
#Sig2=np.sum(cross/(dcross**2))/np.sum(1./dcross**2)
#Noise2=np.sqrt(1./np.sum(1./dcross**2))
#Sig3=np.sum(cross*(theory/dcross)**2)/np.sum((theory/dcross)**2)
#Noise3=np.sqrt(np.sum(theory**2)/np.sum(theory**2/dcross**2))
#SNR2=Sig2/Noise2
#SNR3=Sig3/Noise3
#ipdb.set_trace()
fig,ax1=plt.subplots(1,1)
ax1.plot(a_scales,likelihood,'k.')
ax1.set_title('Faraday Rotation Correlator')
ax1.set_xlabel('Likelihood scalar')
ax1.set_ylabel('Likelihood of Correlation')
fig.savefig('FR_Correlation_Likelihood.png',format='png')
fig.savefig('FR_Correlation_Likelihood.eps',format='eps')
#ipdb.set_trace()
f=open('Maximum_likelihood_{0:0>2d}.txt'.format(cut),'w')
f.write('Maximum Likelihood: {0:2.5f}% for scale factor {1:.2f} \n'.format(float(likelihood[ind]*100),float(a_scales[ind])))
f.write('Probability of scale factor =1: {0:2.5f}% \n \n'.format(float(likelihood[np.where(a_scales ==1)])*100))
f.write('Detection Levels using Standard Deviation \n')
f.write('Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n'.format(SNR,Sig, Noise))
f.write('Weighted Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n \n'.format(SNR1,Sig1,Noise))
#f.write('Detection using Theoretical Noise \n')
#f.write('Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n'.format(SNR2,Sig2, Noise2))
#f.write('Weighted Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n'.format(SNR3,Sig3,Noise3))
f.close()
#if b == 1 :
# xbar= np.matrix(ll[1:]*(cross-np.mean(cross))[1:]).T
# vector=np.matrix(ll[1:]*cross[1:]).T
# mu=np.matrix(ll[1:]*theory[1:]).T
# fact=len(xbar)-1
# cov=(np.dot(xbar,xbar.T)/fact).squeeze()
# ipdb.set_trace()
# likelihood=np.exp(-np.dot(np.dot((vector-mu).T,lin.inv(cov)),(vector-mu))/2. )/(np.sqrt(2*np.pi*lin.det(cov)))
# print('Likelihood of fit is #{0:.5f}'.format(likelihood[0,0]))
# f=open('FR_likelihood.txt','w')
# f.write('Likelihood of fit is #{0:.5f}'.format(likelihood[0,0]))
# f.close()
subprocess.call('mv Maximum_likelihood_cut_{:2>02d}.txt' .format(cut), shell=True)
subprocess.call('mv *01.png bin_01/', shell=True)
subprocess.call('mv *05.png bin_05/', shell=True)
subprocess.call('mv *10.png bin_10/', shell=True)
subprocess.call('mv *20.png bin_20/', shell=True)
subprocess.call('mv *25.png bin_25/', shell=True)
subprocess.call('mv *50.png bin_50/', shell=True)
subprocess.call('mv *.eps eps/'.format(cut), shell=True)
def plot_mc():
bins=[1,5,10,20,25,50]
gal_cut=[-20,-10,-05,00,05,10,20]
bls=hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),3*nside_out-1)**2
l=np.arange(3*nside_out)
ll=l*(l+1)/(2*np.pi)
for cut in gal_cut:
data=np.load('FR_planck_cut_{0:2>02d}.npz'.format(cut))
theory_in=data['theory']
cross1_array_in=data['c1']
cross2_array_in=data['c2']
noise1_array_in=data['n1']
noise2_array_in=data['n2']
Ndq_array_in=data['ndq']
Ndu_array_in=data['ndu']
Naq_array_in=data['naq']
Nau_array_in=data['nau']
#print( str(np.shape(cross1_array_in)) + ' line 596')
if cut >= 0:
fsky= 1. - np.sin(cut*np.pi/180.)
else:
fsky= np.abs(np.sin(cut*np.pi/180.))
L=np.sqrt(fsky*4*np.pi)
dl_eff=2*np.pi/L
#print( str(np.shape(cross1_array_in)) + ' line 604')
#ipdb.set_trace()
cross1_array_in=np.array(cross1_array_in)/fsky
cross2_array_in=np.array(cross2_array_in)/fsky
noise1_array_in=np.array(noise1_array_in)/fsky
noise2_array_in=np.array(noise2_array_in)/fsky
Nau_array_in=np.array(Nau_array_in)/fsky
Naq_array_in=np.array(Naq_array_in)/fsky
Ndu_array_in=np.array(Ndu_array_in)/fsky
Ndq_array_in=np.array(Ndq_array_in)/fsky
#ipdb.set_trace()
for b in bins:
#N_dq=np.mean(Ndq_array_in)
#N_au=np.mean(Nau_array_in)
#Transpose arrays to match dimensions for operations
#cross1_array_in=cross1_array_in.T
#cross2_array_in=cross2_array_in.T
#noise1_array_in=noise1_array_in.T
#noise2_array_in=noise2_array_in.T
#ipdb.set_trace()
delta1=np.sqrt( np.divide(2.*abs( (cross1_array_in - noise1_array_in).T**2 + (cross1_array_in - noise1_array_in). T * (Ndq_array_in+Nau_array_in)/2. + Ndq_array_in*Nau_array_in/2.).T,((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky)))
#N_du=np.mean(Ndu_array_in)
#N_aq=np.mean(Naq_array_in)
delta2=np.sqrt( np.divide(2.*abs( (cross2_array_in - noise2_array_in).T**2 + (cross2_array_in - noise2_array_in).T * (Ndu_array_in+Naq_array_in)/2. + Ndu_array_in*Naq_array_in/2.).T,((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky)))
#Transpose arrays back to match for plotting
#cross1_array=cross1_array_in.T
#cross2_array=cross2_array_in.T
#noise1_array=noise1_array_in.T
#noise2_array=noise2_array_in.T
#ipdb.set_trace()
cosmic=np.sqrt(2./((2.*l+1)*np.sqrt(b**2+dl_eff**2)*fsky)*theory_in**2)
delta_array=np.sqrt(delta1**2+delta2**2)
cross_array = np.add(np.subtract(cross1_array_in,noise1_array_in),np.subtract(cross2_array_in,noise2_array_in))
cross=np.average(cross_array,weights=1./delta_array**2,axis=0)
dcross=np.average(delta_array,weights=1./delta_array**2,axis=0)
plot_l=[]
if( b != 1):
tmp_c1=bin_llcl.bin_llcl(ll*cross/bls,b)
cross=tmp_c1['llcl']
tmp_dc1=bin_llcl.bin_llcl(ll*dcross/bls,b)
dcross= tmp_dc1['llcl']
plot_l=tmp_c1['l_out']
tmp_t1=bin_llcl.bin_llcl(ll*theory_in/bls,b)
theory=tmp_t1['llcl']
tmp_c1=bin_llcl.bin_llcl(ll*cosmic/bls,b)
cosmic=tmp_c1['llcl']
else:
plot_l=l
theory=np.multiply(ll/bls,theory_in)
#cross1_array=np.multiply(ll/bls,cross1_array_in)
#noise1_array=np.multiply(ll/bls,noise1_array_in)
#cross2_array=np.multiply(ll/bls,cross2_array_in)
#noise2_array=np.multiply(ll/bls,noise2_array_in)
cross*=ll/bls
cosmic*=ll/bls
dcross*=ll/bls
#cosmic2*=ll/bls
#delta1*=ll/bls
#delta2*=ll/bls
#ipdb.set_trace()
bad=np.where(plot_l < dl_eff)
#noise1=np.mean(noise1_array,axis=0)
#noise2=np.mean(noise2_array,axis=0)
#theory_array = np.add(theory1_array,theory2_array)
#theory=np.mean(theory_array,axis=0)
#dtheory=np.std(theory_array,axis=0,ddof=1)
#cross_array = np.add(np.subtract(cross1_array,noise1_array),np.subtract(cross2_array,noise2_array))
#delta_array=np.sqrt(delta1**2+delta2**2)
##cross_array=np.add(cross1_array,cross2_array)
#ipdb.set_trace()
#cross=np.average(cross_array,weights=1./delta_array**2,axis=0)
#cosmic=np.sqrt(cosmic1**2+cosmic2**2)
#theory1=np.mean(theory1_array,axis=0)
#dtheory1=np.std(theory1_array,axis=0,ddof=1)
#cross1=np.mean(cross1_array,axis=0)
#dcross1=np.std(np.subtract(cross1_array,noise1),axis=0,ddof=1)
#dcross=np.average(delta_array,weights=1./delta_array**2,axis=0)
#ipdb.set_trace()
plot_binned.plotBinned((cross)*1e12,dcross*1e12,plot_l,b,'Cross_FR_cut_{0:0>2d}'.format(cut), title='Faraday Rotation Correlator',theory=theory*1e12,dtheory=cosmic*1e12)
#theory2=np.mean(theory2_array,axis=0)
#dtheory2=np.std(theory2_array,axis=0,ddof=1)
#cross2=np.mean(cross2_array,axis=0)
##delta2=np.mean(delta2_array,axis=0)
#dcross2=np.std(np.subtract(cross2_array,noise2),axis=0,ddof=1)
##ipdb.set_trace()
#plot_binned.plotBinned((cross2-noise2)*1e12,dcross2*1e12,plot_l,b,'Cross_43x95_FR_UxaQ', title='Cross 43x95 FR UxaQ',theory=theory2*1e12,dtheory=dtheory2*1e12,delta=delta2*1e12,cosmic=cosmic2*1e12)
#ipdb.set_trace()
if b == 25 :
a_scales=np.linspace(-10,10,1001)
chi_array=[]
for a in a_scales:
chi_array.append(np.sum( (cross - a*theory)**2/(dcross)**2))
ind = np.argmin(chi_array)
#likelihood=np.exp(np.multiply(-1./2.,chi_array))/np.sqrt(2*np.pi)
likelihood=np.exp(np.multiply(-1./2.,chi_array))/np.sum(np.exp(np.multiply(-1./2.,chi_array))*.05)
Sig=np.sum(cross/(dcross**2))/np.sum(1./dcross**2)
#Noise=np.std(np.sum(cross_array/dcross**2,axis=1)/np.sum(1./dcross**2)) \
Noise=np.sqrt(1./np.sum(1./dcross**2))
Sig1=np.sum(cross*(theory/dcross)**2)/np.sum((theory/dcross)**2)
Noise1=np.sum(dcross*(theory/dcross)**2)/np.sum((theory/dcross)**2)
SNR=Sig/Noise
SNR1=Sig1/Noise1
#Sig2=np.sum(cross/(dcross**2))/np.sum(1./dcross**2)
#Noise2=np.sqrt(1./np.sum(1./dcross**2))
#Sig3=np.sum(cross*(theory/dcross)**2)/np.sum((theory/dcross)**2)
#Noise3=np.sqrt(np.sum(theory**2)/np.sum(theory**2/dcross**2))
#SNR2=Sig2/Noise2
#SNR3=Sig3/Noise3
#ipdb.set_trace()
fig,ax1=plt.subplots(1,1)
ax1.plot(a_scales,likelihood,'k.')
ax1.set_title('Faraday Rotation Correlator')
ax1.set_xlabel('Likelihood scalar')
ax1.set_ylabel('Likelihood of Correlation')
fig.savefig('FR_Correlation_Likelihood.png',format='png')
fig.savefig('FR_Correlation_Likelihood.eps',format='eps')
#ipdb.set_trace()
f=open('Maximum_likelihood.txt','w')
f.write('Maximum Likelihood: {0:2.5f}% for scale factor {1:.2f} \n'.format(float(likelihood[ind]*100),float(a_scales[ind])))
f.write('Probability of scale factor =1: {0:2.5f}% \n \n'.format(float(likelihood[np.where(a_scales ==1)])*100))
f.write('Detection Levels using Standard Deviation \n')
f.write('Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n'.format(SNR,Sig, Noise))
f.write('Weighted Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n \n'.format(SNR1,Sig1,Noise))
#f.write('Detection using Theoretical Noise \n')
#f.write('Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n'.format(SNR2,Sig2, Noise2))
#f.write('Weighted Detection Level: {0:.4f} sigma, Signal= {1:.4e}, Noise= {2:.4e} \n'.format(SNR3,Sig3,Noise3))
f.close()
#if b == 1 :
# xbar= np.matrix(ll[1:]*(cross-np.mean(cross))[1:]).T
# vector=np.matrix(ll[1:]*cross[1:]).T
# mu=np.matrix(ll[1:]*theory[1:]).T
# fact=len(xbar)-1
# cov=(np.dot(xbar,xbar.T)/fact).squeeze()
# ipdb.set_trace()
# likelihood=np.exp(-np.dot(np.dot((vector-mu).T,lin.inv(cov)),(vector-mu))/2. )/(np.sqrt(2*np.pi*lin.det(cov)))
# print('Likelihood of fit is #{0:.5f}'.format(likelihood[0,0]))
# f=open('FR_likelihood.txt','w')
# f.write('Likelihood of fit is #{0:.5f}'.format(likelihood[0,0]))
# f.close()
subprocess.call('mv Maximum_likelihood_cut_{:2>02d}.txt' .format(cut), shell=True)
subprocess.call('mv *01*.png bin_01/', shell=True)
subprocess.call('mv *05*.png bin_05/', shell=True)
subprocess.call('mv *10*.png bin_10/', shell=True)
subprocess.call('mv *20*.png bin_20/', shell=True)
subprocess.call('mv *25*.png bin_25/', shell=True)
subprocess.call('mv *50*.png bin_50/', shell=True)
subprocess.call('mv *.eps eps/'.format(cut), shell=True)
