def construct_gs_hist(del_bl=8.,num_bl=10,beam_sig=0.09,fq=0.1):
save_tag = 'grid_del_bl_{0:.2f}_num_bl_{1}_beam_sig_{2:.2f}_fq_{3:.3f}'.format(del_bl,num_bl,beam_sig,fq)
save_tag_mc = 'grid_del_bl_{0:.2f}_num_bl_{1}_beam_sig_{2:.2f}_fq_{3}'.format(del_bl,num_bl,beam_sig,fq)
ys = load_mc_data('{0}/monte_carlo/{1}'.format(data_loc,save_tag_mc))
print 'ys ',ys.shape
alms_fg = qgea.generate_sky_model_alms(gsm_fits_file,lmax=3)
alms_fg = alms_fg[:,2]
baselines,Q,lms = load_Q_file(gh='grid',del_bl=del_bl,num_bl=num_bl,beam_sig=beam_sig,fq=fq,lmax=3)
N = total_noise_covar(0.1,baselines.shape[0],'{0}/gsm_matrices/gsm_{1}.npz'.format(data_loc,save_tag))
MQN = return_MQdagNinv(Q,N,num_remov=None)
print MQN
ahat00s = n.array([])
for ii in xrange(ys.shape[1]):
#_,ahat,_ = qgea.test_recover_alms(ys[:,ii],Q,N,alms_fg,num_remov=None)
ahat = uf.vdot(MQN,ys[:,ii])
ahat00s = n.append(n.real(ahat[0]),ahat00s)
#print ahat00s
print ahat00s.shape
_,bins,_ = p.hist(ahat00s,bins=36,normed=True)
# plot best fit line
mu,sigma = norm.fit(ahat00s)
print "mu, sigma = ",mu,', ',sigma
y_fit = mpl.mlab.normpdf(bins,mu,sigma)
p.plot(bins, y_fit, 'r--', linewidth=2)
p.xlabel('ahat_00')
p.ylabel('Probability')
p.title(save_tag)
p.annotate('mu = {0:.2f}\nsigma = {1:.2f}'.format(mu,sigma), xy=(0.05, 0.5), xycoords='axes fraction')
p.savefig('./figures/monte_carlo/{0}.pdf'.format(save_tag))
p.clf()
def construct_gs_hist(del_bl=8., num_bl=10, beam_sig=0.09, fq=0.1):
save_tag = 'grid_del_bl_{0:.2f}_num_bl_{1}_beam_sig_{2:.2f}_fq_{3:.3f}'.format(
del_bl, num_bl, beam_sig, fq)
save_tag_mc = 'grid_del_bl_{0:.2f}_num_bl_{1}_beam_sig_{2:.2f}_fq_{3}'.format(
del_bl, num_bl, beam_sig, fq)
ys = load_mc_data('{0}/monte_carlo/{1}'.format(data_loc, save_tag_mc))
print 'ys ', ys.shape
alms_fg = qgea.generate_sky_model_alms(gsm_fits_file, lmax=3)
alms_fg = alms_fg[:, 2]
baselines, Q, lms = load_Q_file(gh='grid',
del_bl=del_bl,
num_bl=num_bl,
beam_sig=beam_sig,
fq=fq,
lmax=3)
N = total_noise_covar(
0.1, baselines.shape[0],
'{0}/gsm_matrices/gsm_{1}.npz'.format(data_loc, save_tag))
MQN = return_MQdagNinv(Q, N, num_remov=None)
print MQN
ahat00s = n.array([])
for ii in xrange(ys.shape[1]):
#_,ahat,_ = qgea.test_recover_alms(ys[:,ii],Q,N,alms_fg,num_remov=None)
ahat = uf.vdot(MQN, ys[:, ii])
ahat00s = n.append(n.real(ahat[0]), ahat00s)
#print ahat00s
print ahat00s.shape
_, bins, _ = p.hist(ahat00s, bins=36, normed=True)
# plot best fit line
mu, sigma = norm.fit(ahat00s)
print "mu, sigma = ", mu, ', ', sigma
y_fit = mpl.mlab.normpdf(bins, mu, sigma)
p.plot(bins, y_fit, 'r--', linewidth=2)
p.xlabel('ahat_00')
p.ylabel('Probability')
p.title(save_tag)
p.annotate('mu = {0:.2f}\nsigma = {1:.2f}'.format(mu, sigma),
xy=(0.05, 0.5),
xycoords='axes fraction')
p.savefig('./figures/monte_carlo/{0}.pdf'.format(save_tag))
p.clf()
