def run_model(nu_params):
# Get band definition
nu_min, nu_max = nu_params
print "nu_min = %d GHz, nu_max = %d GHz" % (nu_min, nu_max)
nu = bands_log(nu_min, nu_max, nbands)
label = str(nu_min) + '_' + str(nu_max)
# Name of sample file
fname_samples = "output/samples_%s.%s_nb%d_seed%d_%s.dat" \
% (name_in, name_fit, nbands, SEED, label)
# Simulate data and run MCMC fit
D_vec, Ninv = fitting.generate_data(nu, fsigma_T, fsigma_P,
components=mods_in,
noise_file=NOISE_FILE)
gls_cmb, cmb_chisq, cmb_noise \
= fitting.model_test(nu, D_vec, Ninv, mods_fit,
burn=200, steps=800,
cmb_amp_in=cmb_model.amps(),
sample_file=fname_samples)
# FIXME: CMB noise is actually the variance!
#cmb_true = np.array([cmb_model.amp_I, cmb_model.amp_Q, cmb_model.amp_U])
print "chisq =", cmb_chisq, gls_cmb.flatten()
#print "Frac. err. (I,Q,U) =", np.sqrt(cmb_noise.flatten()) / cmb_true
#print "Bias =", (gls_cmb.flatten() - cmb_true) / np.sqrt(cmb_noise.flatten())
# Append summary statistics to file
f = open(filename, 'a')
f.write(9*('%0.6e ') % (nu_min, nu_max, cmb_chisq,
gls_cmb[0], gls_cmb[1], gls_cmb[2],
cmb_noise[0]/cmb_model.amp_I,
cmb_noise[1]/cmb_model.amp_Q,
cmb_noise[2]/cmb_model.amp_U) )
f.write('\n')
f.close()
def run_model(nu_params):
# Get band definition
nu_min, nu_max = nu_params
print "nu_min = %d GHz, nu_max = %d GHz" % (nu_min, nu_max)
nu = bands_log(nu_min, nu_max, nbands)
label = str(nu_min) + '_' + str(nu_max)
# Make copies of models
my_mods_in = mods_in
my_mods_fit = mods_fit
# Name of sample file
#fname_samples = "output/%s_samples_%s.%s_nb%d_seed%d_%s.dat" \
# % (PREFIX, name_in, name_fit, nbands, SEED, label)
fname_samples = None
# Simulate data and run MCMC fit
D_vec, Ninv = fitting.generate_data(nu, fsigma_T, fsigma_P,
components=my_mods_in,
noise_file=NOISE_FILE)
pnames, samples, logp, ini = model_test(nu, D_vec, Ninv, my_mods_fit,
burn=NBURN, steps=NSTEPS,
nwalkers=NWALKERS,
cmb_amp_in=cmb_model.amps(),
sample_file=fname_samples)
# Calculate best-fit chisq.
chisq = -2.*logp
dof = D_vec.size - len(pnames)
# Get best-fit (max. prob.) parameter values
maxl_idx = np.argmax(logp)
bf_params = samples[:, maxl_idx]
# Reshape sample array into (Nparams, Nwalkers, Nsamples)
samples = samples.reshape((samples.shape[0],
NWALKERS,
samples.shape[1]/NWALKERS))
# Loop over different burn-in cuts to produce summary stats
for n, cut in enumerate(cut_range):
# Set output filename
fname = filename + "_cut%d.dat" % cut
# Output mean and bias
summary_data = [nu_min, nu_max, np.min(chisq), maxl_idx]
header = "nu_min nu_max chi2_min maxlike_idx "
# Loop over parameter names
for i in range(len(pnames)):
# Mean, std. dev., and fractional shift from true value
# (for mean, median, and max. likelihood param values)
_mean = np.mean(samples[i,:,cut:])
_std = np.std(samples[i,:,cut:])
_fracbias = (np.mean(samples[i,:,cut:]) - ini[i]) \
/ np.std(samples[i,:,cut:])
_med_fracbias = (np.median(samples[i,:,cut:]) - ini[i]) \
/ np.std(samples[i,:,cut:])
_ml_fracbias = (bf_params[i] - ini[i]) / np.std(samples[i,:,cut:])
stats = [_mean, _std, _fracbias, _med_fracbias, _ml_fracbias]
# Keep summary stats, to be written to file
summary_data += stats
header += "mean_%s std_%s Delta_%s MedDelta_%s MLDelta_%s " \
% (pnames[i], pnames[i], pnames[i], pnames[i], pnames[i])
# Only output summary stats once
if n == 0:
print "%14s: %+3.3e +/- %3.3e [Delta = %+3.3f, MLDelta = %+3.3f]" \
% (pnames[i], stats[0], stats[1], stats[2], stats[3])
# If file is empty, set flag to write header when saving output
has_header = False if os.stat(fname).st_size == 0 else True
# Append summary statistics to file
f = open(fname, 'a')
if has_header:
np.savetxt(f, np.atleast_2d(summary_data))
else:
np.savetxt(f, np.atleast_2d(summary_data), header=header[:-1])
f.close()
def run_model(nu_params, Nside, i_px ):
## Number of maps
n_maps = 0
for ii in range( len( amps_fit ) ):
n_maps += len( amps_fit[ ii ] )
for jj in range( len( params_fit ) ):
n_maps += len( params_fit[ jj ] )
# Get band definition
nu_min, nu_max = nu_params
#print "nu_min = %d GHz, nu_max = %d GHz" % (nu_min, nu_max)
nu = bands_log(nu_min, nu_max, n_bands)
label = str(nu_min) + '_' + str(nu_max)
# Make copies of models
my_mods_in = mods_in
my_mods_fit = mods_fit
fname_samples = None
# Simulate data and run MCMC fit
D_vec, Ninv = fitting.generate_data(nu, fsigma_T, fsigma_P,
components=my_mods_in,
noise_file=NOISE_FILE, idx_px = i_px)
pnames, samples, logp, ini = model_test(nu, D_vec, Ninv, my_mods_fit,
burn=NBURN, steps=NSTEPS,
nwalkers=NWALKERS,
cmb_amp_in=cmb_model.amps(),
sample_file=fname_samples,
i_px = i_px,
nthreads = nthreads )
# Calculate best-fit chisq.
chisq = -2.*logp
dof = D_vec.size - len(pnames)
# Reshape sample array into (Nparams, Nwalkers, Nsamples)
samples = samples.reshape((samples.shape[0],
NWALKERS,
samples.shape[1]/NWALKERS))
# Array to store the results (NB: this is the structure for the final FITS file to be stored with healpy)
data_fits = np.full( ( len( pnames ) * 3 ), hp.UNSEEN, dtype = 'float32' )
# Loop over different burn-in cuts to produce summary stats
## Right now, since it's only printing out some diagnostic, it's set for one case only
## Before: for cut in cut_range:
for cut in [ cut_range[ -1 ] ]:
# Output mean and bias
summary_str = "%4.4e %4.4e %4.4e " % (nu_min, nu_max, np.min(chisq))
summary_data = [nu_min, nu_max, np.min(chisq)]
header = "nu_min nu_max chi2_min "
# Loop over parameter names
for i in range(len(pnames)):
# Mean, std. dev., and fractional shift from true value
_mean = np.mean(samples[i,:,cut:])
_std = np.std(samples[i,:,cut:])
_fracbias = (np.mean(samples[i,:,cut:]) - ini[i]) \
/ np.std(samples[i,:,cut:])
stats = [_mean, _std, _fracbias]
data_fits[ 3 * i ] = ini[ i ]
data_fits[ 3 * i + 1 ] = _mean
data_fits[ 3 * i + 2 ] = _std
# Keep summary stats, to be written to file
summary_data += stats
header += "mean_%s std_%s Delta_%s " \
% (pnames[i], pnames[i], pnames[i])
# Printing out only the first pixel
if i_px == 0:
print "%14s: %+3.3e %+3.3e +/- %3.3e [Delta = %+3.3f]" \
% (pnames[i], ini[ i ], stats[ 0 ], stats[ 1 ], stats[ 2 ] )
return data_fits
def run_model(nu_params, Nside, Npix):
## Number of maps
n_maps = 0
for ii in range(len(amps_fit)):
n_maps += len(amps_fit[ii])
for jj in range(len(params_fit)):
n_maps += len(params_fit[jj])
## For each amplitude & parameter, we store 3 results: in/out/err
n_maps *= 3
if os.path.isfile(filename + '.fits') == 1:
# Check whether there are already some results (if the fit was forced to be re-started, the values are hp.UNSEEN)
data_ini = hp.read_map(filename + '.fits',
range(n_maps),
verbose=False)
# if any is hp.UNSEEN, run the fit, otherwise return
s_tmp = 0
for i in range(n_maps):
s_tmp += np.abs(data_ini[i][Npix])
if s_tmp < np.abs(hp.UNSEEN):
print '(run_joint_mcmc_allsky_serial) Pixel %i already analyzed. Skipping it.' % (
Npix)
return
# creare the output file if it does not exist
if os.path.isfile(filename + '.fits') != 1:
data_start = np.full((n_maps, 12 * Nside * Nside), hp.UNSEEN)
hp.write_map(filename + '.fits', data_start, dtype='float32')
# Get band definition
nu_min, nu_max = nu_params
print "nu_min = %d GHz, nu_max = %d GHz" % (nu_min, nu_max)
nu = bands_log(nu_min, nu_max, nbands)
label = str(nu_min) + '_' + str(nu_max)
# Make copies of models
my_mods_in = mods_in
my_mods_fit = mods_fit
# Name of sample file
#fname_samples = "output_serial/%s_samples_%s.%s_nb%d_seed%d_%s.dat" \
# % (PREFIX, name_in, name_fit, nbands, SEED, label)
fname_samples = None
# Simulate data and run MCMC fit
D_vec, Ninv = fitting.generate_data(nu,
fsigma_T,
fsigma_P,
components=my_mods_in,
noise_file=NOISE_FILE)
pnames, samples, logp, ini = model_test(nu,
D_vec,
Ninv,
my_mods_fit,
burn=NBURN,
steps=NSTEPS,
nwalkers=NWALKERS,
cmb_amp_in=cmb_model.amps(),
sample_file=fname_samples)
# Calculate best-fit chisq.
chisq = -2. * logp
dof = D_vec.size - len(pnames)
# Reshape sample array into (Nparams, Nwalkers, Nsamples)
samples = samples.reshape(
(samples.shape[0], NWALKERS, samples.shape[1] / NWALKERS))
# Loop over different burn-in cuts to produce summary stats
for cut in cut_range:
# Set output filename
fname = filename + "_cut%d.dat" % cut
# Output mean and bias
summary_str = "%4.4e %4.4e %4.4e " % (nu_min, nu_max, np.min(chisq))
summary_data = [nu_min, nu_max, np.min(chisq)]
header = "nu_min nu_max chi2_min "
data_fits = np.full((len(pnames), 3), hp.UNSEEN, dtype='float32')
# Loop over parameter names
for i in range(len(pnames)):
# Mean, std. dev., and fractional shift from true value
_mean = np.mean(samples[i, :, cut:])
_std = np.std(samples[i, :, cut:])
_fracbias = (np.mean(samples[i,:,cut:]) - ini[i]) \
/ np.std(samples[i,:,cut:])
stats = [_mean, _std, _fracbias]
data_fits[i] = [ini[i], _mean, _std]
# Keep summary stats, to be written to file
summary_data += stats
header += "mean_%s std_%s Delta_%s " \
% (pnames[i], pnames[i], pnames[i])
print "%14s: %+3.3e %+3.3e +/- %3.3e [Delta = %+3.3f]" \
% (pnames[i], ini[ i ], stats[ 0 ], stats[ 1 ], stats[ 2 ] )
# Insert the results
data_maps = hp.read_map(filename + '.fits',
range(len(pnames) * 3),
verbose=False)
for i_tmp in range(len(pnames)):
for i_tmp_2 in range(3):
data_maps[i_tmp * 3 +
i_tmp_2][Npix] = data_fits[i_tmp][i_tmp_2]
hp.write_map(filename + '.fits', data_maps, dtype='float32')
params_pMBB_narrow = [pMBB_narrow.amp_I, pMBB_narrow.amp_Q, pMBB_narrow.amp_U, cmb.amp_I, cmb.amp_Q, cmb.amp_U,
sync.amp_I, sync.amp_Q, sync.amp_U, pMBB_narrow.dust_beta, pMBB_narrow.dust_T,
pMBB_narrow.sigma_beta, pMBB_narrow.sigma_temp, sync.sync_beta]
initial_vals_sMBB = (amp_I, amp_Q, amp_U, cmb.amp_I, cmb.amp_Q, cmb.amp_U,
sync.amp_I, sync.amp_Q, sync.amp_U, mean_beta, mean_temp,
sync.sync_beta)
initial_vals_pMBB_broad = (amp_I, amp_Q, amp_U, cmb.amp_I, cmb.amp_Q, cmb.amp_U,
sync.amp_I, sync.amp_Q, sync.amp_U, mean_beta, mean_temp,
sigma_beta, sigma_temp, sync.sync_beta)
initial_vals_pMBB_narrow = (amp_I, amp_Q, amp_U, cmb.amp_I, cmb.amp_U, cmb.amp_Q,
sync.amp_I, sync.amp_Q, sync.amp_U,mean_beta, mean_temp,
.1 * sigma_beta, .1 * sigma_temp, sync.sync_beta)
parent_model = 'mbb'
D_vec_sMBB, Ninv = fitting.generate_data(nu_pico, fsigma_T, fsigma_P, [sMBB, cmb, sync],
noise_file="data/noise_pico.dat" )
D_vec_pMBB_broad, Ninv = fitting.generate_data(nu_pico, fsigma_T, fsigma_P, [pMBB_broad, cmb, sync],
noise_file="data/noise_pico.dat")
D_vec_pMBB_narrow, Ninv = fitting.generate_data(nu_pico, fsigma_T, fsigma_P, [pMBB_narrow, cmb, sync],
noise_file="data/noise_pico.dat")
data_spec_sMBB = (nu_pico, D_vec_sMBB, Ninv, beam_mat)
data_spec_pMBB_broad = (nu_pico, D_vec_pMBB_broad, Ninv, beam_mat)
data_spec_pMBB_narrow = (nu_pico, D_vec_pMBB_narrow, Ninv, beam_mat)
p_spec_sMBB = (pnames_sMBB, initial_vals_sMBB, parent_model)
p_spec_pMBB_broad = (pnames_pMBB_broad, initial_vals_pMBB_broad, parent_model)
p_spec_pMBB_narrow = (pnames_pMBB_narrow, initial_vals_pMBB_narrow, parent_model)
print "running emcee"