ame_model = models.AMEModel( amp_I=rj2cmb(30e9, 30.),
amp_Q=rj2cmb(30e9, 0.),
amp_U=rj2cmb(30e9, 0.),
nu_peak=25. )
ff_model = models.FreeFreeUnpol( amp_I=rj2cmb(30e9, 30.),
amp_Q=rj2cmb(30e9, 0.),
amp_U=rj2cmb(30e9, 0.),
ff_beta=-0.118 )
sync_model = models.SyncPow( amp_I=rj2cmb(30e9, 30.),
amp_Q=rj2cmb(30e9, 10.),
amp_U=rj2cmb(30e9, 10.),
sync_beta=-1.2 )
cmb_model = models.CMB( amp_I=50., amp_Q=0.6, amp_U=0.6 )
# Silicate + Carbonaceous grains as 2 MBBs
two_comp_silcar_model = models.DustGen(
amp_I=rj2cmb(353e9, DUST_I/1.25),
amp_Q=rj2cmb(353e9, DUST_P),
amp_U=rj2cmb(353e9, DUST_P),
beta = 1.6,
dbeta = 0.2,
Td1 = 15.,
Td2 = 24.,
fI = 0.25,
fQ = 0.,
fU = 0. )
def cmb_model(CMB_I=50, CMB_Q=0.6, CMB_U=0.6):
cmb_model = models.CMB(amp_I=CMB_I, amp_Q=CMB_Q, amp_U=CMB_U)
return cmb_model
# Band parameter definitions
nbands = 5
NPROC = 1 #4 #32
filename = "bands_log_%d.dat" % nbands
numin_vals = [5., ] #10., 20., 30., 40., 50., 60., 70.]
numax_vals = [200.,] # 300., 400., 500., 600., 700.]
models_fit = np.array(['mbb', 'pow'])
fsigma_T = 0.01
fsigma_P = 0.01
# Define input models and their amplitudes/parameters
dust_model = models.DustMBB(amp_I=10., amp_Q=1., amp_U=1.2, dust_beta=1.6, dust_T=20.)
sync_model = models.SyncPow(amp_I=10., amp_Q=2., amp_U=1.5, sync_beta=-3.)
cmb_model = models.CMB(amp_I=100., amp_Q=10., amp_U=20.)
mods = [dust_model, sync_model, cmb_model]
models_in = [dust_model.model, sync_model.model]
amps_in = np.array([m.amps() for m in mods])
params_in = np.array([m.params() for m in mods])
def bands_log(nu_min, nu_max, nbands):
"""
Logarithmic set of bands.
"""
freq_vec = np.arange(nbands)
return nu_min * (nu_max/nu_min)**(freq_vec/(nbands-1.)) * 1e9 # in Hz
def bands_3groups(nu_low, nu_mid, nu_high, sep_low, sep_mid, sep_high, nbands):