def dust_model(DUST_I=50,
DUST_Q=10 / 1.41,
DUST_U=10 / 1.41,
DUST_BETA=1.6,
DUST_T=20.):
dust_model = models.DustMBB(amp_I=rj2cmb(353e9, DUST_I),
amp_Q=rj2cmb(353e9, DUST_Q),
amp_U=rj2cmb(353e9, DUST_U),
dust_beta=DUST_BETA,
dust_T=DUST_T)
return dust_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
import models
from utils import rj2cmb
DUST_I = 50.
DUST_P = 5. / 1.41
# Define input models and their amplitudes/parameters
dust_model = models.DustMBB( amp_I=rj2cmb(353e9, DUST_I),
amp_Q=rj2cmb(353e9, DUST_P),
amp_U=rj2cmb(353e9, DUST_P),
dust_beta=1.6, dust_T=20. )
prob1mbb_model = models.ProbSingleMBB( amp_I=rj2cmb(353e9, DUST_I),
amp_Q=rj2cmb(353e9, DUST_P),
amp_U=rj2cmb(353e9, DUST_P),
dust_beta=1.6, dust_T=20.,
sigma_beta=.2, sigma_temp=4. )
simple_dust_model = models.DustSimpleMBB( amp_I=rj2cmb(353e9, DUST_I),
amp_Q=rj2cmb(353e9, DUST_P),
amp_U=rj2cmb(353e9, DUST_P),
dust_beta=1.6, dust_T=20. )
simple_dust_model_shifted = models.DustSimpleMBB(
amp_I=rj2cmb(353e9, DUST_I),
amp_Q=rj2cmb(353e9, DUST_P),
amp_U=rj2cmb(353e9, DUST_P),
dust_beta=1.7, dust_T=20. )
ame_model = models.AMEModel( amp_I=rj2cmb(30e9, 30.),
numin_vals = [15., 20., 25., 30., 35., 40.]
numax_vals = [300., 400., 500., 600., 700., 800.]
#numin_vals = [5., 10., 20., 30., 40., 50., 60., 70.]
#numax_vals = [200., 300., 400., 500., 600., 700.]
#numin_vals = [5., ] #10., 20., 30., 40., 50., 60., 70.]
#numax_vals = [700.,] # 300., 400., 500., 600., 700.]
# Temperature/polarisation noise rms for all bands, as a fraction of T_cmb
fsigma_T = 1. #0.01
fsigma_P = 2. #0.01
# Define input models and their amplitudes/parameters
#dust_model = models.DustMBB(amp_I=150., amp_Q=10., amp_U=10., dust_beta=1.6, dust_T=20.)
dust_model = models.DustMBB( amp_I=rj2cmb(353e9, 150.),
amp_Q=rj2cmb(353e9, 10.),
amp_U=rj2cmb(353e9, 10.),
dust_beta=1.6, dust_T=20. )
sync_model = models.SyncPow( amp_I=30., amp_Q=10., amp_U=10., sync_beta=-3.2 )
cmb_model = models.CMB( amp_I=50., amp_Q=0.6, amp_U=0.6 )
name_in = "MBBSync"
#name_in = "SimpleMBBSync"
mods_in = [cmb_model, dust_model, sync_model]
#mods_in = [sync_model, cmb_model]
amps_in = np.array([m.amps() for m in mods_in])
params_in = np.array([m.params() for m in mods_in])
# Define models to use for the fitting
name_fit = "MBBSync"
#name_fit = "SimpleMBBSync"
mods_fit = [cmb_model, dust_model, sync_model]