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test_config_sampling.yml
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test_config_sampling.yml
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# These parameters are accessible to all stages
global:
# HEALPix resolution parameter
nside: 64
# Use D_l = l*(l+1)*C_l/(2*pi) instead of C_l?
compute_dell: True
BBCompSep:
# Sampler type. Options are:
# - 'emcee': a full MCMC will be run using emcee.
# - 'fisher': only the Fisher matrix (i.e. the likelihood
# Hessian matrix) will be calculated around the fiducial
# parameters chosen as prior centers.
# - 'maximum_likelihood': only the best-fit parameters will
# be searched for.
# - 'single_point': only the chi^2 at the value used as the
# center for all parameter priors will be calculated.
# - 'timing': will compute the average time taken by one
# likelihood computation.
sampler: 'maximum_likelihood'
# If you chose emcee:
# Number of walkers
nwalkers: 24
# Number of iterations per walker
n_iters: 1000
# Likelihood type. Options are:
# - 'chi2': a standard chi-squared Gaussian likelihood.
# - 'h&l': Hamimeche & Lewis likelihood.
likelihood_type: 'h&l'
# Which polarization channels do you want to include?
# Can be ['E'], ['B'] or ['E','B'].
pol_channels: ['E','B']
# Scale cuts (will apply to all frequencies)
l_min: 30
l_max: 120
# CMB model
cmb_model:
# Template power spectrum. Should contained the lensed power spectra
# with r=0 and r=1 respectively.
cmb_templates: ["./examples/data/camb_lens_nobb.dat",
"./examples/data/camb_lens_r1.dat"]
# Free parameters
params:
# tensor-to-scalar ratio
# See below for the meaning of the different elements in the list.
r_tensor: ['r_tensor', 'tophat', [-0.1, 0.00, 0.1]]
# Lensing amplitude
A_lens: ['A_lens', 'tophat', [0.00,1.0,2.00]]
# Foreground model
fg_model:
# Add one section per component. They should be called `component_X`,
# starting with X=1
component_1:
# Name for this component
name: Dust
# Type of SED. Should be one of the classes stored in fgbuster.components
# https://github.com/fgbuster/fgbuster/blob/master/fgbuster/component_model.py
sed: Dust
# Type of power spectra for all possible polarization channel combinations.
# Any combinations not added here will be assumed to be zero.
# The names should be one of the classes in bbpower/fgcls.py. This is quite
# limiter for now, so consider adding to it if you want something fancier.
cl:
EE: ClPowerLaw
BB: ClPowerLaw
# Parameters of the SED
sed_parameters:
# The key can be anything you want, but each parameter in the model
# must have a different name.
# The first item in the list is the name of the parameter used by fgbuster
# The second item is the type of prior. The last item are the numbers
# necessary to define the prior. They should be:
# - Gaussian: [mean,sigma]
# - tophat: [lower edge, start value, upper edge]
# - fixed: [parameter value]
# nu0-type parameters can only be fixed.
beta_d: ['beta_d', 'Gaussian', [1.59, 0.11]]
temp_d: ['temp', 'fixed', [19.6]]
nu0_d: ['nu0', 'fixed', [353.]]
cl_parameters:
# Same for power spectrum parameters
# (broken down by polarization channel combinations)
EE:
amp_d_ee: ['amp', 'tophat', [0., 10., "inf"]]
alpha_d_ee: ['alpha', 'tophat', [-1., -0.42, 0.]]
l0_d_ee: ['ell0', 'fixed', [80.]]
BB:
amp_d_bb: ['amp', 'tophat', [0., 5., "inf"]]
alpha_d_bb: ['alpha', 'tophat', [-1., -0.2, 0.]]
l0_d_bb: ['ell0', 'fixed', [80.]]
# If this component should be correlated with any other, list them here
cross:
# In this case the list should contain:
# [component name, prior type, prior parameters]
# Each of this will create a new parameter, corresponding to a constant
# scale- and frequency-independend correlation coefficient between
# the two components.
epsilon_ds: ['component_2', 'tophat', [-1., 0., 1.]]
component_2:
name: Synchrotron
sed: Synchrotron
cl:
EE: ClPowerLaw
BB: ClPowerLaw
sed_parameters:
beta_s: ['beta_pl', 'Gaussian', [-3.0, 0.3]]
nu0_s: ['nu0', 'fixed', [23.]]
cl_parameters:
EE:
amp_s_ee: ['amp', 'tophat', [0., 4., "inf"]]
alpha_s_ee: ['alpha', 'tophat', [-1., -0.6, 0.]]
l0_s_ee: ['ell0', 'fixed', [80.]]
BB:
amp_s_bb: ['amp', 'tophat', [0., 2., "inf"]]
alpha_s_bb: ['alpha', 'tophat', [-1., -0.4, 0.]]
l0_s_bb: ['ell0', 'fixed', [80.]]
BBPlotter:
# Maximum ell up to which things will be plotted
lmax_plot: 128
# Add total coadds to C_ell plots?
plot_coadded_total: False
# Add noise spectra to C_ell plots?
plot_noise: False
# Plot null tests?
plot_nulls: False
# Plot likelihood?
plot_likelihood: True