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params_cov.yml
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params_cov.yml
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# Global parameters
global:
# Healpix resolution
nside: 2048
# Output directory
output_dir: '/mnt/extraspace/nikfilippas/yxg/output_cov'
jk:
# Do you want to do jackknives?
do: True
# Healpix resolution defining the jackknife regions
nside: 8
# Should we store the mode-coupling matrix?
store_mcm: False
# Information about ell bandpowers
bandpowers:
# Bandpower mode
type: "linlog"
# Constant width
nlb: 20
# Threshold between linear and logarithmic
lsplit: 52
# Width of linar part
nlb_lin: 10
# Number of logarithmic bins
nb_log: 20
# Masks
# Add an entry for each mask used in the analysis
masks:
# Given each mask a unique name (key)
mask_lowz: "data/maps/mask_v3.fits"
mask_tsz: "data/maps/mask_planck60.fits"
mask_545: "data/maps/mask_planck20.fits"
# Maps
maps:
- name: "wisc2"
type: "g"
map: "data/maps/2dstarsub_WISC_cleaned_public.bin_0.15_z_0.2.Pix512.fits"
mask: "mask_lowz"
dndz: "data/dndz/WISC_bin2.txt"
beam: False
model:
M0: 10.549518891252172
M1: 11.794952075732667
Mmin: 10.549518891252172
alpha: 1.0
b_hydro: 0.18613969938649388
beta_gal: 1.0
beta_max: 1.0
fc: 1.0
r_corr: -0.6007288486023787
sigma_lnM: 0.15
width: 0.8510867851364564
systematics:
- 'scos_plates'
- 'Star_contamination/2dstarcorr_WISC_cleaned_public.bin_0.15_z_0.2.Pix512.fits'
- 'data/maps/lambda_sfd_ebv.fits'
- name: "y_milca"
type: "y"
map: "data/maps/milca_ymaps.fits"
mask: "mask_tsz"
beam: 10.
systematics:
- 'data/maps/lambda_sfd_ebv.fits'
- name: "dust_545"
type: "d"
map: "data/maps/HFI_SkyMap_545_2048_R2.02_full.fits"
mask: "mask_545"
beam: 10.
# MCMC
mcmc:
# Name for this run
run_name: 'covariance'
# Maximum wavenumber in units of 1/Mpc
kmax: 1.
# Number of samples in redshift for each bin
nz_points_g: 32
# Use logarithmic sampling in redshift?
z_log_sampling: True
# Correct the halo model in the transition regime?
hm_correct: True
# Print debug information?
debug: False
# Emcee parameters
n_walkers: 24
n_steps: 0
# Continue MCMC from previous iteration?
continue_mcmc: True
# Save parameters (separated by whitespace)
save_par: False
# Cosmological mass function
mfunc: 'tinker'
# Halo bias model
hbias: 'tinker10'
# Model parameters
params:
# Each item here corresponds to one of the parameters
# used in our model
- name: "M0"
# Label for plotting
label: "M_0"
# Is this the same as another parameter? If so, put its name here.
alias: "Mmin"
# Is this a free parameter?
vary: False
# Fiducial value
value: 10.549518891252172
# Prior
prior:
# Allowed: "Gaussian" or "TopHat"
type: "TopHat"
# If "TopHat", put edges here.
# If "Gaussian", put [mean,sigma].
values: [10.,16.]
- name: "M1"
label: "M_1"
vary: True
value: 11.794952075732667
prior:
type: "TopHat"
values: [10.,16.]
- name: "Mmin"
label: "M_{\\rm min}"
vary: True
value: 10.549518891252172
prior:
type: "TopHat"
values: [10.,16.]
- name: "fc"
label: "f_c"
vary: False
value: 1.
prior:
type: "TopHat"
values: [0.,1.]
- name: "alpha"
label: "\\alpha"
vary: False
value: 1.
prior:
type: "TopHat"
values: [0.,3.]
- name: "beta_max"
label: "\\beta_{\\rm max}"
alias: "beta_gal"
- name: "beta_gal"
label: "beta_g"
vary: False
value: 1.
prior:
type: "TopHat"
values: [0.1,10.]
- name: "sigma_lnM"
label: "\\sigma_{{\\rm ln}M}"
vary: False
value: 0.15
prior:
type: "TopHat"
values: [0.01,1.]
- name: "b_hydro"
label: "b_H"
vary: True
value: 0.18613969938649388
prior:
type: "TopHat"
values: [0.,1.]
- name: "r_corr"
label: "\\rho_{gy}"
vary: True
value: -0.6007288486023787
prior:
type: "TopHat"
values: [-1.,1.]
- name: "width"
label: "w_{N(z)}"
vary: True
value: 0.8510867851364564
prior:
type: "TopHat"
values: [0.8, 1.2]
# List all the different combinations of
# power spectra you want to analyze
data_vectors:
- type: 'Cl'
name: 'wisc2'
covar_type: 'comb_m'
twopoints:
- tracers: ['wisc2','wisc2']
lmin: 10
- tracers: ['wisc2','y_milca']
lmin: 0