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defaults.py
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197 lines (191 loc) · 9.11 KB
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"""provides some useful default values"""
from __future__ import division,print_function,absolute_import
import numpy as np
from param_manager import PowerParamManager
# default cosmology is Planck 2015 TT+lowP+lensing+ext (arxiv 1502.01589v3 page 32)
cosmology = { 'Omegabh2':0.02227,
'Omegach2':0.1190390665,#calculated, paper give 0.1184
'Omegab' :0.0483037300370249,#calculated
'Omegac' : 0.258196269962975, #calculated
'Omegamh2': 0.1413090665,#calculated
'OmegaL' : 0.6935,
'OmegaLh2': 0.3197319335,#calculated
'Omegam' : .3065,
'H0' : 67.90,
'sigma8' : 0.8296437384606381,#calculated
'h' :.6790,
'Omegak' : 0.0,
'Omegakh2': 0.0,
'Omegar' : 0.0,
'Omegarh2': 0.0,
'ns' : 0.9681,
'tau' : 0.067,
'Yp' :0.251,
'As' : 2.143*10**-9,
'LogAs' :-19.9619, #calculated
'w' :-1.0,#not from planck
'de_model':'constant_w',#dark energy model
#'zstar' :1089.90,#redshift at last scattering
'mnu' :0.
}
# wmap 9 year cosmology
cosmology_wmap = { 'Omegabh2':0.0227,
'Omegach2':0.1099,
'Omegab' :0.04390715667311412,
'Omegac' :0.21257253384912958,
'Omegamh2':0.1326,
'OmegaL' :0.7435203094777563,
'OmegaLh2':0.3844,
'Omegam' :0.2564796905222437,
'H0' :71.9027120489902,
'sigma8' :0.7924312492067015,# #from the code
'h' :0.719027120489902,
'Omegak' : 0.0,#
'Omegakh2': 0.0,#
'Omegar' : 0.0,#
'Omegarh2': 0.0,#
'ns' : 0.963,
'tau' : 0.087,
'Yp' :0.299,
'As' :2.139272540605297e-09,
'LogAs' :-19.9628,
'w' :-1.0,
'de_model':'constant_w',#dark energy model
'mnu' :0.}
#cosmology from jdem 2008 working group paper arxiv:0901.0721v1
cosmology_jdem = { 'ns' : 0.963,
'Omegamh2': 0.1326,
'Omegabh2': 0.0227,
'Omegakh2': 0.,
'OmegaLh2': 0.3844,
'dGamma' : 0.,#parameter we don't need
'dM' :0.,#parameter we don't need
'LogG0' :0.,#parameter we don't need
'LogAs' :-19.9628,
'w' :-1.,
'de_model':'constant_w',
'mnu' :0.
}
lensing_params = { 'pmodel' :'halofit', #default method for finding p grid
'n_gal' :None,#118000000*6.,#118000000 galaxies/rad^2=10 galaxies/arcmin^2
'delta_l' :1., #binning window
'sigma2_e' :0.27**2*2, #other noise term
'sigma2_mu' :1.2, #noise term for magnification
'smodel' :'constant', #type, current options are 'gaussian','constant','cosmolike','custom_z','nz_matcher'
'z_min_dist' :0.,
'z_max_dist' :np.inf,
'zbar' :0.1, #mean of gaussian source distribution
'sigma' :0.4, #stdev of gaussian source distribution
'epsilon' :0.0001,#small paramater
'l_min' :20,#start of minimum l bin
'l_max' :3000,#start of maximum l bin
'n_l' :20,#number of l bins
}
sw_observable_list = np.array(['len_shear_shear'])
sw_survey_params = { 'needs_lensing' : True,
'cross_bins' : True}
dn_params = {'nz_select' :'CANDELS',
'sigma0' :0.001,
'n_extend' :5}
lw_observable_list = np.array(['d_number_density'])
lw_survey_params = { 'cross_bins': False}
basis_params = { 'n_bessel_oversample' :400000,
'x_grid_size':100000}#convergence related
polygon_params = {'res_healpix':6,'n_double':30}
nz_params = { 'data_source' :'./data/CANDELS-GOODSS2.dat',
'i_cut' :24,
'area_sterad' :0.0409650328530259/3282.80635,
'smooth_sigma' :0.04,
'n_right_extend':4,
'z_resolution' :0.0001,
'mirror_boundary':True,
'suppress' :True,
'z_cut' :0.04}
nz_params_wfirst_gal = { 'data_source' :'./data/CANDELS-GOODSS2.dat',
'area_sterad' :0.0409650328530259/3282.80635,
'smooth_sigma' :0.02,
'n_right_extend':8,
'z_resolution' :0.001,
'mirror_boundary':True,
'suppress' :True,
'z_cut' :0.04,
'i_cut' :25.3
}
nz_params_lsst_use = { 'i_cut' :24}
nz_params_wfirst_lens = { 'data_source' :'./data/H-5x140s.dat',
'area_sterad' : 0.040965*np.pi**2/180**2,
'smooth_sigma' :0.1,
'n_right_extend':5,
'z_resolution' :0.001,
'mirror_boundary':True,
'suppress' :True,
'z_cut' :0.04
}
nz_params_lsst = { 'data_source' :'./data/CANDELS-GOODSS2.dat',
'i_cut' :25.3,#lsst assumes they will get 10 billion galaxies with i<26 in 20000 deg^2, 4 billion lensing quality with i<25.3
'area_sterad' :0.0409650328530259/3282.80635,
'smooth_sigma' :0.05,
'n_right_extend':4,
'z_resolution' :0.0001,
'mirror_boundary':True,
'suppress' :True,
'z_cut' :0.04
}
hmf_params = { 'log10_min_mass' : 11, #saturated mitigation achieved with cutoff ~11
'log10_max_mass' : 18,
'n_grid' : 500}
#'z_resolution' : 0.01,
#'z_min' : 0.,
#'z_max' : 4.05,
#'n_z' : 405}
fpt_params = { 'C_window':0.75,
'n_pad':1000,
'low_extrap':-5,
'high_extrap':5,
'nu' :-2}
camb_params = { 'npoints':1000,
'minkh':1.1e-4,
'maxkh':10., #1e5
'kmax':10.0,#20 #may need to be higher for some purposes,like 100, but makes things slower
'leave_h':False,
'force_sigma8':False,
'return_sigma8':False,
'accuracy':1,
'pivot_scalar':0.05
}
#amara refregier 2006 parameter forecast stuff
prior_fisher_params = { 'row_strip' :np.array([3,5,6,7]),
'fisher_source' :'data/F_Planck_tau0.01.dat',
'n_full' :45,
'n_de' :36,
'z_step' :0.025
}
halofit_params = { 'r_max':6,#5
'r_min':0.05,#0.05
'r_step':0.01,
'max_extend':10,
'extrap_wint':True,#
'k_fix' :500, #require integral in wint to go up this far if extrapolating
'min_kh_nonlinear' :0.005, #transition between linear and nonlinear power camb power spectrum
'smooth_width': 0.002 #scale over which to smooth linear to nonlinear transition to avoid spikes in derivatives
}
wmatcher_params = { 'w_step':0.01,
'w_min':-3.50,
'w_max':0.1,
'a_step':0.0001,
'a_min':0.001,
'a_max':1.00
}
matter_power_params = { 'needs_halofit' :True,
'needs_fpt' :True,
'needs_wmatcher':True,
'needs_camb_w_grid':True,
'w_edge' : 0.08, #0.08
'w_step' : 0.002, #0.0005
'min_n_w' : 3,
'a_min' : 0.05,
'a_max' : 1.0,
'a_step' : 0.05,
}
lw_param_list = np.array([{'dn_params':dn_params,'n_params':nz_params_wfirst_gal,'mf_params':hmf_params}])
power_params = PowerParamManager(matter_power_params,wmatcher_params,halofit_params,camb_params,fpt_params)