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config_test.yml
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general:
ignore_scale_cuts : False # If True ignore any values of kmax
cosmo:
Omega_c : 0.2664315
Omega_b : 0.0491685
h : 0.6727
n_s : 0.9645
#A_s : 2.105e-9
sigma8 : 0.831
extra_parameters :
camb :
dark_energy_model : 'ppf'
# transfer_function : 'eisenstein_hu' # If you want to specify the transfer function you can do so here
# If the transfer function is not specified, it defaults to using CAMB
ccl_accuracy: # Example of a couple of parameters to modify.
spline_params: # You can change here any pyccl.spline_params
K_MAX_SPLINE : 100
gsl_params: # You can change here any pyccl.gsl_params
INTEGRATION_EPSREL: 1e-6
sources: # Sources
nbins : 5
ndens : 10 # in arcmin^-2 (it should be a scalar with the total number density or a list with each bin's)
ellipticity_error : 0.26
# Nz_type : 'SourceSRD2018' # For now, this is the assumed N(z) for sources -- check SRD 2018
# Nz_kwargs : # keyword arguments for SourceSRD2018 class -- can allow easy generalizations for the future
# Nz_alpha : 0.78
# Nz_z0 : 0.13
# Nz_sigmaz : 0.05
Nz_type : 'ZDistFromFile'
Nz_kwargs :
input_file : 'data/srd_source_bins_y1.txt'
format : 'ascii'
# input_file : 'data/srd_source_bins_year_1.npy'
mult_bias : 0 # Scalar or list (if list, then it's a value per bin)
delta_z : 0 # Photo-z shift, Scalar or list (if list, then it's a value per bin)
ia_class: 'wl.LinearAlignmentSystematic'
ia_bias : 0. # ia-bias
alphaz : 0.
z_piv : 1.0
ia_kwargs : # Left here for future extensions to pass as optional parameter to IA-systematics
lenses : # Lenses
nbins : 5
ndens : 18 # in arcmin^-2, same convention as for sources
delta_z : 0 # Photo-z shift for lenses (if ommited it assumes no shift)
Nz_type : 'ZDistFromFile'
Nz_kwargs :
input_file : 'data/srd_lens_bins_y1.txt'
format : 'ascii'
# input_file : 'data/srd_lens_bins_year_1.npy'
# Nz_type : 'LensSRD2018' # For now, this is the assumed N(z) for lenses -- check SRD 2018
# Nz_kwargs : # keyword arguments for LensSRD2018 class -- can allow easy generalizations for the future
# Nz_width : 0.2 # Photo-z bin-width
# Nz_center : np.arange(1, 6)*0.2 + 0.1 # Photo-z bin centers (it can be a list of values or some np array)
# Nz_sigmaz : 0.03 # Sigma of photo-z Gaussian smearing
# Nz_alpha : 0.94 # N(z) alpha parameter
# Nz_z0 : 0.26 # N(z) z-pivot
# bias_type : 'inverse_growth' # Inverse growth bias or custom
# bias_kwargs :
# b0 : 1.33 # Reverse engineeering the bias values from the SRD
# Another option is to use the `custom` bias type, but it requires a value per bin
bias_type : 'custom'
bias_kwargs :
b : [1.562362, 1.732963, 1.913252, 2.100644, 2.293210] # Values from SRD
statistics:
galaxy_density_cl : # The statistics are supposed to be SACC statistics
tracer_combs : [[0, 0], [1, 1], [2, 2], [3, 3], [4, 4]] # These need to be lists for now
ell_edges : np.geomspace(20, 15000, 21, endpoint=True) # bandpower edges
kmax : 0.201 # scale cut (in Mpc^-1)
# kmax : None
galaxy_shear_cl_ee :
tracer_combs : [[0, 0], [0, 1], [0, 2], [0, 3], [0, 4],
[1, 1], [1, 2], [1, 3], [1, 4],
[2, 2], [2, 3], [2, 4],
[3, 3], [3, 4],
[4, 4]]
ell_edges : np.geomspace(20, 15000, 21, endpoint=True)
kmax : None
galaxy_shearDensity_cl_e :
tracer_combs : [[0, 2], [0, 3], [0, 4], [1, 3], [1, 4], [2, 4], [3, 4]]
ell_edges : np.geomspace(20, 15000, 21, endpoint=True)
kmax : 0.201
fiducial_sacc_path : test_sacc.sacc
cov_options:
# Several options implemented -- Gaussian internal
#cov_type : 'gaus_internal'
#fsky : 0.3
# ------
# Or you can also get it from a file
cov_type : 'SRD'
SRD_cov_path : './data/Y1_3x2_SRD_cov.npy'
# Or using TJPCov
#cov_type : 'tjpcov'
#IA : 0.0
#fsky: 0.3
#binning_info :
# ell_edges : np.geomspace(20, 15000, 21, endpoint=True).astype(np.int32)
fisher:
var_pars: ['Omega_c', 'sigma8', 'n_s', 'w0', 'wa', 'Omega_b', 'h', 'lens0_bias', 'lens1_bias',
'lens2_bias', 'lens3_bias', 'lens4_bias',
'src0_mult_bias', 'src1_mult_bias', 'src2_mult_bias', 'src3_mult_bias', 'src4_mult_bias',
'lens0_delta_z', 'lens1_delta_z', 'lens2_delta_z', 'lens3_delta_z', 'lens4_delta_z',
'src0_delta_z', 'src1_delta_z', 'src2_delta_z', 'src3_delta_z', 'src4_delta_z']
# parameters: # TODO: For now priors are ignored
# Omega_c: [0.1, 0.26, 0.9]
# A_s: [1e-9, 4e-9]
# #sigma8: [0.4, 0.81, 1.2]
# w0: [-1.8, -1.0, -0.2]
# wa: [-4, 0.0, 0.5]
# h: [0.5, 0.6727, 0.8]
# n_s: [0.9, 0.9645, 1.0]
# #mult_bias: [-0.1 0.0 0.1]
step: 1e-2
output: 'output/fisher.dat'
fid_output: 'output/fiducials.dat'
fisher_bias:
biased_dv: '' # Path to file containing modified data vector with the systematic shift to probe
# If the file is provided it should be a FITS or ASCII file with the same binning as the
# fiducial data vector and the column dv_sys
bias_params:
Omega_c: 0.27
lens0_bias: 1.3
lens0_delta_z: 0.01
src3_delta_z: 0.005
postprocess:
latex_table: output/latex_table.tex
triangle_plot: output/triangle_plot.pdf
facecolor: blue
pairplots: [(w0, wa), (omega_c, sigma8)]
outdir: 'output/'
CL:
- 0.68
- 0.95