sacc_like
Generic 2-point measurement Gaussian likelihood using sacc format
File |
likelihood/sacc/sacc_like.py |
Attribution |
CosmoSIS Team |
URL |
This module implements a general likelihood of tomographic 2-point measuremnts of various quantities, including galaxy density, cosmic shear, intrinsic alignments, CMB lensing, and the various cross powers between these.
This is a first version of a general code using the SACC format developed for DESC. See github.com/LSSTDESC/sacc for more details
Note a recent change: by default we now do not renormalize any bandpower weights, whereas before we did. This is to allow for non-unit-sum weights such as those produced by NaMaster.
Assumptions
A Gaussian likelihood approximation for two-point measurements
Data supplied in a specific file format
Setup Parameters
Name |
Type |
Default |
Description |
|---|---|---|---|
data_file |
str |
Filename of the sacc file to use. |
|
data_sets |
str |
all |
Space-separated list of which data sets from within the file to use for likelihoods. |
keep_tracers |
str |
Regular expression to select tracers to use. |
|
angle_range_{dataset}_{i}_{j} |
str |
Pair of real numbers. If set, for the given data set and pair of bins, cut down the data used to this angular range (min and max) |
|
cut_{dataset} |
str |
Space-separated list of i,j pairs. (no spaces within the pair, just betwen them, e.g. cut_lss = 1,2 1,1 3,4. Remove this bin from the likelihood. |
|
{name}_section |
str |
Various depending on name. |
For each {name} in the data types used from the file, a cosmosis block section to look for the theory predictions in. |
save_theory |
str |
If set, save the theory predictions used in the likelihood to this sacc file. |
|
save_realization |
str |
If set, save a simulated data set to this sacc file. |
|
covariance_realizations |
int |
-1 |
If >0, assume that the Covariance matrix was estimated from a set of MC simulations and should thus have the Anderson-Hartlap factor applied to increase its size. If zero, assume infinite number of realizations. |
sellentin |
bool |
False |
If set, use the Sellentin-Heavens 2016 change to the likelihood to account for this distribution of the covariance estimates. This changes the likelihood to a student’s-t form. Note that this invalidates the simulated data sets used for the ABC sampler. |
like_name |
str |
2pt |
The name of the likelihood to save. |
likelihood_only |
bool |
False |
Skip saving the covariance, inverse, simulation, etc. Saves some time. |
kind |
str |
cubic |
The interpolation to do into the theory splines. See scipy.interpolate.interp1d. |
Input values
Section |
Name |
Type |
Default |
Description |
|---|---|---|---|---|
shear_cl |
ell |
real 1d |
If a Fourier-space measurement is used, the angular wave-number of the predicted theory curves. The name of the section here depends on the data type used from the file. It might be galaxy_cl or shear_cl, for example. |
|
bin_{i}_{j} |
real 1d |
For various i,j depending what is found in the file, the theory predictions for this value. For example, C_ell. |
Output values
Section |
Name |
Type |
Description |
|---|---|---|---|
likelihoods |
2pt_like |
real |
Gaussian likelihood value. Name can be changed in parameter file (see above) for this and the other outputs below. |
data_vector |
2pt_data |
real 1d |
The full vector of data points used in the likelihood |
2pt_theory |
real 1d |
The full vector of theory points used in the likelihood |
|
2pt_covariance |
real 2d |
The covariance matrix used |
|
2pt_inverse_covariance |
real 2d |
The inverse covariance matrix (precision matrix) used. |
|
2pt_simulation |
real 1d |
A simulated data set from the given theory and covariance matrix. |
|
2pt_angle |
real 1d |
The angular scale used for each data point. |
|
2pt_bin1 |
int 1d |
The first bin index used for each data point |
|
2pt_bin2 |
int 1d |
The second bin index used for each data point |