DESC Blinding Modules

This repostory (under development) contains the blinding modules for blinding at the following levels of the analysis:

Data-vector measurements
Posterior distribution [not yet developed]
(TBC) Catalogues

Other info: the previous README can be found here

⚠️ Important notice ⚠️ : the term "blinding" and the name "smokescreen" are used as placeholders and are not final. The final name will be decided by the DESC Blinding Working Group in discussions with the collaboration.

Installation

Creating a new environment:

You can create a new conda environment with the required packages using the environment.yml file:

conda env create -f environment.yml

This will create a new environment called desc_blinding with the required packages. You can activate the environment with:

conda activate desc_blinding

Using an existing environment:

If you want to install the blinding package in an existing environment, you can install it using:

conda activate myenv
conda env update --name myenv --file environment.yml --prune

After installing the dependencies from environment.yml, you can install the blinding package using:

python -m pip install [-e] .

The -e flag is optional and installs the package in editable mode (useful for development).

Testing the installation

You can test the installation by running the tests:

pytest .

Usage: Data-vector Blinding

Likelihood Requirements

The blinding module requires the Firecrown likelihoods to be built with certain requirements. First we bust be able to build the likelihoods providing a sacc object with the measurements for the data-vector:

def build_likelihood(build_parameters):
    """
    This is a generic likelihood theory model 
    for a generic data vector.
    """
    sacc_data = build_parameters['sacc_data']

(This is simular to what is currently done in TXPipe)

The likelihood module also must have a method .compute_theory_vector(ModellingTools) which calls for the calculation of the theory vector inside the likelihood object.

The likelihood can be provided either as a path to the python file containing the build_likelihood function or as a python module. In the latter case, the module must be imported.

Blinding a data vector

TL;DR: Check the notebooks/test_blinding_prototype.ipynb for a working example using the des_y1_3x2pt_PT example from firecrown.

The blinding module can be used to blind the data-vector measurements. The module can be used as follows:

# import the module
import pyccl as ccl
from blind import Smokescreen
# import the likelihood that contains the model and data vector
[...]
import my_likelihood

# create the cosmology ccl object
cosmo = ccl.Cosmology(Omega_c=0.27, 
                      Omega_b=0.045, 
                      h=0.67, 
                      sigma8=0.8, 
                      n_s=0.96, 
                      transfer_function='bbks')
# load a sacc object with the data vector [FIXME: this is a placeholder, the sacc object should be loaded from the likelihood]
sacc_data = sacc.Sacc.load_fits('path/to/data_vector.sacc')
# create a dictionary of the necessary firecrown nuisance parameters
syst_dict = {
            "ia_a_1": 1.0,
            "ia_a_2": 0.5,
            "ia_a_d": 0.5,
            "lens0_bias": 2.0,
            "lens0_b_2": 1.0,
            "lens0_b_s": 1.0,
            "lens0_mag_bias": 1.0,
            "src0_delta_z": 0.000,
            "lens0_delta_z": 0.000,}
# create the smokescreen object
smkscr = Smokescreen(cosmo, syst_dict, sacc_data, my_likelihood, 
                    {'Omega_c': (0.22, 0.32), 'sigma8': (0.7, 0.9)})
# blind the data vector
smkscr.calculate_blinding_factor()
blinded_dv = smkscr.apply_blinding_to_likelihood_datavec()

Legacy Blinding

Legacy Blinding scripts for 2pt data vector blinding with Cosmosis moved to a new repository.

The Legacy Blinding repositories contains an updated version of Jessie Muir's scripts for blinding data-vectors using Cosmosis. The scripts have been adapted to work as a standalone module with Cosmosis V2 and SACC [Under Construction!].

Under development. For questions contact @arthurmloureiro, @jessmuir, or @jablazek

LSSTDESC / Blinding