Smokescreen 0.0.0

Smokescreen: DESC Blinding Unified Library. Based on the Muir et al. (2021) data-vector blinding method.

Smokescreen: DESC Modules for data concealment (blinding)

This repostory (under development) contains the modules for data concealment (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

:warning: Important notice :warning: : the term "blinding" is used in the context of data concealment for scientific analysis. We understand this is an outdated term and we are working to update it to a more appropriate term. If you have any suggestions, please let us know.

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_smokescreen with the required packages. You can activate the environment with:

conda activate desc_smokescreen

Using an existing environment:

If you want to install the Smokescreen 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 Smokescreen 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 Concealment (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.

Concealing (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.

From the commandline:

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

python -m smokescreen --config configuration_file.yaml

You can find an example of a configuration file in examples/cosmic_shear/blind_cosmic_shear_example.yaml. Or you can use the following command to create a template configuration file:

python -m smokescreen --print_config > template_config.yaml

Note that the reference_cosmology is optional. If not provided, the CCL VanillaLCDM reference cosmology will be the one used to compute the data vector.

From a notebook/your code:

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 smokescreen import ConcealDataVector
# 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
smoke = ConcealDataVector(cosmo, syst_dict, sacc_data, my_likelihood, 
                          {'Omega_c': (0.22, 0.32), 'sigma8': (0.7, 0.9)})
# conceals (blinds) the data vector
smoke.calculate_concealing_factor()
concealed_dv = smoke.apply_concealing_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