labrador

Combine simulation-based inference with gravitational-wave specific tricks such as relative binning, folding, and coordinate transformations, to get the best of both worlds.

Installation

Clone repository:

git clone git@github.com:jroulet/labrador.git

Create environment:

conda create -n ENVIRONMENT_NAME pip cogwheel-pe sbi -c conda-forge
conda activate ENVIRONMENT_NAME

(replace ENVIRONMENT_NAME by a name of your choice, e.g. labrador.)

Note: it's better to install those packages with conda rather than pip, at least in the LDG computers.

Install:

cd labrador
pip install -e .

Usage

See notebooks/workflow.ipynb or use the cheatsheet below.

Cheatsheet

1. Create and populate RUNDIR (uses HTCondor)

lab-setup-rundir PARENTDIR
lab-generate-data-htcondor RUNDIR --submit-arg accounting_group=ACCOUNTING_GROUP --submit

Note: this submits a .dag file that in turn orchestrates several .sub files. If you get a crash due to insufficient resources, you may adjust the requests in the corresponding .sub, delete from the .dag those jobs that have already succeeded, delete the .rescue file, and resubmit the .dag with condor_submit_dag DAGMAN_PATH.

2. Create and populate RESCALERDIR (uses GPU)

lab-setup-rescalerdir PRIORDIR
python -m labrador.rescaling RESCALERDIR

3. Create and populate SBIDIR (uses GPU)

lab-setup-sbidir RESCALERDIR
python -m labrador.training SBIDIR

4. Create and populate UNFOLDERDIR

lab-setup-unfolderdir RESCALERDIR
python -m labrador.unfolding UNFOLDERDIR