supernnova 3.0.1

framework for Bayesian, Neural Network based supernova light-curve classification

Read the documentation

For the main branch: https://supernnova.readthedocs.io

The paper branch differs slightly from the master. Take a look to "changelog_paper_to_new_branch" or Build the docs for this branch.

Installation

Clone this repository (preferred)

git clone https://github.com/supernnova/supernnova.git

or install pip module (check versioning)

pip install supernnova

Read the paper

Links to the publication: MNRAS,ArXiv. All results quoted in these publications were produced using the branch "paper" which is frozen for reproducibility.

Please include the full citation if you use this material in your research: A Möller and T de Boissière, MNRAS, Volume 491, Issue 3, January 2020, Pages 4277–4293.

Table of contents

1. Repository overview
2. Getting Started 0. [Use Poetry in new releases]
   1. With Conda
   2. With Docker
3. Usage
4. Reproduce paper
5. Pipeline Description
6. Running tests
7. Build the docs

Repository overview

├── supernnova              --> main module
    ├──data                 --> scripts to create the processed database
    ├──visualization        --> data plotting scripts
    ├──training             --> training scripts
    ├──validation           --> validation scripts
    ├──utils                --> utilities used throughout the module
├── tests                   --> unit tests to check data processing
├── sandbox                 --> WIP scripts

Getting started

With Conda

cd env

# Create conda environment
conda create --name <env> --file <conda_file_of_your_choice>

# Activate conda environment
source activate <env>

With Docker

cd env

# Build docker images
make cpu  # cpu image
make gpu  # gpu image (requires NVIDIA Drivers + nvidia-docker)

# Launch docker container
python launch_docker.py (--use_gpu to run GPU based container)

For more detailed instructions, check the full setup instructions

Usage

When cloning this repository:

# Create data
python run.py --data  --dump_dir tests/dump --raw_dir tests/raw --fits_dir tests/fits

# Train a baseline RNN
python run.py --train_rnn --dump_dir tests/dump

# Train a variational dropout RNN
python run.py --train_rnn --model variational --dump_dir tests/dump

# Train a Bayes By Backprop RNN
python run.py --train_rnn --model bayesian --dump_dir tests/dump

# Train a RandomForest
python run.py --train_rf --dump_dir tests/dump

When using pip, a full example is https://supernnova.readthedocs.io

# Python
import supernnova.conf as conf
from supernnova.data import make_dataset

# get config args
args =  conf.get_args()

# create database
args.data = True            # conf: making new dataset
args.dump_dir = "tests/dump"        # conf: where the dataset will be saved
args.raw_dir = "tests/raw"      # conf: where raw photometry files are saved 
args.fits_dir = "tests/fits"        # conf: where salt2fits are saved 
settings = conf.get_settings(args)  # conf: set settings
make_dataset.make_dataset(settings) # make dataset

Reproduce paper results

Please change to branch paper:

python run_paper.py

General pipeline description

• Parse raw data in FITS format
• Create processed database in HDF5 format
• Train Recurrent Neural Networks (RNN) or Random Forests (RF) to classify photometric lightcurves
• Validate on test set

Running tests with py.test

PYTHONPATH=$PWD:$PYTHONPATH pytest -W ignore --cov supernnova tests

Build docs

cd docs && make clean && make html && cd ..
firefox docs/_build/html/index.html