Quickstart guide (pip)

Welcome to SuperNNova! This is a quick start guide so you can start testing our framework. This guide assumes you have installed it with pip, if you want to use the GitHub cloning please refer to Quickstart guide (GitHub).

Installation

Pip install

pip install supernnova

Please beware that SuperNNova only runs properly in Unix systems.

Setup your environment. 3 options

Create a conda virtual env Environment configuration (preferred).

Create a docker image: Docker .

Install packages manually. Inspect env/conda_env.yml (or env/conda_gpu_env.yml when using cuda) and pyproject.toml for the list of packages we use.

Usage

For quick tests, a database that contains a limited number of light-curves is provided. It is located in tests/raw. For more information on the available data, check Data walkthrough. An example of running as module can be found in sandbox/example_run_moduler_snn.py.

Build the database

In the parent folder, you can launch python or ipython with the following:

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

# get config args
command_arg = "make_data"
args = conf.get_args(command_arg)

# create database
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(command_arg, args) # conf: set settings
make_dataset.make_dataset(settings)     # make dataset

Train an RNN

import supernnova.conf as conf
from supernnova.training import train_rnn

# get config args
command_arg = "train_rnn"
args = conf.get_args(command_arg)

# train rnn
args.dump_dir = "tests/dump"            # conf: where the dataset is saved
args.nb_epoch = 2                       # conf: training epochs
settings = conf.get_settings(command_arg, args) # conf: set settings
train_rnn.train(settings)               # train rnn

Validate an RNN

import supernnova.conf as conf
from supernnova.validation import validate_rnn

# get config args
command_arg = "validate_rnn"
args = conf.get_args(command_arg)

# validate rnn
args.dump_dir = "tests/dump"                    # conf: where the dataset is saved
settings = conf.get_settings(command_arg, args)         # conf: set settings
validate_rnn.get_predictions(settings)          # classify test set