ztfparsnip 0.2.1

Generate noisified lightcurves based on the BTS sample and retrain Parsnip with these.

ztfparsnip

Retrain Parsnip for ZTF. This is achieved by using fpbot forced photometry lightcurves of the Bright Transient Survey. These are augmented (redshifted, noisifed and - when possible - K-corrected).

The package is maintained by A. Townsend (HU Berlin) and S. Reusch (DESY).

Usage

Create augmented training sample

from pathlib import Path
from ztfparsnip.create import CreateLightcurves
weights = {"sn_ia": 9400, "tde": 9400, "sn_other": 9400, "agn": 9400, "star": 9400}
if __name__ == "__main__":
    sample = CreateLightcurves(
        output_format="parsnip",
        classkey="simpleclasses",
        weights=weights,
        train_dir=Path("train"),
        plot_dir=Path("plot"),
        seed=None,
        phase_lim=True,
        k_corr=True,
    )
    sample.select()
    sample.create(plot_debug=False)

Train Parsnip with the augmented sample

from ztfparsnip.train import Train
if __name__ == "__main__":
    train = Train(classkey="simpleclasses", seed=None)
    train.run()

Evaluate

Coming soon.