lcfit.sncutils

This module provides utilities for executing single lightcurve fits and converting sncosmo results into structured pandas DataFrames or Series.

skysurvey.lcfit.sncutils.sncosmo_results_to_dataframe(result, flatten=True)

Convert a sncosmo fit result into a pandas DataFrame or flattened Series.

Parameters:

• result (result object returned by sncosmo.fit_lc().) –

  dict subclass with attribute access. It must provide:

  • result.param_names

  • result.vparam_names

  • result.parameters

  • result[“errors”]

  • result[“covariance”]

• flatten (bool, default is True.) – If False, return a DataFrame. If True, return a flattened pandas.Series.

Return type:

pandas.DataFrame or pandas.Series

skysurvey.lcfit.sncutils.sncosmo_fit_single(target_data, target_model, free_param, modelcov=True, keymap={}, **kwargs)

Fit a sncosmo model to a single lightcurve dataset and return the fit results as a pandas.Series.

Parameters:

• target_data (pandas.DataFrame) – dataframe containing the lightcurve data. It must contain [“time”, “band”, “flux”, “fluxerr”,”zp”, “zpsys”] (but see keymap).

• target_model (sncosmo.Model) – The model to fit.

• free_param (list) – model parameters to vary in the fit. (all if None)

• modelcov (bool) – Include model covariance when calculating chisq. If true, the fit is performed multiple times until convergence.

• keymap (dict) – Change the key naming convention. For instance to use fluxerr_tot for fluxerr use: keymap = {“fluxerr”: “fluxerr_tot”}

• sncosmo.fit_lc() (kwargs goes to)

Return type:

pandas.Series