TemperatureEstimator (xsnap.TemperatureEstimator)

Methods	Attributes
__init__()	tExplosion
clear()	norm
compute_pl_fit()	lo_norm_err
estimate()	hi_norm_err
	exp
	lo_exp_err
	hi_exp_err
	chi2_red
	temperatures

class TemperatureEstimator(tExplosion=None, norm=None, exponent=None)

Bases: object

Estimate temperature evolution from a supernova explosion given a set of temperature evolution data or power-law parameters.

If given a set of temperature evolution data, the TemperatureEstimator will fit the data in a power-law model and get the best-fit parameters.

tExplosion

Supernova time of explosion in MJD.

Type:

float

norm

Best-fit normalization for the power-law model.

Type:

float

lo_norm_err

Lower uncertainty of the normalization.

Type:

float

hi_norm_err

Upper uncertainty of the normalization.

Type:

float

exp

Best-fit exponent for the power-law model.

Type:

float

lo_exp_err

Lower uncertainty of the exponent.

Type:

float

hi_exp_err

Upper uncertainty of the exponent.

Type:

float

chi2_red

The reduced chi-squared of the fit.

Type:

float

temperatures

Estimated temperatures wrapped in a DataFrame table with columns ['time_since_explosion', 'lo_time_err', 'hi_time_err', 'temperature', 'lo_temp_err', 'hi_temp_err']

Type:

pandas.DataFrame

__init__(tExplosion=None, norm=None, exponent=None)

Initialization of the TemperatureEstimator class

Parameters:

• tExplosion (float, optional) – Supernova time of explosion in MJD. If None, must be set before estimating. Defaults to None.

• norm (float, optional) – Normalization for the power-law model (if known). Defaults to None.

• exponent (float, optional) – Exponent for the power-law model (if known). Defaults to None.

clear()

Reset all fitted parameters and clear the temperature table.

compute_pl_fit(time_since_explosion, temperature, mcmc=True, temp_err_lo=None, temp_err_hi=None, time_err_lo=None, time_err_hi=None, nwalkers=200, nsteps=6000, nburn=1000, show_plots=True)

Fit a power-law \(T(t) = \mathrm{norm} \times t^{\mathrm{exp}}\) to the data, either via Markov chain Monte-Carlo (MCMC) or simple least-squares (curve_fit()).

Parameters:

• time_since_explosion (array_like) – Times since explosion in days.

• temperature (array_like) – Observed/fitted temperatures data.

• mcmc (bool, optional) – If True, use MCMC. If False, use curve_fit(). Defaults to True.

• temp_err_lo (array_like, optional) – Lower uncertainty on temperature. Defaults to None.

• temp_err_hi (array_like, optional) – Upper uncertainty on temperature. Defaults to None.

• time_err_lo (array_like, optional) – Lower uncertainty on time. Defaults to None.

• time_err_hi (array_like, optional) – Upper uncertainty on time. Defaults to None.

• nwalkers (int, optional) – Number of MCMC walkers. Defaults to 200.

• nsteps (int, optional) – Number of MCMC steps per walker. Defaults to 6000.

• nburn (int, optional) – Number of burn-in steps to discard. Defaults to 1000.

• (bool (show_plots) – If True, display fitted and residual plots. Defaults to True.

• optional – If True, display fitted and residual plots. Defaults to True.

Returns:

selfTemperatureEstimator

The same TemperatureEstimator class with norm, exp, lo_norm_err, hi_norm_err, lo_exp_err, hi_exp_err, and chi2_red all set.

estimate(time_since_explosion=None, t_err_lo=0, t_err_hi=0, file=None, files=None, tExplosion=None)

Estimate temperature(s) at given time(s) or from FITS file(s) and append to temperatures.

Parameters:

• time_since_explosion (float or array_like, optional) – Time(s) since explosion in days.

• t_err_lo (float or array_like, optional) – Lower time error(s). Defaults to 0.

• t_err_hi (float or array_like, optional) – Upper time error(s). Defaults to 0.

• file (str, optional) – Single FITS file to read time from.

• files (list of str, optional) – List of FITS files to read times from.

• tExplosion (float, optional) – Explosion time (MJD), if not already set.

Returns:

Temperaturespandas.DataFrame

The temperatures table wrapped in DataFrame with columns ['time_since_explosion', 'lo_time_err', 'hi_time_err', 'temperature', 'lo_temp_err', 'hi_temp_err'].

Raises:

RuntimeError – If neither time_since_explosion nor file or files provided, or if model parameters have not been fitted, or if tExplosion is still unset when reading FITS.