photFit

Photometry fitting python pckage. This package is intended to fit the spectral energy distribution of any one- or two-body system using a theoretial atmopshere models, for a plenty of photmetric bands, of hot and cool stars (e.g., TLUSTY, NEXTGEN, etc...). It also includes a visualization tool to inspect the best fitting parameters (TODO).

Usage

This is a main.py example.

import numpy as np
from sedfit import FitSED, estimate_ML

mags = np.asarray([19.854, 21.4608, 20.18, 20.27, 20.58, 20.26])
merr = np.asarray([0.157, 0.2887, 0.03, 0.05, 0.12, 0.09])

fs = FitSED(mags=mags,
            err_mags=merr,
            passbands=["GALEX_GALEX.FUV",
                       "GALEX_GALEX.NUV",
                       "INT_WFC.Gunn_g",
                       "INT_IPHAS.gR",
                       "INT_IPHAS.Ha",
                       "INT_IPHAS.gI"],
            kernel_model="indexes",
            pn_name=object_name))

# Initial values
pars, mod = fs.prepare_run(thot=90000., tcool=7500., is_binary=True, vary_ebv=True, ebv=0.2, ebv_p=[0.0, 0.62], beta=10.)

res1 = fs.run_fit(pars, mod, emcee_kws=dict(steps=7000, burn=500, nwalkers=100, progress=True, workers=5), use_weights=True)

best_res = estimate_ML(res1)

This will print the best fitting values as well as the ML estimation.

Available kernels

• Indexes

where . This definition is for a two-body SED in the colour A-B filters. In case of one star .

• Pogson

where , with D the distance to the star.

To do list

• Creating a githug repository.
• First release.
• Adding a visualization tool.
• Refactoring.
• Public the first release.