fit_modifiedbb_montecarlo

dust_emissivity.fit_sed.fit_modifiedbb_montecarlo(frequency, flux, err=None, temperature_guess=10, beta_guess=None, column_guess=None, quiet=True, return_MC=False, nsamples=5000, burn=1000, min_temperature=0, max_temperature=100, max_column=1e+30, multivariate=False, **kwargs)

An MCMC version of the fitter.

Parameters:

frequency : array

Array of frequency values

flux : array

array of flux values

err : array (optional)

Array of error values (1-sigma, normal)

temperature_guess : float

Input / starting point for temperature

min_temperature : float

max_temperature : float

Lower/Upper limits on fitted temperature

beta_guess : float (optional)

Opacity beta value

column_guess : float

guess for the column density (cm^-2)

return_MC : bool

Return the pymc.MCMC object?

nsamples : int

Number of samples to use in determining the posterior distribution (the answer)

burn : int

number of initial samples to ignore

kwargs : kwargs

passed to blackbody function