[Feature Request] Is it possible to store/save a starry model?

I have generated a load of starry models by fitting to data, and I would like to save them so I can plot the model in future notebooks. I don’t think there is a feature to do this, and it’s not possible to use pickle on a starry.Map object.

I think the best way for me to do this currently is to save all the map amplitudes and dictionaries of the orbital parameters, and then re-initialize the model when I want to use it in a new notebook. I can totally do this for now, but if there is an easy way to store the models and then re-open them I’d love to use it!

Thanks C

[taylorbell57]

Upvoting this to allow for the combination of emcee and multiprocessing which requires a pickleable object or a new star and planet object to be created at each iteration which is a huge overhead time cost (2 ms for v0.3 or 8 ms for v1.0; that’s 100-400% of my current likelihood computation time without using starry). On a related note, v1.0 takes an order of magnitude longer just to update the map (2 us for v0.3 versus 3 ms for v1.0 for a planet that has a ydeg=1). The “Pre-computing some matrices… Done.” being repeated a million times when initialising a planet in each MCMC step is also pretty rough and is something I’ve commented out locally.

[jvines]

Well, after a clean install I got your snippet there working, and I coded up this:

def LC_model(theta, lds, time):
    """Evaluate light curve model.

    Parameters
    ----------
    theta: array_like
        Array with the orbital parameters.
    theta_ld: array_like
        Array with the kipping limb darkening coefficients.
    time: array_like
        The time array.
    model: starry.kepler.System
        The starry System object.
    law: str
        The limb darkening law.

    Returns
    -------
    flux: array
        The transit flux.
    """
    p = theta[0]
    e = theta[1]
    w = theta[2]
    tp = theta[3]
    rr = theta[4]
    b = theta[5]
    a = theta[6]
    
    inc = np.arccos(b / a) * 180 / np.pi
    star = starry.Primary(starry.Map(udeg=2))
    star.map[1] = lds[0]
    star.map[2] = lds[1]
    planet = starry.kepler.Secondary(starry.Map(amp=0),
                                     m=0, porb=p, inc=inc, ecc=e,
                                     r=rr, w=w, t0=tp)
    model = starry.System(star, planet)

    flux = model.flux(time)
    return flux

# Compile `slow_flux` into a theano function
starry.config.lazy = True
theta = tt.dvector()
lds = tt.dvector()
time = tt.dvector()
fast_flux = theano.function(
    [theta, lds, time],
    LC_model(theta, lds, time))

I’m unsure, however, if that’s the correct approach here, so any guidance would be appreciated.