generation.py 文件源码

def generate_hads_lightcurve(
        times,
        mags=None,
        errs=None,
        paramdists={
            'period':sps.uniform(loc=0.04,scale=0.06),
            'fourierorder':[5,10],
            'amplitude':sps.uniform(loc=0.1,scale=0.6),
            'phioffset':np.pi,
        },
        magsarefluxes=False
):
    '''This generates fake HADS light curves.

    times is an array of time values that will be used as the time base.

    mags and errs will have the model mags applied to them. If either is None,
    np.full_like(times, 0.0) will used as a substitute.

    paramdists is a dict containing parameter distributions to use for the
    transitparams, in order:

    {'period', 'fourierorder', 'amplitude'}

    These are all 'frozen' scipy.stats distribution objects, e.g.:

    https://docs.scipy.org/doc/scipy/reference/stats.html#continuous-distributions

    The minimum light curve epoch will be automatically chosen from a uniform
    distribution between times.min() and times.max().

    The amplitude will be flipped automatically as appropriate if
    magsarefluxes=True.

    '''

    modeldict = generate_sinusoidal_lightcurve(times,
                                               mags=mags,
                                               errs=errs,
                                               paramdists=paramdists,
                                               magsarefluxes=magsarefluxes)
    modeldict['vartype'] = 'HADS'
    return modeldict