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Phase Folding & Model Fitting

from varistar import TimeSeries, LightCurve
from _synthetic import make_sinusoidal_lightcurve, make_eclipsing_lightcurve

Fourier fit (sinusoidal variables)​

ts_sine = TimeSeries(magnitude="mag I", time_scale="HJD")
ts_sine.load_data_from_df(make_sinusoidal_lightcurve(), data_id="sine_variable")

lc_sine = LightCurve(ts_sine)
lc_sine.plot_phased(fit_model="fourier", n_harmonics=4, show_residuals=True)

Gaussian fit (eclipsing binaries)​

The Double Super-Gaussian model handles the sharp, asymmetric dips a Fourier series smooths over:

ts_eb = TimeSeries(magnitude="mag I", time_scale="HJD")
ts_eb.load_data_from_df(make_eclipsing_lightcurve(), data_id="eb_variable")

lc_eb = LightCurve(ts_eb)
lc_eb.plot_phased(fit_model="gaussian", show_residuals=True)

plot_best: one call, no guessing​

plot_best finds the best period, runs the eclipsing-binary check, and picks Fourier or Gaussian (doubling the period if that fits better) automatically:

lc_eb.plot_best()
[score_eb] Possible EB: eb_variable | ρ_dip=62.40  ρ_global=457.00

Next: Classification.