Visualization

Publication and poster styling

viz.style applies a consistent matplotlib theme globally, so any plot made afterwards (including plots on other pages of this docs instance) picks it up.

from varistar import TimeSeries, LightCurve
from varistar.viz.style import apply_science_style
from _synthetic import make_sinusoidal_lightcurve

apply_science_style()

ts = TimeSeries(magnitude="mag I", time_scale="HJD")
ts.load_data_from_df(make_sinusoidal_lightcurve(), data_id="synthetic_001")
ts.plot_timeseries()

apply_poster_style() swaps in larger fonts and thicker lines for talks; reset_style() restores matplotlib’s defaults.

Interactive plots

viz.interactive mirrors the static plotting API with Plotly figures — useful for docs pages where readers can zoom and hover. plotly is already a core dependency of varistar, so no separate install is needed (the module’s own docstring mentions pip install varistar[viz], but that extra isn’t currently declared in pyproject.toml — worth a quick fix on the package side, or dropping that line from the docstring):

from varistar.viz.interactive import plot_phased

lc = LightCurve(ts)
lc.run_ls()
plot_phased(lc, period=lc.periods[0])

Next: API Reference.