sdapy.snerun.snobject._ax2¶

snobject._ax2(show_title=False, show_legend=False, ylabel_2right=False, show_data=True, show_limits=True, show_fits=True, show_gp=True, show_fit_error=True, show_texp=False, color=None, marker=None, markersize=None, label=None, ls=None, fillstyle=None, fontsize=None, **kwargs)¶

Magnitude LC plot.

Parameters

jd_x0float: x axis zeropoint for mag/flux LCs
ax2_xstylestr: x axis for mag (_ax2) LCs: [rp] rest frame since peak [jd] Junlian date since jd_x0
ax2_ystylestr: y axis for mag (_ax2) LCs: [app] apparent mag, or [abs] absolute mag
ax2_xlimlist: x limit
ax2_ylimlist: y limit
plot_sourceslist: which source LCs to show
plot_bandslist: which filters to show
show_titlebool: if show title
show_legendbool: if show legend
ylabel_2rightbool: if put y label to right
corr_mkwstr: when calculating absulte mag, if correct milky way extinction if there’re any
corr_hoststr: when calculating absulte mag, if correct host galaxy extinction if there’re any
show_databool: if show data points
show_limitsbool: if show upper limits
show_fit_errorbool: if False, only show best fit, otherwise, show errors or random samplings
show_fitsbool: if show model fittings
show_gpbool: if show GP modellings
show_texpbool: if show explosion epochs
alphabestfloat between 0 and 1: matplotlib alpha for best fit fitting
alphasamplefloat between 0 and 1: matplotlib alpha for random samplings or errors
plot_mcmctfloat between 0 and 1: a threshold that select good mc samples for plotting, rangiing from 0 to 1, e.g. 0.5 means selecting samplings from the top 50 percent of all samplings relying on the likelihoods
plot_nsamplesint: how many random MC samples to be plotted
multiband_early_xrangeplist: range to reproduce the multiband_early models
gp_xrangeplist: range to reproduce the GP interpolations
verbosebool: Enable progress report