sdapy.snerun.snobject._ax4¶

snobject._ax4(xpred=None, index=0, show_title=False, show_legend=False, ylabel_2right=False, show_data=True, show_fits=True, show_fit_error=True, show_texp=True, color=None, marker=None, markersize=None, label=None, ls=None, fillstyle=None, fontsize=None, **kwargs)¶

luminosity LCs plot

Parameters

make_bollist: how to make bolometric lcs: [lyman] with BC defined by Lyman approach, [bb] blackbody fits on multiband spectra, or [spec] integration on blackbody of absolute calibrated spectra
jd_x0float: x axis zeropoint for mag/flux LCs
ax4_xstylestr: x axis for luminosity (_ax4) LCs: [rp] rest frame since peak [jd] Junlian date since jd_x0
ax4_ystylestr: y axis for luminosity (_ax4) LCs: [linear] or [log] scale
ax4_xlimlist: x limit
ax4_ylimlist: y limit
show_titlebool: if show title
show_legendbool: if show legend
ylabel_2rightbool: if put y label to right
show_databool: if show data points
show_fit_errorbool: if False, only show best fit, otherwise, show errors or random samplings
show_fitsbool: if show model fittings
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
verbosebool: Enable progress report