frommatplotlibimportpyplotfromscipyimportstatsimportprobscale# nothing else neededbeta=stats.beta(a=3,b=4)weibull=stats.weibull_min(c=5)scales= [ {"scale": {"value":"linear"},"label":"Linear (built-in)"}, {"scale": {"value":"log","base":10},"label":"Log. Base 10 (built-in)"}, {"scale": {"value":"log","base":2},"label":"Log. Base 2 (built-in)"}, {"scale": {"value":"logit"},"label":"Logit (built-in)"}, {"scale": {"value":"prob"},"label":"Standard Normal Probability (this package)"}, {"scale": {"value":"prob","dist":weibull},"label":"Weibull probability scale, c=5 (this package)", }, {"scale": {"value":"prob","dist":beta},"label":"Beta probability scale, α=3 & β=4 (this package)", },]N=len(scales)fig,axes=pyplot.subplots(nrows=N,figsize=(9,N-1),constrained_layout=True)forscale,axinzip(scales,axes.flat):ax.set_xscale(**scale["scale"])ax.text(0.0,0.1,scale["label"]+" →",transform=ax.transAxes)ax.set_xlim(left=0.5,right=99.5)ax.set_yticks([])ax.spines.left.set_visible(False)ax.spines.right.set_visible(False)ax.spines.top.set_visible(False)outpath=Path(__file__).parent.joinpath("../img/example.png").resolve()fig.savefig(outpath,dpi=300)
