f1 = plt.figure(figsize=(6,6), dpi=300) x = np.linspace(-2, 2, 100)

plt.title(u"Inverse transforming sampling for normal distribution") plt.plot(x, n.pdf(x), label=r'pdf $f(x)$') # proba density function plt.plot(x, n.cdf(x), label=r'cdf $F(x) = \int_{-\infty}^x f(t) \, dt$') # cumulative distributive function plt.plot(x, n.ppf(x), label='ppf $F^{-1}(x)$') # percent point function plt.plot(x,x,'--k') plt.plot([n.ppf(0.2),n.ppf(0.2),0,0.2,0.2],[0,0.2,0.2,0,n.ppf(0.2)],'o:k') plt.text(n.ppf(0.2)-0.2, -0.2, r"$y \sim \cal N$", size=14) plt.text(0.2, 0.05, r'$x \sim \calU(]0,1[][-2,2]) plt.axhline(color = 'gray') plt.axvline(color = 'gray') plt.rcParams['legend.loc'] = 'upper left' plt.legend()

 

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