写个画图装饰器，通过绘图加深对常见概率分布的理解

import numpy as np
from scipy.stats import beta, norm, uniform, binom
import matplotlib.pyplot as plt
from functools import wraps

# 定义带四个参数的画图装饰器
def my_plot(label0=None, label1=None, ylabel='probability density function', fn=None):
    def decorate(f):
        @wraps(f)
        def myplot():
            fig = plt.figure(figsize=(16, 9))
            ax = fig.add_subplot(111)
            x, y, y1 = f()
            ax.plot(x, y, linewidth=2, c='r', label=label0)
            ax.plot(x, y1, linewidth=2, c='b', label=label1)
            ax.legend()
            plt.ylabel(ylabel)
            # plt.show()
            plt.savefig('./img/%s' % (fn,))
            plt.close()
        return myplot
    return decorate

# 均匀分布(uniform)
@my_plot(label0='b-a=1.0', label1='b-a=2.0', fn='uniform.png')
def unif():
    x = np.arange(-0.01, 2.01, 0.01)
    y = uniform.pdf(x, loc=0.0, scale=1.0)
    y1 = uniform.pdf(x, loc=0.0, scale=2.0)
    return x, y, y1

@my_plot(label0='n=50,p=0.3', label1='n=50,p=0.7', fn='binom.png', ylabel='probability mass function')
def bino():
    x = np.arange(50)
    n, p, p1 = 50, 0.3, 0.7
    y = binom.pmf(x, n=n, p=p)
    y1 = binom.pmf(x, n=n, p=p1)
    return x, y, y1

# 高斯 分布
@my_plot(label0='u=0.,sigma=1.0', label1='u=0.,sigma=2.0', fn='guass.png')
def guass():
    x = np.arange(-5, 5, 0.1)
    y = norm.pdf(x, loc=0.0, scale=1.0)
    y1 = norm.pdf(x, loc=0., scale=2.0)
    return x, y, y1

@my_plot(label0='a=10., b=30.', label1='a=4., b=4.', fn='beta.png')
def bet():
    x = np.arange(-0.1, 1, 0.001)
    y = beta.pdf(x, a=10., b=30.)
    y1 = beta.pdf(x, a=4., b=4.)
    return x, y, y1

distrs = [unif, bino, guass, bet]
for distri in distrs:
    distri()