# Python实现正态分布
  # 绘制正态分布概率密度函数
  u = 0  # 均值μ
  u01 = -2
  sig = math.sqrt(0.2) # 标准差δ
  sig01 = math.sqrt(1)
  sig02 = math.sqrt(5)
  sig_u01 = math.sqrt(0.5)
  x = np.linspace(u - 3*sig, u + 3*sig, 50)
  x_01 = np.linspace(u - 6 * sig, u + 6 * sig, 50)
  x_02 = np.linspace(u - 10 * sig, u + 10 * sig, 50)
  x_u01 = np.linspace(u - 10 * sig, u + 1 * sig, 50)
  y_sig = np.exp(-(x - u) ** 2 /(2* sig **2))/(math.sqrt(2*math.pi)*sig)
  y_sig01 = np.exp(-(x_01 - u) ** 2 /(2* sig01 **2))/(math.sqrt(2*math.pi)*sig01)
  y_sig02 = np.exp(-(x_02 - u) ** 2 / (2 * sig02 ** 2)) / (math.sqrt(2 * math.pi) * sig02)
  y_sig_u01 = np.exp(-(x_u01 - u01) ** 2 / (2 * sig_u01 ** 2)) / (math.sqrt(2 * math.pi) * sig_u01)
  plt.plot(x, y_sig, "r-", linewidth=2)
  plt.plot(x_01, y_sig01, "g-", linewidth=2)
  plt.plot(x_02, y_sig02, "b-", linewidth=2)
  plt.plot(x_u01, y_sig_u01, "m-", linewidth=2)
  # plt.plot(x, y, 'r-', x, y, 'go', linewidth=2,markersize=8)
  plt.grid(True)
  plt.show()