Question

我是编程的初学者。我编写了一个绘制分布的python脚本。我添加了一个控制2个参数（mu和sigma）的滑块小部件。然后，用户可以使用这些参数来查看对分发的影响。代码按预期工作。

然而，我正在寻求即兴发挥，因为我最终得到了一长串重复代码。这是我脚本的一部分

import numpy as np
from scipy.stats import norm, lognorm, uniform
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons, CheckButtons
import math


#####Defining global variables#####

global mu_a1
global sigma_a1
global mu_b1
global sigma_b1
global mu_c1
global sigma_c1
global ix

#####_____#####



#####Generating random data#####

mu_a1 = 1
sigma_a1 =  0.14
mu_b1 = 10
sigma_b1 =  1.16
mu_c1 = -13
sigma_c1 =  2.87
mu_x01 = -11
sigma_x01 =  1.9

a1 = 0.75*mu_a1 + (1.25 - 0.75)*sigma_a1*np.random.sample(10000)
b1 = 8*mu_b1 + (12 - 8)*sigma_b1*np.random.sample(10000)
c1 = -12*mu_c1 + 2*sigma_c1*np.random.sample(10000)
x01 = (-b1 - np.sqrt(b1**2 - (4*a1*c1)))/(2*a1)

#####_____#####



#####Creating subplots & plotting distributions#####

fig = plt.figure()
plt.subplots_adjust(left=0.13,right=0.99,bottom=0.05)

def ax1():
    ax1 = fig.add_subplot(331)
    ax1.clear()
    ax1.set_xlabel('a' , fontsize = 14)
    ax1.grid(True)
    [n1,bins1,patches] = ax1.hist(a1, bins=50, color = 'red',alpha = 0.5, normed = True)
    return ax1

def ax4_ax5():
    ax4 = fig.add_subplot(132)
    ax4.clear()
    ax4.set_xlabel('x0' , fontsize = 14)
    ax4.grid(True)
    [n4,bins4,patches] = ax4.hist(x01, bins=50, color = 'red',alpha = 0.5, normed = True)
    ax4.axvline(np.mean(x01), color = 'black', linestyle = 'dashed', lw = 2)

    ax5 = fig.add_subplot(133)
    ax5.clear()
    ax5.set_xlabel('x0' , fontsize = 14)
    ax5.set_ylabel('CDF', fontsize = 14)
    ax5.grid(True)
    dx = bins4[1] - bins4[0]
    CDF = np.cumsum(n4)*dx
    ax5.plot(bins4[1:], CDF, color = 'red')
    return ax4,ax5

#####_____#####

ax1 = ax1()
ax4, ax5 = ax4_ax5()


#####Creating Sliders#####

axcolor = 'lightgoldenrodyellow'
axmu_331 = plt.axes([0.015, 0.67, 0.07, 0.015], axisbg=axcolor)
axsigma_331 = plt.axes([0.015, 0.65, 0.07, 0.015], axisbg=axcolor)
mu_331 = Slider(axmu_331, 'M', -13.0 , 10.0, valinit = 1.0)
sigma_331 = Slider(axsigma_331, 'SD', -3.0 , 3.0, valinit =1.0)

#####_____#####



#####Updating Sliders#####

def update_slider_331(val):
    mu_a1 = mu_331.val
    print mu_a1
    sigma_a1 = sigma_331.val
    print sigma_a1
    ax1.clear()
    ax4.clear()
    ax5.clear()
    ax1.grid(True)
    ax4.grid(True)
    ax5.grid(True)
    a1 = 0.75*mu_a1 + (1.25 - 0.75)*sigma_a1*np.random.sample(10000)
    [n1,bins1,patches] = ax1.hist(a1, bins=50, color = 'red',alpha = 0.5, normed = True)
    x01 = (-b1 - np.sqrt(b1**2 - (4*a1*c1)))/(2*a1)
    [n4,bins4,patches] = ax4.hist(x01, bins=50, color = 'red',alpha = 0.5, normed = True)
    ax4.axvline(np.mean(x01), color = 'black', linestyle = 'dashed', lw = 2)
    dx = bins4[1] - bins4[0]
    CDF = np.cumsum(n4)*dx
    ax5.plot(bins4[1:], CDF, color = 'red')
    plt.draw()

mu_331.on_changed(update_slider_331)
sigma_331.on_changed(update_slider_331)

#####_____#####

plt.show()

我定义了函数ax1()＆amp; ax4_ax5()绘制分布图。我们的想法是在需要的地方调用这些函数而不是重复代码。

问题：

在调用更新滑块功能时，是否可以调用函数ax1()＆amp; ax4_ax5()以这样的方式使用更新的滑块值执行自己并使用新的分布更新绘图而不是在滑块更新功能中重写代码？

提前致谢： - ）

Matplotlib：使用更新的滑块值调用函数

0 个答案: