使用类而不是全局变量

我有一个程序可以对数组进行一些操作(小波变换和各种其他复杂性),然后将其与先前的数组及其属性进行比较,输出比较两者的图形,最后更新“先前的”数组以包含此信息.
基本上,我的程序开始有点冗长且难以阅读,但是我无法真正将其拆分为多个函数,因为所有函数都正在读入并更改相同的变量.每次我不希望将所有这些变量都定义为全局变量时,都很难.

然后我在网上找到了这个：

You may have several functions that use the same state variables, either reading or writing them. You are passing a lot of parameters around. You have nested functions that have to forward their parameters to the functions they use. You are tempted to make some module variables to hold the state.

You could make a class instead! All methods of a class have access to all istance data of the class. By storing the shared state in the class, you avoid the need to pass it as parameters to the methods.

所以我想知道如何使我的程序改用类来编写？
如果有帮助,我可以附加我的代码,但是很长,我不想填写论坛！

这是代码：

import os, sys, string, math
from optparse import OptionParser
import numpy as np
import pywt
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
from matplotlib.ticker import MaxNLocator
import glob

dir = os.getcwd()
profiles = glob.glob(dir+"/B0740-28/*_edit.FT.ascii")
for x in range(0,len(profiles)):
    profiles[x] = profiles[x][28:]
#produce list of profile file names

mode = 'per'
wavelets = ['db12']
levels = range(3,4)
starts = []
fig = 1
ix = 0 #profile index
changes = np.zeros(len(profiles))
#array to record shape changes

for num_levels in levels:
    for wavelet in wavelets:
        for profile in profiles:

            prof_name = profile.partition('.')[0]
            #remove file extension

            pfile=open(dir+'/B0740-28/'+profile)
            data = []
            for line in pfile:
                data.append(float(line))
            data = np.array(data)
            end = len(data)
            data = np.array(data)/max(data)
            #get pulse profile and normalise
            #ignore first 2 lines

            wav_name = wavelet.partition('.')[0]
            w = pywt.Wavelet(wavelet)
            useful = pywt.dwt_max_level(end,w)
            #find max level of decomposition

            coeffs = pywt.wavedec(data,wavelet,mode,level=num_levels)
            #create wavelet coefficients: cAn, cDn, cD(n-1)... cD1

            lowpass = pywt.upcoef('a',coeffs[0],wavelet,level=num_levels,take=end)
            highpass = np.zeros(end)
            for x in range(1,(num_levels+1)):
                highpass += pywt.upcoef('d',coeffs[len(coeffs)-x],wavelet,\
                                        level=x,take=end)
            #reverse transform by upcoef
            #define highpass and lowpass components

            for n in range(0,len(data)):
                if float(data[n]) > 0.4:
                    value = n
                    starts.append(value)
                    break
            if profile != profiles[0]:
                offset = starts[0]- value
                data = np.roll(data,offset)
                lowpass = np.roll(lowpass,offset)
                highpass = np.roll(highpass,offset)
            #adjust profiles so that they line up

            if profile == profiles[0]:
                data_prev = 0
                lowpass_prev = 0
                highpass_prev = 0
                mxm = data.argmax()

            diff_low = lowpass - lowpass_prev
            diff_high = highpass - highpass_prev
            if max(diff_low) >= 0.15 or min(diff_low) <= -0.15:
                changes[ix] = 1
            else: changes[ix] = 0
            #significant change?

            def doPlotting(name,yaxis):
                plt.plot(name)
                plt.xlim([mxm-80,mxm+100])
                plt.ylabel(yaxis)
                plt.gca().yaxis.set_major_locator(MaxNLocator(nbins=4))

            figure = plt.figure(fig)
            figure.subplots_adjust(hspace =.5)
            plt.suptitle('Comparison of Consecutive Profiles')
            plt.subplot(411); plt.plot(data_prev); \
                              doPlotting(data,'Data'); plt.ylim(ymax=1.1)
            plt.subplot(412); plt.plot(lowpass_prev); \
                              doPlotting(lowpass,'Lowpass'); plt.ylim(ymax=1.1)
            plt.subplot(413); plt.plot(highpass_prev); doPlotting(highpass,'Highpass')
            plt.subplot(414); doPlotting(diff_low,'Lowpass\nChange')
            plotname = 'differences_'+str(ix+1)+'_'+wav_name+'_'+str(num_levels)
            plt.savefig(dir+'/B0740-28/Plots/'+plotname)
            #creates plots of two most recent profiles + their decomposition 

            fig += 1
            ix += 1
            #clears the figure content
            #increase array index

            data_prev = data
            lowpass_prev = lowpass
            highpass_prev = highpass
            #reassigns 'previous profile' values

figure = plt.figure(fig)
plt.plot(changes)
plt.title('Lowpass Changes')
plt.xlabel('Profile Number')
plt.ylabel('Change > Threshold?')
plt.ylim(-0.25,1.25)
plt.xlim(0,48)
plt.savefig(dir+'/B0740-28/Plots/changes')
#Save lowpass changes plot

解决方法:

我可能会对此答案不满意,但是在这种特殊情况下,按照宏伟的计划,在包中添加一些全局变量确实没有问题.

当您要在许多不同的地方使用一堆功能时,类很棒并且很有用,但是,您所描述的内容听起来非常具体,不太可能在其他地方重复使用.创建带有实例变量的一次性类与在具有全局变量的程序包中具有一堆函数没有太大的不同.

标签：global-variables,python,class
来源： https://codeday.me/bug/20191031/1976412.html