Question

我在该类中编写了一个名为BiVAR的类，我有一个名为ResiPlot的函数，用于绘制一个数字。但是，在该函数中，我定义了一个称为self.resi的变量。在那个特定的类中，我有另一个名为hzTest的函数。在这个函数中，我对函数ResiPlot的图或打印不感兴趣。我所需要的只是确保self.resi已被定义。

因此，我实际需要的是如何在函数self.resi中定义hzTest。即调用函数ResiPlot来抑制其数字或任何打印输出。

我的代码：

from statsmodels.tsa.api import VAR
import matplotlib.pyplot as plt, subprocess
# Calling built-in functions from R
Rsummary = robjects.r['summary']

class BiVAR:

    def __init__(self, df, restrict=0): # Initialize when created
        self.data = np.array(df.values, dtype=float) # self is the new object
        self.isrestricted = restrict
        if self.isrestricted ==0:
            self.Model= VAR(self.data)
        else:
            p = int(input("Since, you want a restricted model please enter the lag p: "))
            self.p=p
            if p==0: p=1
            t= Rvars.VAR(self.data, p, type='const')
            self.Model= Rvars.restrict(t,method = "ser")
    def BestLagAic(self):
        if self.isrestricted==1: 
            print('Sorry this can not be excuted since you chose the model to be restricted')
        else:
            R=self.Model.select_order(15)
            return R['aic'] # Split string on blanks
    def Fit(self, *parameters, **keyword_parameters):
    # This function allows you to specify the lag variable. If not specified it will use the p value you previously 
    # give it for the restricted VAR model otherwise it will use the best lag based on AIC 
        if self.isrestricted ==0:
            if len(parameters)==1:
                p=parameters[0]
                results = self.Model.fit(p)
                print(results.summary())
            elif len(parameters)==0:
                p=self.BestLagAic()
                results = self.Model.fit(p)
                print(results.summary())
            else:
                print('You included so many unrequired variables')
        else:
            p=self.p
            t= Rvars.VAR(self.data, p, type='const')
            t1= Rvars.restrict(t, method = "ser")
            H=str(Rsummary(t1))
            start = H.find('VAR Estimation Results:') + 23
            end = H.find('Roots of the characteristic', start)
            pvalue=H[start:end]
            start1 = H.find('Estimation results for equation') + 31
            pvalue1=H[start1::]
            print(pvalue+pvalue1)
    def ResiPlot(self, *parameters):
        # This function plots the residuals when fitted with a VAR(p) model
        if self.isrestricted ==0:
            if len(parameters)==1:
                p=parameters[0]
                results = self.Model.fit(p)
                resi=results.resid
                self.resi=pd.DataFrame(resi, columns=['Bond-Resi','Equity-Resi'])
                pd.DataFrame(resi).plot()
                plt.show()
            elif len(parameters)==0:
                p=self.BestLagAic()
                results = self.Model.fit(p)
                resi=results.resid
                self.resi=pd.DataFrame(resi, columns=['Bond-Resi','Equity-Resi'])
                pd.DataFrame(resi).plot()
                plt.show()
            else:
                print('You included so many unrequired variables')
        else: 
            t= Rvars.VAR(self.data, self.p, type='const')
            t1= Rvars.restrict(t, method = "ser")
            t2=t1.rx2('varresult').rx2('y1').rx2('residuals')
            t3=t1.rx2('varresult').rx2('y2').rx2('residuals')
            resi=pd.DataFrame(np.column_stack((np.array(t2), np.array(t3))), columns=['Bond-Resi','Equity-Resi'])
            self.resi=resi
            pd.DataFrame(resi).plot()
            plt.show()
    def hzTest(self):
        print('This is the Henze-Zirkler Multivariate Normality test applied on the residuals of the fitted model')
        subprocess.call('self.ResiPlot')
        MVNresult =MVN.hzTest(self.resi, qqplot = 0)
        np.array(MVNresult.slots[tuple(MVNresult.slotnames())[1]])[0]

Answer 1

您需要（简单版本）multitier architecture。没有计算事物的功能也会询问用户他们的输入并绘制他们的结果。那你就不必“压制其他功能的输出”;你只需在不同的上下文中调用计算函数，其中一些从中产生用户可见的输出，而其中一些不产生。

Python：如何在同一个类中的另一个函数中运行函数来抑制其他函数输出

1 个答案: