Python 从一个“变量”中获取所有预定义变量的子程序；“主程序”；

我很难做到这一点： 由于我正在使用pyqt编程GUI，因此我希望构建我的工作：

我的GUI上有几个按钮，可以通过使用scikitlearn进行的计算调用“不同的子程序”

我有一个按钮“PRED”用于预测，另一个按钮用于一些绘图，称为“绘图”

单击这些按钮时，将调用一个python“计算程序”

class MyDia(QtGui.QDialog, Dlg): 
    def __init__(self): 
        QtGui.QDialog.__init__(self) 
        self.setupUi(self)

        self.connect(self.buttonOPLOT, 
                QtCore.SIGNAL("clicked()"), self.onPLOT)    
        self.connect(self.buttonPRED, 
                QtCore.SIGNAL("clicked()"), self.onPRED)
    def onPRED
        if self.button_1.checkState(): 
            a=1
        if self.button_2.checkState(): 
            a=2
        query=np.zeros((1,18))
        for i in range(0,18,1):
            try:
                query[0,i]= float(self.tableWidget.item(0,i).text())

        ### when user has made his choices the data goes do this
        from sk_calc import main, pred
        main() #after main, "pred" should be called with some definitions that 
        have been made in "main"
        pred(a) #a is some parameter of a regression (i try to keep it easy)

目前，我在不同的文件中使用不同的“计算”程序“sk_plot和sk_pred”-目标是只更改一个。。。其中“main”在指定作业之前运行（PRED或PLOT…）

唯一计算程序的“外观”/结构应与此类似：

def main():
    import numpy as np
    import #all modules from scikitlearn

    DATA=np.genfromtxt(direc+"\some.csv",delimiter=";",dtype=float ,skip_header=2, usecols=range(0,22)) #reading in a csv file with my data

    features=DATA[:,4:22]#the "X" of my DATA
    targets=DATA[:,1]#the "Y" of my DATA

    svr_rbf = SVR(kernel='rbf', C=2e4, gamma=a) #Regression using the DATA #a comes from user click
    svr_rbf.fit(features, targets).predict(features)# method of scikit-learn

        def pred():
            Prediction=svr_rbf.predict(query)
            #query is defined by the user in the gui typing in some values 
            print(Pred_ic)

        def plot():
            #... something different using pylab but ALSO DATA features and targets

您可以看到，我希望某些代码（main）在单击该按钮时不独立地运行 之后，应执行“计算程序”的一部分，该部分包含main（）中定义的变量和数据

---

我是否为此使用类？如果是，我需要记住什么？这方面的步骤是什么…

类是构造代码的好方法，这是正确的

类可以维护自己的状态，并具有预定义的行为，这些行为可以通过方法和属性进行操作

然而，我不会给出关于使用类的一般性建议，因为这与stackoverflow无关，它关注的是特定的编程问题。如果您想了解更多，只需在web上搜索有关python的书籍/教程就可以了——有几十本不错的

相反，我将尽我所能重新构造问题中的代码，以使用类。以下代码仅用于说明目的。它不是一个完整的、可运行的示例。希望这里有足够的提示让您了解如何继续：

gui.py：

import numpy as np
import sk_calc

class MyDia(QtGui.QDialog, Dlg):
    def __init__(self):
        QtGui.QDialog.__init__(self)
        self.setupUi(self)
        self.buttonOPLOT.clicked.connect(self.onPLOT)
        self.buttonPRED.clicked.connect(self.onPRED)

    def onPRED(self):
        if self.button_1.isChecked():
            a = 1
        elif self.button_2.isChecked():
            a = 2
        else:
            a = 0
        query = np.zeros((1,18))
        # ... etc

        # when user has made his choices the data goes do this

        # create an instance of the Calc class, passing in
        # parameters from the gui
        calc = sk_calc.Calc(a)

        # call methods of the instance, passing in parameters
        # from the gui, and receiving returned values
        prediction = calc.pred(query)

        # calc.plot() ... etc

import numpy as np
from sklearn.svm import SVR
# import other stuff from scikitlearn

DEFAULT_CSVPATH = 'path/to/some/file.csv'

class Calc(object):
    def __init__(self, a, csvpath=None):
        if csvpath is None:
            csvpath = DEFAULT_CSVPATH
        # reading in a csv file with my data
        self.data = np.genfromtxt(
            csvpath , delimiter=';', dtype=float,
            skip_header=2, usecols=range(0,22))

        self.features = data[:,4:22] # the "X" of my DATA
        self.targets = data[:,1]     # the "Y" of my DATA

        # Regression using the DATA, a comes from user click
        self.svr_rbf = SVR(kernel='rbf', C=2e4, gamma=a)

        # method of scikit-learn
        self.svr_rbf.fit(features, targets).predict(features)

    def pred(self, query):
        # query is defined by the user in the gui typing in some values
        prediction = self.svr_rbf.predict(query)
        return prediction

    def plot(self):
        # ... use pylab with DATA features and targets
        # self.data ...
        # self.features ...

sk_calc.py：

import numpy as np
import sk_calc

class MyDia(QtGui.QDialog, Dlg):
    def __init__(self):
        QtGui.QDialog.__init__(self)
        self.setupUi(self)
        self.buttonOPLOT.clicked.connect(self.onPLOT)
        self.buttonPRED.clicked.connect(self.onPRED)

    def onPRED(self):
        if self.button_1.isChecked():
            a = 1
        elif self.button_2.isChecked():
            a = 2
        else:
            a = 0
        query = np.zeros((1,18))
        # ... etc

        # when user has made his choices the data goes do this

        # create an instance of the Calc class, passing in
        # parameters from the gui
        calc = sk_calc.Calc(a)

        # call methods of the instance, passing in parameters
        # from the gui, and receiving returned values
        prediction = calc.pred(query)

        # calc.plot() ... etc

import numpy as np
from sklearn.svm import SVR
# import other stuff from scikitlearn

DEFAULT_CSVPATH = 'path/to/some/file.csv'

class Calc(object):
    def __init__(self, a, csvpath=None):
        if csvpath is None:
            csvpath = DEFAULT_CSVPATH
        # reading in a csv file with my data
        self.data = np.genfromtxt(
            csvpath , delimiter=';', dtype=float,
            skip_header=2, usecols=range(0,22))

        self.features = data[:,4:22] # the "X" of my DATA
        self.targets = data[:,1]     # the "Y" of my DATA

        # Regression using the DATA, a comes from user click
        self.svr_rbf = SVR(kernel='rbf', C=2e4, gamma=a)

        # method of scikit-learn
        self.svr_rbf.fit(features, targets).predict(features)

    def pred(self, query):
        # query is defined by the user in the gui typing in some values
        prediction = self.svr_rbf.predict(query)
        return prediction

    def plot(self):
        # ... use pylab with DATA features and targets
        # self.data ...
        # self.features ...

---

类是构造代码的好方法，这是正确的

类可以维护自己的状态，并具有预定义的行为，这些行为可以通过方法和属性进行操作

然而，我不会给出关于使用类的一般性建议，因为这与stackoverflow无关，它关注的是特定的编程问题。如果您想了解更多，只需在web上搜索有关python的书籍/教程就可以了——有几十本不错的

相反，我将尽我所能重新构造问题中的代码，以使用类。以下代码仅用于说明目的。它不是一个完整的、可运行的示例。希望这里有足够的提示让您了解如何继续：

gui.py：

import numpy as np
import sk_calc

class MyDia(QtGui.QDialog, Dlg):
    def __init__(self):
        QtGui.QDialog.__init__(self)
        self.setupUi(self)
        self.buttonOPLOT.clicked.connect(self.onPLOT)
        self.buttonPRED.clicked.connect(self.onPRED)

    def onPRED(self):
        if self.button_1.isChecked():
            a = 1
        elif self.button_2.isChecked():
            a = 2
        else:
            a = 0
        query = np.zeros((1,18))
        # ... etc

        # when user has made his choices the data goes do this

        # create an instance of the Calc class, passing in
        # parameters from the gui
        calc = sk_calc.Calc(a)

        # call methods of the instance, passing in parameters
        # from the gui, and receiving returned values
        prediction = calc.pred(query)

        # calc.plot() ... etc

import numpy as np
from sklearn.svm import SVR
# import other stuff from scikitlearn

DEFAULT_CSVPATH = 'path/to/some/file.csv'

class Calc(object):
    def __init__(self, a, csvpath=None):
        if csvpath is None:
            csvpath = DEFAULT_CSVPATH
        # reading in a csv file with my data
        self.data = np.genfromtxt(
            csvpath , delimiter=';', dtype=float,
            skip_header=2, usecols=range(0,22))

        self.features = data[:,4:22] # the "X" of my DATA
        self.targets = data[:,1]     # the "Y" of my DATA

        # Regression using the DATA, a comes from user click
        self.svr_rbf = SVR(kernel='rbf', C=2e4, gamma=a)

        # method of scikit-learn
        self.svr_rbf.fit(features, targets).predict(features)

    def pred(self, query):
        # query is defined by the user in the gui typing in some values
        prediction = self.svr_rbf.predict(query)
        return prediction

    def plot(self):
        # ... use pylab with DATA features and targets
        # self.data ...
        # self.features ...

sk_calc.py：

import numpy as np
import sk_calc

class MyDia(QtGui.QDialog, Dlg):
    def __init__(self):
        QtGui.QDialog.__init__(self)
        self.setupUi(self)
        self.buttonOPLOT.clicked.connect(self.onPLOT)
        self.buttonPRED.clicked.connect(self.onPRED)

    def onPRED(self):
        if self.button_1.isChecked():
            a = 1
        elif self.button_2.isChecked():
            a = 2
        else:
            a = 0
        query = np.zeros((1,18))
        # ... etc

        # when user has made his choices the data goes do this

        # create an instance of the Calc class, passing in
        # parameters from the gui
        calc = sk_calc.Calc(a)

        # call methods of the instance, passing in parameters
        # from the gui, and receiving returned values
        prediction = calc.pred(query)

        # calc.plot() ... etc

import numpy as np
from sklearn.svm import SVR
# import other stuff from scikitlearn

DEFAULT_CSVPATH = 'path/to/some/file.csv'

class Calc(object):
    def __init__(self, a, csvpath=None):
        if csvpath is None:
            csvpath = DEFAULT_CSVPATH
        # reading in a csv file with my data
        self.data = np.genfromtxt(
            csvpath , delimiter=';', dtype=float,
            skip_header=2, usecols=range(0,22))

        self.features = data[:,4:22] # the "X" of my DATA
        self.targets = data[:,1]     # the "Y" of my DATA

        # Regression using the DATA, a comes from user click
        self.svr_rbf = SVR(kernel='rbf', C=2e4, gamma=a)

        # method of scikit-learn
        self.svr_rbf.fit(features, targets).predict(features)

    def pred(self, query):
        # query is defined by the user in the gui typing in some values
        prediction = self.svr_rbf.predict(query)
        return prediction

    def plot(self):
        # ... use pylab with DATA features and targets
        # self.data ...
        # self.features ...

---

你能概括你的问题吗？比如，你到底需要什么。从函数导入变量？将变量传递给正在运行的脚本？当我运行函数的“子部分”时，我希望我在函数的main（）中定义的所有内容都可以在此子部分中使用-变量名称、值等…你能概括你的问题吗？比如，你到底需要什么。从函数导入变量？将变量传递给正在运行的脚本？当我运行函数的“子部分”时，我希望我在函数的main（）中定义的所有内容都可以在该子部分中使用—变量名称、值等。感谢您的努力，我将阅读更多关于类的信息，并尝试您的方法！谢谢完成这项工作的工作量并不大——我基本上需要添加一个“self”。在使用“init”部分中定义的变量时，感谢您的努力，我将阅读更多关于类的内容，并尝试您的方法！谢谢完成这项工作的工作量并不大——我基本上需要添加一个“self”。在“init”部分中定义的所有变量都被使用