class MainGui():
def __init__(self, master):
self.master = master
self.StateQueue = Queue.Queue()
self.InfoQueue = Queue.Queue()
self.PredictQueue = Queue.Queue()
self.train_flag = False
##定义功能区：三大块
##上部：菜单行**********************************************************************************************************************
self.frm_top = LabelFrame(root)
self.N_btn = Button(
self.frm_top,
text='Train File',
command=lambda: readnewtrainfile_press(self.StateQueue))
self.N_btn.grid(row=0, column=0, columnspan=1, sticky=W)
self.f_btn = Button(
self.frm_top,
text='Test File',
command=lambda: readtestdata_press(self.StateQueue))
self.f_btn.grid(row=0, column=1, columnspan=1, sticky=W) #
self.l_btn = Button(self.frm_top,
text='Load Model',
command=lambda: openmodel_press(self.StateQueue))
self.l_btn.grid(row=0, column=2, columnspan=1, sticky=W)
# self.SS_btn = Button(self.frm_top,text="Save Model",command = lambda :saveModel_press(self.StateQueue))
# self.SS_btn.grid(row=0,column=3,columnspan=1,sticky = W)
self.graph = tf.Graph()
with self.graph.as_default():
self.p_btn = Button(self.frm_top,
text='Predict',
command=self.startPredict)
self.p_btn.grid(row=0, column=5, columnspan=1, sticky=W)
self.dm_btn = Button(self.frm_top, text="Report", command=None)
self.dm_btn.grid(row=0, column=7, columnspan=1, sticky=W)
self.wri = IntVar()
self.Write2File = Checkbutton(
self.frm_top,
text='Write',
variable=self.wri,
command=lambda: Writetofile_press(self.wri))
self.Write2File.grid(row=0, column=4, columnspan=1, sticky=W)
self.frm_top.grid(row=0, column=0, columnspan=18, sticky='WESN')
##中间：图像结果显示行**************************************************************************************************************************
## frm_middle_left = LabelFrame(root).grid(row= 1,column = 0,sticky ='WESN')
#在frm_middle_left的GUI上放置一个画布，并用.grid()来调整布局
self.fig = Figure(figsize=[7.5, 6], dpi=100)
self.canvas = FigureCanvasTkAgg(self.fig, master=root)
self.fig_sub = self.fig.add_subplot(111)
DrawPic(self.canvas, self.fig_sub)
self.frm_middle_right = LabelFrame(root)
self.text_box = Text(self.frm_middle_right, width=50, heigh=42)
self.text_box.grid(row=0, column=0)
self.text_box.insert('end', "Hello!")
self.scrollY_0 = Scrollbar(self.frm_middle_right,
orient=VERTICAL,
command=self.text_box.yview)
self.text_box['yscrollcommand'] = self.scrollY_0.set
self.scrollY_0.grid(row=0, column=6, sticky='N' + 'S' + 'E' + 'W')
self.frm_middle_right.grid(row=1,
column=4,
columnspan=9,
sticky='WESN')
##下部参数设置以及训练控制行*********************************************************************************************************************
self.frm_bottom1 = LabelFrame(root)
##1*************************** data preprocess****************************
self.LF_datapreprocess = LabelFrame(self.frm_bottom1,
text='NIR Data Preprocess')
self.D_lbl = Label(self.LF_datapreprocess, text="DropL:")
self.D_lbl.grid(row=0, column=0, sticky=W)
self.inputLabel = Entry(self.LF_datapreprocess, width=4)
self.inputLabel.grid(row=0, column=1, sticky=E)
self.inputLabel.insert(0, '5')
self.P_lbl = Label(self.LF_datapreprocess, text="PosiL:")
self.P_lbl.grid(row=0, column=2, sticky=W)
self.posiLabel = Entry(self.LF_datapreprocess, width=4)
self.posiLabel.grid(row=0, column=3, sticky=E)
self.posiLabel.insert(0, '3')
Label(self.LF_datapreprocess, text="ClsMode:").grid(row=0,
column=4,
columnspan=1)
self.clsM = StringVar()
self.BiOrMultiMode = ttk.Combobox(self.LF_datapreprocess,
width=8,
textvariable=self.clsM)
self.BiOrMultiMode['values'] = ('macro', 'binary', 'micro') # 设置下拉列表的值
self.BiOrMultiMode.grid(row=0,
column=5) # 设置其在界面中出现的位置 column代表列 row 代表行
self.BiOrMultiMode.insert(
0, 'macro') # 设置下拉列表默认显示的值，0为 numberChosen['values'] 的下标值
Label(self.LF_datapreprocess, text="TraForm:").grid(row=1,
column=4,
columnspan=1,
sticky=W)
self.trans = StringVar()
self.inputtransform = ttk.Combobox(self.LF_datapreprocess,
width=8,
textvariable=self.trans)
self.inputtransform['values'] = ('norm', 'minmax', 'none') # 设置下拉列表的值
self.inputtransform.grid(row=1, column=5,
sticky=W) # 设置其在界面中出现的位置 column代表列 row 代表行
self.inputtransform.insert(
0, 'norm') # 设置下拉列表默认显示的值，0为 numberChosen['values'] 的下标值
self.lam = IntVar()
self.Apply = Checkbutton(self.LF_datapreprocess,
text='Apply(SpectraData):',
variable=self.lam,
command=lambda: ApplyOr_press(self.lam))
self.Apply.grid(row=0, column=6, sticky=W)
self.inputLambda = Entry(self.LF_datapreprocess, width=12)
self.inputLambda.grid(row=0, column=7, columnspan=1, sticky=W)
self.inputLambda.insert(0, 'x:x*1')
##2***************************2****************************************************
Label(self.LF_datapreprocess, text="WLs:").grid(row=1,
column=0,
sticky=W)
self.inputwlengths = Entry(self.LF_datapreprocess, width=4)
self.inputwlengths.grid(row=1, column=1, sticky=E)
self.inputwlengths.insert(0, '489')
Label(self.LF_datapreprocess, text="WLe:").grid(row=1,
column=2,
sticky=W)
self.inputwlengthe = Entry(self.LF_datapreprocess, width=4)
self.inputwlengthe.grid(row=1, column=3, sticky=W)
self.inputwlengthe.insert(0, '1053')
self.shuf = IntVar()
self.shufflebutton = Checkbutton(
self.LF_datapreprocess,
text='Shuffle',
variable=self.shuf,
command=lambda: shufflebutton_press(self.shuf))
self.shufflebutton.grid(row=1, column=6, columnspan=1, sticky=W)
print "befor first shuffe: ", filesAll.getTrainDataFile()
self.rate = IntVar()
Label(self.LF_datapreprocess, text="DataR:").grid(row=1,
column=7,
columnspan=1,
sticky=W)
self.inputRate = Entry(self.LF_datapreprocess, width=6)
self.inputRate.grid(row=1, column=7, columnspan=1, sticky=E)
self.inputRate.insert(0, '0.7')
self.LF_datapreprocess.grid(row=0,
column=0,
columnspan=8,
sticky='WESN')
##******************************model params***************************************************
self.LF_modelparams = LabelFrame(self.frm_bottom1,
text='DL Model Params')
Label(self.LF_modelparams, text="ACT_Func:").grid(row=0,
column=0,
sticky=W)
self.str_act = StringVar()
self.DL_ActFunc = ttk.Combobox(self.LF_modelparams,
width=8,
textvariable=self.str_act)
self.DL_ActFunc['values'] = ('relu', 'sigmoid', 'tanh', 'linear'
) # 设置下拉列表的值
self.DL_ActFunc.grid(row=0, column=1,
sticky=E) # 设置其在界面中出现的位置 column代表列 row 代表行
self.DL_ActFunc.insert(
0, 'relu') # 设置下拉列表默认显示的值，0为 numberChosen['values'] 的下标值
Label(self.LF_modelparams, text="Optimization:").grid(row=0,
column=4,
columnspan=1,
sticky=W)
self.opt = StringVar()
self.DL_Optimize = ttk.Combobox(self.LF_modelparams,
width=8,
textvariable=self.opt)
self.DL_Optimize['values'] = ('SGD', 'Adam', 'RMSprop', 'Adagrad',
'Adadelta', 'Adamax', 'Nadam'
) # 设置下拉列表的值
self.DL_Optimize.grid(row=0, column=5,
sticky=E) # 设置其在界面中出现的位置 column代表列 row 代表行
self.DL_Optimize.insert(
0, 'SGD') # 设置下拉列表默认显示的值，0为 numberChosen['values'] 的下标值
Label(self.LF_modelparams, text="GN Sigma:").grid(row=0,
column=6,
columnspan=1,
sticky=W)
self.sigma = StringVar()
self.DL_GNsigma = ttk.Combobox(self.LF_modelparams,
width=8,
textvariable=self.sigma)
self.DL_GNsigma['values'] = ('0', '0.1', '0.05', '0.01', '0.005'
) # 设置下拉列表的值
self.DL_GNsigma.grid(row=0, column=7,
sticky=E) # 设置其在界面中出现的位置 column代表列 row 代表行
self.DL_GNsigma.insert(
0, '0.01') # 设置下拉列表默认显示的值，0为 numberChosen['values'] 的下标值
Label(self.LF_modelparams, text="Layers:").grid(row=0,
column=8,
sticky=W)
self.DL_Model_layers = Entry(self.LF_modelparams, width=6)
self.DL_Model_layers.grid(row=0, column=9, columnspan=1, sticky=E)
self.DL_Model_layers.insert(0, '3')
self.uniform = StringVar()
Label(self.LF_modelparams, text="Init Mode:").grid(row=1,
column=0,
columnspan=1,
sticky=W)
self.DL_Initmode = ttk.Combobox(self.LF_modelparams,
width=8,
textvariable=self.uniform)
self.DL_Initmode['values'] = ('uniform', 'glorot_uniform')
self.DL_Initmode.grid(row=1, column=1, sticky=W)
self.DL_Initmode.insert(0, 'uniform')
self.regularization = StringVar()
Label(self.LF_modelparams,
text="Regularization Func:").grid(row=0,
column=2,
columnspan=1,
sticky=W)
self.DL_Regularization = ttk.Combobox(self.LF_modelparams,
width=8,
textvariable=self.regularization)
self.DL_Regularization['values'] = ('l1', 'l2', 'None')
self.DL_Regularization.grid(row=0, column=3, sticky=W)
self.DL_Regularization.insert(0, 'l2')
self.regular_rate = StringVar()
Label(self.LF_modelparams,
text="Regularization Rate:").grid(row=1,
column=2,
columnspan=1,
sticky=W)
self.DL_Regularization_rate = ttk.Combobox(
self.LF_modelparams, width=8, textvariable=self.regular_rate)
self.DL_Regularization_rate['values'] = ('0', '0.001', '0.003', '0.01',
'0.03', '0.1', '0.3', '1',
'3', '10')
self.DL_Regularization_rate.grid(row=1, column=3, sticky=W)
self.DL_Regularization_rate.insert(0, '0.1')
Label(self.LF_modelparams, text="OutDimention:").grid(row=1,
column=4,
columnspan=1,
sticky=W)
self.DL_Model_outputdimention = Entry(self.LF_modelparams, width=10)
self.DL_Model_outputdimention.grid(row=1,
column=5,
columnspan=1,
sticky=E)
self.DL_Model_outputdimention.insert(0, '100')
self.dropout_rate = StringVar()
Label(self.LF_modelparams, text="Dropout Rate:").grid(row=1,
column=6,
columnspan=1,
sticky=W)
self.DL_Model_droprate = ttk.Combobox(self.LF_modelparams,
width=8,
textvariable=self.dropout_rate)
self.DL_Model_droprate['values'] = ('0', '0.1', '0.2', '0.3', '0.4',
'0.5', '0.6', '0.7', '0.8')
self.DL_Model_droprate.grid(row=1, column=7, sticky=W)
self.DL_Model_droprate.insert(0, '0.2')
## weights=None, W_regularizer=None, b_regularizer=None,
## activity_regularizer=None, W_constraint=None, b_constraint=None, bias=True, input_dim=None
self.LF_modelparams.grid(row=1, column=0, columnspan=8, sticky='WESN')
#
self.frm_bottom1.grid(row=2, column=0, columnspan=8, sticky='WESN')
##下部参数设置以及训练控制行222222222*********************************************************************************************************************
self.frm_bottom2 = LabelFrame(root)
##1***************************step1****************************
self.trainstep_1 = LabelFrame(self.frm_bottom2,
text="Train_Step1 Params")
Label(self.trainstep_1, text="L_Rate:").grid(row=1, column=0, sticky=W)
self.step1_DL_Learning_rate = Entry(self.trainstep_1, width=6)
self.step1_DL_Learning_rate.grid(row=1,
column=1,
columnspan=1,
sticky=E)
self.step1_DL_Learning_rate.insert(0, '0.01')
Label(self.trainstep_1, text="Iterations:").grid(row=1,
column=2,
columnspan=1,
sticky=W)
self.step1_Iterations = Entry(self.trainstep_1, width=6)
self.step1_Iterations.grid(row=1, column=3, columnspan=1, sticky=E)
self.step1_Iterations.insert(0, '120')
Label(self.trainstep_1, text="Batchsize:").grid(row=1,
column=4,
columnspan=1,
sticky=W)
self.step1_Batchsize = Entry(self.trainstep_1, width=4)
self.step1_Batchsize.grid(row=1, column=5, columnspan=1, sticky=W)
self.step1_Batchsize.insert(0, '40')
self.trainstep_1.grid(row=0, column=0, columnspan=6, sticky='WESN')
##1***************************step2********************************
self.trainstep_2 = LabelFrame(self.frm_bottom2,
text="Train_Step2 Params")
Label(self.trainstep_2, text="L_Rate:").grid(row=1, column=0, sticky=W)
self.step2_DL_Learning_rate = Entry(self.trainstep_2, width=6)
self.step2_DL_Learning_rate.grid(row=1,
column=1,
columnspan=1,
sticky=E)
self.step2_DL_Learning_rate.insert(0, '0.001')
Label(self.trainstep_2, text="Iterations:").grid(row=1,
column=2,
columnspan=1,
sticky=W)
self.step2_Iterations = Entry(self.trainstep_2, width=6)
self.step2_Iterations.grid(row=1, column=3, columnspan=1, sticky=E)
self.step2_Iterations.insert(0, '60')
Label(self.trainstep_2, text="Batchsize:").grid(row=1,
column=4,
columnspan=1,
sticky=W)
self.step2_Batchsize = Entry(self.trainstep_2, width=4)
self.step2_Batchsize.grid(row=1, column=5, columnspan=1, sticky=W)
self.step2_Batchsize.insert(0, '35')
self.trainstep_2.grid(row=1, column=0, columnspan=6, sticky='WESN')
##1***************************step3********************************
self.trainstep_3 = LabelFrame(self.frm_bottom2,
text="Train_Step3 Params")
Label(self.trainstep_3, text="L_Rate:").grid(row=1, column=0, sticky=W)
self.step3_DL_Learning_rate = Entry(self.trainstep_3, width=6)
self.step3_DL_Learning_rate.grid(row=1,
column=1,
columnspan=1,
sticky=E)
self.step3_DL_Learning_rate.insert(0, '0.0001')
Label(self.trainstep_3, text="Iterations:").grid(row=1,
column=2,
columnspan=1,
sticky=W)
self.step3_Iterations = Entry(self.trainstep_3, width=6)
self.step3_Iterations.grid(row=1, column=3, columnspan=1, sticky=E)
self.step3_Iterations.insert(0, '30')
Label(self.trainstep_3, text="Batchsize:").grid(row=1,
column=4,
columnspan=1,
sticky=W)
self.step3_Batchsize = Entry(self.trainstep_3, width=4)
self.step3_Batchsize.grid(row=1, column=5, columnspan=1, sticky=W)
self.step3_Batchsize.insert(0, '30')
self.trainstep_3.grid(row=2, column=0, columnspan=6, sticky='WESN')
self.startbutton = Button(self.frm_bottom2,
text="Run",
command=self.startTrain,
fg='blue')
self.startbutton.grid(row=0, column=18, columnspan=1, sticky='WESN') #
self.stopbutton = Button(self.frm_bottom2,
text="Stop",
command=self.stop_trainthread,
fg='red')
self.stopbutton.grid(row=1, column=18, columnspan=1, sticky='WESN') #
self.frm_bottom2.grid(row=2, column=4, columnspan=12, sticky='WESN')
def clearCanvas(self):
self.fig.clf()
self.canvas = FigureCanvasTkAgg(self.fig, master=root)
self.fig_sub = self.fig.add_subplot(111)
##将每次训练任务放到一个独立的线程中进行，实现多线程
def startTrain(self):
refreshParam(preproAll, ANNNET, dltrainAll)
self.__threadTrain = threading.Thread(target=self.trainmodel)
self.train_flag = False
self.__threadTrain.setDaemon(True)
self.__threadTrain.start()
# self.currentthread = self.__threadTrain.getName()
if not self.train_flag:
self.periodicTextCall()
else:
self.canvas.show()
self.__threadTrain.stop()
self.__threadTrain.join()
self.__threadTrain.exit()
return self.__threadTrain
def stopTrain(self):
self.StateQueue.put("train stopped.")
self.train_flag = True
threadTrain.stop()
threadTrain.join()
threadTrain.exit()
def stop_trainthread(self):
_async_raise(self.__threadTrain.ident, SystemExit)
self.StateQueue.put("train stopped.")
self.train_flag = True
print 'train stopped.'
## def startPredict(self):
## self.predict_flag = False
## threadPredict = threading.Thread(target = lambda:predict_press(self.predict_flag,self.StateQueue))
## threadPredict.start()
## if not self.predict_flag:
## self.periodicTextCall()
## else:
## self.canvas.show()
## threadPredict.join()
def startPredict(self):
self.predict_flag = False
predict_press(self.predict_flag, self.StateQueue)
# self.insertTextBox()
self.canvas.show()
##用queue来进行多线程之间的信息传递，为什么要用while？再了解下
def insertTextBox(self):
while self.StateQueue.qsize():
try:
msg = self.StateQueue.get(0) ##队列的第一个值是最新的消息
self.text_box.insert("insert", '\n') ##换行
self.text_box.insert("insert", msg)
except Queue.Empty:
pass
##定义一个周期更新GUI方法，查询训练状态以及操作状态信息的方法
def periodicTextCall(self):
self.master.after(200, self.periodicTextCall)
self.insertTextBox()
###############################################################################
def PlotAcryROC(self, classifier, test_y, predict, precision, recall,
accuracy):
from sklearn.metrics import roc_curve, auc, f1_score
print "PlotAcryROC called", precision, recall, accuracy
print "posi label", preproAll.getPosiLabel()
fpr, tpr, thresholds = roc_curve(test_y,
predict,
pos_label=preproAll.getPosiLabel())
ROC.interPMean_tpr(fpr, tpr)
print "fpr, tpr", fpr, tpr
roc_auc = auc(fpr, tpr)
print "roc_auc", roc_auc
self.fig_sub.plot(
fpr,
tpr,
lw=1,
label='%s (precision: %.2f%%,recall: %.2f%%, accuracy: %.2f%%)' %
(classifier, precision * 100, recall * 100, accuracy * 100))
#画ROC折线，只需要plt.plot(fpr,tpr),变量roc_auc只是记录auc的值，通过auc()函数能计算出来
self.fig_sub.plot(fpr,
tpr,
lw=1,
label='ROC %s (area = %0.3f)' %
(classifier, roc_auc))
def GridsearchCv(self, create_model):
from sklearn.grid_search import GridSearchCV
from keras.wrappers.scikit_learn import KerasClassifier
# create model
model = KerasClassifier(build_fn=create_model, verbose=0)
# define the grid search parameters
batch_size = [10, 20, 40, 60, 80, 100]
epochs = [10, 50, 100]
param_grid = dict(batch_size=batch_size, nb_epoch=epochs)
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=1)
return grid
def floatfunction(self, array):
print array.shape
def trainmodel(self): ##subfigue
import numpy as np
from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta, Adam, Adamax, Nadam
import tensorflow as tf
import MethMethod
sess = tf.InteractiveSession()
print "model train start"
self.StateQueue.put("#######################################")
self.StateQueue.put("*ANN model train start")
import FileMethod
self.clearCanvas()
train_x,train_y,test_x,test_y = FileMethod.read_data\
(filesAll,preproAll, boolAll,self.StateQueue)
# print "train_y",train_y.shape
## preproAll.setLabelsNum(len(count))
labelsnum = preproAll.getLabelsNum()
onehottrain_y = tf.one_hot(np.asarray(train_y.T)[0], labelsnum).eval()
# print "onehot train_y",onehottrain_y
optmodel = ANNNET.getOPTFUNC()
print 'optmodel', optmodel
lr1, lr2, lr3 = dltrainAll.getDLLearningRate()
print "lr1,lr2,lr3", lr1, lr2, lr3
nb1, nb2, nb3 = dltrainAll.getDLBatchsize()
print "nb1,nb2,nb3", nb1, nb2, nb3
it1, it2, it3 = dltrainAll.getDLIterations()
print "it1,it2,it3", it1, it2, it3
opt1 = eval(optmodel)(lr=lr1, decay=1e-6)
opt2 = eval(optmodel)(lr=lr2, decay=1e-6)
opt3 = eval(optmodel)(lr=lr3, decay=1e-6)
inputdimention = train_x.shape[1]
print '********labelsnum*******', labelsnum
self.StateQueue.put("%d labels to be classified" % labelsnum)
model = ANNNET.ANN_Custom(inputdimention, labelsnum)
# model = ANNNET.ANN_simple2()
start_time = time.time()
print "step 1...."
self.StateQueue.put("step 1....")
## self.StateQueue.put(sys.stdout)
model.compile(
loss='categorical_crossentropy',
optimizer=opt1,
metrics=['accuracy']
) ##loss:categorical_crossentropy sparse_categorical_crossentropy,
# model = self.GridsearchCv(model)
model.fit(train_x,
onehottrain_y,
nb_epoch=it1,
batch_size=nb1,
shuffle=True,
verbose=2) #4训练模型
# grid_result = model.fit(train_x, train_y)
# # summarize results
# print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
# for params, mean_score, scores in grid_result.grid_scores_:
# print("%f (%f) with: %r" % (scores.mean(), scores.std(), params))
print "step 2...."
self.StateQueue.put("step 2....")
## self.StateQueue.put(sys.stdout)
model.compile(loss='categorical_crossentropy',
optimizer=opt2,
metrics=['accuracy'])
model.fit(train_x,
onehottrain_y,
nb_epoch=it2,
batch_size=nb2,
shuffle=True,
verbose=2) #4训练模型
print "step 3...."
self.StateQueue.put("step 3....")
## self.StateQueue.put(sys.stdout)
model.compile(loss='categorical_crossentropy',
optimizer=opt3,
metrics=['accuracy'])
model.fit(train_x,
onehottrain_y,
nb_epoch=it3,
batch_size=nb3,
shuffle=True,
verbose=2) #4训练模型
# json_string = model.to_json()
# open('my_model_architecture.json','wb+').write(json_string)
# model.save_weights('ANN_model_weights.h5',overwrite=True)
# print"train end!"
self.StateQueue.put("######################################")
self.StateQueue.put("model train end")
costtime = time.time() - start_time
print 'training took %fs!' % costtime
self.StateQueue.put('training took %fs!' % (costtime))
self.StateQueue.put("%s train done." % model)
predict = model.predict_classes(test_x)
print "True label is", test_y.T
print "predict is ", predict
# print "predict score is %0.3f"%predictprob
self.StateQueue.put("predict is %s" % predict)
precision,recall,accuracy = \
MethMethod.CalculateMixtureMetrics(test_y, predict, preproAll.getBiOrMultiMode())
classifier = 'ANN'
self.PlotAcryROC(classifier, test_y, predict, precision, recall,
accuracy)
FileMethod.SaveModelParam(filesAll, preproAll, ANNNET)
self.train_flag = True
DrawPic(self.canvas, self.fig_sub)
saveModel_press(model, self.StateQueue)
print "model train end"
rateDLabel = Label(inputFrame, text="rateD:", pady=5) rateAEntry = Entry(inputFrame, textvariable=rateA) rateBEntry = Entry(inputFrame, textvariable=rateB) rateCEntry = Entry(inputFrame, textvariable=rateC) rateDEntry = Entry(inputFrame, textvariable=rateD) initialXEntry = Entry(inputFrame, textvariable=initialX) initialYEntry = Entry(inputFrame, textvariable=initialY) goButton = Button(inputFrame, text="Go!", command=graphIt) inputRequestWindowLabel.grid(row=0, columnspan=2) selectionFrame.pack() initialXLabel.grid(row=0, column=0) initialYLabel.grid(row=1, column=0) initialXEntry.grid(row=0, column=1) initialYEntry.grid(row=1, column=1) rateALabel.grid(row=2, column=0) rateBLabel.grid(row=3, column=0) rateCLabel.grid(row=4, column=0) rateDLabel.grid(row=5, column=0) rateAEntry.grid(row=2, column=1) rateBEntry.grid(row=3, column=1) rateCEntry.grid(row=4, column=1) rateDEntry.grid(row=5, column=1) goButton.grid(row=6, columnspan=2) inputFrame.pack() root.mainloop()
elif selected == []:
messagebox.showinfo("Error", "Please insert at least 2 stocks")
selected.append(entry3.get())
selected.append(entry4.get())
selected.append(entry5.get())
root.destroy()
global start_year
start_year = change_dropdown1()
global end_year
end_year = change_dropdown2()
btn_add = Button(root, text="Add more +", command=addBox, bg="#54DCFF")
btn_add.config(width=20)
btn_add.grid(row=2, columnspan=2)
submit = Button(root, text="Submit", command=submit, bg="#54DCFF")
submit.config(width=20)
submit.grid(row=3, columnspan=2)
# Create a Tkinter variable
year1 = StringVar(root)
# ------------------YEAR OPTIONS -------------------------
choices = [] # list of years
for i in range(1980, 2020):
choices.append(i)
choices.reverse()
year1.set('2014') # set the default option
