Графический интерфейс Tkinter часто зависает

Недавно я создал графический интерфейс, который получает файл CSV и отображает среднее значение и стандартное значение для выбранной переменной в выбранном месте.

Графический интерфейс работал отлично, пока несколько дней назад. Он начал падать (не отвечает) либо сразу после загрузки CSV-файла, либо после выбора переменной местоположения. Иногда он отображает первый набор данных, а затем перестает работать (я должен иметь возможность повторно выбрать данные и повторно построить график).

Однако очень часто он работает и отображает среднее значение и стандартное значение.

Я искал Google снова и снова, но не мог найти ответа на свою проблему.

Буду признателен, если кто поможет и подскажет решение.

import matplotlib
from Tkinter import *
import csv
matplotlib.use("TkAgg")
from matplotlib import pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg,    NavigationToolbar2TkAgg
from matplotlib.figure import Figure
import numpy as np
import pylab as pl
from matplotlib import style
style.use('ggplot')
from tkFileDialog import askopenfilename
import inspect, os


root=Tk()
root.geometry("%dx%d+%d+%d" % (1500, 900, 500, 150))
root.title("Spectroscopy Library 1.0")
root.configure(bg='white')
#root.iconbitmap(iconpath)   # setting the icon 

T = Text(root, height=1, width=40,bg='white', fg='gray13',font=("Helvetica",24))
T.pack()
T.insert(END, "Tumor Average Spectra, Short Echo Time at 1.5 T")

filename = askopenfilename() 
Label(root, text="Location:",bg='white').grid(row=3, sticky=W)
var1 = IntVar()
Checkbutton(root, text="Posterior Fossa", variable=var1,bg='white').grid(row=4, sticky=W)
var2 = IntVar()
Checkbutton(root, text="Supratentorial", variable=var2,bg='white').grid(row=5, sticky=W)


def tumor_select():


    #----------------- calculating the file length---------------------------------
    csv_file_size = open(filename, 'rU')
    dataforsize = csv.reader(csv_file_size, delimiter=',')
    row_count = pl.sum(1 for row2 in dataforsize)

    #------------------defining matrixes-------------------------------------------

    csv_file = open(filename, 'rU')
    #    csv_file = open("/Users/Emsku/Documents/Python/modeldata5.csv", "rU")
    data = csv.reader(csv_file, delimiter=',')

    tumor = ["" for x in range(row_count)]  # defining an empty string
    region = ["" for y in range(row_count)]  #defining an empty string
    fit = np.zeros((row_count, 456))  #defining a int array

    #     data into arrays
    f = 0
    for row in data:
        tumor[f] = row[0]
        region[f] = row[1]
        fit[f] = row[2:]
        f += 1

    #---------------- devining data into region and tumor types -------------------

    atrt = [];atrtfit =[];atrtloc =[]
    gbm = [];gbmfit = [];gbmloc = []
    gng = [];gngfit = [];gngloc = []
    dast = [];dastfit = [];dastloc = []
    aast = [];aastfit = [];aastloc = []
    carn = [];carnfit = [];carnloc = []
    cpp = [];cppfit = [];cpploc = []
    cnspri = [];cnsprifit = [];cnspriloc = []
    dnt = [];dntfit = [];dntloc = []
    acpp = [];acppfit = [];acpploc = []
    past = [];pastfit = [];pastloc = []
    pin = [];pinfit = [];pinloc = []
    cpc = [];cpcfit = [];cpcloc = []
    germ = [];germfit = [];germloc = []
    epen = [];epenfit = [];epenloc = []
    anepen = [];anepenfit = [];anepenloc = []
    med = [];medfit = [];medloc = []
    dnmed = [];dnmedfit = [];dnmedloc = []
    lmed = [];lmedfit = [];lmedloc = []
    ast = [];astfit = [];astloc = []
    sepen = [];sepenfit = [];sepenloc = []
    glicer = [];glicerfit = [];glicerloc = []
    gcast = [];gcastfit = [];gcastloc = []
    swm = [];swmfit = [];swmloc = []
    pong = [];pongfit = [];pongloc = []
    ppm = []


    for index, type in enumerate(tumor):
        if type == 'Atypical Teratoid - Rhabdoid Tumour':
            atrt.append(index)
            atrtfit.append(fit[index,])
            atrtloc.append(region[index])
        if type == 'Glioblastoma':
            gbm.append(index)
            gbmfit.append(fit[index,])
            gbmloc.append(region[index])
        if type == 'ganglioglioma':
            gng.append(index)
            gngfit.append(fit[index,])
            gngloc.append(region[index])
        if type == 'diffuse_astrocytoma':
            dast.append(index)
            dastfit.append(fit[index,])
            dastloc.append(region[index])


       # part of the code (same as above for other tumor types) not included here


    ppm = fit[0,]

     # ATRT tumor
    atrt_pf=[];atrt_sp=[]
    for index, type in enumerate(atrtloc):
        if type == 'posterior fossa':
            atrt_pf.append(atrtfit[index])
        elif type == 'Supratentorial':
            atrt_sp.append(atrtfit[index])

    if len(atrt_pf)>1:
        atrtave_pf = np.mean(atrt_pf, axis=0);atrtsd_pf = np.std(atrt_pf, axis=0)
    elif len(atrt_pf)==1:
        atrtave_pf = np.mean(atrt_pf, axis=0) ;atrtsd_pf = np.zeros((456, 1))

    if len(atrt_sp)>1:
        atrtave_sp = np.mean(atrt_sp, axis=0);atrtsd_sp = np.std(atrt_sp, axis=0)
    elif len(atrt_sp)==1:
        atrtave_sp = np.mean(atrt_sp, axis=0); atrtsd_sp = np.zeros((456, 1))

    # Glioblastoma

    gbm_pf=[]; gbm_sp=[]
    for index, type in enumerate(gbmloc):
        if type == 'posterior fossa':
            gbm_pf.append(gbmfit[index])
        elif type == 'Supratentorial':
            gbm_sp.append(gbmfit[index])

    if len(gbm_pf)>1:
        gbmave_pf = np.mean(gbm_pf, axis=0);gbmsd_pf = np.std(gbm_pf, axis=0)
    elif len(gbm_pf)==1:
        gbmave_pf = np.mean(gbm_pf, axis=0) ;gbmsd_pf = np.zeros((456, 1))

    if len(gbm_sp)>1:
        gbmave_sp = np.mean(gbm_sp, axis=0);gbmsd_sp = np.std(gbm_sp, axis=0)
    elif len(gbm_sp)==1:
        gbmave_sp = np.mean(gbm_sp, axis=0); gbmsd_sp = np.zeros((456, 1))

    # Ganglioglioma

    gng_pf=[]; gng_sp=[]
    for index, type in enumerate(gngloc):
        if type == 'posterior fossa':
            gng_pf.append(gngfit[index])
        elif type == 'Supratentorial':
            gng_sp.append(gngfit[index])

    if len(gng_pf)>1:
        gngave_pf = np.mean(gng_pf, axis=0);gngsd_pf = np.std(gng_pf, axis=0)
    elif len(gng_pf)==1:
        gngave_pf = np.mean(gng_pf, axis=0) ;gngsd_pf = np.zeros((456, 1))

    if len(gng_sp)>1:
        gngave_sp = np.mean(gng_sp, axis=0);gngsd_sp = np.std(gng_sp, axis=0)
    elif len(gng_sp)==1:
        gngave_sp = np.mean(gng_sp, axis=0); gngsd_sp = np.zeros((456, 1))


    dast_pf=[]; dast_sp=[]
    for index, type in enumerate(dastloc):
        if type == 'posterior fossa':
            dast_pf.append(dastfit[index])
        elif type == 'Supratentorial':
            dast_sp.append(dastfit[index])



    if len(dast_pf)>1:
        dastave_pf = np.mean(dast_pf, axis=0);dastsd_pf = np.std(dast_pf, axis=0)
    elif len(dast_pf)==1:
        dastave_pf = np.mean(dast_pf, axis=0) ;dastsd_pf = np.zeros((456, 1))

    if len(dast_sp)>1:
        dastave_sp = np.mean(dast_sp, axis=0);dastsd_sp = np.std(dast_sp, axis=0)
    elif len(dast_sp)==1:
        dastave_sp = np.mean(dast_sp, axis=0); dastsd_sp = np.zeros((456, 1))



       # part of the code (same as above for other tumor types) not included here


var3 = StringVar(root)
# initial value
var3.set(' Select Tumor Type ')
choices = ['Atypical Teratoid Rhabdoid Tumour', 'Anaplastic astrocytoma','Anaplastic ependymoma','Atypical Choroid Plexus Papilloma', 'Craniopharyngioma adamantinomatous', 'Choroid Plexus Carcinoma', 'Choroid plexus papilloma', 'CNS_primitive_neuroectodermal_tumour_94733', 'Diffuse astrocytoma', 'Dysembryoplastic Neuroepithelial Tumour','Desmoplastic-Nodular Medulloblastoma','Ependymoma ', 'Glioblastoma', 'Gliomatosis cerberi', 'Ganglioglioma', 'Germinoma','Large Cell Medulloblastoma','Medulloblastoma ', 'Pilomyxoid astrocytoma', 'Pinoblastoma', 'Pontine glioma', 'Pilocytic astrocytoma ', 'Schwannoma', 'Subependymoma', 'Unbiopsied Subependymal Giant Cell Astrocytoma']
option =OptionMenu(root, var3, *choices).grid(row=10, sticky=W)
button = Button(root, text="       Plot average Spectra   ", command=tumor_select,bg="alice blue").grid(row=12, sticky=W)
#button.pack(side='left', padx=20, pady=10)
close_button = Button(root, text="                   Quit                   ", command=root.destroy).grid(row=16, sticky=W)


root.mainloop()