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Недавно я создал графический интерфейс, который получает CSV-файл и отображает среднее значение и std для выбранной переменной в выбранном месте.Tkinter GUI часто зависает
GUI работал отлично, пока несколько дней назад. Он начал сбой (не отвечает), как только я загружаю файл CSV или после выбора переменной местоположения. Иногда он отображает первый набор данных, а затем прекращает работу (я должен иметь возможность повторно выбирать данные и переделывать).
Однако очень часто это работает и отображает среднее значение и std.
У меня есть поиск google снова и снова, но не нашел ответа на мою проблему.
Я был бы признателен, если поможет кто угодно и разрешите 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()