def print_unusual_amino_acid(self):
'''Check if ico file exist'''
if self.check_if_ico_exist() == False:
return f'"logo.ico" file doesnt exist. File has to be added to the directory;{self.ico_path()}'
'''check if unusual_amino.csv" exist'''
if self.check_if_data_exist() == False:
return f'"unusual_amino.csv" doesnt exist. Amino acid has to be added first;{self.data_path()}'
'''check if unusual_amino.csv is empty - first checked if exist'''
if self.check_if_unusual_amino_is_empty() == 'True':
return f'Please note that file "unusual_amino.csv" is empty; Or it contains only one row'
else:
'''Sets icon'''
root = tk.Tk()
root.title('PeptideMassCalculator')
root.iconbitmap(self.ico_path())
frame = tk.Frame(root)
frame.pack(fill='both', expand=True)
datatable = Table(frame, showtoolbar=False, showstatusbar=True)
datatable.show()
'''shows unusual_amino.csv in the table'''
datatable.importCSV(filename=self.data_path(), dialog=False)
root.mainloop()
def retrievePassword(root, userSecret):
root.destroy()
hoje = Tk()
hoje.geometry("480x300")
hoje.title('SmartLocker')
FILENAME = Image.open('photo.png')
tk_img = ImageTk.PhotoImage(FILENAME)
frame1 = tk.Frame(hoje, bg='black')
frame1.place(relwidth=1.0, relheight=1.0)
frame1.place(relwidth=1.0, relheight=1.0)
labetse2 = Label(frame1, image=tk_img)
labetse2.pack()
labetset = Label(frame1, image=tk_img)
#labetset.grid(row=0, column=0)
labetset.pack()
labetset.place(relwidth=1.0, relheight=0.6)
frameText = Label(labetset,
text="Take a look on what info we have!",
bg='black',
fg='white',
font=('Courier', 17))
#frameText.grid(row=2, columnspan=3)
frameText.place(x=15, y=280)
#userSecret.showServices()
toolbar = tk.Frame(labetset)
pt = Table(labetset, width=480, height=150)
button = tk.Button(frame1,
text="Quit",
font='Courier',
fg='red',
background='blue',
highlightbackground='green',
command=quit)
button.place(x=220, y=250)
buttonSwitch = tk.Button(frame1,
text="Add New Account",
font='Courier',
fg='red',
background='blue',
highlightbackground='green',
command=lambda: frameAddPassword(hoje))
buttonSwitch.place(x=280, y=250)
pt.importCSV(filename='password.csv')
pt.show()
pt.setColumnColors(cols=2, clr='red')
#button.place(x=100, y =100)
hoje.mainloop()
def getCSV(self):
root = tk.Tk()
root.title('PandasTable')
frame = tk.Frame(root)
frame.pack(fill='both', expand=True)
pt = Table(frame)
pt.show()
import_file_path = filedialog.askopenfilename()
pt.importCSV(filename=import_file_path, dialog=True)
root.mainloop()
def load_dataset():
dataset_window = Tk()
dataset_window.geometry('1000x800')
dataset_window.title('Dataset')
f = Frame(dataset_window)
tb = Table(f)
f.pack(fill=BOTH, expand=True)
tb.importCSV('train.csv')
tb.show()
dataset_window.mainloop()
def Employee():
filename = "EmployeeDetails\EmployeeDetails.csv"
root3 = Tk()
root3.title("Employee Table")
frame = Frame(root3)
frame.pack()
pt = Table(frame)
pt.importCSV(filename)
pt.show()
root3.mainloop()
def open_file():
file = filedialog.askopenfilename(
initialdir="/home/pi/Desktop/AutomatedCableTester",
title="Select file",
filetypes=(("csv files", "*.csv"), ("all files", "*.*")))
if len(file) != 0 and ".csv" in file:
t = tk.Toplevel()
pt = Table(t)
pt.importCSV(file)
pt.redraw()
pt.show()
def attendance():
ts = time.time()
date = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d')
filename = "Attendance\Attendance_" + date + ".csv"
root2 = Tk()
root2.title("Attendance Table")
frame = Frame(root2)
frame.pack()
pt = Table(frame)
pt.importCSV(filename)
pt.show()
root2.mainloop()
def loaddataset():
#root.destroy()
root1 = Tk()
root1.geometry('1600x8000')
root1.title('Dataset')
f = Frame(root1)
tb = Table(f)
f.pack(fill=BOTH, expand=True)
tb.importCSV('data.csv')
tb.show()
root1.mainloop()
def show_data_table():
path = open_path()
if path == 'FileNotFoundError':
error_box('Please upload data first.')
return
root = tk.Tk()
root.title('STOCK ANALYST')
frame = tk.Frame(root)
frame.pack(fill='both', expand=True)
datatable = Table(frame, showtoolbar=True, showstatusbar=True)
datatable.show()
'''shows data in the table'''
datatable.importCSV(filename=path, dialog=False)
root.mainloop()
class DataPanel():
def __init__(self, root):
self.frame3 = Tk.Frame(root)
self.frame3.__init__()
self.frame3.pack(fill=Tk.BOTH, expand=1)
df = pandas.DataFrame()
self.table = Table(self.frame3,
dataframe=df,
showtoolbar=True,
showstatusbar=True)
self.table.show()
def update(self, file_path):
self.table.importCSV(file_path)
self.table.redraw()
def show_table():
try:
try:
if root.state() == 'normal':
root.focus()
except Exception as e:
root = tk.Toplevel()
root.title("Show Table")
root.geometry('800x500')
root.configure(bg='#FFFFCC')
frame = tk.Frame(root)
frame.pack(fill=BOTH)
table = Table(frame,showstatusbar=True,showtoolbar=True)
table.importCSV(filename=r'G:\PYTHON\GUI\Passbuddy project\webinfo.csv')
table.show()
except Exception as e:
permanent_row = ['Platformname','Username','Password','Email']
f = open(r'G:\PYTHON\GUI\Passbuddy project\webinfo.csv','a')
write = csv.writer(f)
write.writerow(permanent_row)
f.close()
Created on Sat Feb 22 19:33:18 2020
@author: Zhimin
"""
from tkinter import *
from pandastable import Table, TableModel
#assuming parent is the frame in which you want to place the table
root = Tk()
frame = Frame(root)
frame.pack()
#root.mainloop()
pt = Table(frame)
pt.show()
pt.importCSV('test.csv')
pt.mainloop()
class popupWindow(object):
def changeBlock( self,event ):
self.location = (event.x,event.y)
print(str(event.x) + str(event.y))
self.canvas.create_rectangle(event.x,event.y,event.x+10,event.y+10,fill='red')
def __init__(self, master):
top=self.top=Toplevel(master)
top.minsize(width=800, height=600)
self.canvas = Canvas(top, width=120, height=80, bg='black')
self.canvas.pack(expand=YES, fill=BOTH)
self.gif1 = PhotoImage(file='BP Baltimore Satellite.PNG')
from tkinter import *
from pandastable import Table
#assuming parent is the frame in which you want to place the table
pt = Table(parent)
pt.importCSV('example.csv')
pt.show()
def merge_data():
mergeWindow = Toplevel()
mergeWindow.title("Merged Data Window")
reader = csv.reader(open(dataFile[0]))
reader1 = csv.reader(open(dataFile[1]))
f = open("combined.csv", "w")
writer = csv.writer(f)
next(reader1)
for row in reader:
writer.writerow(row)
for row in reader1:
writer.writerow(row)
f.close()
frame = Frame(mergeWindow)
frame.pack()
pt = Table(frame)
pt.show()
pt.importCSV(filename='combined.csv', dialog=True)
label = Label(mergeWindow, text="This is merged file", font=50)
label.pack()
dataPreprocessingLabel = Label(
mergeWindow,
text="Do you want data preprocessing on data?",
font=100)
dataPreprocessingLabel.pack()
def yes_clicked():
#Here we have to do data preprocessing
dataPreprocessingWindow = Toplevel()
dataPreprocessingWindow.title("Data Preprocessing Window")
dataPreprocessingWindow.geometry("700x700")
missing_values_indexes = StringVar()
icategorical_values_indexes = StringVar()
dcategorical_values_indexes = StringVar()
def ok_clicked():
try:
listOfMissingValuesIndexes = []
listOfICategoricalValuesIndexes = []
listOfDCategoricalValuesIndexes = []
index = missing_values_indexes.get()
iindex = icategorical_values_indexes.get()
dindex = dcategorical_values_indexes.get()
for i in index:
if (i == ' '):
pass
else:
ind = int(i)
listOfMissingValuesIndexes.append(ind)
for i in iindex:
if (i == ' '):
pass
else:
ind = int(i)
listOfICategoricalValuesIndexes.append(ind)
for i in dindex:
if (i == ' '):
pass
else:
ind = int(i)
listOfDCategoricalValuesIndexes.append(ind)
print(listOfMissingValuesIndexes)
print(listOfICategoricalValuesIndexes)
print(listOfDCategoricalValuesIndexes)
except:
print(
"Your data doesn't have any categorical value")
finally:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('combined.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values
print(X)
print(y)
try:
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan,
strategy='mean')
imputer.fit(X[:, listOfMissingValuesIndexes])
X[:,
listOfMissingValuesIndexes] = imputer.transform(
X[:, listOfMissingValuesIndexes])
print(X)
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[
('encoder', OneHotEncoder(),
listOfICategoricalValuesIndexes)
],
remainder='passthrough')
X = np.array(ct.fit_transform(X))
print(X)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(y)
print(y)
except:
print("No need")
finally:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=1)
Label(
dataPreprocessingWindow,
text=
"This is metrices of features X after data preprocessing",
bg="orange",
font=50).grid()
Label(dataPreprocessingWindow, text=X,
font=50).grid()
Label(
dataPreprocessingWindow,
text=
"This is dependent variable y after data preprocessing",
bg="orange",
font=50).grid()
Label(dataPreprocessingWindow, text=y,
font=50).grid()
Label(
dataPreprocessingWindow,
text=
"Data set splitted into training set(80%) and test set(20%)",
bg="orange",
font=70).grid()
def move_forward_clicked():
chooseAlgoWindow = Toplevel()
chooseAlgoWindow.title("Choice is hard")
chooseAlgoWindow.geometry("700x700")
Label(
chooseAlgoWindow,
text=
"Choose what you want to do on your data(REGRESSION, CLASSIFICATION OR CLUSTERING)",
bg="antique white",
font=80).grid(row=0, column=0)
def regression():
def linear_regression():
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
#Predicting the test set results
y_pred = regressor.predict(X_test)
#Visualizing the training set results
def train_set():
plt.scatter(X_train[:, 0],
y_train,
color='red')
plt.plot(X_train,
regressor.predict(X_train),
color='blue')
plt.title('Training set')
plt.show()
#Visualizing the test set results
def test_set():
plt.scatter(X_test[:, 0],
y_test,
color='red')
plt.plot(X_train,
regressor.predict(X_train),
color='blue')
plt.title("Test set")
plt.show()
#Predicting new value
def predict_new_value():
new_value_predict = IntVar()
Label(
chooseAlgoWindow,
text="Enter the value to predict",
font=70).grid()
Entry(chooseAlgoWindow,
textvariable=new_value_predict,
width=50).grid()
def predict_clicked():
value_to_predict = new_value_predict.get(
)
X_predict = [value_to_predict]
y_predict = regressor.predict(
[X_predict])
Label(chooseAlgoWindow,
text="Predicted value is :-",
font=100).grid()
Label(chooseAlgoWindow,
text=y_predict,
font=150).grid()
Button(chooseAlgoWindow,
text="Predict",
fg="orange",
command=predict_clicked,
cursor="hand2").grid()
Button(
chooseAlgoWindow,
text=
"Click to visualize the train test result",
bg="orange",
fg="black",
command=train_set,
cursor="hand2").grid()
Button(
chooseAlgoWindow,
text=
"Click to visualize the test test result",
bg="orange",
fg="black",
command=test_set,
cursor="hand2").grid()
Button(
chooseAlgoWindow,
text="Click here to predict new value",
bg="orange",
fg="black",
command=predict_new_value,
cursor="hand2").grid()
def multiple_linear_regression():
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
#Predicting the Test set results
y_pred = regressor.predict(X_test)
np.set_printoptions(precision=2)
print(
np.concatenate(
(y_pred.reshape(len(y_pred), 1),
y_test.reshape(len(y_test), 1)),
1))
Label(
chooseAlgoWindow,
text=
"Here is the predicted test set result(FIRST COLUMN = TEST SET VALUE, SECOND COLUMN = PREDICTED VALUE)",
bg="orange").grid()
Label(chooseAlgoWindow,
text=np.concatenate(
(y_pred.reshape(len(y_pred), 1),
y_test.reshape(len(y_test), 1)),
1)).grid()
Label(chooseAlgoWindow,
text="Ok...Which algo??",
bg="orange",
font=40).grid(row=4, column=1)
Button(chooseAlgoWindow,
text="1.Simple Linear Regression",
bg="linen",
fg="black",
command=linear_regression,
cursor="hand2").grid(row=5, column=1)
Button(chooseAlgoWindow,
text="2.Multiple Linear Regression",
bg="linen",
fg="black",
command=multiple_linear_regression,
cursor="hand2").grid(row=6, column=1)
Button(chooseAlgoWindow,
text="3.Polynomial Regression",
bg="linen",
fg="black",
cursor="hand2").grid(row=7, column=1)
Button(chooseAlgoWindow,
text="4.Support Vector Regression",
bg="linen",
fg="black",
cursor="hand2").grid(row=8, column=1)
Button(chooseAlgoWindow,
text="5.Decision Tree Regression",
bg="linen",
fg="black",
cursor="hand2").grid(row=9, column=1)
Button(chooseAlgoWindow,
text="6.Random Forest Regression",
bg="linen",
fg="black",
cursor="hand2").grid(row=10, column=1)
def classification():
Label(chooseAlgoWindow,
text="Ok...Which algo??",
bg="orange",
font=40).grid(row=4, column=2)
Button(chooseAlgoWindow,
text="1.Logistic Regression",
bg="linen",
fg="black",
cursor="hand2").grid(row=5, column=2)
Button(chooseAlgoWindow,
text="2.K-Nearest Neighbours",
bg="linen",
fg="black",
cursor="hand2").grid(row=6, column=2)
Button(chooseAlgoWindow,
text="3.Support Vector Machine",
bg="linen",
fg="black",
cursor="hand2").grid(row=7, column=2)
Button(chooseAlgoWindow,
text="4.Kernel SVM",
bg="linen",
fg="black",
cursor="hand2").grid(row=8, column=2)
Button(chooseAlgoWindow,
text="5.Naive Byes",
bg="linen",
fg="black",
cursor="hand2").grid(row=9, column=2)
Button(chooseAlgoWindow,
text="6.Decision Tree Classification",
bg="linen",
fg="black",
cursor="hand2").grid(row=10, column=2)
Button(chooseAlgoWindow,
text="7.Random Forest Classification",
bg="linen",
fg="black",
cursor="hand2").grid(row=11, column=2)
def clustering():
Label(chooseAlgoWindow,
text="Ok...Which algo??",
bg="orange",
font=40).grid(row=4, column=3)
Button(chooseAlgoWindow,
text="1.K-Means Clustering",
bg="linen",
fg="black",
cursor="hand2").grid(row=5, column=3)
Button(chooseAlgoWindow,
text="2.Heirarchical Clustering",
bg="linen",
fg="black",
cursor="hand2").grid(row=6, column=3)
Button(chooseAlgoWindow,
text="REGRESSION",
bg="linen",
fg="black",
height=2,
width=30,
command=regression,
cursor="hand2").grid(row=3, column=1)
Button(chooseAlgoWindow,
text="CLASSIFICATION",
bg="linen",
fg="black",
height=2,
width=30,
command=classification,
cursor="hand2").grid(row=3, column=2)
Button(chooseAlgoWindow,
text="CLUSTERING",
bg="linen",
fg="black",
height=2,
width=30,
command=clustering,
cursor="hand2").grid(row=3, column=3)
Button(dataPreprocessingWindow,
text="Move Forward",
bg="orange",
fg="linen",
command=move_forward_clicked,
cursor="hand2").grid()
Label(
dataPreprocessingWindow,
text=
"Enter indexes(space separated) that can contain missing data",
font=70).grid(row=0, column=0)
Entry(dataPreprocessingWindow,
textvariable=missing_values_indexes,
width=50).grid()
Label(
dataPreprocessingWindow,
text=
"Enter indexes(space separated) of independent variables that can contain categorical data",
font=70).grid()
Entry(dataPreprocessingWindow,
textvariable=icategorical_values_indexes,
width=50).grid()
Button(dataPreprocessingWindow,
text="OK",
command=ok_clicked,
cursor="hand2").grid()
def no_clicked():
messagebox.showinfo(
"Recommendation",
"We recommend you to do data preprocessing before moving forward!"
)
yesButton = Button(mergeWindow,
text="Yes(RECOMMENDED)",
height=2,
width=20,
bg="linen",
command=yes_clicked,
cursor="hand2")
yesButton.pack()
noButton = Button(mergeWindow,
text="No",
height=2,
width=20,
command=no_clicked,
bg="linen",
cursor="hand2")
noButton.pack()
Label(
mergeWindow,
text=
"Make sure before clicking on YES that the dependent variable is the last column in data",
font=100).pack()
Label(
mergeWindow,
text=
"Or if you want clustering on data then click the below button",
font=100,
bg="orange",
fg="black").pack()
def clusteringClicked():
Label(
mergeWindow,
text=
"Which algo?? Choose the right algo according to the data",
bg="green yellow").pack()
Button(mergeWindow,
text="K-Means Algorithm",
bg="linen",
fg="black",
cursor="hand2").pack()
heirarchicalButton = Button(mergeWindow,
text="Heirarchical Clustering",
bg="linen",
fg="black",
cursor="hand2").pack()
clusteringButton = Button(mergeWindow,
text="Click Here For Clustering",
height=2,
width=20,
command=clusteringClicked,
cursor="hand2")
clusteringButton.pack()