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Major update of all demo programs to use new PEP8 bindings, etc

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PySimpleGUI 2019-10-23 16:10:03 -04:00
parent 3f7c87c562
commit 7f52778bcc
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DemoPrograms/Demo_Machine_Learning.py
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@ -1,65 +1,75 @@
#!/usr/bin/env python
import sys
if sys.version_info[0] >= 3:
import PySimpleGUI as sg
else:
import PySimpleGUI27 as sg
import PySimpleGUI as sg
def MachineLearningGUI():
sg.SetOptions(text_justification='right')
sg.set_options(text_justification='right')
flags = [[sg.Checkbox('Normalize', size=(12, 1), default=True), sg.Checkbox('Verbose', size=(20, 1))],
[sg.Checkbox('Cluster', size=(12, 1)), sg.Checkbox('Flush Output', size=(20, 1), default=True)],
[sg.Checkbox('Write Results', size=(12, 1)), sg.Checkbox('Keep Intermediate Data', size=(20, 1))],
[sg.Checkbox('Normalize', size=(12, 1), default=True), sg.Checkbox('Verbose', size=(20, 1))],
[sg.Checkbox('Cluster', size=(12, 1)), sg.Checkbox('Flush Output', size=(20, 1), default=True)],
[sg.Checkbox('Write Results', size=(12, 1)), sg.Checkbox('Keep Intermediate Data', size=(20, 1))],]
flags = [[sg.CB('Normalize', size=(12, 1), default=True), sg.CB('Verbose', size=(20, 1))],
[sg.CB('Cluster', size=(12, 1)), sg.CB(
'Flush Output', size=(20, 1), default=True)],
[sg.CB('Write Results', size=(12, 1)), sg.CB(
'Keep Intermediate Data', size=(20, 1))],
[sg.CB('Normalize', size=(12, 1), default=True),
sg.CB('Verbose', size=(20, 1))],
[sg.CB('Cluster', size=(12, 1)), sg.CB(
'Flush Output', size=(20, 1), default=True)],
[sg.CB('Write Results', size=(12, 1)), sg.CB('Keep Intermediate Data', size=(20, 1))], ]
loss_functions = [[sg.Radio('Cross-Entropy', 'loss', size=(12, 1)), sg.Radio('Logistic', 'loss', default=True, size=(12, 1))],
[sg.Radio('Hinge', 'loss', size=(12, 1)), sg.Radio('Huber', 'loss', size=(12, 1))],
[sg.Radio('Kullerback', 'loss', size=(12, 1)), sg.Radio('MAE(L1)', 'loss', size=(12, 1))],
[sg.Radio('MSE(L2)', 'loss', size=(12, 1)), sg.Radio('MB(L0)', 'loss', size=(12, 1))],]
loss_functions = [[sg.Rad('Cross-Entropy', 'loss', size=(12, 1)), sg.Rad('Logistic', 'loss', default=True, size=(12, 1))],
[sg.Rad('Hinge', 'loss', size=(12, 1)),
sg.Rad('Huber', 'loss', size=(12, 1))],
[sg.Rad('Kullerback', 'loss', size=(12, 1)),
sg.Rad('MAE(L1)', 'loss', size=(12, 1))],
[sg.Rad('MSE(L2)', 'loss', size=(12, 1)), sg.Rad('MB(L0)', 'loss', size=(12, 1))], ]
command_line_parms = [[sg.Text('Passes', size=(8, 1)), sg.Spin(values=[i for i in range(1, 1000)], initial_value=20, size=(6, 1)),
sg.Text('Steps', size=(8, 1), pad=((7,3))), sg.Spin(values=[i for i in range(1, 1000)], initial_value=20, size=(6, 1))],
[sg.Text('ooa', size=(8, 1)), sg.In(default_text='6', size=(8, 1)), sg.Text('nn', size=(8, 1)),
sg.In(default_text='10', size=(10, 1))],
[sg.Text('q', size=(8, 1)), sg.In(default_text='ff', size=(8, 1)), sg.Text('ngram', size=(8, 1)),
sg.In(default_text='5', size=(10, 1))],
[sg.Text('l', size=(8, 1)), sg.In(default_text='0.4', size=(8, 1)), sg.Text('Layers', size=(8, 1)),
sg.Drop(values=('BatchNorm', 'other'), auto_size_text=True)],]
sg.Text('Steps', size=(8, 1), pad=((7, 3))), sg.Spin(values=[i for i in range(1, 1000)], initial_value=20, size=(6, 1))],
[sg.Text('ooa', size=(8, 1)), sg.Input(default_text='6', size=(8, 1)), sg.Text('nn', size=(8, 1)),
sg.Input(default_text='10', size=(10, 1))],
[sg.Text('q', size=(8, 1)), sg.Input(default_text='ff', size=(8, 1)), sg.Text('ngram', size=(8, 1)),
sg.Input(default_text='5', size=(10, 1))],
[sg.Text('l', size=(8, 1)), sg.Input(default_text='0.4', size=(8, 1)), sg.Text('Layers', size=(8, 1)),
sg.Drop(values=('BatchNorm', 'other'))], ]
layout = [[sg.Frame('Command Line Parameteres', command_line_parms, title_color='green', font='Any 12')],
[sg.Frame('Flags', flags, font='Any 12', title_color='blue')],
[sg.Frame('Loss Functions', loss_functions, font='Any 12', title_color='red')],
[sg.Frame('Loss Functions', loss_functions,
font='Any 12', title_color='red')],
[sg.Submit(), sg.Cancel()]]
window = sg.Window('Machine Learning Front End', font=("Helvetica", 12)).Layout(layout)
button, values = window.Read()
sg.SetOptions(text_justification='left')
sg.set_options(text_justification='left')
window = sg.Window('Machine Learning Front End',
layout, font=("Helvetica", 12))
button, values = window.read()
window.close()
print(button, values)
def CustomMeter():
# layout the form
layout = [[sg.Text('A custom progress meter')],
[sg.ProgressBar(1000, orientation='h', size=(20,20), key='progress')],
[sg.ProgressBar(1000, orientation='h',
size=(20, 20), key='progress')],
[sg.Cancel()]]
# create the form`
window = sg.Window('Custom Progress Meter').Layout(layout)
progress_bar = window.FindElement('progress')
window = sg.Window('Custom Progress Meter', layout)
progress_bar = window['progress']
# loop that would normally do something useful
for i in range(1000):
# check to see if the cancel button was clicked and exit loop if clicked
event, values = window.Read(timeout=0, timeout_key='timeout')
event, values = window.read(timeout=0, timeout_key='timeout')
if event == 'Cancel' or event == None:
break
# update bar with loop value +1 so that bar eventually reaches the maximum
progress_bar.UpdateBar(i+1)
progress_bar.update_bar(i+1)
# done with loop... need to destroy the window as it's still open
window.CloseNonBlocking()
if __name__ == '__main__':
CustomMeter()
MachineLearningGUI()