def buildTab(self):
self.background_image = "images/gui_elements/tab_darkblue.png"
with open("config/blocks.json") as json_data:
blocks_config = json.load(json_data)
for tab in blocks_config["tabs"]:
#newTab = TabbedPanelItem(text = str(tab["id"]))
block_type = str(tab["id"])[0]
newTab = TabbedPanelItem()
color_tab = []
for color in tab["rgba"]:
color_tab.append(color)
newTab.background_color = color_tab
newLayout = StackLayout(spacing=10)
for block in tab["blocks"]:
newBlock = Block(block_type, str(block["id"]),
str(block["source"]))
newLayout.add_widget(newBlock)
newTab.content = newLayout
self.add_widget(newTab)
def __init__(self, df, security_name, **kwargs):
super(Application, self).__init__(**kwargs)
self.orientation = 'vertical'
tab_panel = TabbedPanel()
tab_panel.do_default_tab = False
tab_panel.background_color = (7 / 255, 0, 13 / 255, 1)
tab_menu = TabbedPanelItem(text="Menu")
tab_menu.background_color = (62 / 255, 142 / 255, 222 / 255, 1)
tab_chart = TabbedPanelItem(text='Chart')
tab_chart.background_color = (62 / 255, 142 / 255, 222 / 255, 1)
tab_training = TabbedPanelItem(text='Training')
tab_training.background_color = (62 / 255, 142 / 255, 222 / 255, 1)
tab_validate = TabbedPanelItem(text='Validate')
tab_validate.background_color = (62 / 255, 142 / 255, 222 / 255, 1)
tab_future = TabbedPanelItem(text='Prediction')
tab_future.background_color = (62 / 255, 142 / 255, 222 / 255, 1)
tab_panel.add_widget(tab_menu)
tab_panel.add_widget(tab_chart)
tab_panel.add_widget(tab_training)
tab_panel.add_widget(tab_validate)
tab_panel.add_widget(tab_future)
tab_menu.bind(on_press=lambda x: change_screen('menu'))
chart_layout = FloatLayout()
fig = plot_data(df, security_name.upper(), 30, 200)
canvas = fig.canvas
chart_layout.add_widget(canvas)
tab_chart.add_widget(chart_layout)
predict_frame = FloatLayout(opacity=1)
predict_btn = Button(text='Run Prediction',
background_color=(62 / 255, 142 / 255, 222 / 255,
1),
font_size=20,
pos_hint={
"center_x": 0.5,
"bottom": 0
},
size_hint=(0.3, 0.075))
predict_btn.bind(on_press=lambda x: start_predict(df, security_name))
predict_frame.add_widget(predict_btn)
chart_layout.add_widget(predict_frame)
def start_predict(df, security_name):
que = queue.Queue()
par_modeling = partial(modeling, security_name=security_name)
process = threading.Thread(
target=lambda q, arg1: q.put(par_modeling(arg1)),
args=(que, df),
daemon=True)
process.start()
Clock.schedule_once(
lambda *args: tab_panel.switch_to(tab_training))
train_fig = plt.figure(facecolor='#07000d')
train_canvas = train_fig.canvas
train_layout = FloatLayout()
train_layout.add_widget(train_canvas)
tab_training.add_widget(train_layout)
if os.path.exists('training.csv'):
os.remove('training.csv')
def update_plot(fig):
train_canvas = fig.canvas
train_layout.clear_widgets()
train_layout.add_widget(train_canvas)
plt.close(fig)
# for child in train_layout.children[:1]:
# train_layout.remove_widget(child)
# Clock.schedule_once(lambda *args: tab_panel.switch_to(tab_training))
def read_training(self):
if os.path.exists('training.csv'):
loss_df = None
try:
loss_df = pd.read_csv('training.csv')
except Exception as e:
print(e)
pass
if loss_df is not None:
train_fig = plot_loss(loss_df)
update_plot(train_fig)
if not process.is_alive():
Clock.unschedule(read_training)
val_fig, future_fig = que.get()
val_canvas = val_fig.canvas
val_layout = FloatLayout()
val_layout.add_widget(val_canvas)
tab_validate.add_widget(val_layout)
future_canvas = future_fig.canvas
future_layout = FloatLayout()
future_layout.add_widget(future_canvas)
tab_future.add_widget(future_layout)
Clock.schedule_once(
lambda *args: tab_panel.switch_to(tab_validate))
Clock.schedule_interval(read_training, 0.1)
Clock.schedule_once(lambda *args: tab_panel.switch_to(tab_chart))
self.add_widget(tab_panel)
