fixes

finance.py

@@ -67,7 +67,8 @@ stock_info_in_time_period_df = pd.DataFrame(columns=stock_list, index=["średnia
 
 mean_prices_and_dividends = fullTableDf[['Close', 'Dividends']].mean(axis=0).unstack(level=0)
 mean_prices_and_dividends_and_market_cap = pd.concat([mean_prices_and_dividends, market_cap], axis=1)
-mean_prices_and_dividends_figure = px.scatter(mean_prices_and_dividends_and_market_cap.reset_index(), size="marketCap", x='Dividends', y='Close',  text="index", template='plotly_dark')
+mean_prices_and_dividends_figure = px.scatter(mean_prices_and_dividends_and_market_cap.reset_index(), size="marketCap",
+                                              x='Dividends', y='Close', text="index", template='plotly_dark')
 
 
 def make_gauge(title, min_v, value, max_v):
@@ -151,7 +152,8 @@ app.layout = html.Div(
                                           dcc.Graph(id='timeseries', config={'displayModeBar': False}, animate=True),
                                       ]),
                                       dcc.Tab(id='tab-2', label='Chart 2', value='tab-2', children=[
-                                        dcc.Graph(id='price-dividends', figure=mean_prices_and_dividends_figure, config={'displayModeBar': False}, animate=True),
+                                          dcc.Graph(id='price-dividends', figure=mean_prices_and_dividends_figure,
+                                                    config={'displayModeBar': False}, animate=True),
                                       ]),
                                   ]),
                                   dash_table.DataTable(
@@ -167,6 +169,7 @@ app.layout = html.Div(
     ]
 )
 
+
 # Callback for downloading file
 @app.callback(
     Output("download-data", "data"),
@@ -178,6 +181,7 @@ def create_download_file(n_clicks, selected_table):
     global selected_stock_in_table_df
     return dcc.send_data_frame(selected_stock_in_table_df.to_csv, "data-{table}.csv".format(table=selected_table))
 
+
 # Callback for timeseries price
 @app.callback(Output('timeseries', 'figure'), [Input('stockselector', 'value')])
 def update_graph(selected_dropdown_value):
@@ -190,16 +194,56 @@ def update_graph(selected_dropdown_value):
     return figure
 
 
-@app.callback([Output("stock_price_table", "data"), Output('stock_price_table', 'columns')],
+@app.callback([Output("stock_price_table", "data"),
-              [Input('table_selector', 'value')])
+               Output('stock_price_table', 'columns'),
-def update_table(selected_dropdown_value):
+               Output('average_gauge', 'figure'),
+               Output('volatility_gauge', 'figure'),
+               Output('info_in_time_period', 'data')],
+              [Input('timeseries', 'relayoutData'),
+               Input('table_selector', 'value')])
+def common_table_callback(callback_data_time_period, callback_data_table_selector):
     global selected_stock_in_table
     global selected_stock_in_table_df
-    selected_stock_in_table = selected_dropdown_value
+    global from_time
+    global to_time
+    global average_gauge
+    global volatility_gauge
+    global stock_info_in_time_period_df
+    selected_stock_in_table_changed = False
+    change_time_period = False
+    for trigger in dash.callback_context.triggered:
+        if trigger['prop_id'] == 'table_selector.value':
+            selected_stock_in_table_changed = True
+        if trigger['prop_id'] == 'timeseries.relayoutData':
+            change_time_period = True
+    if selected_stock_in_table_changed:
+        selected_stock_in_table = callback_data_table_selector
         selected_stock_in_table_df = fullTableDf.xs(selected_stock_in_table, axis=1, level=1)
-    data = selected_stock_in_table_df.to_dict('records')
+    if change_time_period:
-    columns = [{"name": i, "id": i} for i in selected_stock_in_table_df.columns]
+        if "xaxis.range[0]" in callback_data_time_period and "xaxis.range[1]" in callback_data_time_period:
-    return data, columns
+            from_time = callback_data_time_period["xaxis.range[0]"]
+            to_time = callback_data_time_period["xaxis.range[1]"]
+        else:
+            from_time = selected_stock_in_table_df.index.min()
+            to_time = selected_stock_in_table_df.index.max()
+        from_time = round_to_nearest_weekday(from_time)
+        to_time = round_to_nearest_weekday(to_time)
+    if change_time_period or selected_stock_in_table_changed:
+        full_table_in_time_period = fullTableDf.loc[from_time:to_time]
+        mean = full_table_in_time_period.mean(axis=0)
+        mean = mean.xs('Close', level=0)
+        std = full_table_in_time_period.std(axis=0)
+        std = std.xs('Close', level=0)
+        time_period = selected_stock_in_table_df.loc[from_time:to_time]
+        mean = time_period.mean(axis=0)
+        std = time_period.std(axis=0)
+        # TODO: oblicz stock_info_in_time_period_df tutuaj !!!
+        average_gauge = make_gauge('średnia', 0, mean['Close'], 400)
+        volatility_gauge = make_gauge('wolatylność', 0, std['Close'], 400)
+    stock_price_table_data = selected_stock_in_table_df.to_dict('records')
+    stock_price_table_columns = [{"name": i, "id": i} for i in selected_stock_in_table_df.columns]
+    return stock_price_table_data, stock_price_table_columns, average_gauge, volatility_gauge, stock_info_in_time_period_df.T.to_dict(
+        'records')
 
 
 average_gauge = make_gauge('średnia', 0, 0, 400)
@@ -225,39 +269,5 @@ def round_to_nearest_weekday(date):
         return date
 
 
-@app.callback([Output('average_gauge', 'figure'),
-               Output('volatility_gauge', 'figure'),
-               Output('info_in_time_period', 'data')],
-              Input('timeseries', 'relayoutData'))
-def change_time_period(selectedData):
-    global from_time
-    global to_time
-    global average_gauge
-    global volatility_gauge
-    global stock_info_in_time_period_df
-    if selectedData is not None:
-        if "xaxis.range[0]" in selectedData and "xaxis.range[1]" in selectedData:
-            from_time = selectedData["xaxis.range[0]"]
-            to_time = selectedData["xaxis.range[1]"]
-        else:
-            from_time = selected_stock_in_table_df.index.min()
-            to_time = selected_stock_in_table_df.index.max()
-        from_time = round_to_nearest_weekday(from_time)
-        to_time = round_to_nearest_weekday(to_time)
-        full_table_in_time_period = fullTableDf.loc[from_time:to_time]
-        mean = full_table_in_time_period.mean(axis=0)
-        mean = mean.xs('Close', level=0)
-        std = full_table_in_time_period.std(axis=0)
-        std = std.xs('Close', level=0)
-        time_period = selected_stock_in_table_df.loc[from_time:to_time]
-        mean = time_period.mean(axis=0)
-        std = time_period.std(axis=0)
-        # TODO: oblicz stock_info_in_time_period_df tutuaj !!!
-        average_gauge = make_gauge('średnia', 0, mean['Close'], 400)
-        volatility_gauge = make_gauge('wolatylność', 0, std['Close'], 400)
-    return average_gauge, volatility_gauge, stock_info_in_time_period_df.T.to_dict('records')
-
-
-
 if __name__ == '__main__':
     app.run_server(debug=True)