带有多个输入的短跑输出

将回调更改为类似的方式应该有效，@app.callback( Output('indicator-graphic', 'figure'), [Input('xaxis-column', 'value'), Input('xaxis-type', 'value')])def update_graph(xaxis_column_name, xaxis_type): graph = [] for i in range(0, len(xaxis_column_name)): graph_obj = go.Scatter( x=df.index, y=df[xaxis_column_name[i]]) graph.append(graph_obj) return { 'data': graph }I am trying to have a dash component properly input variables and give appropriate output.Currently multiple inputs will make the functionality not work.I've put multi=true for my dcc dropdown - hasn't worked successfully yet.This is the code I have used.import dashimport dash_core_components as dccimport dash_html_components as htmlfrom dash.dependencies import Input, Outputimport pandas as pdimport plotly.graph_objs as godf = pd.read_excel('FreewayFDSData.xlsx', 'Volume', parse_dates=True, index_col="Time")df = df.TDetectors = list(df.columns)mf = pd.read_excel('FreewayFDSData.xlsx', 'Coordinates')mapbox_access_token = 'pk.eyJ1IjoibWFwYm94IiwiYSI6ImNpejY4NXVycTA2emYycXBndHRqcmZ3N3gifQ.rJcFIG214AriISLbB6B5aw'# Layoutslayout_map = dict( autosize=True, height=500, font=dict(color="#191A1A"), titlefont=dict(color="#191A1A", size='18'), margin=dict( l=35, r=35, b=35, t=45 ), hovermode="closest", plot_bgcolor='#fffcfc', paper_bgcolor='#fffcfc', legend=dict(font=dict(size=10), orientation='h'), title='Freeway detectors', mapbox=dict( accesstoken=mapbox_access_token, style="light", center=dict( lon=145.061, lat=-37.865 ), zoom=12, ))def generate_table(dataframe, max_rows=10): return html.Table( # Header [html.Tr([html.Th(col) for col in dataframe.columns])] + # Body [html.Tr([ html.Td(dataframe.iloc[i][col]) for col in dataframe.columns ]) for i in range(min(len(dataframe), max_rows))] #Styling )external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']app = dash.Dash(__name__, external_stylesheets=external_stylesheets)app.layout = html.Div(children=[ html.H1(children='Managed Motorway'), html.Div([ html.Div([ dcc.Dropdown( id='xaxis-column', options=[{'label': i, 'value': i} for i in Detectors], value='Volume per 15 seconds', style={"width" : '48%'} ), dcc.RadioItems( id='xaxis-type', options=[{'label': i, 'value': i} for i in ['Linear', 'Log']], value='Linear', labelStyle={'display': 'inline-block'} ) ]), dcc.Graph(id='indicator-graphic'), dcc.Graph( id='graph', figure={ 'data': [{ 'lat': mf.Y, 'lon': mf.X, 'type': 'scattermapbox' }], 'layout': layout_map } ) ], style={'display': 'block'}), html.Div([ html.H4(children='Example of Freeway FDS Data'), html.Div([ generate_table(df) ], style={'overflowX': 'scroll','overflowY': 'scroll', 'width':'48%','height':'300px'})])])@app.callback( Output('indicator-graphic', 'figure'), [Input('xaxis-column', 'value'), Input('xaxis-type', 'value')])def update_graph(xaxis_column_name, xaxis_type): # xaxis column name will assign the x axis data being sought return { 'data': [go.Scatter( x=df.index, y=df[xaxis_column_name])] }if __name__ == '__main__': app.run_server(debug=True)This is an example of the data being inputted.Time 00:00 - 00:15 00:15 - 00:30 00:30 - 00:45 00:45 - 01:00 01:00 - 01:15 01:15 - 01:30 01:30 - 01:45 01:45 - 02:00 02:00 - 02:15 02:15 - 02:30 02:30 - 02:45 02:45 - 03:00 03:00 - 03:15 03:15 - 03:30 03:30 - 03:45 03:45 - 04:00 04:00 - 04:15 04:15 - 04:30 04:30 - 04:45 04:45 - 05:00 05:00 - 05:15 05:15 - 05:30 05:30 - 05:45 05:45 - 06:00 06:00 - 06:15 06:15 - 06:30 06:30 - 06:45 06:45 - 07:00 07:00 - 07:15 07:15 - 07:30 07:30 - 07:45 07:45 - 08:00 08:00 - 08:15 08:15 - 08:30 08:30 - 08:45 08:45 - 09:00 09:00 - 09:15 09:15 - 09:30 09:30 - 09:45 09:45 - 10:00 10:00 - 10:15 10:15 - 10:30 10:30 - 10:45 10:45 - 11:00 11:00 - 11:15 11:15 - 11:30 11:30 - 11:45 11:45 - 12:00 12:00 - 12:15 12:15 - 12:30 12:30 - 12:45 12:45 - 13:00 13:00 - 13:15 13:15 - 13:30 13:30 - 13:45 13:45 - 14:00 14:00 - 14:15 14:15 - 14:30 14:30 - 14:45 14:45 - 15:00 15:00 - 15:15 15:15 - 15:30 15:30 - 15:45 15:45 - 16:00 16:00 - 16:15 16:15 - 16:30 16:30 - 16:45 16:45 - 17:00 17:00 - 17:15 17:15 - 17:30 17:30 - 17:45 17:45 - 18:00 18:00 - 18:15 18:15 - 18:30 18:30 - 18:45 18:45 - 19:00 19:00 - 19:15 19:15 - 19:30 19:30 - 19:45 19:45 - 20:00 20:00 - 20:15 20:15 - 20:30 20:30 - 20:45 20:45 - 21:00 21:00 - 21:15 21:15 - 21:30 21:30 - 21:45 21:45 - 22:00 22:00 - 22:15 22:15 - 22:30 22:30 - 22:45 22:45 - 23:00 23:00 - 23:15 23:15 - 23:30 23:30 - 23:45 23:45 - 24:003674S_P1 88 116 84 68 76 56 56 48 72 48 76 40 76 44 36 76 76 116 124 176 236 352 440 624 1016 1172 1260 1280 1304 1312 1252 1344 1324 1336 1212 1148 1132 1120 1084 996 924 1040 952 900 900 1116 1136 1044 1144 1152 1224 1088 1132 1184 1208 1120 1240 1196 1116 1264 1196 1240 1308 1192 1164 1096 1080 1160 1112 1244 1244 1184 1232 996 1108 876 864 776 644 520 684 724 632 620 680 724 516 504 432 396 264 252 272 256 100 1443674S_P0 88 116 76 68 76 56 56 48 68 48 76 48 80 44 32 76 76 108 120 180 240 340 456 624 1088 1268 1352 1384 1412 1376 1356 1372 1400 1436 1296 1240 1200 1256 1120 1028 1008 1072 980 944 932 1148 1192 1040 1188 1220 1292 1140 1116 1268 1292 1172 1272 1236 1216 1280 1248 1280 1388 1244 1224 1076 1096 1148 1108 1256 1356 1308 1236 992 1100 880 872 768 640 520 680 720 636 620 660 716 512 504 428 396 260 244 272 252 100 136The end result is a bug with the output where instead of line graphs the base variable is changed.Any help on fixing this issue is much appreciated (and if you're able to make my code less convulted I'd appreciate it.Cheers!Finished code with debug completedef update_graph(xaxis_column_name, xaxis_type): graph = [] if xaxis_column_name != None : for i in range(0, len(xaxis_column_name)): graph_obj = go.Scatter( x=df.index, y=df[xaxis_column_name[i]]) graph.append(graph_obj) return { 'data': graph } return 解决方案 I'm not able to fully run your code to debug it well, I found this after a quick glance.When the xaxis-column Dropdown component is changed to do a multi-select, it will return a list rather than a value, so the callback you have for xaxis-column will be erroneous,Changing the callback to something like this should work,@app.callback( Output('indicator-graphic', 'figure'), [Input('xaxis-column', 'value'), Input('xaxis-type', 'value')])def update_graph(xaxis_column_name, xaxis_type): graph = [] for i in range(0, len(xaxis_column_name)): graph_obj = go.Scatter( x=df.index, y=df[xaxis_column_name[i]]) graph.append(graph_obj) return { 'data': graph } 这篇关于带有多个输入的短跑输出的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 上岸，阿里云！