Я столкнулся со странным поведением - я вижу похожие вопросы на форумах Plotly и Stackoverflow, но решения не найдено. По сути, я пытаюсь сохранить промежуточное значение (для повторного использования в других обратных вызовах) в скрытом div 'data-storage-json', но обратный вызов, который имеет его как Input, похоже, не выполняется. На сервере нет ошибок. В интерфейсе я получаю сообщение «Ошибка обратного вызова при обновлении plot-div.children» (это компонент, указанный как «Вывод»)
import dash
from dash.dependencies import Input, Output, State
import dash_core_components as dcc
import dash_html_components as html
import dash_table
from dash.exceptions import PreventUpdate
########### Layout:
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div(children=[
html.Div(id='data-storage-json', style={'display': 'none'}),
html.Div(children=[
dash_table.DataTable(
id='event-table',
style_data={'whiteSpace': 'normal'}, #'border': '1px solid blue'},
style_cell={'textAlign': 'center'},
#style_header={ 'border': '1px solid pink' },
css=[{
'selector': '.dash-cell div.dash-cell-value',
'rule': 'display: inline; white-space: inherit; overflow: inherit; text-overflow: inherit;'
}],
columns=[{"name": i, "id": i} for i in event_df.columns if i is not 'id'],
style_table={'overflowX': 'scroll'},
row_selectable='single',
selected_rows=[],
page_current=0,
page_size=PAGE_SIZE,
page_action='custom',
filter_action='custom',
filter_query='',
sort_action='custom',
sort_mode='multi',
sort_by=[]
),
html.Div(id='event-stats', style={'width': '80%', 'color': 'black', 'font-size': '9'})],
style={'width': '90%', 'margin-left': '20px', 'font-size': '9', 'horizontal-align': 'middle', 'vertical-align': 'middle'}),
html.Div(children=[html.Br()]),
html.Button('Plot', id='show-button'),
html.Div(id='plot-div', children=[], style={'width': '95%', 'font-size': '9', 'vertical-align': 'middle'}),
])
########### Callbacks:
'''
Callback for sorting/filtering table
'''
@app.callback(
[Output('event-table', 'data'),
Output('event-table', 'page_count'),
Output('event-stats', 'children')],
[Input('event-table', 'sort_by'),
Input('event-table', 'filter_query'),
Input('event-table', 'page_current'),
Input('event-table', 'page_size')])
def update_event_selection(sort_by, filter_query,page_current, page_size):
dff = sort_filter_table(event_df, filter_query, sort_by)
res = dff.iloc[page_current*page_size: (page_current + 1)*page_size]
page_count = int(dff.shape[0]/page_size)+1
stat_str = '{} events in the table. Displaying page {} of {}'.format(dff.shape[0], page_current+1, page_count)
return res.to_dict('records'), page_count, stat_str
@app.callback(
Output('data-storage-json','children'),
[Input('show-button', 'n_clicks')],
[State('event-table','selected_row_ids')
])
def prepare_data(n_clicks,selected_id):
duration=1
print('Selected id: ',selected_id)
if n_clicks is None or selected_id is None or len(selected_id)==0:
raise PreventUpdate
duration=int(duration)
selected_id=selected_id[0]
row=event_df.loc[selected_id,:]
print(row)
event_time=pd.to_datetime(row['Start'],errors='ignore')
# sensors to load:
flist=['ip_m','vp_m','f','df']
print('Duration {}'.format(duration))
res_df=get_event_data(interconnect,event_time,duration, feature_list=flist)
print(res_df.shape)
js=res_df.to_json(date_format='iso', orient='split')
print('In Prep: ',len(js))
return js
@app.callback(
Output('plot-div','children'),
[Input('data-storage-json','children')],
[State('event-table','selected_row_ids')])
def generate_plots(data_storage,selected_id):
if data_storage is None:
print('None!!!')
raise PreventUpdate
else:
print('InDisplay -storage: '+str(len(data_storage)))
res_df = pd.read_json(data_storage, orient='split')
print('InDisplay ',res_df.shape)
selected_id=selected_id[0]
row=event_df.loc[selected_id,:]
event_time=pd.to_datetime(row['Start'],errors='ignore')
event_type=row['Event']+': '+row['Cause']
event_pid=''
# columns sorted in reverse alphabetical
flist=sorted(np.unique([c.split('__')[1] for c in res_df.columns]))[::-1]
print('To plot: ',res_df.shape)
# generate plots for each type of sensor:
fig_list=[]
for feature in flist:
col_list = [c for c in res_df.columns if not c.startswith('_') and c.endswith('_'+feature)]
temp_df = res_df[col_list]
# plot results
print('Preparing figure '+feature)
fig=temp_df.iplot(kind='scatter',mode='markers',size=3, title="Plot {}: {} {} {}".format(feature,event_time,event_type,event_pid), asFigure=True)
#fig_list.append(fig)
fig_list.append((html.Div(children=[dcc.Graph(id=feature+'-scatter',figure=fig)])))
print('Figure done')
return fig_list
########### Run the app:
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='number of GPU to use for calculations')
parser.add_argument('--port', type=int, default=8050, help='port on which to run (default: 8050)')
options,_ = parser.parse_known_args()
os.environ['CUDA_VISIBLE_DEVICES'] = str(options.gpu)
app.run_server(debug=True, port = options.port)
UPD: событие_df является чем-то вроде:
event_df = pd.DataFrame({"id": [0,1,2],
"Start": ["2016-01-01 14:33","2016-01-01 16:45","2016-01-01 17:46"],
"Event": ["Line Outage","Line Outage","Line Outage"],
})
Я также включил пример автономного кода в ответ ниже
Версии пакета:
dash 1.8.0 py_0 conda-forge
dash-core-components 1.7.0 py_0 conda-forge
dash-html-components 1.0.2 py_0 conda-forge
dash-renderer 1.2.3 py_0 conda-forge
dash-table 4.6.0 py_0 conda-forge