问题
Say I have a model with 20 parameters and I made one input component for each param.
[dcc.Input(type = 'number', id = 'input %i'%i) for i in range(20)]
I want to have one button html.Button('populate parameters', id = 'button populate')
that is supposed to populate best pre-fitted value for all the inputs.
Code should look like below, except it doesn't work.
for i in range(20):
@app.callback(
dash.dependencies.Output('input %i'%i, 'value'),
[dash.dependencies.Input('button populate', 'n_clicks')]
)
def update(ignore):
return np.random.uniform()
Do I have to write 20 callbacks for each output with identical functionality? I can't find a way to make them in one go (loop?)
回答1:
I have dealt with the same issue and found a solution. What you do is bypass the decorator and call the app.callback
function directly:
def update(ignore):
return np.random.uniform()
for i in range(20):
app.callback(
dash.dependencies.Output('input %i' % i, 'value'),
[dash.dependencies.Input('button populate', 'n_clicks')]
)(update)
回答2:
you can have as many input parameters/argument on a callback as you want. But only one output.
What solved a similar case for me was:
@app.callback(
[Output('output-id', 'children')],
[Input('button-trigger', 'n_clicks'],
[State('input-one', 'value'),
...
[State('input-twenty', 'value')]
)
def my_fancy_function(n_clicks, v1, ..., v20):
return sth_awesome
State()
as opposed to Input()
does not trigger the callback when an input value is changed.
n_clicks
changes +1 with each click but doesn't need to be used.
If your parameters are dependent on each other you would need more callbacks.
But...with 20 parameters There must be a better way
回答3:
I have done something similar to populate my layout component after page reload.
Thanks to the first callback, the state of the components are store in a dcc.Store component. The second callback is to populate the layout components when their state changes or when you access the tab ( the layout is in a dcc.Tabs)
dash_layout_components = {
'time_slider_app2': 'value',
'backtest_choice_app2': 'values',
'asset_selection_app2': 'value',
'graph_selection_app2': 'values'
}
stored_layout_value_name = [key[:key.rfind('a')] + value for key, value in
dash_layout_components.items()]
set_back_and_display_graph_input = {
'store_layout_data': 'modified_timestamp',
'tabs': 'value'
}
@app.callback(
Output('store_layout_data', 'data'),
[Input(key, value) for key, value in dash_layout_components.items()])
def store_layout(time_slider_value, backtest_choice_values, assets_selection_values, graph_selection_values):
data_json = {
'time_slider_value': time_slider_value,
'backtest_choice_values': backtest_choice_values,
'asset_selection_value': assets_selection_values,
'graph_selection_values': graph_selection_values
}
return data_json
for component_id, component_property in dash_layout_components.items():
@app.callback(
Output(component_id, component_property),
[Input(key, value) for key, value in set_back_and_display_graph_input.items()],
[State('store_layout_data', 'data'),
State(component_id, 'id')]
)
def set_back_component(bouton_ts, tabs_value, layout_state_data, component): # dynamiser l'arrivée des paramètres. piste, en créer une liste entre le for et le callback
if tabs_value != '/app2':
raise PreventUpdate
if layout_state_data is None:
return []
else:
store_layout_component_name = stored_layout_value_name[list(dash_layout_components.keys()).index(component)]
return layout_state_data[store_layout_component_name]
Note that you won't have access to the iterated values (component_id and component_property) inside the function ( set_back_component(...) )
来源:https://stackoverflow.com/questions/53917648/plotly-dash-create-multiple-callbacks-with-loop