CristalCaveGem » LustreC

init_signals = init_df['varid'].unique()

init_signals_option = [{'label': i, 'value': i} for i in init_signals]

# TODO: extraire les suffixes de contexte booleens pour construire, pour chaque signal, la liste des signaux de partitionnement

# #     return fig

    color='rgba(0,250,0,0.4)'

    x, y_min, y_max = 'timestep', 'min', 'max'

    fig.add_traces(

        go.Scatter(

            x = signal_df[x],

            name=name+'_min'),

        rows=row,

        cols=col)

    return fig

    # fig.add_traces(go.Scatter(x = signal_df[x], y = signal_df[y_min],

    #                       line = dict(color='rgba(0,0,0,0)')))

# Changing dropdown menu or same/split view updates the figure

@app.callback(

    Output("graph", "figure"),

    [Input("signal_sel", "value"),

     Input("sameplotcheck", "value"),

     Input("global_df", 'children')

    color='rgba(0,250,0,0.4)'

    x, y_min, y_max = 'timestep', 'min', 'max'

    if selection is None or selection == [] or jsonified == None:

        #selection=signals

    else:

        df=pd.read_json(jsonified, orient='split')

                row_id=idx+1

            onesigfig(fig,signal_df,sig,row_id,1)

        fig.update_layout(height=300 * nb_rows)

# Changing global_df or choice of types updates dropdown menu

@app.callback(Output("signal_sel", "options"),

              Output("signal_sel", "value"),

              Input("global_df", 'children'),

              Input('typesel', 'value')

              )

        signals_data=df.query('type in ' +str(typesel))

        signals=signals_data['varid'].unique()

        signals_options=[{'label': i, 'value': i} for i in signals]

def parse_contents(contents, filename, date):