仪表板绘图仪表板更新仪表板按钮

Question

这是我的仪表板的一个较小版本，真正的仪表板有多个选项卡等。仪表板从4个文件创建所有图表

• dfresult=pd.read_csv（“result.csv”，sep=“，”）
• dfradar=pd.read_csv（“radar.csv”，sep=“，”）
• dfoutput=pd.read_csv（“output.csv”，sep=“，”）
• dfavg=pd.read\u csv（“avg\u input.csv”，sep=“，”）

有时这些文件可以更改其内容（名称相同，但值不同），我需要一个仪表板更新，以便所有这些文件都是新加载的。仪表板再次运行时，我创建了一个仪表板更新按钮，但如何实现这一点

import dash import dash_html_components as html import dash_core_components as dcc from dash.dependencies import Input, Output, State import plotly.graph_objs as go from plotly import tools import pandas as pd import datetime import plotly.tools from plotly.subplots import make_subplots import numpy as np import plotly.io as pio import sys external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] dfresult = pd.read_csv("result.csv", sep=",") dfradar = pd.read_csv("radar.csv", sep=",") dfoutput = pd.read_csv("output.csv", sep=",") dfavg = pd.read_csv("avg_input.csv",sep=",") #avg_input.csv dfavg = round(dfavg,2) avgtable = dfresult.drop(["Feed 1", "Feed 2","Sum Feed In", "Wind 4600","Solar 1900","Holiday", "Sun 2300", "Total Feed In"],axis=1) avgtable = avgtable.iloc[1:] avgtable = round(avgtable.groupby("Date").mean(),2) avgtable.reset_index( level=None, drop=False, inplace=True, col_level=2, col_fill='') print(avgtable) #radar.csv dfradar = round(dfradar,2) #output.csv dfoutput= round(dfoutput,2) dfoutput= dfoutput.fillna(0) #result.csv dfresult dfresult.fillna(0) #dfresult["Temperature"] = pd.to_numeric(dfresult["Temperature"]) #dfresavg = round(dfresavg,2) #create new dataframe: grouped result table by date, calculate mean of consumption and round by two decimals dfb = pd.DataFrame(round(dfresult.groupby("Date")["Consumption"].mean(),2)) #sort dataframe by date dfb = dfb.sort_values("Date",ascending=False) #split dataframe into first row and other rows first= dfb.iloc[:1] others = dfb.iloc[1:] #sort all rows exept first row by consumption from high to low others = others.sort_values("Consumption",ascending=False) #create new Dataframe by concatinating first and others (both are Dataframes) dfbar1 = pd.concat([first,others]) #reset index of Dataframe, replace Column and fill automaticly dfbar1.reset_index( level=None, drop=False, inplace=True, col_level=2, col_fill='') dfbar1.Date = dfbar1.Date.astype(str) dfresult = dfresult.iloc[1:] app = dash.Dash(__name__)#, external_stylesheets=external_stylesheets) server = app.server app.config.suppress_callback_exceptions = True app.scripts.config.serve_locally = True ################################################### #demand subplots ################################################### figd = tools.make_subplots( rows=2, cols=2, print_grid=True, specs=[[{"type": "xy"}, {"type": "xy"}],[{"type": "xy"}, {"type": "polar"}]], subplot_titles=("A", "B", "C", "D")) c = ["#67c2dc","#16336d","#ffbb0e","#213939","#cc66ff"] c1 = ["#ffff01","#67c2dc","#16336d","#ffbb0e","#213939","#cc66ff"] #check order of daylist from dfbar1 daylist = dfbar1.Date.unique() daylist_small = daylist[1:] #dfresult.sort_values(["Date","daylist"]) daylistresult = dfresult.Date.unique() ###Chart1 for i,j,a in zip(daylist,dfbar1["Consumption"],range(len(dfbar1.Date))): figd.append_trace( go.Bar( x=[i], y=[j], #name = dfbar1.Date, name=str(i), legendgroup='group'+str(i), showlegend=True, marker=dict(color=c1[a]), ),1,1 ), figd.update_xaxes(type = 'category') ###Chart2 for i,j in zip(daylist_small,range(len(dfresult.Date.unique()))): data = dfresult.loc[dfresult.Date == str(i)] figd.append_trace( go.Scatter( x=data["Time"], y=data["Consumption"], #name=str(data["Date"].unique())[2:-2], legendgroup='group'+str(i), showlegend=False, marker=dict(color=c[j]), mode="lines+markers" ),1,2 ), ###Chart4 for i,j,a in zip(daylist,dfoutput["Consumption"],range(len(dfoutput.Date))): figd.append_trace( go.Bar( x=[i], y=[j], #name=str(i), legendgroup='group'+str(i), showlegend=False, marker=dict(color=c1[a]), ),2,1 ), figd.update_xaxes(type = 'category') ###Chart4 for a, i,j in zip(daylist, dfradar.Date.unique(),range(len(dfradar.Date.unique()))): figd.append_trace(go.Scatterpolar( r=dfradar["Total"].loc[dfradar["Date"]==i], theta=dfradar["Parameters"].loc[dfradar["Date"]==i], showlegend=False, legendgroup='group'+str(a), marker=dict(color=c1[j]), line_color = c1[j], mode = "lines+markers", opacity = round(j * (0.4)+0.2,0), fill ='toself' ),2,2) figd.update_layout( #height=500, #width =500, autosize=True, polar = dict(angularaxis = dict( rotation = 45) )) #set layout for each graph figd['layout'].update(height=1100,autosize=True,polar = dict(angularaxis = dict( rotation = 45))) bgc = "#f7f9ef" background_color = "#f7f9ef" graphwidth = "33%" graphheight = "450px" formwidth = "170px" formheight = "30px" templates = ['ggplot2', 'seaborn', 'simple_white', 'plotly','plotly_white', 'plotly_dark', 'presentation', 'xgridoff', 'ygridoff', 'gridon'] app.layout =html.Div([ #header container with name, update button and logo dcc.Dropdown(id='drop-temp', style={ "float":"right", "display": 'inline-block', "width": "220px", }, options=[ {'label': str(i), 'value': i} for i in templates] ,placeholder="Select Chart Template" ), html.Button("Update Dashboard",id="update", style={ "width": "220px", "float":"left", 'display': 'inline-block', "background":"#e29820", "color":"white", "height":"35px" }), html.Div(id="empty-slot1",style={"height":"5px"}), dcc.Tabs(id="tabs", value='tab2', children=[ dcc.Tab(label='Overview', value='tab2', style={"height":"7%","width":"16.6%","background":"#e29820","color":"white"}), ],style={"background":background_color} ), html.Div(id='tabs-content'), ],style={"width":"100%", "background":background_color, }) @app.callback(Output('tabs-content', 'children'), [Input('tabs', 'value'), ]) def contents(tab): if tab == 'tab2': return html.Div( [ html.H3('Overview'), dcc.Graph(id="g1",style={"width":"99%"}, figure=figd, ), ], style={"width":"99%", "background":background_color} ) if __name__ == '__main__': app.run_server(debug=False)

Answer 1

您必须使用dcc.interval组件来实现这一点，在应用程序布局中添加此组件，然后为文件更新进行相应的回调

下面是一个很好的例子：

https://community.plot.ly/t/dash-app-not-reading-modified-file-on-refreshing-the-browser/27145/8

以及文件：

https://dash.plot.ly/live-updates