情节：单击按钮后如何显示图形？
我只想在单击按钮后才使用图来显示图形，但不确定如何使它工作。我的身材存储在以下代码位中
fig1 = go.Figure(data=plot_data, layout=plot_layout)
然后，我使用以下代码位定义应用布局：
app.layout = html.Div([
#button
html.Div(className='submit', children=[
html.Button('Forecast', id='submit', n_clicks=0)
]),
#loading
dcc.Loading(
id="loading-1",
type="default",
children=html.Div(id="loading-output-1")
),
#graph
dcc.Graph(id= 'mpg-scatter',figure=fig),
#hoverdata
html.Div([
dcc.Markdown(id='hoverdata-text')
],style={'width':'50%','display':'inline-block'})
])
@app.callback(Output('hoverdata-text','children'),
[Input('mpg-scatter','hoverData')])
def callback_stats(hoverData):
return str(hoverData)
if __name__ == '__main__':
app.run_server()
但是问题是我只希望首先显示按钮。然后，当有人单击“预测”按钮时，将显示加载功能，并在稍后显示图形。我定义了dcc.loading组件，但是不确定如何定义此功能的回调。
1 个答案:
答案 0 :(得分：2)
建议3-dcc.Store()和dcc.Loading
此建议使用一个dcc.Store()组件，一个html.Button()和一个dcc.Loading组件来生成我现在所理解的理想设置：
- 启动仅显示按钮的应用。
- 单击按钮以显示加载图标，然后
- 显示数字。
- 再次单击以显示三个数字的序列中的下一个数字。
- 用完图形序列后再次开始。
启动后，该应用将如下所示：
现在，您可以单击一次Figures进入下面的Figure 1，但是只有在享受了以下加载图标之一之后：['graph', 'cube', 'circle', 'dot', or 'default']，其中'dot'将触发ptsd，并且'cube'恰好是我的最爱：
正在加载...
图1
现在，您无法继续单击Figure 2和Figure 3。我将Figure 1的加载时间设置为不少于5秒，然后将Figure 2和Figure 3的加载时间设置为2秒。但是您可以轻松更改它。
当您单击三次以上时，我们将从头开始：
我希望我终于找到了您真正想要的解决方案。下面的代码片段中的设置基于here中描述的设置，但是已经过调整，希望可以满足您的需求。让我知道这对您有何帮助！
import pandas as pd
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
import plotly.graph_objects as go
from jupyter_dash import JupyterDash
import dash_table
from dash.exceptions import PreventUpdate
import dash_bootstrap_components as dbc
import time
time.sleep(5) # Delay for 5 seconds.
global_df = pd.DataFrame({'value1':[1,2,3,4],
'value2':[10,11,12,14]})
# app = JupyterDash(__name__)
app = JupyterDash(external_stylesheets=[dbc.themes.SLATE])
df = pd.DataFrame({'Value 1': [1,2,3],
'Value 2':[10,11,12],
'Value 3':[14,12,9]})
df.set_index('Value 1', inplace = True)
app.layout = html.Div([
# The memory store reverts to the default on every page refresh
dcc.Store(id='memory'),
# The local store will take the initial data
# only the first time the page is loaded
# and keep it until it is cleared.
# Same as the local store but will lose the data
# when the browser/tab closes.
html.Table([
html.Thead([
html.Tr(html.Th('Click to launch figure:')),
html.Tr([
html.Th(html.Button('Figures', id='memory-button')),
]),
]),
]),
dcc.Loading(id = "loading-icon",
#'graph', 'cube', 'circle', 'dot', or 'default'
type = 'cube',
children=[html.Div(dcc.Graph(id='click_graph'))])
])
# Create two callbacks for every store.
# add a click to the appropriate store.
@app.callback(Output('memory', 'data'),
[Input('memory-button', 'n_clicks')],
[State('memory', 'data')])
def on_click(n_clicks, data):
if n_clicks is None:
# prevent the None callbacks is important with the store component.
# you don't want to update the store for nothing.
raise PreventUpdate
# Give a default data dict with 0 clicks if there's no data.
data = data or {'clicks': 0}
data['clicks'] = data['clicks'] + 1
if data['clicks'] > 3: data['clicks'] = 0
return data
# output the stored clicks in the table cell.
@app.callback(Output('click_graph', 'figure'),
# Since we use the data prop in an output,
# we cannot get the initial data on load with the data prop.
# To counter this, you can use the modified_timestamp
# as Input and the data as State.
# This limitation is due to the initial None callbacks
# https://github.com/plotly/dash-renderer/pull/81
[Input('memory', 'modified_timestamp')],
[State('memory', 'data')])
def on_data(ts, data):
if ts is None:
#raise PreventUpdate
fig = go.Figure()
fig.update_layout(plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)',
yaxis = dict(showgrid=False, zeroline=False, tickfont = dict(color = 'rgba(0,0,0,0)')),
xaxis = dict(showgrid=False, zeroline=False, tickfont = dict(color = 'rgba(0,0,0,0)')))
return(fig)
data = data or {}
0
# plotly
y = 'Value 2'
y2 = 'Value 3'
fig = go.Figure()
fig.update_layout(plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)',
yaxis = dict(showgrid=False, zeroline=False, tickfont = dict(color = 'rgba(0,0,0,0)')),
xaxis = dict(showgrid=False, zeroline=False, tickfont = dict(color = 'rgba(0,0,0,0)')))
if data.get('clicks', 0) == 1:
fig = go.Figure(go.Scatter(name=y, x=df.index, y=df[y], mode = 'lines'))
fig.add_traces(go.Scatter(name=y, x=df.index, y=df[y2], mode = 'lines'))
fig.update_layout(template='plotly_dark',
title = 'Plot number ' + str(data.get('clicks', 0)))
# delay only after first click
time.sleep(2)
if data.get('clicks', 0) == 2:
fig = go.Figure((go.Scatter(name=y, x=df.index, y=df[y], mode = 'lines')))
fig.add_traces(go.Scatter(name=y, x=df.index, y=df[y2], mode = 'lines'))
fig.update_layout(template='seaborn',
title = 'Plot number ' + str(data.get('clicks', 0)))
if data.get('clicks', 0) == 3:
fig = go.Figure((go.Scatter(name=y, x=df.index, y=df[y], mode = 'lines')))
fig.add_traces(go.Scatter(name=y, x=df.index, y=df[y2], mode = 'lines'))
fig.update_layout(template='plotly_white',
title = 'Plot number ' + str(data.get('clicks', 0)))
# Aesthetics
fig.update_layout(margin= {'t':30, 'b':0, 'r': 50, 'l': 50, 'pad': 0},
hovermode = 'x',
legend=dict(x=1,y=0.85),
uirevision='constant')
# delay for every figure
time.sleep(2)
return fig
app.run_server(mode='external', port = 8070, dev_tools_ui=True,
dev_tools_hot_reload =True, threaded=True)
建议2
经过一些交流之后，我们现在知道您想要：
- 仅先显示一个按钮（问题）
- 在图1的底部显示一次单击按钮时，在图2的第二次单击中显示，在图3的第三次单击中显示（注释）
我进行了新设置，应符合上面的所有条件。首先，仅显示控件选项。然后，您可以选择要显示的图形：Fig1, Fig2 or Fig3。在我看来，如果必须循环浏览数字以选择要显示的图形，这似乎是一个非最佳的用户界面。所以我选择了如下单选按钮：
现在，您可以自由选择要显示的图形，或返回到不再显示任何图形，就像这样：
在启动时或选择None时显示：
Figure 1已选择
您仍然没有提供数据示例，因此我仍在使用Suggestion 1中的合成数据，而是让不同的布局指示显示的是哪个图。我希望能满足您的需求，因为您似乎希望为不同的图形使用不同的布局。
完整代码2
from jupyter_dash import JupyterDash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State, ClientsideFunction
import dash_bootstrap_components as dbc
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import plotly.graph_objs as go
from dash.dependencies import Input, Output
import numpy as np
from plotly.subplots import make_subplots
import plotly.express as px
pd.options.plotting.backend = "plotly"
from datetime import datetime
palette = px.colors.qualitative.Plotly
# sample data
df = pd.DataFrame({'Prices': [1,10,7,5, np.nan, np.nan, np.nan],
'Predicted_prices':[np.nan, np.nan, np.nan, 5, 8,6,9]})
# app setup
app = JupyterDash(external_stylesheets=[dbc.themes.SLATE])
# controls
controls = dbc.Card(
[dbc.FormGroup(
[
dbc.Label("Options"),
dcc.RadioItems(id="display_figure",
options=[ {'label': 'None', 'value': 'Nope'},
{'label': 'Figure 1', 'value': 'Figure1'},
{'label': 'Figure 2', 'value': 'Figure2'},
{'label': 'Figure 3', 'value': 'Figure3'}
],
value='Nope',
labelStyle={'display': 'inline-block', 'width': '10em', 'line-height':'0.5em'}
)
],
),
dbc.FormGroup(
[dbc.Label(""),]
),
],
body=True,
style = {'font-size': 'large'})
app.layout = dbc.Container(
[
html.H1("Button for predictions"),
html.Hr(),
dbc.Row([
dbc.Col([controls],xs = 4),
dbc.Col([
dbc.Row([
dbc.Col(dcc.Graph(id="predictions")),
])
]),
]),
html.Br(),
dbc.Row([
]),
],
fluid=True,
)
@app.callback(
Output("predictions", "figure"),
[Input("display_figure", "value"),
],
)
def make_graph(display_figure):
# main trace
y = 'Prices'
y2 = 'Predicted_prices'
# print(display_figure)
if 'Nope' in display_figure:
fig = go.Figure()
fig.update_layout(plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)',
yaxis = dict(showgrid=False, zeroline=False, tickfont = dict(color = 'rgba(0,0,0,0)')),
xaxis = dict(showgrid=False, zeroline=False, tickfont = dict(color = 'rgba(0,0,0,0)')))
return fig
if 'Figure1' in display_figure:
fig = go.Figure(go.Scatter(name=y, x=df.index, y=df[y], mode = 'lines'))
fig.add_traces(go.Scatter(name=y, x=df.index, y=df[y2], mode = 'lines'))
fig.update_layout(template='plotly_dark')
# prediction trace
if 'Figure2' in display_figure:
fig = go.Figure((go.Scatter(name=y, x=df.index, y=df[y], mode = 'lines')))
fig.add_traces(go.Scatter(name=y, x=df.index, y=df[y2], mode = 'lines'))
fig.update_layout(template='seaborn')
if 'Figure3' in display_figure:
fig = go.Figure((go.Scatter(name=y, x=df.index, y=df[y], mode = 'lines')))
fig.add_traces(go.Scatter(name=y, x=df.index, y=df[y2], mode = 'lines'))
fig.update_layout(template='plotly_white')
# Aesthetics
fig.update_layout(margin= {'t':30, 'b':0, 'r': 0, 'l': 0, 'pad': 0})
fig.update_layout(hovermode = 'x')
fig.update_layout(showlegend=True, legend=dict(x=1,y=0.85))
fig.update_layout(uirevision='constant')
fig.update_layout(title = "Prices and predictions")
return(fig)
app.run_server(mode='external', port = 8005)
建议1
此建议将直接针对：
我想仅在单击按钮后才使用绘图显示图形
这意味着我不认为dcc.Loading() 已经是答案的一部分。
我发现dcc.Checklist()是一种用途广泛且用户友好的组件。正确设置后，它将显示为必须单击的按钮（或必须标记的选项），以触发某些功能或可视化。
这是一个基本设置：
dcc.Checklist(
id="display_columns",
options=[{"label": col + ' ', "value": col} for col in df.columns],
value=[df.columns[0]],
labelStyle={'display': 'inline-block', 'width': '12em', 'line-height':'0.5em'}
这是它的样子：
除其他几行外，dcc.Checklist()组件将使您可以随意打开和关闭Prediction跟踪。
# main trace
y = 'Prices'
fig = make_subplots(specs=[[{"secondary_y": True}]])
if 'Prices' in display_columns:
fig.add_trace(go.Scatter(name=y, x=df.index, y=df[y], mode = 'lines'), secondary_y=False)
# prediction trace
if 'Predicted_prices' in display_columns:
fig.add_trace(go.Scatter(name = 'predictions', x=df.index, y=df['Predicted_prices'], mode = 'lines'), secondary_y=False
此外，如果您想进一步扩展此示例，此设置将使您可以轻松地处理 multiple 跟踪的 multiple 预测。试试看，让我知道如何为您解决问题。如果不清楚，那么当您发现时间时，我们可以深入研究细节。
在未启用预测功能的情况下，应用程序的外观如下：
关闭
开启
完整代码：
from jupyter_dash import JupyterDash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State, ClientsideFunction
import dash_bootstrap_components as dbc
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import plotly.graph_objs as go
from dash.dependencies import Input, Output
import numpy as np
from plotly.subplots import make_subplots
import plotly.express as px
pd.options.plotting.backend = "plotly"
from datetime import datetime
palette = px.colors.qualitative.Plotly
# sample data
df = pd.DataFrame({'Prices': [1,10,7,5, np.nan, np.nan, np.nan],
'Predicted_prices':[np.nan, np.nan, np.nan, 5, 8,6,9]})
# app setup
app = JupyterDash(external_stylesheets=[dbc.themes.SLATE])
# input controls
controls = dbc.Card(
[dbc.FormGroup(
[
dbc.Label("Options"),
dcc.Checklist(
id="display_columns",
options=[{"label": col + ' ', "value": col} for col in df.columns],
value=[df.columns[0]],
labelStyle={'display': 'inline-block', 'width': '12em', 'line-height':'0.5em'}
#clearable=False,
#multi = True
),
],
),
dbc.FormGroup(
[dbc.Label(""),]
),
],
body=True,
style = {'font-size': 'large'})
app.layout = dbc.Container(
[
html.H1("Button for predictions"),
html.Hr(),
dbc.Row([
dbc.Col([controls],xs = 4),
dbc.Col([
dbc.Row([
dbc.Col(dcc.Graph(id="predictions")),
])
]),
]),
html.Br(),
dbc.Row([
]),
],
fluid=True,
)
@app.callback(
Output("predictions", "figure"),
[Input("display_columns", "value"),
],
)
def make_graph(display_columns):
# main trace
y = 'Prices'
fig = make_subplots(specs=[[{"secondary_y": True}]])
if 'Prices' in display_columns:
fig.add_trace(go.Scatter(name=y, x=df.index, y=df[y], mode = 'lines'), secondary_y=False)
# prediction trace
if 'Predicted_prices' in display_columns:
fig.add_trace(go.Scatter(name = 'predictions', x=df.index, y=df['Predicted_prices'], mode = 'lines'), secondary_y=False)
# Aesthetics
fig.update_layout(margin= {'t':30, 'b':0, 'r': 0, 'l': 0, 'pad': 0})
fig.update_layout(hovermode = 'x')
fig.update_layout(showlegend=True, legend=dict(x=1,y=0.85))
fig.update_layout(uirevision='constant')
fig.update_layout(template='plotly_dark',
plot_bgcolor='#272B30',
paper_bgcolor='#272B30')
fig.update_layout(title = "Prices and predictions")
return(fig)
app.run_server(mode='external', port = 8005)
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