I have tried different attributes ( title, data, value, etc ) but they dont do the trick. Below is the sample code taken directly from the dash documentation. I simply would like to be able to push the button and have the button text change to something else besides "Submit", preferably the input text.
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
app = dash.Dash(__name__)
app.layout = html.Div([
html.Div(dcc.Input(id='input-on-submit', type='text')),
html.Button('Submit', id='submit-val', n_clicks=0),
html.Div(id='container-button-basic',
children='Enter a value and press submit')
])
#app.callback(
Output('container-button-basic', 'children'),
Input('submit-val', 'n_clicks'),
dash.dependencies.State('input-on-submit', 'value')
)
def update_output(n_clicks, value):
return ('The input value was "{}" and the button has been clicked {} times'.format(
value,
n_clicks
))
if __name__ == '__main__':
app.run_server(debug=True)
Well, I found that I needed to update the "children" property. Below is code that performs the function I was looking for:
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
app = dash.Dash(__name__)
app.layout = html.Div([
html.Div(dcc.Input(id='input-on-submit', type='text')),
html.Button('Submit', id='submit-val', n_clicks=0),
html.Div(id='container-button-basic',
children='Enter a value and press submit')
])
#app.callback(
Output('container-button-basic', 'children'),
Output ('submit-val','children'),
Input('submit-val', 'n_clicks'),
dash.dependencies.State('input-on-submit', 'value')
)
def update_output(n_clicks, value):
if n_clicks==0:
return
else:
buttonText = value
return ('The input value was "{}" and the button has been clicked {} times'.format(
value,
n_clicks
), buttonText)
if __name__ == '__main__':
app.run_server(debug=True)
Related
I have an animated graph that I update in a clientside callback. However, I want to update the text as well as the x and y values of the traces in Graph.extendData(), but it seems that that doesn't work. Is there something I'm missing? Alternatively, is there a different method I should be using instead?
Adopting the code from this post (Plotly/Dash display real time data in smooth animation), I'd like something like this, but where updating the text with extendData actually worked:
import dash
import dash_html_components as html
import dash_core_components as dcc
import numpy as np
from dash.dependencies import Input, Output, State
# Example data (a circle).
resolution = 1000
t = np.linspace(0, np.pi * 2, resolution)
x, y = np.cos(t), np.sin(t)
text = str(t)
# Example app.
figure = dict(data=[{'x': [], 'y': []}], text = [], layout=dict(xaxis=dict(range=[-1, 1]), yaxis=dict(range=[-1, 1])))
app = dash.Dash(__name__, update_title=None) # remove "Updating..." from title
app.layout = html.Div([
dcc.Graph(id='graph', figure=dict(figure)), dcc.Interval(id="interval", interval=25),
dcc.Store(id='offset', data=0), dcc.Store(id='store', data=dict(x=x, y=y, text=text, resolution=resolution)),
])
# This makes the graph fail to draw instead of just extending the text as wel!
app.clientside_callback(
"""
function (n_intervals, data, offset) {
offset = offset % data.x.length;
const end = Math.min((offset + 10), data.x.length);
return [[{x: [data.x.slice(offset, end)], y: [data.y.slice(offset, end)], text: [data.text.slice(offset, end)]}, [0], 500], end]
}
""",
[Output('graph', 'extendData'), Output('offset', 'data')],
[Input('interval', 'n_intervals')], [State('store', 'data'), State('offset', 'data')]
)
if __name__ == '__main__':
app.run_server()
Alternatively, is there a different method I should be using instead?
The dashboard include a dropdown, a table and a graph.
By selecting a item from dropdown, some rows of the table is shown and figure plot the data in the table.
I have two callback,
at the first one:
input : dropdown
output: table
second one:
input: table
output: figure
So the output of first callback is input of the second one.
The following code output look like this:
import dash
import dash_table
import pandas as pd
from copy import deepcopy
import plotly.graph_objects as go
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
data = pd.DataFrame({"a": ["a1", "a2"],
"b": [1, 2]})
app = dash.Dash(
__name__,
external_stylesheets=[dbc.themes.BOOTSTRAP],
prevent_initial_callbacks=True,
)
app.layout = dbc.Container(
[
dbc.Row(dbc.Col([dcc.Dropdown(
id='dropdown1',
options=[
{'label': '1', 'value': 1},
{'label': '2', 'value': 2}
],
value=1)])),
dbc.Row(
dbc.Col([
dash_table.DataTable(
id="datatable",
columns=[dict(id=i,
name=j,
)
for i, j in zip(
["a", "b"],
["a", "b"],
)],
data=data.loc[data['a'] ==
"a1"].to_dict("records"),
sort_mode="single",
)
])),
dbc.Row([dbc.Col([html.Div(
children=dcc.Graph(id="graph"), className="card")
])]),
], fluid=True,
)
########################################################
#app.callback(
Output("datatable", "data"),
[Input("dropdown1", "value")],
prevent_initial_call=True
)
def update_current_table(value):
idx = int(value)
print(idx)
if idx == 1:
df = (data.loc[data["a"] == "a1"])
return df.to_dict("records")
else:
df = deepcopy(data.loc[data["a"] == "a2"])
return df.to_dict("records")
########################################################
# app.callback(
Output('graph', 'figure'),
[Input("datatable", "derived_virtual_data"),
Input("dropdown1", "value")
],
prevent_initial_call=True
)
def update_figure(table, value):
df = pd.DataFrame(table)
print(df)
fig = go.Figure()
return fig
if __name__ == "__main__":
app.run_server(debug=True, port=8000)
The problem here is callback run twice:
a b
0 a1 1
a b
0 a2 2
and for the first time print the previous values of the table
This cause error in the main code that I am working on.
How to prevent dash from runnig callback twice?
I found some other questions complaining about running the callback twic, but I could not found a proper solution for that.
The problem is that you have two inputs for the callback that's running twice:
# app.callback(
Output('graph', 'figure'),
[Input("datatable", "derived_virtual_data"),
Input("dropdown1", "value")
],
prevent_initial_call=True
)
def update_figure(table, value):
The dropdown changes, and triggers this callback, but it also triggers the table to update. The updated table causes the derived_virtual_datato trigger this callback again. You can fix this by making the dropdown value a State instead, like this:
# app.callback(
Output('graph', 'figure'),
[Input("datatable", "derived_virtual_data")],
[State("dropdown1", "value"),]
prevent_initial_call=True
)
def update_figure(table, value):
Edit:
On another read through, you don't even use the value from the dropdown in the second input. Just removing it entirely will also work:
# app.callback(
Output('graph', 'figure'),
[Input("datatable", "derived_virtual_data")],
prevent_initial_call=True
)
def update_figure(table):
I'm attempting to implement a live-update graph that is only displayed if the user selected it from the drop down menu. I've made a callback with input from "intervals" and from the dropdown menu "oxygen". I thought if "value" is "0" then no chart has been selected and I could return "null"(???), and if 'value' = '1', then the graph would update. Is this possible? or is my approach incorrect?
dcc.Graph(id='live-graph2', animate=False),
dcc.Interval(
id='graph-update',
interval=10000,
n_intervals=0
)),
dbc.Col(html.Div([
dcc.Dropdown(
id='oxygen',
options=[{'label': s, 'value': s}
for s in main_graph.keys()],
value=[],
multi=True),
#app.callback(
dash.dependencies.Output('live-graph2', 'figure'),
[dash.dependencies.Input('oxygen', 'value'),
dash.dependencies.Input('graph-update', 'n_intervals')],
)
def update_graph_scatter_2(n,value):
if value == 0:
....
else:
data = {}
I would approach the problem differently.
If you don't want to display the graph at all when no dropdown option is selected then the Graph component doesn't need to be in the layout by default.
Instead you could create a container component that your Graph component is appended to dynamically based on the option values of your Dropdown component.
Below is a simplified example using sample data, adjust per your requirements:
import dash
import dash_html_components as html
import dash_core_components as dcc
import plotly.express as px
import dash_bootstrap_components as dbc
df = px.data.iris()
app = dash.Dash(__name__)
app.layout = html.Div(
[
dcc.Interval(id="graph-update", interval=10000, n_intervals=0),
html.Div(id="graph-container"),
dbc.Col(
html.Div(
[
dcc.Dropdown(
id="oxygen",
options=[{"label": s, "value": s} for s in [1, 2, 3]],
value=[],
multi=True,
)
]
)
),
]
)
#app.callback(
dash.dependencies.Output("graph-container", "children"),
[
dash.dependencies.Input("oxygen", "value"),
dash.dependencies.Input("graph-update", "n_intervals"),
],
)
def update_graph_scatter_2(value, n):
if value:
# Change the line below to dynamically create the figure based on value
fig = px.scatter(df, x="sepal_width", y="sepal_length")
return dcc.Graph(id="live-graph2", figure=fig)
return html.Div()
I like to put plotly graph1 in tab1 and graph2 in tab 2, but there is no trace visible.
Why are traces not visible?
The graph in tab2 is also showing no traces.
It would be great to know whats wrong.
Simular example code would also be a big help.
Many thanks
''' import pandas as pd
import numpy as np
import plotly.express as px
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.graph_objs as go
import requests
import dash_bootstrap_components as dbc
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
app = dash.Dash(external_stylesheets=[dbc.themes.BOOTSTRAP])
#fig1 = px.line(title='title1')
#fig2 = px.line(title='title2')
app.layout = dbc.Container(fluid=True, children=[
html.H1('Title',id="Titel",className='ml-1',style={'text-align': 'center', 'font-weight': 'bold', 'text-decoration': 'underline'}),
## Body
dbc.Row([
dbc.Col(md=6,children=[
html.Br(),html.Br(),
dbc.Col(md=9, children=[
dbc.Col(html.H4("graphs in tabs"), width={"size":6,"offset":3}),
dbc.Tabs(className="nav nav-pills", children=[
dbc.Tab(dcc.Graph(id="grafiek1"), label="tab1"),
dbc.Tab(dcc.Graph(id="grafiek2"), label="tab2")
])
])
#html.Div(id="tab-content", className="mx-auto"), #m=marge p=padding 0-5 of auto vb: P-4
]), # einde kolom 1
dbc.Col(md=6,children=[
]), # einde kolom 2
]), #einde rij
])
#,style={"background-image": 'url(image)'})
#app.callback(
Output("grafiek1", "figure"),
[Input("tabs", "active_tab")])
def tab_content(active_tab):
if active_tab is not None:
if active_tab == "tab1":
fig1 = go.Figure()
fig.add_trace(go.Scatter(x=df['Date'], y=df['AAPL.Low'], mode='markers', name='data', line={"color":"black"}))
## add slider
fig.update_xaxes(rangeslider_visible=True)
## set background color
fig.update_layout(plot_bgcolor='white', autosize=False, width=1000, height=550)
return fig1
#app.callback(
Output("grafiek2", "figure"),
[Input("tabs", "active_tab")])
def tab_content(active_tab):
if active_tab is not None:
if active_tab == "tab2":
fig2 = go.Figure()
fig.add_trace(go.Scatter(x=df['Date'], y=df['AAPL.High'], mode='markers', name='data', line={"color":"black"}))
## add slider
fig.update_xaxes(rangeslider_visible=True)
## set background color
fig.update_layout(plot_bgcolor='white', autosize=False, width=1000, height=550)
return fig2
if __name__ == '__main__':
app.run_server(debug='True')
'''
The 'tabs' id you reference in your callbacks doesn't exist in your layout. Dash doesn't tell you about this because you do this
app.run_server(debug='True')
instead of
app.run_server(debug=True)
You also check if active_tab equals "tab1" in the first callback and "tab2" in the second, but the active_tab prop of your Tabs component isn't set to either of these values. The default active_tab value for the first tab is actually tab-0.
active_tab (string, optional): The tab_id of the currently active tab. If tab_id has not been specified for the active tab, this will default to tab-i, where i is the index (starting from 0) of the tab.
https://dash-bootstrap-components.opensource.faculty.ai/docs/components/tabs/
Since this if statement fails nothing is returned from either callback.
Also the callback function have the same function name, this is not allowed.
Lastly in both callbacks you create a figure, but you add the trace to another figure, so the trace also doesn't show up because you're returning an empty figure.
So instead you could do something like this:
import pandas as pd
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
import plotly.graph_objects as go
df = pd.read_csv(
"https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv"
)
app = dash.Dash(external_stylesheets=[dbc.themes.BOOTSTRAP])
app.layout = dbc.Container(
fluid=True,
children=[
html.H1(
"Title",
id="Titel",
className="ml-1",
style={
"text-align": "center",
"font-weight": "bold",
"text-decoration": "underline",
},
),
dbc.Row(
[
dbc.Col(
md=6,
children=[
html.Br(),
html.Br(),
dbc.Col(
md=9,
children=[
dbc.Col(
html.H4("graphs in tabs"),
width={"size": 6, "offset": 3},
),
dbc.Tabs(
id="tabs",
className="nav nav-pills",
children=[
dbc.Tab(
label="tab1",
children=[dcc.Graph(id="grafiek1")],
),
dbc.Tab(
label="tab2",
children=[dcc.Graph(id="grafiek2")],
),
],
),
],
),
],
),
dbc.Col(md=6, children=[]),
]
),
],
)
#app.callback(Output("grafiek1", "figure"), [Input("tabs", "active_tab")])
def tab_content1(active_tab):
if active_tab is not None:
if active_tab == "tab-0":
fig1 = go.Figure()
fig1.add_trace(
go.Scatter(
x=df["Date"],
y=df["AAPL.Low"],
mode="markers",
name="data",
line={"color": "black"},
)
)
return fig1
return go.Figure()
#app.callback(Output("grafiek2", "figure"), [Input("tabs", "active_tab")])
def tab_content2(active_tab):
if active_tab is not None:
if active_tab == "tab-1":
fig2 = go.Figure()
fig2.add_trace(
go.Scatter(
x=df["Date"],
y=df["AAPL.High"],
mode="markers",
name="data",
line={"color": "black"},
)
)
# add slider
fig2.update_xaxes(rangeslider_visible=True)
# set background color
fig2.update_layout(
plot_bgcolor="white", autosize=False, width=1000, height=550
)
return fig2
return go.Figure()
if __name__ == "__main__":
app.run_server(debug=True)
I am trying to make a dashboard where the output from shap forceplot is illustrated. Shap.forceplot is HTML decorated with json. The example is here
I made a very simple dashboard using the tutorial which should plot the desirable figure after clicking the submit
here is the code
# -*- coding: utf-8 -*-
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
import pandas as pd
from sqlalchemy import create_engine
import shap
from sources import *
import xgboost
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div([
dcc.Input(id='input-cvr-state', type='text', value='12'),
html.Button(id='submit-button', n_clicks=0, children='Submit'),
html.Div(id='output-state'),
html.Div(id='output-shap')
])
#app.callback(Output('output-shap', 'children'),
[Input('submit-button', 'n_clicks')],
[State('input-cvr-state', 'value')])
def update_shap_figure(n_clicks, input_cvr):
shap.initjs()
# train XGBoost model
X,y = shap.datasets.boston()
model = xgboost.train({"learning_rate": 0.01}, xgboost.DMatrix(X, label=y), 100)
# explain the model's predictions using SHAP values(same syntax works for LightGBM, CatBoost, and scikit-learn models)
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X)
# visualize the first prediction's explanation
return(shap.force_plot(explainer.expected_value, shap_values[0,:], X.iloc[0,:])) # matplotlib=True
if __name__ == '__main__':
app.run_server(debug=True)
I managed it by following steps:
import shap
from shap.plots._force_matplotlib import draw_additive_plot
# ... class dashApp
# ... callback as method
# matplotlib=False => retrun addaptativevisualizer,
# if set to True the visualizer will render the result is the stdout directly
# x is index of wanted input
# class_1 is ma class to draw
force_plot = shap.force_plot(
self.explainer.expected_value[class_1],
self.shap_values[class_1][x[0], :],
self.data.iloc[x, :].drop(columns=["TARGET"], errors="ignore"),
matplotlib=False
)
# set show=False to force the figure to be returned
force_plot_mpl = draw_additive_plot(force_plot.data, (30, 7), show=False)
return figure_to_html_img(force_plot_mpl)
def figure_to_html_img(figure):
""" figure to html base64 png image """
try:
tmpfile = io.BytesIO()
figure.savefig(tmpfile, format='png')
encoded = base64.b64encode(tmpfile.getvalue()).decode('utf-8')
shap_html = html.Img(src=f"data:image/png;base64, {encoded}")
return shap_html
except AttributeError:
return ""
The result will be like it
An alternative is to use html.IFrame which will produce a better looking and fully interactive plot.
Here's an example that can be used directly as an Output
def _force_plot_html(*args):
force_plot = shap.force_plot(*args, matplotlib=False)
shap_html = f"<head>{shap.getjs()}</head><body>{force_plot.html()}</body>"
return html.Iframe(srcDoc=shap_html,
style={"width": "100%", "height": "200px", "border": 0})