dash-extensions 0.0.18

Extensions for Plotly Dash.

dash-extensions

The purpose of this package is to provide various extensions to the Plotly Dash framework. It is essentially a collection of code snippets that i have been reusing across multiple projects.

Download

The Download component provides an easy way to download data from a Dash application. Simply add the Download component to the app layout, and add a callback which targets its data property. Here is a small example,

import dash
import dash_html_components as html
from dash.dependencies import Output, Input
from dash_extensions import Download

app = dash.Dash(prevent_initial_callbacks=True)
app.layout = html.Div([html.Button("Download", id="btn"), Download(id="download")])

@app.callback(Output("download", "data"), [Input("btn", "n_clicks")])
def func(n_clicks):
    return dict(content="Hello world!", filename="hello.txt")

if __name__ == '__main__':
    app.run_server()

To ease downloading files, a send_file utility method is included,

import dash
import dash_html_components as html  
from dash.dependencies import Output, Input
from dash_extensions import Download
from dash_extensions.snippets import send_file

app = dash.Dash(prevent_initial_callbacks=True)
app.layout = html.Div([html.Button("Download", id="btn"), Download(id="download")])

@app.callback(Output("download", "data"), [Input("btn", "n_clicks")])
def func(n_clicks):
    return send_file("/home/emher/Documents/Untitled.png")

if __name__ == '__main__':
    app.run_server()

To ease downloading data frames (which seems to be a common use case for Dash users), a send_data_frame utility method is also included,

import dash
import pandas as pd
import dash_html_components as html

from dash.dependencies import Output, Input
from dash_extensions import Download
from dash_extensions.snippets import send_data_frame

# Example data.
df = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [2, 1, 5, 6], 'c': ['x', 'x', 'y', 'y']})
# Create app.
app = dash.Dash(prevent_initial_callbacks=True)
app.layout = html.Div([html.Button("Download", id="btn"), Download(id="download")])

@app.callback(Output("download", "data"), [Input("btn", "n_clicks")])
def func(n_nlicks):
    return send_data_frame(df.to_excel, "mydf.xls")

if __name__ == '__main__':
    app.run_server()

Lottie

The Lottie component makes it possible to run Lottie animations in Dash. Here is a small example,

import dash
import dash_html_components as html
import dash_extensions as de

# Setup options.
url = "https://assets9.lottiefiles.com/packages/lf20_YXD37q.json"
options = dict(loop=True, autoplay=True, rendererSettings=dict(preserveAspectRatio='xMidYMid slice'))
# Create example app.
app = dash.Dash(__name__)
app.layout = html.Div(de.Lottie(options=options, width="25%", height="25%", url=url))

if __name__ == '__main__':
    app.run_server()

DashCallbackBlueprint

A known limitation of Dash is the inability to assign multiple callbacks to the same output. Hence the following code will not work,

import dash
import dash_html_components as html
from dash.dependencies import Output, Input

app = dash.Dash()
app.layout = html.Div([html.Button("Button 1", id="btn1"), html.Button("Button 2", id="btn2"), html.Div(id="div")])


@app.callback(Output("div", "children"), [Input("btn1", "n_clicks")])
def click_btn1(n_clicks):
    return "You clicked btn1"


@app.callback(Output("div", "children"), [Input("btn2", "n_clicks")])
def click_btn2(n_clicks):
    return "You clicked btn2"


if __name__ == '__main__':
    app.run_server()

Specifically, a dash.exceptions.DuplicateCallbackOutput exception will be raised as an attempt is made to assign the output Output("div", "children") a second time.

To address this problem, this package provides the DashCallbackBlueprint class. It acts as a proxy for the Dash application during callback registration, but unlike the Dash application, it supports assignment of multiple callbacks to the same output. When all callbacks have been assigned, the blueprint is registered on the Dash application,

import dash
import dash_html_components as html
from dash.dependencies import Output, Input
from dash_extensions.callback import DashCallbackBlueprint


app = dash.Dash()
app.layout = html.Div([html.Button("Button 1", id="btn1"), html.Button("Button 2", id="btn2"), html.Div(id="div")])
dcb = DashCallbackBlueprint() 


@dcb.callback(Output("div", "children"), [Input("btn1", "n_clicks")])
def click_btn1(n_clicks):
    return "You clicked btn1"


@dcb.callback(Output("div", "children"), [Input("btn2", "n_clicks")]) 
def click_btn2(n_clicks):
    return "You clicked btn2"


dcb.register(app)  

if __name__ == '__main__':
    app.run_server()

Under the hood, the two callbacks are merged into one with the appropriate function handler invoked depending on the input trigger. In this simple case, the two callbacks could easily have been merged by hand. However, in more complex cases, the callback merging and control flow delegation can be cumbersome to implement by hand.