dash-extensions 0.0.30

Extensions for Plotly Dash.

dash-extensions

The purpose of this package is to provide various extensions to the Plotly Dash framework. It can be divided into four main blocks,

• The snippets module, which contains a collection of utility functions
• The transpile module, which contains convenience wrappers for transpiling Python code to javascript
• The enrich module, which contains various enriched versions of Dash components
• A number of custom components, e.g. the Download component

While the snippets module documentation will be limited to source code comments, the enrich module, the transpile module and the custom components are documented below.

Transpiling

The transpile module translates Python code into JavaScript using the transcrypt library. Since transcrypt is a rather large library, it is not included in the requirements, but it can be installed via pip

pip install transcrypt

One of the main use cases for transpiling is clientside callbacks (which are usually written in JavaScript). The functions to be transpiled must be placed in a separate module (file), say logic.py. In this example, we will consider a simple add function,

def add(a, b):
    return a + b

Before the add function can be used as a clientside callback, the logic module must be passed through the to_clientside_functions function. In addition to transpiling the module into JavaScript, it replaces the functional attributes of the module with appropriate ClientsideFunction objects so that they can be used in clientside callbacks,

from dash_extensions.transpile import to_clientside_functions, inject_js
...
inject_js(app, to_clientside_functions(logic))  # this is where the magic happens
app.clientside_callback(logic.add, ...)

The to_clientside_functions returns the path to a JavaScript index file, which must be made available by the app (that's what inject_js does). For completeness, here is the full example app,

import dash
import dash_core_components as dcc
import dash_html_components as html
import logic

from dash.dependencies import Output, Input
from dash_extensions.transpile import to_clientside_functions, inject_js

# Create example app.
app = dash.Dash()
app.layout = html.Div([
    dcc.Input(id="a", value=2, type="number"), html.Div("+"),
    dcc.Input(id="b", value=2, type="number"), html.Div("="), html.Div(id="c"),
])
# Create clientside callback.
inject_js(app, to_clientside_functions(logic))
app.clientside_callback(logic.add, Output("c", "children"), [Input("a", "value"), Input("b", "value")])

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

The other main use case for the transpile module is for passing function handles as Dash properties. Again, the functions to be transpiled must be placed in a separate module (file), say styles.py,

marks = [0, 10, 20, 50, 100, 200, 500, 1000]
colorscale = ['#FFEDA0', '#FED976', '#FEB24C', '#FD8D3C', '#FC4E2A', '#E31A1C', '#BD0026', '#800026']

def style(feature):
    color = None
    for i, item in enumerate(marks):
        if feature["properties"]["density"] > item:
            color = colorscale[i]
    return dict(fillColor=color, weight=2, opacity=1, color='white', dashArray='3', fillOpacity=0.7)

def hover_style(feature):
    return dict(weight=5, color='#666', dashArray='')

The style function above was designed to match the signature of the style option of the Leaflet GeoJSON object. Before the functions can be used as properties, the module must be passed through the to_js_functions function. In addition to transpiling the module into JavaScript, it replaces the functional attributes of the module with strings that are translated into functions in the JavaScript layer,

from dash_extensions.transpile import to_js_functions, inject_js
...
index = to_js_functions(styles) 
geojson = dl.GeoJSON(data=data, id="geojson", options=dict(style=styles.style), hoverStyle=styles.hover_style)
...
inject_js(app, index)

For completeness, here is the full example app (tested with dash-leaflet==0.0.23),

import dash
import dash_html_components as html
import json
import dash_leaflet as dl
import styles

from dash_extensions.transpile import to_js_functions, inject_js

# Create geojson.
with open("assets/us-states.json", 'r') as f:
    data = json.load(f)
index = to_js_functions(styles) 
geojson = dl.GeoJSON(data=data, id="geojson", options=dict(style=styles.style), hoverStyle=styles.hover_style)
# Create app.
app = dash.Dash(prevent_initial_callbacks=True)
app.layout = html.Div([dl.Map(children=[dl.TileLayer(), geojson], center=[39, -98], zoom=4, id="map")],
                      style={'width': '100%', 'height': '50vh', 'margin': "auto", "display": "block"})
# Inject transcrypted JavaScript.
inject_js(app, index)


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

Enrichments

At the time of writing, the following enrichments (as compared to Dash 1.14.0) have been implemented,

• Ordering and form (single element versus list) of (Output, Input, State) does not matter. Hence, you could do this,

    @app.callback(Input("input_id", "input_prop"), Output("output_id", "output_prop"))
  

• A new Trigger component has been added. Like an Input, it can trigger callbacks, but its value is not passed on to the callback,

    @app.callback(Output("output_id", "output_prop"), Trigger("button", "n_clicks"))
    def func():  # note that "n_clicks" is not included as an argument 
  

• It is now possible to have callbacks without an Output,

    @app.callback(Trigger("button", "n_clicks"))  # note that the callback has no output
  

• A new group keyword makes it possible to bundle callbacks together. This feature serves as a work around for Dash not being able to target an output multiple times. Here is a small example,

    @app.callback(Output("log", "children"), Trigger("left", "n_clicks"), group="my_group") 
    def left():
        return "left"
        
    @app.callback(Output("log", "children"), Trigger("right", "n_clicks"), group="my_group") 
    def right():
        return "right"
  

• A new ServersideOutput component has been added. It works like a normal Output, but keeps the data on the server. By skipping the data transfer between server/client, the network overhead is reduced drastically, and the serialization to JSON can be avoided. Hence, you can now return complex objects, such as a pandas data frame, directly,

    @app.callback(ServersideOutput("store", "data"), Trigger("left", "n_clicks")) 
    def query():
        return pd.DataFrame(data=list(range(10)), columns=["value"])
        
    @app.callback(Output("log", "children"), Input("store", "data")) 
    def right(df):
        return df["value"].mean()
  

  The reduced network overhead along with the avoided serialization to/from JSON can yield significant performance improvements, in particular for large data. Note that content of a ServersideOutput cannot be accessed by clientside callbacks.

• A new memoize keyword makes it possible to memoize the output of a callback. That is, the callback output is cached, and the cached result is returned when the same inputs occur again.

    @app.callback(ServersideOutput("store", "data"), Trigger("left", "n_clicks"), memoize=True) 
    def query():
        return pd.DataFrame(data=list(range(10)), columns=["value"])
  

  Used with a normal Output, this keyword is essentially equivalent to the @flask_caching.memoize decorator. For a ServersideOutput, the backend to do server side storage will also be used for memoization. Hence you avoid saving each object two times, which would happen if the @flask_caching.memoize decorator was used with a ServersideOutput.

To enable the enrichments, simply replace the imports of the Dash object and the (Output, Input, State) objects with their enriched counterparts,

from dash_extensions.enrich import Dash, Output, Input, State

The syntax in the enrich module should be considered alpha stage. It might change without notice.

Components

The components listed here can be used in the layout of your Dash app.

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()

Keyboard

The Keyboard component makes it possible to capture keyboard events at the document level. Here is a small example,

import dash
import dash_html_components as html
import json

from dash.dependencies import Output, Input
from dash_extensions import Keyboard

app = dash.Dash()
app.layout = html.Div([Keyboard(id="keyboard"), html.Div(id="output")])


@app.callback(Output("output", "children"), [Input("keyboard", "keydown")])
def keydown(event):
    return json.dumps(event)


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