Skip to main content

Transcrypt bindings for Plotly Dash.

Project description

The purpose of dash-transcrypt is making it easy to

  • Write clientside callbacks in Python
  • Pass function handles as component properties

Under the hood, it utilizes transcrypt for the transpiling of Python to JavaScript.

Getting started

The recommended way to install dash-transcrypt is via pip,

 pip install dash-transcrypt  

In addition, a working java installation is required (it's used in the minification process). To run the examples related to function properties, dash-leaflet and geobuf are also needed,

 pip install geobuf dash-leaflet   

Clientside callbacks

The functions to be used as clientside callbacks must be placed in a separate module (file), say calculator_cf.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 calculator_cf module must be passed to the to_clientside_functions function. In addition to transpiling the module into JavaScript, it replaces the function attributes of the module with ClientsideFunction objects so that they can be used in clientside callbacks,

 import caculator_cf as ccf 
 from dash_transcrypt import module_to_clientside_functions, inject_js 
 ... 
 inject_js(app, module_to_clientside_functions(ccf))
 app.clientside_callback(ccf.add, ...)  

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

import dash  
import dash_core_components as dcc  
import dash_html_components as html  
import caculator_cf as ccf  

from dash.dependencies import Output, Input  
from dash_transcrypt import module_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, module_to_clientside_functions(ccf))  
app.clientside_callback(ccf.add, Output("c", "children"), [Input("a", "value"), Input("b", "value")])  

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

Functions as properties

As you might already know, it is not possible to pass function handles as component properties in Dash. To circumvent this limitation, dash-transcrypt passes the full path to the function as a string. It's then up to the component to create the function.

An example of a component that supports this flow is the GeoJSON component in dash-leaflet. One of the function properties is the pointToLayer option, which must be a function (or rather a full path to a function) that matches the signature of the pointToLayer option of the underlying Leaflet GeoJSON object. The relevant function(s) must be placed in a separate module (file), say scatter_js.py,

def point_to_layer(feature, latlng, context):
    radius = feature.properties.value*10
    return L.circleMarker(latlng, dict(radius=radius))

Before the function(s) can be assigned as a property, the module must be passed through the module_to_props function. In addition to transpiling the module into JavaScript, it replaces the function attributes of the module with the appropriate strings,

import scatter_js as sjs 
import dash_leaflet as dl
from dash_transcrypt import inject_js, module_to_props 
... 
js = module_to_props(sjs)
geojson = dl.GeoJSON(data=data, options=dict(pointToLayer=sjs.point_to_layer))  # pass function as prop
...
inject_js(app, js)  

For completeness, here is the full example app

import random
import dash
import dash_html_components as html
import dash_leaflet as dl
import scatter_js as sjs
import dash_leaflet.express as dlx

from dash_transcrypt import inject_js, module_to_props

# Create some markers.
points = [dict(lat=55.5 + random.random(), lon=9.5 + random.random(), value=random.random()) for i in range(100)]
data = dlx.dicts_to_geojson(points)
# Create geojson.
js = module_to_props(sjs)  # compiles the js
geojson = dl.GeoJSON(data=data, options=dict(pointToLayer=sjs.point_to_layer))  # pass function as prop
# Create the app.
app = dash.Dash()
app.layout = html.Div([dl.Map([dl.TileLayer(), geojson], center=(56, 10), zoom=8, style={'height': '50vh'})])
inject_js(app, js)  # adds the js to the app

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

Passing arguments at compile time

Variable assignments followed by # <kwarg> are overwritten at compile time by the dash-transcypt preprocessor. As an extension of the previous example, say one would like to be able to vary the scaling of the radius. This could be achieved by modifying scatter_js.py to

scale = 10  # <kwarg>

def point_to_layer(feature, latlng, context):
    radius = feature.properties.value * scale
    return L.circleMarker(latlng, dict(radius=radius))

The default scale 10 as before, but the value can now be modified by changing a single line in the application code,

js = module_to_props(cjs, scale=20)  # compiles the js

Passing arguments at runtime

While not enforced by dash-transcrypt, it is recommended that a context (typically a reference to this) is passed to all functional properties. Furthermore, it is recommended that a hideout property is added which does nothing, but serves as a container arguments at runtime. The GeoJSON component from the previous example(s) follows these guidelines. Hence by modifying scatter_js.py to

def point_to_layer(feature, latlng, context):  
    scale = context.props.hideout.scale  
    radius = feature.properties.value * scale  
    return L.circleMarker(latlng, dict(radius=radius))

the scale can now be changed on runtime. That is, the map visualization can now be interactive. Here is a small app, where a slider changes the scale and thus the size of the makers,

import random
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_leaflet as dl
import scatter_rt_js as rjs
import dash_leaflet.express as dlx

from dash.dependencies import Input, Output
from dash_transcrypt import inject_js, module_to_props

# Create some markers.
points = [dict(lat=55.5 + random.random(), lon=9.5 + random.random(), value=random.random()) for i in range(100)]
data = dlx.dicts_to_geojson(points)
# Create geojson.
js = module_to_props(rjs)  # compiles the js
geojson = dl.GeoJSON(data=data, options=dict(pointToLayer=rjs.point_to_layer),  # pass function as prop
                     hideout=dict(scale=10), id="geojson")  # pass variables to function
# Create the app.
app = dash.Dash()
app.layout = html.Div([
    dl.Map([dl.TileLayer(), geojson], center=(56, 10), zoom=8, style={'height': '50vh'}),
    dcc.Slider(min=1, max=100, value=10, id="slider")
])
inject_js(app, js)  # adds the js to the app

@app.callback(Output("geojson", "hideout"), [Input("slider", "value")], prevent_initial_call=False)
def update_scale(value):
    return dict(scale=value)

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

Notes

  • Browsers tend to cache javascript assets. When changes have been made to the python functions, it might therefore be necessary to force a reload of the page (ctrl+F5) to get the updated function definitions.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dash-transcrypt-0.0.6.tar.gz (5.7 kB view hashes)

Uploaded source

Built Distribution

dash_transcrypt-0.0.6-py3-none-any.whl (5.5 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page