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dash-transcrypt-0.0.6rc1
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Transcrypt bindings for Plotly Dash.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dash-transcrypt-0.0.6rc1 |
| نام | dash-transcrypt |
| نسخه کتابخانه | 0.0.6rc1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Emil Haldrup Eriksen |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/thedirtyfew/dash-transcrypt/ |
| آدرس اینترنتی | https://pypi.org/project/dash-transcrypt/ |
| مجوز | MIT |
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](https://www.transcrypt.org/) 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]([https://pypi.org/project/dash-leaflet/](https://pypi.org/project/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](https://leafletjs.com/reference-0.7.7.html#geojson-pointtolayer). 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.
نیازمندی
| مقدار | نام |
|---|---|
| - | transcrypt |
نحوه نصب
نصب پکیج whl dash-transcrypt-0.0.6rc1:
pip install dash-transcrypt-0.0.6rc1.whl
نصب پکیج tar.gz dash-transcrypt-0.0.6rc1:
pip install dash-transcrypt-0.0.6rc1.tar.gz