plotly-dash custom component. Adds Plotly.extendTraces() support to dash_core_components.Graph()
Project description
dash-extendable-graph
dash-extendable-graph is a Dash component library. This library contains a single component: ExtendableGraph
. The component is a fork of the Graph() component of dash-core-components (v 0.43.1), with an extra property (extendData
) that allows Graph traces to be drawn through Plotly.extendTraces()
instead of Plotly.react()
.
Note: plotly.js is required. However, the library is NOT explicitly listed in MANIFEST.in
or in dash_extendable_graph\__init__.py
as a way to reduce bundle size. Plotly.js is already distributed with the dash-core-components package, and most projects will import dcc as well as dash-extendable-graph.
Installation
Get started with:
- Install Dash and dependencies: https://dash.plot.ly/installation
$ pip install -r requirements.txt
- Install dash-extendable-graph
$ pip install dash-extendable-graph
- Run
python usage.py
- Visit http://localhost:8050 in your web browser
Usage
import dash_extendable_graph as deg
import dash
from dash.dependencies import Input, Output, State
import dash_html_components as html
import dash_core_components as dcc
import random
app = dash.Dash(__name__)
app.scripts.config.serve_locally = True
app.css.config.serve_locally = True
app.layout = html.Div([
deg.ExtendableGraph(
id='extendablegraph_example',
config={'showAxisDragHandles': True,
'showAxisRangeEntryBoxes': True,
'modeBarButtonsToRemove': [
'sendDataToCloud',
'lasso2d',
'autoScale2d',
'hoverClosestCartesian',
'hoverCompareCartesian',
'toggleSpikelines'],
'displaylogo': False,
},
figure=dict(
data=[{'x': [0],
'y': [0],
'mode':'lines+markers'
}],
)
),
dcc.Interval(
id='interval_extendablegraph_update',
interval=1000,
n_intervals=0,
max_intervals=-1),
html.Div(id='output')
])
@app.callback(Output('extendablegraph_example', 'extendData'),
[Input('interval_extendablegraph_update', 'n_intervals')],
[State('extendablegraph_example', 'figure')])
def update_extendData(n_intervals, existing):
x_new = existing['data'][0]['x'][-1] + 1
y_new = random.random()
return [dict(x=[x_new], y=[y_new])]
if __name__ == '__main__':
app.run_server(debug=True)
Contributing
See CONTRIBUTING.md
Local Installation
- Dependencies
$ npm install $ virtualenv venv $ . venv/bin/activate $ pip install -r requirements.txt $ pip install -r tests/requirements.txt
- Build
$ npm run build:all
- Check out the component via component-playground
$ npm run start
The demo app is insrc/demo
- Check out the sample Dash application using the component
$ python usage.py
Tests
- Write tests for your component.
- A sample test is available in
tests/test_usage.py
, it will loadusage.py
and you can then automate interactions with selenium. - Run the tests with
$ pytest tests
. - The Dash team uses these types of integration tests extensively. Browse the Dash component code on GitHub for more examples of testing (e.g. https://github.com/plotly/dash-core-components)
- A sample test is available in
- Add custom styles to your component by putting your custom CSS files into your distribution folder (
dash_extendable_graph
).- Make sure that they are referenced in
MANIFEST.in
so that they get properly included when you're ready to publish your component. - Make sure the stylesheets are added to the
_css_dist
dict indash_extendable_graph/__init__.py
so dash will serve them automatically when the component suite is requested.
- Make sure that they are referenced in
- Review your code
Create a production build and publish:
```bash
$ npm run build:all
$ rm -rf dist
$ python setup.py sdist
$ twine upload dist/*
$ npm publish
```
Test your tarball by copying it into a new environment and installing it locally:
$ pip install dash_extendable_graph-X.X.X.tar.gz
Publishing your component to NPM will make the JavaScript bundles available on the unpkg CDN. By default, Dash servers the component library's CSS and JS from the remote unpkg CDN, so if you haven't published the component package to NPM you'll need to set the serve_locally
flags to True
(unless you choose False
on publish_on_npm
). We will eventually make serve_locally=True
the default, follow our progress in this issue.
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