myd

mydcc

My Dash Core Components.

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32

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3yrs ago

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5

License

MIT

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Readme

Mydcc

My Dash Core Components

Installing :

pip install mydcc

Requirements:

  • dash -- The core dash backend
  • dash-renderer -- The dash front-end
  • dash-html-components -- HTML components
  • dash-core-components -- Supercharged components
  • plotly -- Plotly graphing library used in examples

Usage :

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, Event, State
import mydcc

app = dash.Dash()
app.layout = html.Div(...)

@app.callback(...)
def myfun(...):
    ...
    return ...

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

1. mydcc.Listener :

Get mouse position of plotly graph

Usage :

app.layout = html.Div([
    mydcc.Listener( id = "uuu", aim = 'graph' ),
    dcc.Graph( id = 'graph',
               figure = { 'data': [  {'x': [1, 2, 3], 'y': [4, 1, 2]}  ]  }
              ),
    html.Div( id = 'text' )
])

@app.callback(
    Output('text', 'children'),
    [Input('uuu', 'data')])
def myfun(ddd):
    return str(ddd['x']) + ' and ' + str(ddd['y']) 

2. mydcc.Listener_mapbox :

Get mouse position for plotly mapbox graph

Usage :

app.layout = html.Div([
    mydcc.Listener_mapbox( id = "uuu", aim = 'graph'  ),
    dcc.Graph( id = 'ggg', figure = figgure_with_mapbox ),
    html.Div( id = 'text' )
])
  
@app.callback(
    Output('text', 'children'),
    [Input('uuu', 'data')])
def myfun(ddd):
    return str(ddd['x']) + ' and ' + str(ddd['y']) 

3. mydcc.Relayout :

Relayout plotly graph

Usage :

app.layout = html.Div([
    mydcc.Relayout( id = "rrr", aim = 'graph' ),
    dcc.Graph( id = 'graph',
               figure = { 'data': [  {'x': [1, 2, 3], 'y': [4, 1, 2]}  ]  }
              ),
    dcc.Input(id = 'in',
              placeholder = 'Enter a title ...',
              type = 'text',
              value = ''  
              )
])

@app.callback(
    Output('rrr', 'layout'),
    [Input('in', 'value')])
def myfun(x):
    return {'title':x}

4. mydcc.Change_trace :

Change plotly graph trace (only for graph with one trace )

Usage :

app.layout = html.Div([
    dcc.Graph(
        id = 'graph',    
        figure = {
            'data': [
                { 'x': [1, 2, 3], 'y': [4, 1, 2] },
            ],
            'layout': {
                'title': 'Dash Data Visualization',
                'xaxis': { 'range': [0,50] },
                'yaxis': { 'range': [0,50] }
            }
        }
    ),
    mydcc.Listener(id = "uuu", aim = 'graph'),
    mydcc.Change_trace(id = 'ii', aim = 'graph')
])

@app.callback(
    Output('ii', 'data'),
    [Input('uuu', 'data')])
def myfun(ddd):
    data = {'disable':'y'}
    if type(ddd['x']) != str and ddd['x'] < 30:
        data = dict(x = [ddd['x']],
                    y = [ddd['y']],
                    opacity = 1
                    )
    return data

5. mydcc.Change_trace_mapbox :

Change plotly mapbox graph trace (only for graph with one trace )

Usage :

app.layout = html.Div([
    dcc.Graph( id = 'graph', figure = figgure_with_mapbox ),
    mydcc.Listener_mapbox(id = "uuu", aim = 'graph'),
    mydcc.Change_trace_mapbox(id = 'ii', aim = 'graph')
])

@app.callback(
    Output('ii', 'data'),
    [Input('uuu', 'data')])
def myfun(ddd):
    data = {'lon':[1], 'lat':[1], 'type': 'scattermapbox'}
    if type(ddd['x']) != str :
        data = dict(lon = [ ddd['x'] ],
                    lat = [ ddd['y'] ],
                    type = 'scattermapbox',
                    opacity = 1
                    )
    return data

Dash

Go to this link to learn about Dash.

Getting started

# Install dependencies
$ npm install

# Watch source for changes and build to `lib/`
$ npm start

Development

Demo server

You can start up a demo development server to see a demo of the rendered components:

$ builder run demo
$ open http://localhost:9000

You have to maintain the list of components in demo/Demo.react.js.

Code quality and tests

To run lint and unit tests:

$ npm test

To run unit tests and watch for changes:

$ npm run test-watch

To debug unit tests in a browser (Chrome):

$ npm run test-debug
  1. Wait until Chrome launches.
  2. Click the "DEBUG" button in the top right corner.
  3. Open up Chrome Devtools (Cmd+opt+i).
  4. Click the "Sources" tab.
  5. Find source files
  • Navigate to webpack:// -> . -> spec/components to find your test source files.
  • Navigate to webpack:// -> [your/repo/path]] -> mydcc -> src to find your component source files.
  1. Now you can set breakpoints and reload the page to hit them.
  2. The test output is available in the "Console" tab, or in any tab by pressing "Esc".

To run a specific test

In your test, append .only to a describe or it statement:

describe.only('Foo component', () => {
    // ...
})l

Testing your components in Dash

  1. Build development bundle to lib/ and watch for changes

    # Once this is started, you can just leave it running.
    $ npm start
    
  2. Install module locally (after every change)

    # Generate metadata, and build the JavaScript bundle
    $ npm run install-local
    
    # Now you're done. For subsequent changes, if you've got `npm start`
    # running in a separate process, it's enough to just do:
    $ python setup.py install
    
  3. Run the dash layout you want to test

    # Import mydcc to your layout, then run it:
    $ python my_dash_layout.py
    

TODO: There is a workflow that links your module into site-packages which would make it unnecessary to re-run 2. on every change: python setup.py develop. Unfortunately, this doesn't seem to work with resources defined in package_data.

See https://github.com/plotly/dash-components-archetype/issues/20

Installing python package locally

Before publishing to PyPi, you can test installing the module locally:

# Install in `site-packages` on your machine
$ npm run install-local

Uninstalling python package locally

$ npm run uninstall-local

Publishing

For now, multiple steps are necessary for publishing to NPM and PyPi, respectively. TODO: #5 will roll up publishing steps into one workflow.

Ask @chriddyp to get NPM / PyPi package publishing accesss.

  1. Preparing to publish to NPM

    # Bump the package version
    $ npm version major|minor|patch
    
    # Push branch and tags to repo
    $ git push --follow-tags
    
  2. Preparing to publish to PyPi

    # Bump the PyPi package to the same version
    $ vi setup.py
    
    # Commit to github
    $ git add setup.py
    $ git commit -m "Bump pypi package version to vx.x.x"
    
  3. Publish to npm and PyPi

    $ npm run publish-all
    

Builder / Archetype

We use Builder to centrally manage build configuration, dependencies, and scripts.

To see all builder scripts available:

$ builder help

See the dash-components-archetype repo for more information.

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