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A Dash Graph component modified to support use of figure.data-structured input to extend and/or add traces.

Project description

dash-extendable-graph

dash-extendable-graph is a Dash component library containing a single component: ExtendableGraph. The component was forked from Plotly's core Graph component (dash-core-components). dash-extendable-graph has modified extendData and prependData properties that accept trace data matching the format for figure["data"]. These properties support (1) adding new traces and (2) allow multiple trace types to be extended/prepended within a single callback (not supported by the core component)

Note: As of version 1.1.0, dash-extendable-graph includes a minimized plotly.js as an internal dependency. Previously, the component assumed it would be used in conjunction with dash-core-components.

PyPI PyPI - Python Version PyPI - License

Installation

Get started with:

  1. Install Dash and dependencies: https://dash.plot.ly/installation
$ pip install -r requirements.txt
  1. Install dash-extendable-graph
$ pip install dash-extendable-graph
  1. Run python usage.py
  2. Visit http://localhost:8050 in your web browser

Usage

General examples may be found in usage.py

extendData

  1. updateData [list]: a list of dictionaries, each dictionary representing trace data in a format matching figure['data'] (e.g dict(x=[1], y=[1]))
  2. traceIndices [list, optional]: identify the traces that should be extended. If the specified trace index does not exist, a (new) corresponding trace shall be appended to the figure.
  3. maxPoints [number, optional]: define the maximum number of points to plot in the figure (per trace).

Based on the Plotly.extendTraces() api. However, the updateData key has been modified to better match the contents of Plotly.plot() (e.g. Graph.figure). Aside from following dash-familiar styling, this component allows the user to extend traces of different types in a single call (Plotly.extendTraces() takes a map of key:val and assumes all traces will share the same data keys).

prependData

  1. updateData [list]: a list of dictionaries, each dictionary representing trace data in a format matching figure['data'] (e.g dict(x=[1], y=[1]))
  2. traceIndices [list, optional]: identify the traces that should be extended. If the specified trace index does not exist, a (new) corresponding trace shall be appended to the figure.
  3. maxPoints [number, optional]: define the maximum number of points to plot in the figure (per trace).

Based on the Plotly.prependTraces() api. However, the updateData key has been modified to better match the contents of Plotly.plot() (e.g. Graph.figure). Aside from following dash-familiar styling, this component allows the user to prepend traces of different types in a single call (Plotly.prependTraces() takes a map of key:val and assumes all traces will share the same data keys).

Code

Extend a trace once per second, limited to 100 maximum points.

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',
        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])], [0], 100


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

Contributing

See CONTRIBUTING.md

Local Installation

  1. Dependencies
$ npm install
$ virtualenv venv
$ . venv/bin/activate
$ pip install -r requirements.txt

For developers:

$ pip install -r tests/requirements.txt
  1. Build
$ npm run build
  1. Check out the component via component-playground
$ npm run start
The demo app is in `src/demo`
  1. Check out the sample Dash application using the component
$ python setup.py install
$ python usage.py

Tests

lgtm

Run locally

Run linting + integration tests in one command:

$ npm run test

Or run tests individually:

Code style

Uses flake8, eslint, and prettier. Check package.json, .eslintrc, .eslintignore for configuration settings.

$ npm run lint

Also you can apply formatting settings.

$ npm run format

Integration Tests

Integration tests for the component can be found in tests/

$ pytest

Selenium test runner configuration options are located in pytest.ini (e.g. --webdriver, --headless). See dash[testing] documentation for more information on built-ins provided by the dash test fixture.

Run individual integration tests based on the filename.

$ pytest tests/test_extend_maxpoints.py

Continuous Integration

CI

This repository uses a github action to automate integration testing. Linting and Tests are triggered for each pull request created in the master branch.

Package Publishing

Publish

This repository uses a github action to automate package deployment (in this case, compiling a source archive and binary wheel using setuptools). Publishing is triggered on each published release.

Project details


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