Interactive plotting for the Jupyter notebook, using d3.js and ipywidgets.
Project description
bqplot
2-D plotting library for Project Jupyter
Introduction
bqplot is a 2-D visualization system for Jupyter, based on the constructs of
the Grammar of Graphics.
Usage
In bqplot, every component of a plot is an interactive widget. This allows the user to integrate visualizations with other Jupyter interactive widgets to create integrated GUIs with a few lines of Python code.
Goals
- Provide a unified framework for 2-D visualizations with a pythonic API
- Provide a sensible API for adding user interactions (panning, zooming, selection, etc)
Two APIs are provided
Object Model, which is inspired by the constructs of the Grammar of Graphics (figure, marks, axes, scales). This API is verbose but is fully customizablepyplot, which is a context-based API similar to Matplotlib's pyplot.pyplotprovides sensible default choices for most parameters
Trying it online
To try out bqplot interactively in your web browser, just click on the binder link:
Dependencies
This package depends on the following packages:
ipywidgets(version >=7.0.0, <8.0)traitlets(version >=4.3.0, <5.0)traittypes(Version >=0.2.1, <0.3)numpypandas
Installation
Using pip:
$ pip install bqplot
Using conda
$ conda install -c conda-forge bqplot
If you are using JupyterLab <=2:
$ jupyter labextension install @jupyter-widgets/jupyterlab-manager bqplot
Development installation
For a development installation (requires JupyterLab (version >= 3) and yarn):
$ git clone https://github.com/bqplot/bqplot.git
$ cd bqplot
$ pip install -e .
$ jupyter nbextension install --py --overwrite --symlink --sys-prefix bqplot
$ jupyter nbextension enable --py --sys-prefix bqplot
Note for developers: the --symlink argument on Linux or OS X allows one to
modify the JavaScript code in-place. This feature is not available
with Windows.
For the experimental JupyterLab extension, install the Python package, make sure the Jupyter widgets extension is installed, and install the bqplot extension:
$ pip install "ipywidgets>=7.6"
$ jupyter labextension develop . --overwrite
Whenever you make a change of the JavaScript code, you will need to rebuild:
cd js
yarn run build
Then refreshing the JupyterLab/Jupyter Notebook is enough to reload the changes.
Running tests
You can install the dependencies necessary to run the tests with:
conda env update -f test-environment.yml
And run it with for Python tests:
pytest
And cd js to run the JS tests with:
yarn run test
Every time you make a change on your tests it's necessary to rebuild the JS side:
yarn run build
Examples
Using the pyplot API
Using the Object Model API
Documentation
Full documentation is available at https://bqplot.readthedocs.io/
Install a previous bqplot version (Only for JupyterLab <= 2)
In order to install a previous bqplot version, you need to know which front-end version (JavaScript) matches with the back-end version (Python).
For example, in order to install bqplot 0.11.9, you need the labextension version 0.4.9.
$ pip install bqplot==0.11.9
$ jupyter labextension install bqplot@0.4.9
Versions lookup table:
back-end (Python) |
front-end (JavaScript) |
|---|---|
| 0.12.14 | 0.5.14 |
| 0.12.13 | 0.5.13 |
| 0.12.12 | 0.5.12 |
| 0.12.11 | 0.5.11 |
| 0.12.10 | 0.5.10 |
| 0.12.9 | 0.5.9 |
| 0.12.8 | 0.5.8 |
| 0.12.7 | 0.5.7 |
| 0.12.6 | 0.5.6 |
| 0.12.4 | 0.5.4 |
| 0.12.3 | 0.5.3 |
| 0.12.2 | 0.5.2 |
| 0.12.1 | 0.5.1 |
| 0.12.0 | 0.5.0 |
| 0.11.9 | 0.4.9 |
| 0.11.8 | 0.4.8 |
| 0.11.7 | 0.4.7 |
| 0.11.6 | 0.4.6 |
| 0.11.5 | 0.4.5 |
| 0.11.4 | 0.4.5 |
| 0.11.3 | 0.4.4 |
| 0.11.2 | 0.4.3 |
| 0.11.1 | 0.4.1 |
| 0.11.0 | 0.4.0 |
Development
See our contributing guidelines to know how to contribute and set up a development environment.
License
This software is licensed under the Apache 2.0 license. See the LICENSE file for details.
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Source Distribution
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