interplot 0.1.1

Create matplotlib/plotly hybrid plots with a few lines of code.

interplot

Create matplotlib/plotly hybrid plots with a few lines of code.

It combines the best of the matplotlib and the plotly worlds through a unified, flat API. All the necessary boilerplate code is contained in this module.

Currently supported building blocks:

• scatter plots
  • line
  • scatter
  • linescatter
• histogram hist
• boxplot boxplot
• heatmap heatmap
• linear regression regression
• line fill fill
• annotations text

Supported

• 2D subplots
• automatic color cycling
• 3 different API modes

  • One line of code

    >>> interplot.line([0,4,6,7], [1,2,4,8])
    [plotly line figure]
    
    >>> interplot.hist(np.random.normal(40, 8, 1000), interactive=False)
    [plotly hist figure]
    
    >>> interplot.boxplot(
    >>>     [
    >>>         np.random.normal(20, 5, 1000),
    >>>         np.random.normal(40, 8, 1000),
    >>>         np.random.normal(60, 5, 1000),
    >>>     ],
    >>> )
    [matplotlib boxplots]
    

  • Decorator to auto-initialize plots to use in your methods

    >>> @interplot.magic_plot
    >>> def plot_my_data(fig=None):
    >>>     # import and process your data...
    >>>     data = np.random.normal(2, 3, 1000)
    >>>     # draw with the fig instance obtained from the decorator function
    >>>     fig.add_line(data, label="my data")
    >>>     fig.add_fill((0, 999), (-1, -1), (5, 5), label="sigma")
    
    >>> plot_my_data(title="My Recording")
    [plotly figure "My Recording"]
    
    >>> @interplot.magic_plot_preset(interactive=False, title="Preset Title")
    >>> def plot_my_data_preconfigured(fig=None):
    >>>     # import and process your data...
    >>>     data = np.random.normal(2, 3, 1000)
    >>>     # draw with the fig instance obtained from the decorator function
    >>>     fig.add_line(data, label="my data")
    >>>     fig.add_fill((0, 999), (-1, -1), (5, 5), label="sigma")
    
    >>> plot_my_data_preconfigured()
    [matplotlib figure "Preset Title"]
    

  • The interplot.Plot class for full control

    >>> fig = interplot.Plot(
    >>>     interactive=True,
    >>>     title="Everything Under Control",
    >>>     fig_size=(800, 500),
    >>>     rows=1,
    >>>     cols=2,
    >>>     shared_yaxes=True,
    >>>     # ...
    >>> )
    >>> fig.add_hist(np.random.normal(1, 0.5, 1000), row=0, col=0)
    >>> fig.add_boxplot(
    >>>     [
    >>>         np.random.normal(20, 5, 1000),
    >>>         np.random.normal(40, 8, 1000),
    >>>         np.random.normal(60, 5, 1000),
    >>>     ],
    >>>     row=0,
    >>>     col=1,
    >>> )
    >>> # ...
    >>> fig.post_process()
    >>> fig.show()
    [plotly figure "Everything Under Control"]
    
    >>> fig.save("export/path/file.html")
    saved figure at export/path/file.html
    

Resources

• Documentation: https://interplot.janjo.ch
• Source Code: https://github.com/janjoch/interplot
• PyPI: https://pypi.org/project/interplot/

Licence

Demo

View on NBViewer:

Try on Binder:

Install

pip install interplot

dev installation

1. git clone https://github.com/janjoch/interplot
2. cd interplot
3. pip install -e .