https://github.com/janjoch/interplot

Create matplotlib and plotly charts with the same few lines of code.
https://github.com/janjoch/interplot

jupyter jupyter-notebook matplotlib-pyplot plotly

Last synced: about 1 month ago
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Create matplotlib and plotly charts with the same few lines of code.

• Host: GitHub
• URL: https://github.com/janjoch/interplot
• Owner: janjoch
• License: gpl-3.0
• Created: 2023-12-30T12:25:42.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-07-12T13:39:58.000Z (4 months ago)
• Last Synced: 2024-09-30T10:39:31.359Z (about 2 months ago)
• Topics: jupyter, jupyter-notebook, matplotlib-pyplot, plotly
• Language: Python
• Homepage: https://interplot.janjo.ch/
• Size: 112 MB
• Stars: 5
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# interplot

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/janjoch/interplot/HEAD) [![NBViewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/janjoch/interplot/tree/main/demo/)

Create `matplotlib` and `plotly` charts with the same few lines of code.

It combines the best of the `matplotlib` and the `plotly` worlds through a unified, flat API.

Switch between `matplotlib` and `plotly` with the single keyword `interactive`. All the necessary boilerplate code to translate between the packages is contained in this module.

Currently supported building blocks:

- scatter plots

    - `line`

    - `scatter`

    - `linescatter`

- bar charts `bar`

- 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

        ```python

        >>> interplot.line([0,4,6,7], [1,2,4,8])

        [plotly line figure]

        >>> interplot.hist(np.random.normal(40, 8, 1000), interactive=False)

        [matplotlib hist figure]

        >>> interplot.boxplot(

        >>>     [

        >>>         np.random.normal(20, 5, 1000),

        >>>         np.random.normal(40, 8, 1000),

        >>>         np.random.normal(60, 5, 1000),

        >>>     ],

        >>> )

        [plotly boxplots]

        ```

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

        ```python

        >>> @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

        ```python

        >>> 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

- **Demo Notebooks:** https://nbviewer.org/github/janjoch/interplot/tree/main/demo/

- **Source Code:** https://github.com/janjoch/interplot

- **PyPI:** https://pypi.org/project/interplot/

## Licence

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)

## Demo

View on `NBViewer`:

[![NBViewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/janjoch/interplot/tree/main/demo/)

Try on `Binder`:

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/janjoch/interplot/HEAD)

## Install

```pip install interplot```

### dev installation

1. ```git clone https://github.com/janjoch/interplot```

2. ```cd interplot```

2. ```pip install -e .```

## Contribute

Ideas, bug reports/fixes, feature requests and code submissions are very welcome! Please write to [[email protected]](mailto:[email protected]) or directly into a pull request.