interplot

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

>>> 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

>>> @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