mpl_interactions: Easy interactive Matplotlib plots¶

mpl_interactions’ aims to make it as easy as possible to create responsive Matplotlib plots. In particular, you can:

• Better understand a function’s change with respect to a parameter.

To achieve this, mpl_interactions provides:

• A way to control the output of pyplot functions (e.g. plot and hist) with sliders

• A function to compare horizontal and vertical slices of heatmaps.

• A function allowing zooming using the scroll wheel.

Installation¶

To install, simply run: pip install mpl_interactions

To also install version of ipympl and ipywidgets that are known to work install the optional jupyter dependencies by running pip install mpl_interactions[jupyter]

Further instructions for installation from JupyterLab can be found on the Installation page.

Basic example¶

To control a plot with a slider:

# if running this code in a Jupter notbeook or JupyterLab
%matplotlib ipympl

import mpl_interactions.ipyplot as iplt
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, np.pi, 100)
tau = np.linspace(0.5, 10, 100)

def f1(x, tau, beta):
return np.sin(x * tau) * x * beta
def f2(x, tau, beta):
return np.sin(x * beta) * x * tau

fig, ax = plt.subplots()
controls = iplt.plot(x, f1, tau=tau, beta=(1, 10, 100), label="f1")
iplt.plot(x, f2, controls=controls, label="f2")
_ = plt.legend()
plt.show()


If you are in a Jupyter Notebook the output will look like this:

and from a script or ipython the output will use Matplotlib sliders:

Matplotlib backends¶

mpl_interactions’ functions will work in any Matplotlib backend. In most backends they will use the Matplotlib Slider and Radio button widgets. However, if you are working in a Jupyter notebook the ipympl backend then ipywidgets sliders will be used as the controls. Further discussion of the behavior as a function of backend can be found on the Matplotlib Backends page.

Follow the links below for further information on installation, functions, and plot examples.