10. Violin plot with linear colormap

This script demonstrates a clever technique for creating a violin plot where the fill color is a continuous, linear colormap mapped to the y-axis values. 🎻

Instead of using a simple solid fill, it uses the paths of the violin bodies as clipping masks. An imshow gradient is then displayed underneath, making it visible only within the violin shapes. A corresponding colorbar is added to complete the visualization.

Out:

C:\Users\kelda\Desktop\repositories\github\python-spare-code\main\examples\matplotlib\plot_main10_violin_cmap.py:52: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.

C:\Users\kelda\Desktop\repositories\github\python-spare-code\main\examples\matplotlib\plot_main10_violin_cmap.py:56: UserWarning:

FigureCanvasAgg is non-interactive, and thus cannot be shown

# ``mpl.violin``

import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.path import Path
from matplotlib.patches import PathPatch
from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable


x = [np.random.normal(loc=i, scale=1, size=(100,)) for i in range(5)]

fig, ax = plt.subplots()
violins = ax.violinplot(x)

ymin, ymax = ax.get_ylim()
xmin, xmax = ax.get_xlim()

# create a numpy image to use as a gradient
Nx,Ny=1,1000
imgArr = np.tile(np.linspace(0,1,Ny), (Nx,1)).T
cmap = 'hsv'

for violin in violins['bodies']:
    path = Path(violin.get_paths()[0].vertices)
    patch = PathPatch(path, facecolor='none', edgecolor='none')
    ax.add_patch(patch)
    img = ax.imshow(imgArr, origin="lower", extent=[xmin,xmax,ymin,ymax], aspect="auto",
                    cmap=cmap,
                    clip_path=patch)

# colorbar
ax_divider = make_axes_locatable(ax)
cax = ax_divider.append_axes("right", size="5%", pad="2%")
norm = matplotlib.colors.Normalize(vmin=ymin, vmax=ymax)
cb = matplotlib.colorbar.ColorbarBase(cax, cmap=matplotlib.cm.get_cmap(cmap),
                                norm=norm,
                                orientation='vertical')

plt.show()