The mne package

It is possible to build chord diagrams from a connectivity matrix thanks to the neuroscience library MNE. It comes with a visual function called plot_connectivity_circle() that is pretty handy to get good-looking chord diagrams in minutes!

Let's load the library and see what it can make!

from mne.viz import plot_connectivity_circle

# only for the exemple
import numpy as np

Most basic chord diagram with mne

Let's start with a basic examples. 20 nodes that are randomly connected. Two objects are created:

  • node_names that is a list of 20 node names
  • con that is an object containing some random links between nodes.

Both object are passed to the plot_connectivity_circle() function that automatically builds the chord diagram.

N = 20  # Number of nodes
node_names = [f"N{i}" for i in range(N)]  # List of labels [N]

# Random connectivity
ran = np.random.rand(N,N)
con = np.where(ran > 0.9, ran, np.nan)  # NaN so it doesn't display the weak links
fig, axes = plot_connectivity_circle(con, node_names) 

Split the chord

It is possible to split the chord diagram in several parts. It can be handy to build chord diagrams where nodes are split in 2 groups, like origin and destination for instance.

start, end = 45, 135
first_half = (np.linspace(start, end, len(node_names)//2) +90).astype(int)[::+1] %360
second_half = (np.linspace(start, end, len(node_names)//2) -90).astype(int)[::-1] %360
node_angles = np.array(list(first_half) + list(second_half))
fig, axes = plot_connectivity_circle(con, node_names, 

Style: node customization

Pretty much all parts of the chord diagram can be customized. Let's start by changing the node width (with node_width) and filtering the links that are shown (with vmin and vmax)

fig, axes = plot_connectivity_circle(con, node_names, 
    node_width=2, vmin=0.97, vmax=0.98)

Now let's customize the nodes a bit more:

  • node_colors for the fill color
  • node_edgecolor for the edges
  • node_linewidth for the width
node_edgecolor = N//2 * [(0,0,0,0.)] + N//2 * ['green']
node_colors = N//2 * ['crimson'] + N//2 * [(0,0,0,0.)]
fig, axes = plot_connectivity_circle(con, node_names,
    node_colors=node_colors, node_edgecolor=node_edgecolor, node_linewidth=2)

Style: labels and links

Now some customization for labels, links and background:

  • colormap
  • facecolor
  • textcolor
  • colorbar
  • linewidth
fig, axes = plot_connectivity_circle(con, node_names,
    colormap='Blues', facecolor='white', textcolor='black', colorbar=False,


Let's get some fun and build a data art brocoli like chord diagram 😊 !

N = 200
node_names = N * ['']
ran = np.random.rand(N,N)
con = np.where(ran > 0.95, ran, np.nan)
first_half = (np.linspace(0, 180, len(node_names)//2)).astype(int)[::+1] %360
second_half = (np.linspace(70, 110, len(node_names)//2)-180).astype(int)[::-1] %360
node_angles = np.array(list(first_half) + list(second_half))
node_colors = node_edgecolor = N * ['green']
fig, axes = plot_connectivity_circle(con, node_names,
    colormap='Greens', facecolor='w', textcolor='k', colorbar=False,
    node_colors=node_colors, node_edgecolor=node_edgecolor,
    node_width=0.1, node_linewidth=1, linewidth=1)

Contact & Edit

👋 This document is a work by Yan Holtz. You can contribute on github, send me a feedback on twitter or subscribe to the newsletter to know when new examples are published! 🔥

This page is just a jupyter notebook, you can edit it here. Please help me making this website better 🙏!