Dendrogram

Polar Ward Clustering Light

Light theme Ward hierarchical clustering in polar format

Output
Polar Ward Clustering Light
Python
import matplotlib.pyplot as plt
import numpy as np
from scipy.cluster.hierarchy import dendrogram, linkage, set_link_color_palette

np.random.seed(321)

n = 24
labels = [chr(65 + i) if i < 26 else f'A{i-26}' for i in range(n)]
data = np.random.rand(n, 6) * 80
Z = linkage(data, method='ward')

fig_temp, ax_temp = plt.subplots()
set_link_color_palette(['#27D3F5', '#F5276C', '#6CF527', '#F5B027', '#5314E6', '#F527B0'])
dn = dendrogram(Z, labels=labels, no_plot=False, color_threshold=0.55*max(Z[:,2]),
                above_threshold_color='#9ca3af', ax=ax_temp)
plt.close(fig_temp)

icoord = np.array(dn['icoord'])
dcoord = np.array(dn['dcoord'])
colors = dn['color_list']

x_min, x_max = icoord.min(), icoord.max()
y_max = dcoord.max()

def to_polar(x, y):
    theta = (x - x_min) / (x_max - x_min) * 2 * np.pi * 0.95
    r = y / y_max * 0.55 + 0.4
    return theta, r

fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={'projection': 'polar'}, facecolor='#ffffff')
ax.set_facecolor('#ffffff')

# Concentric rings
for r, alpha in [(0.5, 0.08), (0.7, 0.06), (0.9, 0.04)]:
    circle_theta = np.linspace(0, 2*np.pi, 100)
    ax.fill(circle_theta, [r]*100, color='#27D3F5', alpha=alpha)

for ic, dc, color in zip(icoord, dcoord, colors):
    thetas, rs = [], []
    for x, y in zip(ic, dc):
        t, r = to_polar(x, y)
        thetas.append(t)
        rs.append(r)
    
    ax.plot([thetas[0], thetas[1]], [rs[0], rs[1]], color=color, linewidth=2.5, alpha=0.95)
    ax.plot([thetas[2], thetas[3]], [rs[2], rs[3]], color=color, linewidth=2.5, alpha=0.95)
    if thetas[1] != thetas[2]:
        arc_thetas = np.linspace(min(thetas[1], thetas[2]), max(thetas[1], thetas[2]), 50)
        ax.plot(arc_thetas, [rs[1]]*len(arc_thetas), color=color, linewidth=2.5, alpha=0.95)

leaf_positions = np.linspace(x_min, x_max, n)
for i, (pos, label) in enumerate(zip(leaf_positions, dn['ivl'])):
    theta, _ = to_polar(pos, 0)
    rotation = np.degrees(theta) - 90 if np.pi/2 < theta < 3*np.pi/2 else np.degrees(theta) + 90
    ha = 'right' if np.pi/2 < theta < 3*np.pi/2 else 'left'
    ax.text(theta, 0.28, label, ha=ha, va='center', fontsize=7, color='#374151',
            rotation=rotation, rotation_mode='anchor', fontweight='bold')
    
    color = ['#27D3F5', '#F5276C', '#6CF527', '#F5B027', '#5314E6', '#F527B0'][i % 6]
    ax.scatter(theta, 0.4, c=color, s=35, zorder=5, edgecolor='#ffffff', linewidth=0.8)

ax.set_ylim(0, 1.02)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.spines['polar'].set_visible(False)
ax.grid(False)

ax.set_title('Ward Clustering - Polar View', fontsize=14, color='#1f2937', fontweight='bold', y=1.08)

plt.tight_layout()
plt.show()
Library

Matplotlib

Category

Statistical

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