Soft Glow Clusters Light

import matplotlib.pyplot as plt
import numpy as np
from scipy.cluster.hierarchy import dendrogram, linkage, fcluster, set_link_color_palette
from matplotlib.patches import FancyBboxPatch

np.random.seed(789)

labels = ['Node_' + str(i) for i in range(1, 13)]
data = np.random.rand(len(labels), 5) * 60
Z = linkage(data, method='complete')

fig, ax = plt.subplots(figsize=(14, 8), facecolor='#ffffff')
ax.set_facecolor('#ffffff')

set_link_color_palette(['#F5276C', '#27D3F5', '#6CF527', '#F5B027'])

dn = dendrogram(Z, labels=labels, leaf_rotation=0, leaf_font_size=11,
                color_threshold=0.5*max(Z[:,2]), above_threshold_color='#9ca3af', ax=ax)

# Soft cluster boxes
clusters = fcluster(Z, t=4, criterion='maxclust')
glow_colors = [('#F5276C', '#F5276C15'), ('#27D3F5', '#27D3F515'), 
               ('#6CF527', '#6CF52715'), ('#F5B027', '#F5B02715')]
ivl = dn['ivl']
leaves = dn['leaves']

cluster_ranges = {}
for i, leaf in enumerate(leaves):
    c = clusters[leaf]
    if c not in cluster_ranges:
        cluster_ranges[c] = [i, i]
    else:
        cluster_ranges[c][1] = i

for c, (start, end) in cluster_ranges.items():
    edge_color, fill_color = glow_colors[(c-1) % len(glow_colors)]
    rect = FancyBboxPatch((start*10 - 5, 0), (end - start + 1)*10, max(Z[:,2])*0.9,
                          boxstyle='round,pad=0.02', facecolor=fill_color, 
                          edgecolor=edge_color, linewidth=1.5, zorder=0)
    ax.add_patch(rect)

ax.set_title('Network Clustering Analysis', fontsize=15, 
             color='#1f2937', fontweight='bold', pad=20)
ax.set_xlabel('Nodes', fontsize=11, color='#374151')
ax.set_ylabel('Distance', fontsize=11, color='#374151')
ax.tick_params(axis='both', colors='#374151', labelsize=10)
for spine in ax.spines.values():
    spine.set_visible(False)

plt.tight_layout()
plt.show()