Genome Expression Heatmap

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.patches import FancyBboxPatch

fig, ax = plt.subplots(figsize=(12, 10), facecolor='#ffffff')
ax.set_facecolor('#ffffff')

np.random.seed(42)
genes = [f'Gene_{i+1}' for i in range(15)]
samples = [f'S{i+1}' for i in range(8)]
data = np.random.randn(len(genes), len(samples))

colors = ['#3b82f6', '#1e3a8a', '#1e293b', '#065f46', '#22c55e']
cmap = LinearSegmentedColormap.from_list('gene', colors, N=256)

cell_w, cell_h = 0.88, 0.6
for i in range(len(genes)):
    for j in range(len(samples)):
        val = data[i, j]
        rect = FancyBboxPatch((j - cell_w/2, i - cell_h/2), cell_w, cell_h,
                               boxstyle="round,pad=0.02,rounding_size=0.08",
                               facecolor=cmap((val+3)/6), edgecolor='#e2e8f0', linewidth=1)
        ax.add_patch(rect)

ax.set_xlim(-0.5, len(samples)-0.5)
ax.set_ylim(-0.5, len(genes)-0.5)
ax.set_aspect('equal')
ax.invert_yaxis()
ax.set_xticks(range(len(samples)))
ax.set_yticks(range(len(genes)))
ax.set_xticklabels(samples, color='#64748b', fontsize=10, fontweight='500')
ax.set_yticklabels(genes, color='#1e293b', fontsize=9, fontweight='500')

sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(-3, 3))
cbar = plt.colorbar(sm, ax=ax, shrink=0.8, pad=0.02)
cbar.set_label('Expression (z-score)', color='#1e293b', fontsize=11)
cbar.outline.set_edgecolor('#e2e8f0')
plt.setp(plt.getp(cbar.ax.axes, 'yticklabels'), color='#64748b')

for spine in ax.spines.values(): spine.set_visible(False)
ax.tick_params(length=0)
ax.set_title('Gene Expression Heatmap', fontsize=18, color='#1e293b', fontweight='bold', pad=20)
plt.tight_layout()
plt.show()