Risk Assessment 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=(10, 9), facecolor='#ffffff')
ax.set_facecolor('#ffffff')

impact = ['Negligible', 'Minor', 'Moderate', 'Major', 'Severe']
likelihood = ['Rare', 'Unlikely', 'Possible', 'Likely', 'Certain']
data = np.array([[1, 2, 3, 4, 5], [2, 4, 6, 8, 10], [3, 6, 9, 12, 15], [4, 8, 12, 16, 20], [5, 10, 15, 20, 25]])

colors = ['#22c55e', '#84cc16', '#eab308', '#f97316', '#dc2626']
cmap = LinearSegmentedColormap.from_list('risk', colors, N=256)

cell_w, cell_h = 0.88, 0.88
for i in range(5):
    for j in range(5):
        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=0.12",
                               facecolor=cmap((val-1)/24), edgecolor='#e2e8f0', linewidth=1.5)
        ax.add_patch(rect)
        ax.text(j, i, str(val), ha='center', va='center', color='#1e293b', fontsize=12, fontweight='bold')

ax.set_xlim(-0.5, 4.5)
ax.set_ylim(-0.5, 4.5)
ax.set_aspect('equal')
ax.invert_yaxis()
ax.set_xticks(range(5))
ax.set_yticks(range(5))
ax.set_xticklabels(likelihood, rotation=45, ha='right', color='#64748b', fontsize=9, fontweight='500')
ax.set_yticklabels(impact, color='#1e293b', fontsize=10, fontweight='500')

sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(1, 25))
cbar = plt.colorbar(sm, ax=ax, shrink=0.8, pad=0.02)
cbar.set_label('Risk 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_xlabel('Likelihood', fontsize=12, color='#64748b', labelpad=10)
ax.set_ylabel('Impact', fontsize=12, color='#64748b', labelpad=10)
ax.set_title('Risk Assessment Matrix', fontsize=18, color='#1e293b', fontweight='bold', pad=20)
plt.tight_layout()
plt.show()