Automotive Performance Matrix

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

np.random.seed(42)

# Simulated automotive data
cars = ['Model_' + chr(65+i) for i in range(20)]
metrics = ['MPG', 'HP', 'Weight', 'Accel', 'Disp', 'Torque', 'Price', 'Range']

data = np.random.rand(20, 8)
# Add structure
data[:7, [1, 4, 5]] += 0.5  # High performance cars
data[7:14, [0, 7]] += 0.5   # Efficient cars
data[14:, [2, 6]] += 0.5    # Heavy/expensive cars

df = pd.DataFrame(data, index=cars, columns=metrics)

# Cylinder groups
cylinders = [8]*7 + [4]*7 + [6]*6
cyl_palette = {4: '#6CF527', 6: '#F5B027', 8: '#F5276C'}
row_colors = pd.Series(cylinders, index=cars).map(cyl_palette)

# NEON blue-pink colormap
neon_cmap = LinearSegmentedColormap.from_list('neon_bp', ['#0a0a0f', '#4927F5', '#F5276C', '#F5B027'])

g = sns.clustermap(df, cmap=neon_cmap, row_colors=row_colors,
                   standard_scale=1, method='average',
                   linewidths=0.3, linecolor='#1a1a2e',
                   figsize=(8, 7), dendrogram_ratio=(0.12, 0.12),
                   cbar_pos=(0.01, 0.08, 0.008, 0.12),
                   tree_kws={'linewidths': 1.5, 'colors': '#27D3F5'})

g.fig.patch.set_facecolor('#0a0a0f')
g.ax_heatmap.set_facecolor('#0a0a0f')
g.ax_heatmap.tick_params(colors='white', labelsize=8)

for ax in [g.ax_row_dendrogram, g.ax_col_dendrogram]:
    ax.set_facecolor('#0a0a0f')

g.fig.suptitle('Automotive Performance Clustering', color='white', fontsize=14, fontweight='bold', y=1.02)


plt.show()