Rosenbrock Optimization Surface

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

x = np.linspace(-2, 2, 200)
y = np.linspace(-1, 3, 200)
X, Y = np.meshgrid(x, y)
Z = (1 - X)**2 + 100 * (Y - X**2)**2
Z = np.log1p(Z)

fig, ax = plt.subplots(figsize=(10, 8), facecolor='#ffffff')
ax.set_facecolor('#ffffff')

# Custom colormap: blue to purple
colors = ['#dbeafe', '#3b82f6', '#4927F5', '#5314E6']
cmap = LinearSegmentedColormap.from_list('blue_purple', colors, N=256)

cs = ax.contourf(X, Y, Z, levels=30, cmap=cmap)
ax.contour(X, Y, Z, levels=15, colors='#1e3a5f', linewidths=0.5, alpha=0.6)
cbar = plt.colorbar(cs, ax=ax, pad=0.02)
cbar.set_label('log(f(x,y))', color='#374151', fontsize=11)
cbar.ax.yaxis.set_tick_params(color='#374151')
plt.setp(plt.getp(cbar.ax.axes, 'yticklabels'), color='#374151')

ax.set_xlabel('X', fontsize=11, color='#374151', fontweight='500')
ax.set_ylabel('Y', fontsize=11, color='#374151', fontweight='500')
ax.set_title('Rosenbrock Optimization Surface', fontsize=14, color='#1f2937', fontweight='bold', pad=15)

ax.tick_params(colors='#6b7280', labelsize=9)
for spine in ax.spines.values():
    spine.set_color('#d1d5db')

plt.tight_layout()
plt.show()