Wind Speed vs Power Output

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

np.random.seed(42)

# Wind energy data
wind_speed = np.clip(np.random.weibull(2, 5000) * 10, 0, 30)  # m/s
# Power curve: cubic up to rated, then flat
power = np.minimum(wind_speed**3 * 0.5, 1000) + np.random.normal(0, 50, 5000)
power = np.clip(power, 0, 1200)

fig, ax = plt.subplots(figsize=(10, 8), facecolor='#020B14')
ax.set_facecolor('#020B14')

# Custom colormap: lime to cyan
colors = ['#020B14', '#0d2d1a', '#6CF527', '#27D3F5']
cmap = LinearSegmentedColormap.from_list('wind', colors, N=256)

h = ax.hist2d(wind_speed, power, bins=45, cmap=cmap, cmin=1)
cbar = plt.colorbar(h[3], ax=ax, pad=0.02)
cbar.set_label('Readings', color='white', fontsize=11)
cbar.ax.yaxis.set_tick_params(color='white')
plt.setp(plt.getp(cbar.ax.axes, 'yticklabels'), color='white')

ax.set_xlabel('Wind Speed (m/s)', fontsize=11, color='white', fontweight='500')
ax.set_ylabel('Power Output (kW)', fontsize=11, color='white', fontweight='500')
ax.set_title('Wind Speed vs Power Output', fontsize=14, color='white', fontweight='bold', pad=15)

ax.tick_params(colors='white', labelsize=9)
for spine in ax.spines.values():
    spine.set_color('#333333')

plt.tight_layout()
plt.show()