Climate Anomaly 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)

# Years and months
years = list(range(2000, 2025))
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 
          'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']

# Temperature anomalies with warming trend
anomalies = np.random.randn(len(years), 12) * 0.5
trend = np.linspace(-0.3, 0.8, len(years))
anomalies += trend[:, np.newaxis]

df = pd.DataFrame(anomalies, index=years, columns=months)

# Diverging colormap (blue cold, red warm)
neon_temp = LinearSegmentedColormap.from_list('neon_temp', ['#27D3F5', '#0a0a0f', '#F5276C'])

fig, ax = plt.subplots(figsize=(9, 6), facecolor='#0a0a0f')
ax.set_facecolor('#0a0a0f')

sns.heatmap(df, cmap=neon_temp, center=0, linewidths=0.5, linecolor='#1a1a2e',
            cbar_kws={'shrink': 0.7, 'label': 'Anomaly (°C)'}, ax=ax)

ax.set_title('Global Temperature Anomalies (2000-2024)', color='white', fontsize=14, fontweight='bold', pad=15)
ax.set_xlabel('Month', color='white', fontsize=11)
ax.set_ylabel('Year', color='white', fontsize=11)
ax.tick_params(colors='#888888', labelsize=9)

# Colorbar styling
cbar = ax.collections[0].colorbar
cbar.ax.yaxis.set_tick_params(color='white')
cbar.ax.yaxis.label.set_color('white')
plt.setp(plt.getp(cbar.ax.axes, 'yticklabels'), color='white')

plt.tight_layout()
plt.show()