Temperature Weather Calendar

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

np.random.seed(999)

days = 365
# Simulate seasonal temperature pattern (Northern Hemisphere)
base_temp = 15 + 15 * np.sin(2 * np.pi * (np.arange(days) - 80) / 365)
temp = base_temp + np.random.normal(0, 5, days)
temp = np.clip(temp, -10, 40)

temp_levels = np.digitize(temp, bins=[-20, 0, 10, 18, 25, 32]) - 1

weeks = 53
data = np.zeros((7, weeks))
for i, val in enumerate(temp_levels):
    week = i // 7
    day = i % 7
    if week < weeks:
        data[day, week] = val

# Cold to hot gradient
colors = ['#1e3a8a', '#3b82f6', '#22d3ee', '#fbbf24', '#f97316', '#dc2626']
cmap = LinearSegmentedColormap.from_list('temp', colors, N=256)

fig, ax = plt.subplots(figsize=(16, 4), facecolor='#0a0a0f')
ax.set_facecolor('#0a0a0f')

im = ax.imshow(data, cmap=cmap, aspect='auto', vmin=0, vmax=5)

ax.set_yticks(range(7))
ax.set_yticklabels(['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'], fontsize=9, color='#e2e8f0')
ax.set_xticks(range(0, 52, 4))
ax.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec', ''], 
                   fontsize=9, color='#e2e8f0')

ax.set_title('Daily Temperature - Weather Patterns', fontsize=16, color='white', fontweight='bold', pad=15)

for i in range(8):
    ax.axhline(y=i-0.5, color='#1e293b', linewidth=0.5)
for i in range(weeks+1):
    ax.axvline(x=i-0.5, color='#1e293b', linewidth=0.5)

ax.tick_params(colors='#e2e8f0', length=0)
for spine in ax.spines.values():
    spine.set_visible(False)

legend_elements = [mpatches.Patch(facecolor=c, label=l, edgecolor='#334155') 
                   for c, l in zip(colors, ['<0C', '0-10C', '10-18C', '18-25C', '25-32C', '>32C'])]
ax.legend(handles=legend_elements, loc='upper left', bbox_to_anchor=(0, -0.15), ncol=6, 
          fontsize=8, facecolor='#1e293b', edgecolor='#334155', labelcolor='white')

avg = np.mean(temp)
max_t = np.max(temp)
min_t = np.min(temp)
ax.annotate(f'Avg: {avg:.1f}C | Range: {min_t:.0f}C to {max_t:.0f}C', xy=(0.98, 1.1), xycoords='axes fraction',
            fontsize=11, color='#fbbf24', ha='right', fontweight='bold')

plt.tight_layout()
plt.subplots_adjust(bottom=0.25)
plt.show()