Mood Tracker Calendar

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

np.random.seed(555)

days = 365
mood = np.random.choice([1, 2, 3, 4, 5], size=days, p=[0.05, 0.15, 0.35, 0.30, 0.15])

weeks = 53
data = np.zeros((7, weeks))
for i, val in enumerate(mood):
    week = i // 7
    day = i % 7
    if week < weeks:
        data[day, week] = val - 1  # 0-4 scale

# Mood gradient: red -> yellow -> green
colors = ['#fecaca', '#fde68a', '#fef08a', '#bef264', '#86efac']
cmap = LinearSegmentedColormap.from_list('mood', colors, N=256)

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

im = ax.imshow(data, cmap=cmap, aspect='auto', vmin=0, vmax=4)

ax.set_yticks(range(7))
ax.set_yticklabels(['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'], fontsize=9, color='#374151')
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='#374151')

ax.set_title('Daily Mood Tracker - Mental Wellness', fontsize=16, color='#111827', fontweight='bold', pad=15)

for i in range(8):
    ax.axhline(y=i-0.5, color='#e5e7eb', linewidth=0.5)
for i in range(weeks+1):
    ax.axvline(x=i-0.5, color='#e5e7eb', linewidth=0.5)

ax.tick_params(colors='#374151', length=0)
for spine in ax.spines.values():
    spine.set_visible(False)

legend_elements = [mpatches.Patch(facecolor=c, label=l, edgecolor='#d1d5db') 
                   for c, l in zip(colors, ['Very Low', 'Low', 'Neutral', 'Good', 'Great'])]
ax.legend(handles=legend_elements, loc='upper left', bbox_to_anchor=(0, -0.15), ncol=5, 
          fontsize=8, facecolor='white', edgecolor='#d1d5db', labelcolor='#374151')

avg = np.mean(mood)
good_days = int(np.sum(mood >= 4))
ax.annotate(f'Avg Mood: {avg:.1f}/5 | {good_days} great days', xy=(0.98, 1.1), xycoords='axes fraction',
            fontsize=10, color='#16a34a', ha='right', fontweight='bold')

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