Calendar Heatmap

Screen Time Calendar

Daily screen time tracking with usage pattern analysis.

Output
Screen Time Calendar
Python
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.patches as mpatches

np.random.seed(258)

# Generate screen time (hours, 0-12)
days = 365
screen = np.random.normal(5, 2, days)
screen = np.clip(screen, 0.5, 12)

screen_levels = np.digitize(screen, bins=[0, 2, 4, 6, 8, 10]) - 1

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

# Red gradient (more = worse)
colors = ['#ffffff', '#fef2f2', '#fecaca', '#f87171', '#dc2626', '#7f1d1d']
cmap = LinearSegmentedColormap.from_list('screen', 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=5)

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 Screen Time - Digital 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, ['<2h', '2-4h', '4-6h', '6-8h', '8-10h', '10h+'])]
ax.legend(handles=legend_elements, loc='upper left', bbox_to_anchor=(0, -0.15), ncol=6, 
          fontsize=8, facecolor='white', edgecolor='#d1d5db', labelcolor='#374151')

low_days = int(np.sum(screen <= 4))
avg = np.mean(screen)
ax.annotate(f'Low usage days: {low_days} | Avg: {avg:.1f}h/day', xy=(0.98, 1.1), xycoords='axes fraction',
            fontsize=10, color='#dc2626', ha='right', fontweight='bold')

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

Matplotlib

Category

Time Series

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