Calendar Heatmap
Screen Time Calendar
Daily screen time tracking with usage pattern analysis.
Output
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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