Calendar Heatmap
Social Calls Calendar
Daily social calls tracked in neon pink gradient for relationship tracking.
Output
Python
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.patches as mpatches
np.random.seed(137)
days = 365
calls = np.random.poisson(1.5, days)
calls = np.clip(calls, 0, 6)
weeks = 53
data = np.zeros((7, weeks))
for i, val in enumerate(calls):
week = i // 7
day = i % 7
if week < weeks:
data[day, week] = val
# CLAUDE.md Neon Pink palette
colors_neon = ['#ffffff', '#fce0f3', '#f97dc4', '#F527B0']
cmap = LinearSegmentedColormap.from_list('neon_pink', colors_neon, 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=6)
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('Social Calls - Calls Per Day', fontsize=16, color='#1f2937', 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_neon, ['0', '1', '2-3', '4+'])]
ax.legend(handles=legend_elements, loc='upper left', bbox_to_anchor=(0, -0.15), ncol=4,
fontsize=8, facecolor='#f9fafb', edgecolor='#d1d5db', labelcolor='#374151', title='Calls',
title_fontsize=9)
total = int(np.sum(calls))
ax.annotate(f'{total} calls made', xy=(0.98, 1.1), xycoords='axes fraction',
fontsize=11, color='#F527B0', ha='right', fontweight='bold')
plt.tight_layout()
plt.subplots_adjust(bottom=0.25)
plt.show()
Library
Matplotlib
Category
Time Series
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