A heatmap wrapped around a circle where the angular axis represents a cyclic variable (e.g., hour of day, month) and the radial axis represents a second categorical or ordinal variable (e.g., day of week, year). Cell color encodes the measured value. This visualization reveals patterns in data with inherent cyclical structure that rectangular heatmaps distort, since the first and last angular bins are visually adjacent rather than separated at opposite ends of a row.
""" anyplot.ai
heatmap-polar: Polar Heatmap for Cyclic Two-Dimensional Data
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 85/100 | Created: 2026-05-13
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
sns.set_theme(
style="ticks",
rc={
"figure.facecolor": PAGE_BG,
"axes.facecolor": PAGE_BG,
"text.color": INK,
"xtick.color": INK_SOFT,
"ytick.color": INK_SOFT,
"grid.color": INK,
"grid.alpha": 0.10,
},
)
# Data: hourly website traffic by day of week
np.random.seed(42)
days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
n_days = len(days)
n_hours = 24
# Synthetic traffic with realistic weekday/weekend patterns
traffic = np.zeros((n_days, n_hours))
for d in range(n_days):
for h in range(n_hours):
if d >= 5: # Weekend: afternoon peak
traffic[d, h] = 200 + 400 * np.exp(-0.5 * ((h - 14) / 4) ** 2)
else: # Weekday: morning and afternoon peaks
morning = 300 * np.exp(-0.5 * ((h - 9) / 2) ** 2)
afternoon = 500 * np.exp(-0.5 * ((h - 15) / 2.5) ** 2)
traffic[d, h] = 150 + morning + afternoon
traffic += np.random.normal(0, 20, traffic.shape)
traffic = np.clip(traffic, 0, None)
# Plot — polar projection for the circular heatmap
fig = plt.figure(figsize=(12, 12), facecolor=PAGE_BG)
ax = fig.add_subplot(111, projection="polar")
ax.set_facecolor(PAGE_BG)
theta_edges = np.linspace(0, 2 * np.pi, n_hours + 1)
r_edges = np.arange(n_days + 1, dtype=float)
mesh = ax.pcolormesh(theta_edges, r_edges, traffic, cmap="viridis", shading="flat")
# Clock orientation: midnight at top, clockwise like a 24-hour clock face
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
# Hour labels at the four cardinal positions only
hour_positions = np.linspace(0, 2 * np.pi, n_hours, endpoint=False)
ax.set_xticks(hour_positions)
hour_labels = [
"12am" if h == 0 else "6am" if h == 6 else "12pm" if h == 12 else "6pm" if h == 18 else "" for h in range(n_hours)
]
ax.set_xticklabels(hour_labels, fontsize=18, color=INK_SOFT, fontweight="medium")
# Day-of-week labels at the center of each radial ring
ax.set_yticks(np.arange(n_days) + 0.5)
ax.set_yticklabels(days, fontsize=16, color=INK_SOFT)
ax.set_ylim(0, n_days)
ax.tick_params(axis="y", length=0)
# Subtle cell gridlines to separate rings and sectors
ax.grid(True, alpha=0.12, color=INK, linewidth=0.6)
# Colorbar legend
cbar = plt.colorbar(mesh, ax=ax, pad=0.13, fraction=0.03, aspect=30)
cbar.set_label("Hourly Visits", fontsize=20, color=INK)
cbar.ax.tick_params(labelsize=16, colors=INK_SOFT)
plt.setp(cbar.ax.yaxis.get_ticklabels(), color=INK_SOFT)
cbar.outline.set_edgecolor(INK_SOFT)
ax.set_title(
"Website Traffic · heatmap-polar · seaborn · anyplot.ai", fontsize=24, fontweight="medium", color=INK, pad=25
)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Polar Heatmap for Cyclic Two-Dimensional Data on anyplot.ai.