A hive plot arranges network nodes on radial axes based on node properties (such as degree, category, or other attributes), enabling reproducible and directly comparable network visualizations. Unlike force-directed layouts which can produce different arrangements for identical networks, hive plots always render the same network identically, solving the "hairball" problem of traditional network graphs and making structural comparisons reliable.
""" anyplot.ai
hive-basic: Basic Hive Plot
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-07
"""
import os
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
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"
# Okabe-Ito palette (canonical order)
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
# Apply seaborn theme with theme-adaptive colors
sns.set_theme(
style="white",
context="talk",
font_scale=1.2,
rc={
"figure.facecolor": PAGE_BG,
"axes.facecolor": PAGE_BG,
"axes.edgecolor": INK_SOFT,
"axes.labelcolor": INK,
"text.color": INK,
"xtick.color": INK_SOFT,
"ytick.color": INK_SOFT,
"grid.color": INK,
"grid.alpha": 0.10,
"legend.facecolor": ELEVATED_BG,
"legend.edgecolor": INK_SOFT,
},
)
# Data: Software module dependency network
np.random.seed(42)
# Define nodes with module types (3 categories for 3 axes)
node_data = {
"id": [f"M{i}" for i in range(30)],
"type": ["core"] * 10 + ["utility"] * 10 + ["interface"] * 10,
"degree": np.random.randint(1, 15, 30),
}
nodes_df = pd.DataFrame(node_data)
# Define edges between modules
edges = [
# Core to utility connections
("M0", "M10"),
("M0", "M11"),
("M1", "M12"),
("M2", "M13"),
("M3", "M14"),
("M4", "M15"),
("M5", "M16"),
("M6", "M17"),
("M7", "M18"),
("M8", "M19"),
# Utility to interface connections
("M10", "M20"),
("M11", "M21"),
("M12", "M22"),
("M13", "M23"),
("M14", "M24"),
("M15", "M25"),
("M16", "M26"),
("M17", "M27"),
("M18", "M28"),
("M19", "M29"),
# Core to interface connections (direct)
("M0", "M20"),
("M1", "M23"),
("M3", "M25"),
("M5", "M27"),
("M9", "M29"),
# Internal connections
("M0", "M1"),
("M2", "M3"),
("M10", "M11"),
("M20", "M21"),
]
# Hive plot configuration: 3 axes at 120 degrees apart
axis_angles = {"core": np.pi / 2, "utility": np.pi / 2 + 2 * np.pi / 3, "interface": np.pi / 2 + 4 * np.pi / 3}
# Use Okabe-Ito palette for axis colors
axis_colors = {
"core": IMPRINT[0], # #009E73 (brand green)
"utility": IMPRINT[1], # #C475FD (vermillion)
"interface": IMPRINT[2], # #4467A3 (blue)
}
# Calculate node positions on radial axes (normalized by degree within each type)
nodes_df["angle"] = nodes_df["type"].map(axis_angles)
for node_type in ["core", "utility", "interface"]:
mask = nodes_df["type"] == node_type
degrees = nodes_df.loc[mask, "degree"]
min_deg, max_deg = degrees.min(), degrees.max()
if max_deg > min_deg:
nodes_df.loc[mask, "radius"] = 0.3 + 0.7 * (degrees - min_deg) / (max_deg - min_deg)
else:
nodes_df.loc[mask, "radius"] = 0.65
# Convert to cartesian coordinates
nodes_df["x"] = nodes_df["radius"] * np.cos(nodes_df["angle"])
nodes_df["y"] = nodes_df["radius"] * np.sin(nodes_df["angle"])
# Create figure
fig, ax = plt.subplots(figsize=(12, 12), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Draw axis lines (radial spokes)
for axis_name, angle in axis_angles.items():
ax.plot([0, 1.1 * np.cos(angle)], [0, 1.1 * np.sin(angle)], color=INK_SOFT, linewidth=2, alpha=0.3, zorder=1)
ax.text(
1.2 * np.cos(angle),
1.2 * np.sin(angle),
axis_name.upper(),
fontsize=18,
fontweight="bold",
ha="center",
va="center",
color=axis_colors[axis_name],
)
# Build edge curves as DataFrame for seaborn lineplot
node_pos = nodes_df.set_index("id")[["x", "y"]]
edge_curves = []
for edge_id, (source, target) in enumerate(edges):
src_x, src_y = node_pos.loc[source]
tgt_x, tgt_y = node_pos.loc[target]
t = np.linspace(0, 1, 50)
ctrl_x, ctrl_y = 0.1 * (src_x + tgt_x), 0.1 * (src_y + tgt_y)
curve_x = (1 - t) ** 2 * src_x + 2 * (1 - t) * t * ctrl_x + t**2 * tgt_x
curve_y = (1 - t) ** 2 * src_y + 2 * (1 - t) * t * ctrl_y + t**2 * tgt_y
for i in range(len(t)):
edge_curves.append({"edge_id": edge_id, "x": curve_x[i], "y": curve_y[i]})
edges_df = pd.DataFrame(edge_curves)
# Draw edges using seaborn lineplot with theme-adaptive color
sns.lineplot(
data=edges_df,
x="x",
y="y",
hue="edge_id",
palette=[INK_SOFT] * len(edges),
alpha=0.2,
linewidth=1.5,
legend=False,
units="edge_id",
estimator=None,
ax=ax,
zorder=2,
)
# Draw nodes using seaborn scatterplot
sns.scatterplot(
data=nodes_df,
x="x",
y="y",
hue="type",
palette=[axis_colors[t] for t in ["core", "utility", "interface"]],
s=400,
alpha=0.9,
edgecolor=PAGE_BG,
linewidth=2,
ax=ax,
legend=False,
zorder=3,
)
# Create legend
legend_handles = [
mpatches.Patch(color=axis_colors["core"], label="Core Modules"),
mpatches.Patch(color=axis_colors["utility"], label="Utility Modules"),
mpatches.Patch(color=axis_colors["interface"], label="Interface Modules"),
]
ax.legend(
handles=legend_handles, loc="upper right", fontsize=16, framealpha=0.95, facecolor=ELEVATED_BG, edgecolor=INK_SOFT
)
# Styling
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
ax.set_aspect("equal")
ax.axis("off")
ax.set_title("hive-basic · seaborn · anyplot.ai", fontsize=24, fontweight="bold", pad=20, color=INK)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Basic Hive Plot on anyplot.ai.