An arc diagram arranges nodes along a single horizontal line and draws connections between them as curved arcs above the line. This layout excels at revealing patterns in sequential or ordered data while minimizing visual clutter compared to force-directed layouts. Arc height typically indicates the distance between connected nodes, making it easy to spot long-range versus short-range connections.
""" anyplot.ai
arc-basic: Basic Arc Diagram
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 86/100 | Updated: 2026-05-30
"""
import os
import sys
sys.path = [p for p in sys.path if p and not p.endswith("implementations") and not p.endswith("/python")]
import numpy as np
import pandas as pd
from plotnine import (
aes,
coord_cartesian,
element_rect,
element_text,
geom_path,
geom_point,
geom_segment,
geom_text,
ggplot,
guide_colorbar,
labs,
scale_alpha_identity,
scale_color_gradient,
scale_size_identity,
theme,
theme_void,
)
# Theme tokens (Imprint palette — theme-adaptive chrome)
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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Imprint sequential gradient for arc weight encoding (position 1 → position 3)
ARC_LOW = "#009E73" # weak connections — Imprint brand green
ARC_HIGH = "#4467A3" # strong connections — Imprint blue
# Data: Character interactions in a story chapter
nodes = ["Alice", "Bob", "Carol", "David", "Eve", "Frank", "Grace", "Henry", "Iris", "Jack"]
n_nodes = len(nodes)
edges = [
(0, 1, 3), # Alice-Bob (strong)
(0, 3, 2), # Alice-David
(1, 2, 2), # Bob-Carol
(2, 4, 1), # Carol-Eve
(3, 5, 2), # David-Frank
(4, 6, 1), # Eve-Grace
(0, 7, 1), # Alice-Henry (long-range)
(1, 5, 2), # Bob-Frank
(2, 3, 3), # Carol-David (strong)
(5, 8, 1), # Frank-Iris
(6, 9, 2), # Grace-Jack
(0, 9, 1), # Alice-Jack (longest range)
(3, 7, 2), # David-Henry
(7, 8, 1), # Henry-Iris
(8, 9, 2), # Iris-Jack
]
x_positions = np.linspace(0, 1, n_nodes)
y_baseline = 0.0
# Node degree for proportional sizing (degree 2→6, 3→7.5, 4→9)
node_degree = [0] * n_nodes
for s, e, _ in edges:
node_degree[s] += 1
node_degree[e] += 1
node_sizes = [3.0 + d * 1.5 for d in node_degree]
n_points = 60
theta = np.linspace(0, np.pi, n_points)
arc_rows = []
for arc_id, (start, end, weight) in enumerate(edges):
x_start, x_end = x_positions[start], x_positions[end]
x_center = (x_start + x_end) / 2
arc_radius = abs(x_end - x_start) / 2
height = 0.08 * abs(end - start)
x_arc = x_center - arc_radius * np.cos(theta)
y_arc = y_baseline + height * np.sin(theta)
arc_rows.append(
pd.DataFrame(
{
"x": x_arc,
"y": y_arc,
"arc_id": arc_id,
"weight": float(weight),
"size": 1.0 + weight * 0.45, # weight=1: 1.45, weight=3: 2.35
"alpha": 0.62 + weight * 0.10, # weight=1: 0.72, weight=3: 0.92
}
)
)
arc_df = pd.concat(arc_rows, ignore_index=True)
baseline_df = pd.DataFrame({"x": [x_positions[0]], "xend": [x_positions[-1]], "y": [y_baseline], "yend": [y_baseline]})
node_df = pd.DataFrame({"x": x_positions, "y": [y_baseline] * n_nodes, "size": node_sizes})
label_df = pd.DataFrame({"x": x_positions, "y": [y_baseline - 0.035] * n_nodes, "name": nodes})
# Callout annotation for Alice–Jack: the longest-range arc (nodes 0→9, height=0.72)
alice_jack_apex_x = (x_positions[0] + x_positions[9]) / 2 # 0.5
alice_jack_apex_y = 0.08 * abs(9 - 0) # 0.72
callout_df = pd.DataFrame(
{"x": [alice_jack_apex_x], "y": [alice_jack_apex_y + 0.04], "label": ["Alice–Jack: longest-range arc"]}
)
# Title: "Character Interactions · arc-basic · python · plotnine · anyplot.ai" = 67 chars, no scaling
title = "Character Interactions · arc-basic · python · plotnine · anyplot.ai"
plot = (
ggplot()
+ geom_segment(
baseline_df, aes(x="x", y="y", xend="xend", yend="yend"), color=INK_MUTED, size=0.5, linetype="solid"
)
+ geom_path(arc_df, aes(x="x", y="y", group="arc_id", color="weight", size="size", alpha="alpha"))
+ scale_color_gradient(
low=ARC_LOW,
high=ARC_HIGH,
name="Interaction\nStrength",
breaks=[1, 2, 3],
labels=["Weak", "Medium", "Strong"],
guide=guide_colorbar(direction="vertical"),
)
+ scale_size_identity()
+ scale_alpha_identity()
+ geom_point(node_df, aes(x="x", y="y", size="size"), color=INK, stroke=1.2, fill=PAGE_BG)
+ geom_text(label_df, aes(x="x", y="y", label="name"), size=5, color=INK, fontweight="bold", va="top")
+ geom_text(callout_df, aes(x="x", y="y", label="label"), size=3.5, color=INK_SOFT, ha="center")
+ coord_cartesian(xlim=(-0.06, 1.06), ylim=(-0.12, 0.82))
+ labs(
title=title, subtitle="Narrative connections in Chapter 1 — arc thickness and color encode interaction strength"
)
+ theme_void()
+ theme(
figure_size=(8, 4.5),
plot_title=element_text(size=12, ha="center", weight="bold", color=INK),
plot_subtitle=element_text(size=8, ha="center", color=INK_SOFT),
plot_margin=0.02,
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
legend_position="right",
legend_title=element_text(size=8, weight="bold", color=INK),
legend_text=element_text(size=7, color=INK_SOFT),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_key_height=30,
legend_key_width=8,
)
)
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Part of Basic Arc Diagram on anyplot.ai.