A Circos plot is a circular visualization that displays data on concentric tracks arranged around a circle, with ribbons or arcs connecting related segments across the circular layout. Originally designed for genomic data visualization, it excels at showing relationships between segments while simultaneously displaying multiple data attributes on different tracks. The circular arrangement makes efficient use of space and reveals patterns in complex relational data.
""" anyplot.ai
circos-basic: Circos Plot
Library: letsplot 4.9.0 | Python 3.13.13
Quality: 91/100 | Updated: 2026-05-15
"""
import math
import os
import numpy as np
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
coord_fixed,
element_blank,
element_rect,
element_text,
geom_polygon,
geom_text,
ggplot,
ggsize,
labs,
scale_fill_manual,
scale_x_continuous,
scale_y_continuous,
theme,
)
from lets_plot.export import ggsave
LetsPlot.setup_html()
np.random.seed(42)
# 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"
# Genomic-style data: 8 chromosomes with connections and expression data
chromosomes = ["Chr1", "Chr2", "Chr3", "Chr4", "Chr5", "Chr6", "Chr7", "Chr8"]
n_chromosomes = len(chromosomes)
# Chromosome sizes (proportional to their arc length)
chr_sizes = [120, 95, 85, 75, 70, 65, 55, 50] # Megabases
# Connections between chromosomes (inter-chromosomal rearrangements)
connections = [
("Chr1", "Chr3", 25),
("Chr1", "Chr5", 18),
("Chr2", "Chr4", 22),
("Chr2", "Chr7", 15),
("Chr3", "Chr6", 20),
("Chr4", "Chr8", 12),
("Chr5", "Chr7", 16),
("Chr6", "Chr8", 10),
("Chr1", "Chr8", 8),
("Chr3", "Chr5", 14),
]
# Track data: expression levels for each chromosome (inner tracks)
expression_track1 = [0.85, 0.72, 0.93, 0.68, 0.81, 0.55, 0.78, 0.62] # Normalized 0-1
expression_track2 = [0.45, 0.82, 0.38, 0.91, 0.55, 0.73, 0.42, 0.88] # Normalized 0-1
# Colors for chromosomes (colorblind-friendly palette)
chr_colors = ["#306998", "#FFD43B", "#27AE60", "#E74C3C", "#9B59B6", "#1ABC9C", "#F39C12", "#3498DB"]
# Calculate angular positions for each chromosome
total_size = sum(chr_sizes)
gap_angle = 0.08 # Gap between chromosomes in radians
total_gap = gap_angle * n_chromosomes
available_angle = 2 * math.pi - total_gap
# Assign angular positions to each chromosome
chr_arcs = {}
current_angle = 0
for i, chrom in enumerate(chromosomes):
arc_size = (chr_sizes[i] / total_size) * available_angle
chr_arcs[chrom] = {
"start": current_angle,
"end": current_angle + arc_size,
"mid": current_angle + arc_size / 2,
"size": chr_sizes[i],
"color": chr_colors[i],
"idx": i,
}
current_angle += arc_size + gap_angle
# Radii for different elements
outer_radius = 1.0 # Outer chromosome ring
inner_ring_radius = 0.92 # Inner edge of chromosome ring
track1_outer = 0.88 # Expression track 1
track1_inner = 0.78
track2_outer = 0.74 # Expression track 2
track2_inner = 0.64
chord_radius = 0.60 # Ribbons connecting chromosomes
# Build outer arc segments (chromosome ring)
arc_data = []
n_arc_points = 50
for chrom in chromosomes:
arc = chr_arcs[chrom]
angles = np.linspace(arc["start"], arc["end"], n_arc_points)
# Outer edge
for angle in angles:
arc_data.append(
{
"x": outer_radius * np.cos(angle),
"y": outer_radius * np.sin(angle),
"chromosome": chrom,
"arc_id": f"{chrom}_arc",
}
)
# Inner edge (reversed)
for angle in reversed(angles):
arc_data.append(
{
"x": inner_ring_radius * np.cos(angle),
"y": inner_ring_radius * np.sin(angle),
"chromosome": chrom,
"arc_id": f"{chrom}_arc",
}
)
arc_df = pd.DataFrame(arc_data)
# Build track 1 data (bar heights based on expression)
track1_data = []
for chrom in chromosomes:
arc = chr_arcs[chrom]
expr = expression_track1[arc["idx"]]
# Create arc segment for this track
angles = np.linspace(arc["start"], arc["end"], n_arc_points)
bar_height = track1_inner + (track1_outer - track1_inner) * expr
# Outer edge at expression level
for angle in angles:
track1_data.append(
{
"x": bar_height * np.cos(angle),
"y": bar_height * np.sin(angle),
"chromosome": chrom,
"track_id": f"{chrom}_track1",
}
)
# Inner edge
for angle in reversed(angles):
track1_data.append(
{
"x": track1_inner * np.cos(angle),
"y": track1_inner * np.sin(angle),
"chromosome": chrom,
"track_id": f"{chrom}_track1",
}
)
track1_df = pd.DataFrame(track1_data)
# Build track 2 data
track2_data = []
for chrom in chromosomes:
arc = chr_arcs[chrom]
expr = expression_track2[arc["idx"]]
angles = np.linspace(arc["start"], arc["end"], n_arc_points)
bar_height = track2_inner + (track2_outer - track2_inner) * expr
# Outer edge at expression level
for angle in angles:
track2_data.append(
{
"x": bar_height * np.cos(angle),
"y": bar_height * np.sin(angle),
"chromosome": chrom,
"track_id": f"{chrom}_track2",
}
)
# Inner edge
for angle in reversed(angles):
track2_data.append(
{
"x": track2_inner * np.cos(angle),
"y": track2_inner * np.sin(angle),
"chromosome": chrom,
"track_id": f"{chrom}_track2",
}
)
track2_df = pd.DataFrame(track2_data)
# Build ribbon connections between chromosomes
ribbon_data = []
ribbon_id = 0
# Track offsets for connection placement within each chromosome
chr_offsets = {chrom: chr_arcs[chrom]["start"] for chrom in chromosomes}
for src, tgt, val in connections:
src_arc = chr_arcs[src]
tgt_arc = chr_arcs[tgt]
# Calculate angular width proportional to connection value
width_factor = val / 100.0 # Normalize
src_width = (src_arc["end"] - src_arc["start"]) * width_factor * 0.8
tgt_width = (tgt_arc["end"] - tgt_arc["start"]) * width_factor * 0.8
# Source position
src_start = chr_offsets[src]
src_end = src_start + src_width
chr_offsets[src] = src_end + 0.01
# Target position
tgt_start = chr_offsets[tgt]
tgt_end = tgt_start + tgt_width
chr_offsets[tgt] = tgt_end + 0.01
# Create bezier-like ribbon
n_bezier = 40
polygon_x = []
polygon_y = []
# Source arc at chord radius
src_angles = np.linspace(src_start, src_end, 10)
for angle in src_angles:
polygon_x.append(chord_radius * np.cos(angle))
polygon_y.append(chord_radius * np.sin(angle))
# Bezier curve from source end to target start
src_end_x = chord_radius * np.cos(src_end)
src_end_y = chord_radius * np.sin(src_end)
tgt_start_x = chord_radius * np.cos(tgt_start)
tgt_start_y = chord_radius * np.sin(tgt_start)
for i in range(1, n_bezier):
t = i / n_bezier
x = (1 - t) ** 2 * src_end_x + 2 * (1 - t) * t * 0 + t**2 * tgt_start_x
y = (1 - t) ** 2 * src_end_y + 2 * (1 - t) * t * 0 + t**2 * tgt_start_y
polygon_x.append(x)
polygon_y.append(y)
# Target arc (reversed)
tgt_angles = np.linspace(tgt_end, tgt_start, 10)
for angle in tgt_angles:
polygon_x.append(chord_radius * np.cos(angle))
polygon_y.append(chord_radius * np.sin(angle))
# Bezier curve back from target end to source start
tgt_end_x = chord_radius * np.cos(tgt_end)
tgt_end_y = chord_radius * np.sin(tgt_end)
src_start_x = chord_radius * np.cos(src_start)
src_start_y = chord_radius * np.sin(src_start)
for i in range(1, n_bezier):
t = i / n_bezier
x = (1 - t) ** 2 * tgt_end_x + 2 * (1 - t) * t * 0 + t**2 * src_start_x
y = (1 - t) ** 2 * tgt_end_y + 2 * (1 - t) * t * 0 + t**2 * src_start_y
polygon_x.append(x)
polygon_y.append(y)
# Add points to dataframe
for x, y in zip(polygon_x, polygon_y, strict=False):
ribbon_data.append({"x": x, "y": y, "ribbon_id": f"ribbon_{ribbon_id}", "source": src})
ribbon_id += 1
ribbon_df = pd.DataFrame(ribbon_data)
# Create chromosome labels
label_data = []
label_radius = 1.12
for chrom in chromosomes:
arc = chr_arcs[chrom]
mid_angle = arc["mid"]
label_data.append({"x": label_radius * np.cos(mid_angle), "y": label_radius * np.sin(mid_angle), "label": chrom})
label_df = pd.DataFrame(label_data)
# Build the circos plot
plot = (
ggplot()
# Ribbons connecting chromosomes (innermost, with transparency)
+ geom_polygon(
aes(x="x", y="y", group="ribbon_id", fill="source"), data=ribbon_df, alpha=0.45, color=PAGE_BG, size=0.2
)
# Expression track 2 (inner track)
+ geom_polygon(
aes(x="x", y="y", group="track_id", fill="chromosome"), data=track2_df, alpha=0.65, color=PAGE_BG, size=0.3
)
# Expression track 1 (middle track)
+ geom_polygon(
aes(x="x", y="y", group="track_id", fill="chromosome"), data=track1_df, alpha=0.8, color=PAGE_BG, size=0.3
)
# Outer chromosome ring
+ geom_polygon(
aes(x="x", y="y", group="arc_id", fill="chromosome"), data=arc_df, alpha=0.95, color=PAGE_BG, size=0.8
)
# Chromosome labels
+ geom_text(aes(x="x", y="y", label="label"), data=label_df, size=14, color=INK, fontface="bold")
+ scale_fill_manual(values=chr_colors, name="Chromosome")
+ coord_fixed(ratio=1)
+ scale_x_continuous(limits=(-1.45, 1.45))
+ scale_y_continuous(limits=(-1.45, 1.45))
+ labs(title="circos-basic · letsplot · anyplot.ai")
+ ggsize(1200, 1200)
+ theme(
plot_title=element_text(size=26, face="bold", color=INK),
axis_title=element_blank(),
axis_text=element_blank(),
axis_ticks=element_blank(),
axis_line=element_blank(),
panel_grid=element_blank(),
legend_text=element_text(size=14, color=INK_SOFT),
legend_title=element_text(size=16, face="bold", color=INK),
legend_position="bottom",
panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
)
)
# Save as PNG (scale 3x for 3600x3600 px output)
ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)
# Save as HTML for interactivity
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Circos Plot on anyplot.ai.