The Hertzsprung-Russell (HR) diagram is the iconic astrophysics scatter plot that shows stellar luminosity (or absolute magnitude) versus surface temperature (or spectral class). Stars naturally cluster along the main sequence diagonal, with distinct regions for red giants, supergiants, and white dwarfs, revealing the fundamental relationship between stellar temperature and brightness.
""" anyplot.ai
scatter-hr-diagram: Hertzsprung-Russell Diagram
Library: letsplot 4.10.1 | Python 3.13.13
Quality: 84/100 | Updated: 2026-06-02
"""
import os
import numpy as np
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_point,
geom_text,
ggplot,
ggsize,
guides,
labs,
layer_tooltips,
scale_fill_manual,
scale_x_continuous,
scale_y_log10,
theme,
theme_minimal,
)
from lets_plot.export import ggsave
LetsPlot.setup_html()
# 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"
# Spectral type colors — closest Imprint palette members to astrophysical convention
spectral_colors = {
"O": "#C475FD", # lavender — nearest Imprint member to hot-violet O-type
"B": "#4467A3", # blue
"A": "#2ABCCD", # cyan — lighter blue-white A-type
"F": "#BD8233", # ochre — nearest to yellow-white F-type
"G": "#99B314", # lime — nearest to solar-yellow G-type
"K": "#954477", # rose — closest Imprint member to orange K-type
"M": "#AE3030", # matte red — cool red M-type
}
# Data — synthetic stellar populations
np.random.seed(42)
# Main sequence (diagonal band from hot/bright to cool/dim)
n_main = 200
main_temp = 10 ** np.random.uniform(np.log10(3000), np.log10(35000), n_main)
main_log_lum = 4.0 * (np.log10(main_temp) - np.log10(5778))
main_log_lum += np.random.normal(0, 0.25, n_main)
main_luminosity = 10**main_log_lum
# Red giants (cool but bright)
n_giants = 40
giant_temp = np.random.uniform(3200, 5500, n_giants)
giant_luminosity = 10 ** np.random.uniform(1.0, 3.5, n_giants)
# Supergiants (very bright, wide temp range)
n_super = 25
super_temp = np.random.uniform(3500, 30000, n_super)
super_luminosity = 10 ** np.random.uniform(3.5, 5.5, n_super)
# White dwarfs (hot but very dim)
n_dwarfs = 30
dwarf_temp = np.random.uniform(5000, 30000, n_dwarfs)
dwarf_luminosity = 10 ** np.random.uniform(-4, -1.5, n_dwarfs)
temperature = np.concatenate([main_temp, giant_temp, super_temp, dwarf_temp])
luminosity = np.concatenate([main_luminosity, giant_luminosity, super_luminosity, dwarf_luminosity])
region = (
["Main Sequence"] * n_main + ["Red Giants"] * n_giants + ["Supergiants"] * n_super + ["White Dwarfs"] * n_dwarfs
)
spectral_type = np.select(
[
temperature >= 30000,
temperature >= 10000,
temperature >= 7500,
temperature >= 6000,
temperature >= 5200,
temperature >= 3700,
],
["O", "B", "A", "F", "G", "K"],
default="M",
)
df = pd.DataFrame(
{"temperature": temperature, "luminosity": luminosity, "region": region, "spectral_type": spectral_type}
)
sun_df = pd.DataFrame({"temperature": [5778], "luminosity": [1.0]})
sun_label_df = pd.DataFrame({"temperature": [7800], "luminosity": [4.0], "label": ["☉ Sun"]})
region_labels = pd.DataFrame(
{
"temperature": [25000, 4500, 14000, 18000],
"luminosity": [0.012, 8000, 120000, 0.0005],
"label": ["Main Sequence", "Red Giants", "Supergiants", "White Dwarfs"],
}
)
# Spectral class markers — staggered y-positions to avoid bunching on the linear scale
spectral_axis_labels = pd.DataFrame(
{
"temperature": [35000, 18000, 8500, 6800, 5500, 4200, 3100],
"luminosity": [1200000, 500000, 1200000, 500000, 1200000, 500000, 1200000],
"label": ["O", "B", "A", "F", "G", "K", "M"],
}
)
# Plot
TITLE = "scatter-hr-diagram · python · letsplot · anyplot.ai"
title_size = round(16 * 67 / len(TITLE)) if len(TITLE) > 67 else 16
plot = (
ggplot(df, aes(x="temperature", y="luminosity"))
+ geom_point(
size=4.5,
alpha=0.75,
shape=21,
stroke=1.0,
mapping=aes(fill="spectral_type"),
color=INK_MUTED,
tooltips=layer_tooltips()
.line("@region")
.line("Temperature|@temperature K")
.line("Luminosity|@luminosity L☉")
.line("Spectral Type|@spectral_type"),
)
+ geom_point(
data=sun_df,
mapping=aes(x="temperature", y="luminosity"),
color="#FFD700",
fill="#FFD700",
size=9,
shape=21,
stroke=2.0,
inherit_aes=False,
)
+ geom_text(
data=sun_label_df,
mapping=aes(x="temperature", y="luminosity", label="label"),
size=5,
color="#FFD700",
fontface="bold",
inherit_aes=False,
)
+ geom_text(
data=region_labels,
mapping=aes(x="temperature", y="luminosity", label="label"),
size=4,
color=INK_MUTED,
fontface="bold_italic",
inherit_aes=False,
)
+ geom_text(
data=spectral_axis_labels,
mapping=aes(x="temperature", y="luminosity", label="label"),
size=4.5,
color=INK_SOFT,
fontface="bold",
inherit_aes=False,
)
+ scale_x_continuous(
trans="reverse",
name="Surface Temperature (K)",
breaks=[40000, 30000, 20000, 10000, 5000, 3000],
labels=["40,000", "30,000", "20,000", "10,000", "5,000", "3,000"],
)
+ scale_y_log10(name="Luminosity (L☉)", limits=[0.00005, 2000000])
+ scale_fill_manual(
values=[spectral_colors[k] for k in ["O", "B", "A", "F", "G", "K", "M"]],
limits=["O", "B", "A", "F", "G", "K", "M"],
name="Spectral Type",
)
+ guides(color="none")
+ labs(title=TITLE)
+ ggsize(800, 450)
+ theme_minimal()
+ theme(
axis_text=element_text(size=10, color=INK_SOFT),
axis_title=element_text(size=12, color=INK),
plot_title=element_text(size=title_size, color=INK),
legend_text=element_text(size=10, color=INK_SOFT),
legend_title=element_text(size=12, color=INK),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
panel_grid_major=element_line(color=INK_MUTED, size=0.2),
panel_grid_minor=element_blank(),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
axis_line=element_line(color=INK_SOFT),
axis_ticks=element_line(color=INK_SOFT, size=0.3),
plot_margin=[30, 40, 20, 20],
)
)
# Save
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Hertzsprung-Russell Diagram on anyplot.ai.