A grid of scatter plots showing all pairwise relationships between multiple variables, with histograms or kernel density estimates on the diagonal. This comprehensive visualization enables simultaneous exploration of correlations and distributions across an entire dataset, making it invaluable for understanding multivariate data structure at a glance. Also known as a pairplot or SPLOM (Scatter Plot Matrix).
""" anyplot.ai
scatter-matrix: Scatter Plot Matrix
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 91/100 | Updated: 2026-05-09
"""
import os
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.layouts import gridplot
from bokeh.models import ColumnDataSource, Title
from bokeh.plotting import figure
from bokeh.transform import factor_cmap
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# 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 (first series always #009E73)
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
species_list = ["setosa", "versicolor", "virginica"]
# Data - Iris-like dataset with 4 variables and species groups
np.random.seed(42)
species = np.repeat(species_list, 50)
sepal_length = np.concatenate(
[np.random.normal(5.0, 0.35, 50), np.random.normal(5.9, 0.52, 50), np.random.normal(6.6, 0.64, 50)]
)
sepal_width = np.concatenate(
[np.random.normal(3.4, 0.38, 50), np.random.normal(2.8, 0.31, 50), np.random.normal(3.0, 0.32, 50)]
)
petal_length = np.concatenate(
[np.random.normal(1.5, 0.17, 50), np.random.normal(4.3, 0.47, 50), np.random.normal(5.6, 0.55, 50)]
)
petal_width = np.concatenate(
[np.random.normal(0.2, 0.10, 50), np.random.normal(1.3, 0.20, 50), np.random.normal(2.0, 0.27, 50)]
)
variables = [sepal_length, sepal_width, petal_length, petal_width]
var_names = ["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"]
n_vars = len(variables)
# Create scatter matrix grid
cell_size = 900
grid = []
for i in range(n_vars):
row = []
for j in range(n_vars):
x_label = var_names[j] if i == n_vars - 1 else ""
y_label = var_names[i] if j == 0 else ""
if i == j:
# Diagonal: Histogram
p = figure(width=cell_size, height=cell_size, x_axis_label=x_label, y_axis_label=y_label, tools="")
hist_data = variables[i]
bins = np.linspace(hist_data.min() - 0.1, hist_data.max() + 0.1, 20)
for k, sp in enumerate(species_list):
mask = species == sp
hist, edges = np.histogram(hist_data[mask], bins=bins)
source = ColumnDataSource(data={"top": hist, "left": edges[:-1], "right": edges[1:]})
p.quad(
top="top",
bottom=0,
left="left",
right="right",
source=source,
fill_color=IMPRINT[k],
line_color=PAGE_BG,
line_width=1.5,
alpha=0.7,
legend_label=sp.capitalize(),
)
p.y_range.start = 0
if i == 0:
p.legend.location = "top_right"
p.legend.label_text_font_size = "16pt"
p.legend.glyph_height = 20
p.legend.glyph_width = 20
p.legend.spacing = 8
else:
p.legend.visible = False
else:
# Off-diagonal: Scatter plot with refined styling
source = ColumnDataSource(data={"x": variables[j], "y": variables[i], "species": species})
p = figure(width=cell_size, height=cell_size, x_axis_label=x_label, y_axis_label=y_label, tools="")
p.scatter(
x="x",
y="y",
source=source,
size=14,
alpha=0.65,
fill_color=factor_cmap("species", IMPRINT, species_list),
line_color="white" if THEME == "light" else "#2A2A27",
line_width=0.8,
)
# Style axes — minimal, refined aesthetic
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_width = 0 # Remove spines for cleaner look
p.title.text_color = INK
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_color = INK
p.xaxis.major_label_text_color = INK_SOFT
p.yaxis.major_label_text_color = INK_SOFT
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.xaxis.major_tick_line_color = INK_SOFT
p.yaxis.major_tick_line_color = INK_SOFT
p.xaxis.major_tick_line_width = 1
p.yaxis.major_tick_line_width = 1
p.xaxis.axis_label_text_font_size = "20pt"
p.yaxis.axis_label_text_font_size = "20pt"
p.xaxis.major_label_text_font_size = "16pt"
p.yaxis.major_label_text_font_size = "16pt"
p.xgrid.grid_line_color = INK
p.ygrid.grid_line_color = INK
p.xgrid.grid_line_alpha = 0.08
p.ygrid.grid_line_alpha = 0.08
if p.legend:
p.legend.background_fill_color = ELEVATED_BG
p.legend.border_line_color = INK_SOFT
p.legend.label_text_color = INK_SOFT
p.legend.border_line_width = 1
p.legend.label_text_font_size = "16pt"
row.append(p)
grid.append(row)
# Add title
grid[0][0].add_layout(
Title(text="scatter-matrix · bokeh · anyplot.ai", text_font_size="28pt", align="left", text_color=INK), "above"
)
# Create grid layout
layout = gridplot(grid, toolbar_location=None, merge_tools=False)
# Save HTML
output_file(f"plot-{THEME}.html")
save(layout)
# Screenshot with headless Chrome
W, H = 3600, 3600
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
driver.set_window_size(W, H)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Part of Scatter Plot Matrix on anyplot.ai.