A Bland-Altman plot (also known as a difference plot or Tukey mean-difference plot) visualizes the agreement between two measurement methods by plotting the difference against the average of paired observations. It displays the mean difference (bias) as a horizontal line and limits of agreement (mean ± 1.96 SD) to assess whether the methods are interchangeable within acceptable tolerances.
""" anyplot.ai
bland-altman-basic: Bland-Altman Agreement Plot
Library: bokeh 3.9.2 | Python 3.13.14
Quality: 90/100 | Updated: 2026-08-11
"""
import os
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import BoxAnnotation, ColumnDataSource, HoverTool, Label, Span
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
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"
BRAND = "#009E73"
ACCENT = "#C475FD"
np.random.seed(42)
n = 80
true_bp = np.random.normal(125, 15, n)
method1 = true_bp + np.random.normal(0, 5, n)
method2 = true_bp + np.random.normal(2, 6, n)
mean_values = (method1 + method2) / 2
diff_values = method1 - method2
mean_diff = np.mean(diff_values)
std_diff = np.std(diff_values, ddof=1)
upper_loa = mean_diff + 1.96 * std_diff
lower_loa = mean_diff - 1.96 * std_diff
source = ColumnDataSource(data={"mean": mean_values, "diff": diff_values})
# `width` / `height` are the TOTAL canvas; axis labels at the 36-42pt
# native-pixel sizes need explicit `min_border_*` reservations or they get
# clipped at the edges of the rendered PNG.
p = figure(
width=3200,
height=1800,
title="bland-altman-basic · python · bokeh · anyplot.ai",
x_axis_label="Mean of Two Methods (mmHg)",
y_axis_label="Difference (Method 1 - Method 2) (mmHg)",
toolbar_location=None,
min_border_bottom=160,
min_border_left=180,
min_border_top=110,
min_border_right=50,
)
# Subtle shaded band for the limits-of-agreement zone (design refinement:
# gives the +-1.96 SD region a visual footprint without competing with data).
agreement_zone = BoxAnnotation(bottom=lower_loa, top=upper_loa, fill_color=INK, fill_alpha=0.05)
p.add_layout(agreement_zone)
scatter_renderer = p.scatter(
x="mean",
y="diff",
source=source,
size=14,
color=BRAND,
alpha=0.7,
line_color=PAGE_BG,
line_width=1,
legend_label="Observations",
)
p.add_tools(
HoverTool(tooltips=[("Mean", "@mean{0.2f} mmHg"), ("Difference", "@diff{0.2f} mmHg")], renderers=[scatter_renderer])
)
mean_line = Span(location=mean_diff, dimension="width", line_color=BRAND, line_width=3.5, line_dash="solid")
p.add_layout(mean_line)
upper_line = Span(location=upper_loa, dimension="width", line_color=ACCENT, line_width=2.5, line_dash="dashed")
p.add_layout(upper_line)
lower_line = Span(location=lower_loa, dimension="width", line_color=ACCENT, line_width=2.5, line_dash="dashed")
p.add_layout(lower_line)
x_min = np.min(mean_values)
x_label_pos = x_min + (np.max(mean_values) - x_min) * 0.02
mean_label = Label(
x=x_label_pos,
y=mean_diff,
text=f"Mean Bias: {mean_diff:.2f} mmHg",
text_font_size="28pt",
text_color=BRAND,
text_baseline="bottom",
y_offset=5,
)
p.add_layout(mean_label)
upper_label = Label(
x=x_label_pos,
y=upper_loa,
text=f"+1.96 SD: {upper_loa:.2f} mmHg",
text_font_size="28pt",
text_color=ACCENT,
text_baseline="bottom",
y_offset=5,
)
p.add_layout(upper_label)
lower_label = Label(
x=x_label_pos,
y=lower_loa,
text=f"−1.96 SD: {lower_loa:.2f} mmHg",
text_font_size="28pt",
text_color=ACCENT,
text_baseline="top",
y_offset=-5,
)
p.add_layout(lower_label)
p.title.text_font_size = "50pt"
p.title.text_color = INK
p.xaxis.axis_label_text_font_size = "42pt"
p.yaxis.axis_label_text_font_size = "42pt"
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_color = INK
p.xaxis.major_label_text_font_size = "34pt"
p.yaxis.major_label_text_font_size = "34pt"
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.xgrid.grid_line_color = INK
p.ygrid.grid_line_color = INK
p.xgrid.grid_line_alpha = 0.15
p.ygrid.grid_line_alpha = 0.15
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
# No-frame treatment: bokeh's `outline_line_color` draws a single rectangle
# around the whole plot area (no per-side spine control like matplotlib), so
# a full box is the only "outline" option — skip it in favor of the axis
# lines alone for a cleaner, less boxed-in look.
p.outline_line_color = None
if p.legend:
p.legend.label_text_font_size = "34pt"
p.legend.label_text_color = INK_SOFT
p.legend.location = "top_left"
p.legend.background_fill_color = ELEVATED_BG
p.legend.border_line_color = INK_SOFT
output_file(f"plot-{THEME}.html")
save(p)
# IMPORTANT: W,H must match the figure's width/height above, otherwise the
# bokeh canvas paints into the upper-left corner with white space around it.
W, H = 3200, 1800
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()}")
# IMPORTANT: headless Chrome's --window-size sets the OUTER window, which still
# reserves a phantom title-bar height even headless — innerHeight (and thus the
# screenshot) comes out short of H. Pin the viewport exactly via CDP.
driver.execute_cdp_cmd(
"Emulation.setDeviceMetricsOverride", {"width": W, "height": H, "deviceScaleFactor": 1, "mobile": False}
)
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/bland-altman-basic/bokeh/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "bland-altman-basic",
"language": "python",
"library": "bokeh",
"page": "https://anyplot.ai/bland-altman-basic/python/bokeh",
"hub": "https://anyplot.ai/bland-altman-basic",
"code_json": "https://api.anyplot.ai/specs/bland-altman-basic/bokeh/code",
"spec_json": "https://api.anyplot.ai/specs/bland-altman-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/bland-altman-basic/python/bokeh/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/bland-altman-basic/python/bokeh/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/bland-altman-basic/python/bokeh/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/bland-altman-basic/python/bokeh/plot-dark.html",
"quality_score": 90.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Bland-Altman Agreement Plot on anyplot.ai.