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: seaborn 0.13.2 | Python 3.13.14
Quality: 90/100 | Updated: 2026-08-11
"""
import os
import sys
# Remove current directory from path to avoid importing this file as 'seaborn'
sys.path = [p for p in sys.path if os.path.abspath(p) != os.getcwd()]
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
# 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"
# Imprint palette (categorical) — first series always #009E73
IMPRINT_PALETTE = sns.color_palette(["#009E73", "#C475FD"])
BRAND = IMPRINT_PALETTE[0] # scatter + mean line
ACCENT_1 = IMPRINT_PALETTE[1] # limits of agreement
NEUTRAL = INK # semantic anchor: baseline / reference line, same hex as text
# Theme-adaptive chrome, seaborn-native (see prompts/library/seaborn.md)
sns.set_theme(
style="ticks",
rc={
"figure.facecolor": PAGE_BG,
"axes.facecolor": PAGE_BG,
"axes.edgecolor": INK_SOFT,
"axes.labelcolor": INK,
"text.color": INK,
"xtick.color": INK_SOFT,
"ytick.color": INK_SOFT,
"grid.color": INK,
"grid.alpha": 0.15,
"legend.facecolor": ELEVATED_BG,
"legend.edgecolor": INK_SOFT,
},
)
# Data - Simulated blood pressure measurements from two sphygmomanometers
np.random.seed(42)
n = 80
# Method 1: Reference standard (e.g., mercury sphygmomanometer)
method1 = np.random.normal(120, 15, n)
# Method 2: New device with slight systematic bias and proportional error
method2 = method1 + np.random.normal(2, 5, n) + 0.02 * (method1 - 120)
# Calculate Bland-Altman statistics
mean_values = (method1 + method2) / 2
differences = method1 - method2
mean_diff = np.mean(differences)
std_diff = np.std(differences, ddof=1)
upper_loa = mean_diff + 1.96 * std_diff
lower_loa = mean_diff - 1.96 * std_diff
# Create plot — canonical landscape canvas (3200x1800 @ dpi=400)
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400)
# Scatter plot of differences vs means
sns.scatterplot(x=mean_values, y=differences, s=100, alpha=0.7, color=BRAND, edgecolor=PAGE_BG, linewidth=0.5, ax=ax)
# Rug of the raw differences on the y-axis — a seaborn-native way to surface
# the shape of the difference distribution, which the ±1.96 SD limits assume
# is approximately normal.
sns.rugplot(y=differences, ax=ax, height=0.03, color=BRAND, alpha=0.5, lw=1)
# Shaded band between the limits of agreement — reinforces the ±1.96 SD
# envelope as a single focal region before the individual lines are read.
ax.axhspan(lower_loa, upper_loa, color=ACCENT_1, alpha=0.08, zorder=0)
# Mean difference line (bias)
ax.axhline(y=mean_diff, color=BRAND, linewidth=2.5, label=f"Mean: {mean_diff:.2f} mmHg")
# Limits of agreement (±1.96 SD)
ax.axhline(y=upper_loa, color=ACCENT_1, linewidth=1.75, linestyle="--", label=f"+1.96 SD: {upper_loa:.2f} mmHg")
ax.axhline(y=lower_loa, color=ACCENT_1, linewidth=1.75, linestyle="--", label=f"-1.96 SD: {lower_loa:.2f} mmHg")
# Zero reference line
ax.axhline(y=0, color=NEUTRAL, linewidth=1, linestyle=":", alpha=0.5)
# Annotate values on the right side
x_max = ax.get_xlim()[1]
ax.annotate(
f"{mean_diff:.1f}",
xy=(x_max, mean_diff),
xytext=(5, 0),
textcoords="offset points",
fontsize=9,
color=BRAND,
fontweight="bold",
va="center",
)
ax.annotate(
f"{upper_loa:.1f}",
xy=(x_max, upper_loa),
xytext=(5, 0),
textcoords="offset points",
fontsize=8,
color=ACCENT_1,
va="center",
)
ax.annotate(
f"{lower_loa:.1f}",
xy=(x_max, lower_loa),
xytext=(5, 0),
textcoords="offset points",
fontsize=8,
color=ACCENT_1,
va="center",
)
# Labels and styling
ax.set_xlabel("Mean of Two Methods (mmHg)", fontsize=10)
ax.set_ylabel("Difference (Method 1 - Method 2) (mmHg)", fontsize=10)
ax.set_title("bland-altman-basic · python · seaborn · anyplot.ai", fontsize=12, fontweight="medium")
ax.tick_params(axis="both", labelsize=8)
# Spines — seaborn-native despine (default: top + right removed)
sns.despine(ax=ax)
# Grid - subtle, both axes (scatter plot)
ax.grid(True, axis="both", alpha=0.15, linewidth=0.8, color=INK)
# Legend, positioned and styled via seaborn's move_legend — lower-right,
# clear of both the y-axis rug ticks and the right-edge value annotations
ax.legend(fontsize=8)
sns.move_legend(ax, "lower right", frameon=True, facecolor=ELEVATED_BG, edgecolor=INK_SOFT, framealpha=1.0)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/bland-altman-basic/seaborn/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
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"language": "python",
"library": "seaborn",
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"code_json": "https://api.anyplot.ai/specs/bland-altman-basic/seaborn/code",
"spec_json": "https://api.anyplot.ai/specs/bland-altman-basic",
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"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/bland-altman-basic/python/seaborn/plot-dark.png",
"quality_score": 90.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Bland-Altman Agreement Plot on anyplot.ai.