A standardized clinical audiogram that displays hearing test results, plotting hearing threshold (dB HL) against test frequency for each ear. The y-axis is inverted so that 0 dB HL (best hearing) sits at the top and increasing hearing loss extends downward, while the x-axis is logarithmic spanning the standard audiometric frequencies (125 Hz to 8 kHz). Thresholds are marked with the conventional symbols — a circle (O) for the right ear in red and a cross (X) for the left ear in blue — and connected per ear, with shaded horizontal bands indicating severity of hearing loss (normal, mild, moderate, severe, profound).
""" anyplot.ai
audiogram-clinical: Clinical Audiogram
Library: plotnine 0.15.7 | Python 3.13.13
Quality: 89/100 | Created: 2026-06-15
"""
import sys
sys.path.pop(0) # prevent this file (plotnine.py) from shadowing the plotnine library
import os
import pandas as pd
from plotnine import (
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_line,
geom_point,
geom_rect,
geom_text,
ggplot,
guides,
labs,
scale_color_manual,
scale_fill_manual,
scale_linetype_manual,
scale_shape_manual,
scale_x_log10,
scale_y_reverse,
theme,
theme_minimal,
)
# 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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Audiogram uses strict clinical color conventions (semantic exception)
RIGHT_COLOR = "#AE3030" # Imprint matte red — right ear standard
LEFT_COLOR = "#4467A3" # Imprint blue — left ear standard
ANYPLOT_AMBER = "#DDCC77" # speech frequency range indicator
# Data: noise-induced high-frequency sensorineural hearing loss pattern
frequencies = [125, 250, 500, 1000, 2000, 4000, 8000]
right_thresh = [10, 10, 15, 20, 30, 55, 65]
left_thresh = [15, 15, 20, 25, 40, 60, 75]
df = pd.concat(
[
pd.DataFrame({"frequency": frequencies, "threshold": right_thresh, "ear": "Right Ear (O)"}),
pd.DataFrame({"frequency": frequencies, "threshold": left_thresh, "ear": "Left Ear (X)"}),
],
ignore_index=True,
)
# Severity bands — contiguous boundaries for clean shading
bands = pd.DataFrame(
{
"xmin": [100] * 6,
"xmax": [10000] * 6,
"ymin": [-10, 25, 40, 55, 70, 90],
"ymax": [25, 40, 55, 70, 90, 120],
"severity": ["Normal", "Mild", "Moderate", "Mod. Severe", "Severe", "Profound"],
}
)
# Subtle severity band fills per theme
if THEME == "light":
band_colors = {
"Normal": "#DFF2EC",
"Mild": "#ECF4D9",
"Moderate": "#F5EDD6",
"Mod. Severe": "#F2E3CE",
"Severe": "#F0D7D7",
"Profound": "#EDD7EC",
}
else:
band_colors = {
"Normal": "#14291E",
"Mild": "#1C2414",
"Moderate": "#242012",
"Mod. Severe": "#241A12",
"Severe": "#241212",
"Profound": "#20121E",
}
# Severity band labels: midpoint y positions, placed near right edge
band_labels = pd.DataFrame(
{
"x": [9400] * 6,
"y": [7.5, 32.5, 47.5, 62.5, 80.0, 105.0],
"label": ["Normal", "Mild", "Moderate", "Mod. Severe", "Severe", "Profound"],
}
)
# Speech frequency reference band (500–4000 Hz is clinically critical for speech intelligibility)
speech_ref = pd.DataFrame({"xmin": [500], "xmax": [4000], "ymin": [-10], "ymax": [120]})
speech_label = pd.DataFrame({"x": [1414], "y": [-7.0], "label": ["Speech range"]})
# Title font scaling
title = "audiogram-clinical · python · plotnine · anyplot.ai"
n = len(title)
ratio = 67 / n if n > 67 else 1.0
title_fontsize = max(8, round(12 * ratio))
# Plot
plot = (
ggplot(df, aes(x="frequency", y="threshold"))
# Severity shading (drawn first so data sits on top)
+ geom_rect(
data=bands, mapping=aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax", fill="severity"), inherit_aes=False
)
+ scale_fill_manual(values=band_colors)
+ guides(fill=False)
# Speech frequency range: subtle amber vertical band highlighting clinically important region
+ geom_rect(
data=speech_ref,
mapping=aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax"),
inherit_aes=False,
fill=ANYPLOT_AMBER,
alpha=0.12,
)
# Connecting lines per ear
+ geom_line(aes(color="ear", linetype="ear"), size=1.0)
# Threshold markers: O for right, X for left
+ geom_point(aes(color="ear", shape="ear"), size=3.5, stroke=0.8)
# Severity band labels inside plot near right edge
+ geom_text(
data=band_labels,
mapping=aes(x="x", y="y", label="label"),
inherit_aes=False,
size=3.5,
color=INK_MUTED,
ha="right",
)
# Speech range label near top of band (y=-7 is near the top of the inverted axis)
+ geom_text(
data=speech_label,
mapping=aes(x="x", y="y", label="label"),
inherit_aes=False,
size=2.5,
color=INK_MUTED,
ha="center",
)
# Color: right=red, left=blue (clinical convention); same name merges legend entries
+ scale_color_manual(values={"Right Ear (O)": RIGHT_COLOR, "Left Ear (X)": LEFT_COLOR}, name=" ")
+ scale_shape_manual(values={"Right Ear (O)": "o", "Left Ear (X)": "x"}, name=" ")
+ scale_linetype_manual(values={"Right Ear (O)": "solid", "Left Ear (X)": "dashed"}, name=" ")
# Logarithmic frequency axis with standard audiometric ticks
+ scale_x_log10(
breaks=[125, 250, 500, 1000, 2000, 4000, 8000],
labels=["125", "250", "500", "1k", "2k", "4k", "8k"],
limits=[100, 10000],
)
# Inverted hearing level axis: 0 dB at top, loss increases downward
+ scale_y_reverse(limits=(-10, 120), breaks=list(range(-10, 130, 10)))
+ labs(x="Frequency (Hz)", y="Hearing Level (dB HL)", title=title)
+ theme_minimal()
+ theme(
figure_size=(6, 6),
text=element_text(size=7),
plot_title=element_text(size=title_fontsize, color=INK, weight="bold"),
axis_title=element_text(size=10, color=INK),
axis_text=element_text(size=8, color=INK_SOFT),
legend_text=element_text(size=8, color=INK_SOFT),
legend_title=element_text(size=9, color=INK),
legend_background=element_rect(fill=ELEVATED_BG, color=None),
legend_key=element_blank(),
panel_background=element_rect(fill=PAGE_BG, color=None),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_grid_major=element_line(color=INK_SOFT, size=0.3, alpha=0.5),
panel_grid_minor=element_blank(),
panel_border=element_rect(color=INK_SOFT, fill=None, size=0.5),
legend_position="right",
)
)
# Save
plot.save(f"plot-{THEME}.png", dpi=400, width=6, height=6, units="in")
Part of Clinical Audiogram on anyplot.ai.