A pulmonary function test visualization that plots airflow rate (L/s) against lung volume (L) during forced expiration and inspiration, forming a characteristic loop shape. The expiratory limb rises sharply to Peak Expiratory Flow (PEF) then declines, while the inspiratory limb forms a more symmetric curve below the x-axis. This plot is essential for diagnosing obstructive and restrictive lung diseases by comparing measured loops against predicted normal values.
""" anyplot.ai
spirometry-flow-volume: Spirometry Flow-Volume Loop
Library: bokeh 3.9.1 | Python 3.13.14
Quality: 91/100 | Updated: 2026-06-17
"""
import os
import sys
# Prevent self-import: this file is named bokeh.py, which shadows the installed
# bokeh package when its directory sits at the front of sys.path.
_this_dir = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if os.path.abspath(p or ".") != _this_dir]
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource, HoverTool, Label, Legend, LegendItem
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# Theme tokens (see prompts/default-style-guide.md "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"
# Imprint palette — first series ALWAYS #009E73
BRAND = "#009E73" # measured loop (the patient)
PREDICTED = "#4467A3" # predicted normal reference loop
ACCENT_PEF = "#AE3030" # matte-red focal anchor for Peak Expiratory Flow
ACCENT_FEV1 = "#BD8233" # ochre focal for FEV1 landmark
# Data — Spirometry flow-volume loop (measured vs predicted normal)
np.random.seed(42)
n_points = 160
# Clinical landmarks for a mildly-obstructed adult
fvc_measured = 4.2 # forced vital capacity (L)
pef_measured = 9.5 # peak expiratory flow (L/s)
fev1_measured = 3.3 # volume exhaled in 1 s (L)
fvc_predicted = 4.8
pef_predicted = 10.8
# Expiratory limb (measured): sharp rise to PEF then concave decline
volume_exp = np.linspace(0, fvc_measured, n_points)
t_exp = volume_exp / fvc_measured
pef_fraction = 0.12 # PEF occurs early in the forced exhalation
rise = pef_measured * np.sin(np.pi / 2 * t_exp / pef_fraction)
decline = pef_measured * (1 - (t_exp - pef_fraction) / (1 - pef_fraction))
flow_exp = np.where(t_exp <= pef_fraction, rise, decline)
flow_exp = np.maximum(flow_exp, 0.0)
flow_exp[t_exp > pef_fraction] *= 1 - 0.15 * ((t_exp[t_exp > pef_fraction] - pef_fraction) / (1 - pef_fraction)) ** 2
# Inspiratory limb (measured): symmetric U-shape below zero
volume_insp = np.linspace(fvc_measured, 0, n_points)
t_insp = np.linspace(0, 1, n_points)
peak_insp_flow = -6.5
flow_insp = peak_insp_flow * np.sin(np.pi * t_insp)
# Predicted normal — expiratory limb
volume_pred_exp = np.linspace(0, fvc_predicted, n_points)
t_pred_exp = volume_pred_exp / fvc_predicted
pef_frac_pred = 0.10
rise_pred = pef_predicted * np.sin(np.pi / 2 * t_pred_exp / pef_frac_pred)
decline_pred = pef_predicted * (1 - (t_pred_exp - pef_frac_pred) / (1 - pef_frac_pred))
flow_pred_exp = np.where(t_pred_exp <= pef_frac_pred, rise_pred, decline_pred)
flow_pred_exp = np.maximum(flow_pred_exp, 0.0)
flow_pred_exp[t_pred_exp > pef_frac_pred] *= (
1 - 0.12 * ((t_pred_exp[t_pred_exp > pef_frac_pred] - pef_frac_pred) / (1 - pef_frac_pred)) ** 2
)
# Predicted normal — inspiratory limb
volume_pred_insp = np.linspace(fvc_predicted, 0, n_points)
peak_insp_pred = -7.5
flow_pred_insp = peak_insp_pred * np.sin(np.pi * t_insp)
# Close each loop into a single connected path
volume_measured = np.concatenate([volume_exp, volume_insp])
flow_measured = np.concatenate([flow_exp, flow_insp])
volume_predicted = np.concatenate([volume_pred_exp, volume_pred_insp])
flow_predicted = np.concatenate([flow_pred_exp, flow_pred_insp])
source_measured = ColumnDataSource(data={"volume": volume_measured, "flow": flow_measured})
source_predicted = ColumnDataSource(data={"volume": volume_predicted, "flow": flow_predicted})
# Landmark points on the measured curve
pef_idx = int(np.argmax(flow_exp))
pef_volume = volume_exp[pef_idx]
pef_flow = flow_exp[pef_idx]
fev1_idx = int(np.argmin(np.abs(volume_exp - fev1_measured)))
fev1_flow = flow_exp[fev1_idx]
# Plot
p = figure(
width=3200,
height=1800,
title="spirometry-flow-volume · python · bokeh · anyplot.ai",
x_axis_label="Volume (L)",
y_axis_label="Flow (L/s)",
toolbar_location=None,
min_border_bottom=160,
min_border_left=180,
min_border_top=110,
min_border_right=60,
)
# Diagnostic gap: shade between measured and predicted expiratory limbs
pred_exp_interp = np.interp(volume_exp, volume_pred_exp, flow_pred_exp)
patch_vol = np.concatenate([volume_exp, volume_exp[::-1]])
patch_flow = np.concatenate([flow_exp, pred_exp_interp[::-1]])
p.patch(x=patch_vol, y=patch_flow, fill_color=BRAND, fill_alpha=0.10, line_color=None)
# Zero-flow reference line
p.line(
x=[-0.2, max(fvc_measured, fvc_predicted) + 0.3], y=[0, 0], line_color=INK_MUTED, line_width=2, line_dash="dotted"
)
# Predicted normal loop (dashed, background)
r_pred = p.line(
x="volume",
y="flow",
source=source_predicted,
line_color=PREDICTED,
line_width=5,
line_dash="dashed",
line_alpha=0.85,
)
# Measured loop (solid, foreground)
r_meas = p.line(x="volume", y="flow", source=source_measured, line_color=BRAND, line_width=6)
# PEF focal marker + label
p.scatter(x=[pef_volume], y=[pef_flow], size=26, fill_color=ACCENT_PEF, line_color=PAGE_BG, line_width=4)
p.add_layout(
Label(
x=pef_volume,
y=pef_flow,
text=f"PEF = {pef_measured:.1f} L/s",
text_font_size="30pt",
text_color=ACCENT_PEF,
text_font_style="bold",
x_offset=24,
y_offset=18,
)
)
# FEV1 marker + label
p.scatter(x=[fev1_measured], y=[fev1_flow], size=22, fill_color=ACCENT_FEV1, line_color=PAGE_BG, line_width=4)
p.add_layout(
Label(
x=fev1_measured,
y=fev1_flow,
text=f"FEV1 = {fev1_measured:.1f} L",
text_font_size="30pt",
text_color=ACCENT_FEV1,
text_font_style="bold",
x_offset=22,
y_offset=-44,
)
)
# Clinical values box — placed in the empty upper region, clear of both limbs
clinical_text = (
f"FVC = {fvc_measured:.1f} L\n"
f"FEV1 = {fev1_measured:.1f} L\n"
f"FEV1/FVC = {fev1_measured / fvc_measured:.0%}\n"
f"PEF = {pef_measured:.1f} L/s"
)
p.add_layout(
Label(
x=3.0,
y=9.6,
text=clinical_text,
text_font_size="28pt",
text_color=INK,
text_font_style="bold",
text_baseline="top",
background_fill_color=ELEVATED_BG,
background_fill_alpha=0.92,
border_line_color=INK_SOFT,
border_line_alpha=0.35,
padding=18,
)
)
# Legend
legend = Legend(
items=[LegendItem(label="Measured", renderers=[r_meas]), LegendItem(label="Predicted Normal", renderers=[r_pred])],
location="bottom_right",
label_text_font_size="34pt",
label_text_color=INK_SOFT,
glyph_width=70,
glyph_height=34,
spacing=16,
padding=22,
background_fill_color=ELEVATED_BG,
background_fill_alpha=0.9,
border_line_color=INK_SOFT,
border_line_alpha=0.35,
)
p.add_layout(legend)
# Hover tool — Bokeh's interactive inspection (retained in the HTML artifact)
p.add_tools(HoverTool(tooltips=[("Volume", "@volume{0.2f} L"), ("Flow", "@flow{0.2f} L/s")], mode="mouse"))
# Style — theme-adaptive chrome
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.major_label_text_font_size = "34pt"
p.yaxis.major_label_text_font_size = "34pt"
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.axis_line_width = 2
p.yaxis.axis_line_width = 2
# Grid — subtle y-grid only for reading flow values
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = INK
p.ygrid.grid_line_alpha = 0.15
# Backgrounds
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = None
# Save — write interactive HTML, then screenshot via headless Chrome
output_file(f"plot-{THEME}.html")
save(p)
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)
# CDP override forces an exact W×H viewport regardless of outer window chrome
driver.execute_cdp_cmd(
"Emulation.setDeviceMetricsOverride", {"width": W, "height": H, "deviceScaleFactor": 1, "mobile": False}
)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Part of Spirometry Flow-Volume Loop on anyplot.ai.