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: matplotlib 3.11.0 | Python 3.13.13
Quality: 93/100 | Updated: 2026-06-17
"""
import os
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import FancyArrowPatch
# 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
BRAND = "#009E73" # measured loop — always first series
MATTE_RED = "#AE3030" # semantic anchor: obstruction / loss
# Data — simulate a realistic flow-volume loop (mild obstruction pattern)
np.random.seed(42)
# Measured values (typical adult, mild obstruction)
fvc = 4.2 # Forced Vital Capacity (L)
pef = 8.5 # Peak Expiratory Flow (L/s)
fev1 = 3.1 # FEV1 (L)
# Predicted normal values
fvc_pred = 4.8
pef_pred = 10.2
fev1_pred = 4.0
N = 200
def expiratory_limb(capacity, peak, n=N):
"""Sharp rise to PEF then a roughly linear decline (ascending volume)."""
vol = np.linspace(0, capacity, n)
t = vol / capacity
raw = (1 - np.exp(-30 * t)) * (1 - t) ** 0.5
return vol, raw / raw.max() * peak
def inspiratory_limb(capacity, peak, n=N):
"""Symmetric U-shaped curve below zero flow (ascending volume)."""
vol = np.linspace(0, capacity, n)
t = vol / capacity
return vol, -peak * np.sin(np.pi * t) ** 0.8
vol_exp_m, flow_exp_m = expiratory_limb(fvc, pef)
vol_insp_m, flow_insp_m = inspiratory_limb(fvc, 5.5)
vol_exp_p, flow_exp_p = expiratory_limb(fvc_pred, pef_pred)
vol_insp_p, flow_insp_p = inspiratory_limb(fvc_pred, 6.5)
# Closed measured loop: expiration (0→FVC) then inspiration back (FVC→0)
vol_loop_m = np.concatenate([vol_exp_m, vol_insp_m[::-1]])
flow_loop_m = np.concatenate([flow_exp_m, flow_insp_m[::-1]])
vol_loop_p = np.concatenate([vol_exp_p, vol_insp_p[::-1]])
flow_loop_p = np.concatenate([flow_exp_p, flow_insp_p[::-1]])
# Peak Expiratory Flow point on the measured curve
pef_idx = int(np.argmax(flow_exp_m))
pef_volume, pef_flow = vol_exp_m[pef_idx], flow_exp_m[pef_idx]
# Common grid for obstruction-gap fills — single interpolation per limb
grid = np.linspace(0, fvc_pred, N)
exp_m_g = np.interp(grid, vol_exp_m, flow_exp_m, right=0.0)
exp_p_g = np.interp(grid, vol_exp_p, flow_exp_p)
insp_m_g = np.interp(grid, vol_insp_m, flow_insp_m, right=0.0)
insp_p_g = np.interp(grid, vol_insp_p, flow_insp_p)
# Plot — landscape 3200×1800
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Obstruction gap between predicted and measured limbs
ax.fill_between(grid, exp_m_g, exp_p_g, color=MATTE_RED, alpha=0.13, zorder=1, label="Obstruction gap")
ax.fill_between(grid, insp_m_g, insp_p_g, color=MATTE_RED, alpha=0.09, zorder=1)
# Zero-flow reference line
ax.axhline(y=0, color=INK_SOFT, linewidth=0.8, alpha=0.5, zorder=1)
# Predicted normal loop (dashed muted reference)
ax.plot(vol_loop_p, flow_loop_p, color=INK_MUTED, linewidth=1.6, linestyle="--", zorder=2, label="Predicted Normal")
# Measured loop (solid brand green)
ax.plot(vol_loop_m, flow_loop_m, color=BRAND, linewidth=2.5, solid_capstyle="round", zorder=3, label="Measured")
# Directional arrows along the measured loop
for vol, flow, idx in (
(vol_exp_m, flow_exp_m, N // 5),
(vol_exp_m, flow_exp_m, int(N * 0.65)),
(vol_insp_m[::-1], flow_insp_m[::-1], N // 2),
):
ax.add_patch(
FancyArrowPatch(
(vol[idx], flow[idx]),
(vol[idx + 4], flow[idx + 4]),
arrowstyle="-|>",
mutation_scale=11,
color=BRAND,
linewidth=2.0,
zorder=4,
)
)
# Mark PEF
ax.scatter(pef_volume, pef_flow, s=90, color=BRAND, edgecolors=PAGE_BG, linewidth=1.2, zorder=5)
ax.annotate(
f"PEF = {pef_flow:.1f} L/s",
xy=(pef_volume, pef_flow),
xytext=(pef_volume + 0.55, pef_flow - 0.7),
fontsize=8,
fontweight="bold",
color=INK,
arrowprops={"arrowstyle": "->", "color": INK_SOFT, "lw": 1.2, "connectionstyle": "arc3,rad=0.2"},
)
# Breathing-direction labels
ax.text(2.1, pef * 0.82, "EXPIRATION →", fontsize=8, color=INK_MUTED, fontweight="bold", ha="center")
ax.text(2.1, -5.5 * 0.72, "← INSPIRATION", fontsize=8, color=INK_MUTED, fontweight="bold", ha="center")
# Clinical values text box
textstr = (
f"FVC = {fvc:.1f} L (pred {fvc_pred:.1f})\n"
f"FEV₁ = {fev1:.1f} L (pred {fev1_pred:.1f})\n"
f"FEV₁/FVC = {fev1 / fvc:.0%}\n"
f"PEF = {pef_flow:.1f} L/s (pred {pef_pred:.1f})"
)
ax.text(
0.975,
0.96,
textstr,
transform=ax.transAxes,
fontsize=8,
color=INK,
va="top",
ha="right",
family="monospace",
bbox={
"boxstyle": "round,pad=0.5",
"facecolor": ELEVATED_BG,
"edgecolor": INK_SOFT,
"alpha": 0.95,
"linewidth": 0.8,
},
)
# Axes & chrome
ax.set_xlabel("Volume (L)", fontsize=10, color=INK)
ax.set_ylabel("Flow (L/s)", fontsize=10, color=INK)
ax.set_title("spirometry-flow-volume · python · matplotlib · anyplot.ai", fontsize=12, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=8, colors=INK_SOFT, labelcolor=INK_SOFT)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
ax.spines[s].set_color(INK_SOFT)
ax.yaxis.grid(True, alpha=0.15, color=INK, linewidth=0.8)
leg = ax.legend(fontsize=8, loc="lower left", framealpha=0.95)
leg.get_frame().set_facecolor(ELEVATED_BG)
leg.get_frame().set_edgecolor(INK_SOFT)
plt.setp(leg.get_texts(), color=INK_SOFT)
fig.subplots_adjust(left=0.07, right=0.97, top=0.91, bottom=0.11)
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
Part of Spirometry Flow-Volume Loop on anyplot.ai.