pyspiro 0.9.1

pyspiro

pyspiro

pyspiro is a Python package implementing lung function reference equations for spirometry, static lung volumes, diffusion capacity, and oscillometry. It provides predicted values, z-scores, % predicted, lower limits of normal (LLN), and upper limits of normal (ULN) for research and clinical data analysis pipelines.

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[!WARNING] For research use only. pyspiro is intended exclusively for scientific research and data analysis. It must not be used as a clinical decision-support tool, for patient diagnosis, or for any medical or clinical purpose. The results produced by this package have not been validated for clinical use and do not constitute medical advice. Always consult a qualified healthcare professional for clinical interpretation of lung function tests.

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Available reference equations

Spirometry

Class	Population	Age range	Publication
BOWERMAN_2022	Race-neutral (GLI global)	3–95 y	Bowerman et al. 2023, DOI: 10.1164/rccm.202205-0963OC
AGARWAL_2020	Western Indian (rural Pune)	20–80 y	Agarwal et al. 2020, DOI: 10.1183/13993003.02129-2019
JO_2018	Korean (KNHANES IV & V)	19–90 y	Jo et al. 2018, DOI: 10.3346/jkms.2018.33.e16
JIAN_2017	Han Chinese	4–80 y	Jian et al. 2017, DOI: 10.21037/jtd.2017.10.110
DESAI_2016	Western Indian (Mumbai)	18–82 y (M) / 18–72 y (F)	Desai et al. 2016, DOI: 10.1016/j.ijtb.2016.08.005
KUBOTA_2014	JRS (Japanese)	17–95 y	Kubota et al. 2014, DOI: 10.1016/j.resinv.2014.03.003
CHHABRA_2014	Northern Indian (Delhi, Punjab, Haryana, UP)	18–71 y (M) / 18–65 y (F)	Chhabra et al. 2014, DOI: 10.5005/ijcdas-56-4-221
GLI_2012	Multi-ethnic	3–95 y	Quanjer et al. 2012, DOI: 10.1183/09031936.00080312
KUSTER_2008	Swiss LuftiBus	18–80 y	Kuster et al. 2008, DOI: 10.1183/09031936.00091407
CHOI_2005	Korean (KNHANES 2001–2002)	18–80 y	Choi et al. 2005, DOI: 10.4046/trd.2005.58.3.230
HANKINSON_1999	NHANES III (US); Caucasian, Black, Mexican-American	8–80 y	Hankinson et al. 1999, DOI: 10.1164/ajrccm.159.1.9712108
QUANJER_1995	White European children	6–18 y	Quanjer et al. 1995, DOI: 10.1002/ppul.1950190209
WANG_1993	White & Black children (US)	6–18 y (M) / 7–18 y (F)	Wang et al. 1993, DOI: 10.1002/ppul.1950150204
ECCS_1993	European Caucasian	18–70 y	Quanjer et al. 1993, DOI: 10.1183/09041950.005s1693
ROBERTS_1991	White urban (UK)	18–86 y	Roberts et al. 1991, DOI: 10.1136/thx.46.9.643
ZAPLETAL_1987	European children	6–18 y	Zapletal 1987, DOI: 10.1159/isbn.978-3-318-04125-5
KNUDSON_1983	Caucasian (US)	6–90 y (M) / 6–88 y (F)	Knudson et al. 1983, PMID: 6859656
CRAPO_1981	Caucasian (US), non-smokers	15–91 y (M) / 17–84 y (F)	Crapo et al. 1981, PMID: 7271065
WARWICK_1980	Caucasian children (US)	0–18 y	Warwick 1980, PMID: 7374636
HSU_1979	White, Black, Mexican-American	7–20 y (M) / 7–18 y (F)	Hsu et al. 1979, DOI: 10.1016/S0022-3476(79)80075-X
MORRIS_1973	Caucasian (US), non-smokers	20–90 y	Morris et al. 1973, PMID: 5540840
CHERNIACK_1972	Caucasian (US)	15–79 y	Cherniack & Raber 1972, DOI: 10.1164/arrd.1972.106.1.38
POLGAR_1971	Children	4–17 y	Polgar & Promadhat 1971, DOI: 10.7326/0003-4819-75-5-819_2

Note on classic equations (POLGAR_1971 – QUANJER_1995): These are regression-based equations from the pre-LMS era. Only percent() returns a value; lln(), uln(), and zscore() return pd.NA because no lower/upper limits of normal were published. Among polynomial equations, HANKINSON_1999, KUSTER_2008, CHHABRA_2014, and DESAI_2016 provide LLN.

Parameter availability matrix

✓ = available, — = not available

Equation	FVC	FEV1	FEV0.5	FEV0.75	FEV3	FEV6	SVC	VC	FIVC	FEV1/FVC	FEV1/FEV6	FEV0.75/FVC	FEV3/FVC
BOWERMAN_2022	✓	✓	—	—	—	—	—	—	—	✓	—	—	—
AGARWAL_2020	✓	✓	—	—	—	—	—	—	—	✓	—	—	—
JO_2018	✓	✓	—	—	—	—	—	—	—	✓	—	—	—
JIAN_2017	✓	✓	—	—	—	—	—	—	—	✓	—	—	—
DESAI_2016	✓	✓	—	—	—	—	—	—	—	✓	—	—	—
KUBOTA_2014	✓	✓	—	—	—	—	—	✓	—	✓	—	—	—
CHHABRA_2014	✓	✓	—	—	—	—	—	—	—	✓	—	—	—
GLI_2012	✓	✓	—	✓	—	—	—	—	—	✓	—	✓	—
KUSTER_2008	✓	✓	—	—	—	—	—	—	—	✓	—	—	—
CHOI_2005	✓	✓	—	—	—	✓	—	—	—	✓	—	—	—
HANKINSON_1999	✓	✓	—	—	—	✓	—	—	—	✓	✓	—	—
QUANJER_1995	✓	✓	—	—	—	—	—	—	—	✓	—	—	—
WANG_1993	✓	✓	—	—	—	—	—	—	—	✓	—	—	—
ECCS_1993	✓	✓	—	—	—	—	—	—	✓	✓	—	—	—
ROBERTS_1991	✓	✓	—	—	—	—	—	—	—	✓	—	—	—
ZAPLETAL_1987	✓	✓	—	—	—	—	✓	—	—	✓	—	—	—
KNUDSON_1983	✓	✓	—	—	—	—	—	—	—	✓	—	—	—
CRAPO_1981	✓	✓	✓	—	✓	—	—	—	—	✓	—	—	✓
WARWICK_1980	✓	✓	—	—	—	—	—	—	—	✓	—	—	—
HSU_1979	✓	✓	—	—	—	—	—	—	—	—	—	—	—
MORRIS_1973	✓	✓	—	—	—	—	—	—	—	✓	—	—	—
CHERNIACK_1972	✓	✓	—	—	—	—	—	—	—	—	—	—	—
POLGAR_1971	✓	✓	—	—	—	—	—	—	—	—	—	—	—

Equation	FEF25-75%	FEF25%	FEF50%	FEF75%	PEF	MVV	FET	LLN / z-score
BOWERMAN_2022	—	—	—	—	—	—	—	✓
AGARWAL_2020	—	—	—	—	—	—	—	✓
JO_2018	—	—	—	—	—	—	—	✓
JIAN_2017	✓	—	—	—	✓	—	—	✓
DESAI_2016	✓	—	✓	✓	✓	—	—	✓
KUBOTA_2014	—	—	—	—	—	—	—	✓
CHHABRA_2014	✓	—	✓	✓	✓	—	—	✓
GLI_2012	✓	—	—	✓	—	—	—	✓
KUSTER_2008	—	✓	✓	✓	✓	—	—	✓
CHOI_2005	—	—	—	—	—	—	—	—
HANKINSON_1999	✓	—	—	—	✓	—	—	✓
QUANJER_1995	—	—	—	—	—	—	—	—
WANG_1993	✓	—	—	—	—	—	—	—
ECCS_1993	✓	✓	✓	✓	✓	—	—	—
ROBERTS_1991	—	—	✓	—	✓	—	—	—
ZAPLETAL_1987	✓	✓	✓	✓	✓	✓	—	—
KNUDSON_1983	✓	—	✓	✓	—	—	—	—
CRAPO_1981	✓	—	—	—	—	—	—	—
WARWICK_1980	—	—	✓	✓	✓	—	✓	—
HSU_1979	✓	—	—	—	✓	—	—	—
MORRIS_1973	✓	—	—	—	—	—	—	—
CHERNIACK_1972	✓	✓	✓	✓	✓	✓	—	—
POLGAR_1971	✓	—	—	—	✓	✓	—	—

AGARWAL_2020 uses pre-computed lookup tables (integer age 20–80, integer height 137–185 cm); inputs are rounded to the nearest integer. lln() returns the 5th centile; uln() is approximated as 2×predicted − p5. zscore() uses the normal approximation (accurate for males; approximate for females where non-normal GAMLSS families were fitted). lms() returns (NA, NA, NA).
JIAN_2017 FEV1FVC is expressed as a percentage (0–100), not a unitless ratio. Pass the measured FEV1/FVC in % (e.g. 83.0) for percent(), zscore(), lln(), and uln(); these methods return LLN/ULN in % as well. MMEF corresponds to FEF25-75%.
DESAI_2016 FEV1FVC is expressed as a percentage (0–100). Weight is required for male PEFR and female FEF75; pass weight=None for all other parameters. LLN is computed as predicted − 1.645 × SE.
CHHABRA_2014 FEV1FVC is expressed as a percentage (0–100). Weight is required for male FVC, male FEF75, and male FEV1FVC; pass weight=None for all other parameters. LLN is computed as predicted − 1.645 × SEE.
KUSTER_2008 names its flow parameters MEF75 (= FEF25%), MEF50 (= FEF50%), and MEF25 (= FEF75%).
CHOI_2005 FEV1FVC is expressed as a percentage (0–100). Weight is required for FVC and FEV6. lln(), uln(), and zscore() return pd.NA (no limits of normal were published). HSU_1979 and WANG_1993 require an ethnicity argument.
WANG_1993 currently implements the Male White subgroup only; other subgroups can be added to wang_1993_coefficients.csv.

Multiple breath washout (MBW)

Class	Population	Age range	Parameters	Publication
RAMSEY_2024	GLI international (race-neutral)	2–80 y	LCI, FRC	Ramsey et al. 2024, DOI: 10.1183/13993003.00524-2024

RAMSEY_2024 uses the GAMLSS BCCG model with age-dependent spline corrections from the GLI 2024 supplementary material. LCI has no sex or height dependence; FRC depends on sex, age, and height.

Lung diffusion capacity

Class	Population	Publication
GLI_2017	Caucasian, age 5–80 y	Stanojevic et al. 2017, DOI: 10.1183/13993003.00010-2017
SCAPIS_2023	Swedish, age 50–65 y	Malinovschi et al. 2023, DOI: 10.1164/rccm.202212-2341OC

Static lung volumes

Class	Population	Publication
GLI_2021	European ancestry, age 5–80 y	Hall et al. 2021, DOI: 10.1183/13993003.00289-2020

Oscillometry (IOS/FOT)

Class	Population	Age / height range	Parameters	Publication
SCHULZ_2013	KORA (German), adults	40–65 y	R10/15/25/35, X10/20/25/35	Schulz et al. 2013, DOI: 10.1371/journal.pone.0063366
CALOGERO_2013	Caucasian children (Perth/Viterbo)	92–159 cm	Rrs6/8/10, Xrs6/8/10, AX, Fres	Calogero et al. 2013, DOI: 10.1002/ppul.22699

CALOGERO_2013 uses direct height+sex regression with log/sqrt transformations (not LMS). Z-scores are on the transformed scale; positive z always means worse than predicted. percent() is defined for Rrs, AX, and Fres; it returns pd.NA for Xrs (signed values). predicted() returns the median value directly.

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Severity classifiers

Class	Basis	Stages / outputs
ATS_ERS_2022	FEV1/FVC, FVC, FEV1, TLC z-scores	Normal / Obstructive / Restrictive / Mixed / Non-specific
LF_SEVERITY_2022	Any lung function z-score (parameter-agnostic)	Normal / Mild / Moderate / Severe
BDR_2022	Pre/post FEV1 and/or FVC vs predicted	Positive (FEV1) / Positive (FVC) / Positive (FEV1 and FVC) / Negative
GOLD	FEV1 % predicted (COPD)	I–IV (mild to very severe)
GOLD_ABE	Exacerbation history + CAT / mMRC (COPD)	A / B / E
STAR	FEV1/FVC ratio (COPD)	I–IV (mild to very severe)
BODE	BMI + FEV1 % predicted + mMRC + 6MWT (COPD)	Score 0–10, Quartiles 1–4
GAP	Sex + Age + FVC % predicted + DLCO % predicted (IPF)	Score 0–8, Stages I–III
WODEHOUSE_2003	nNO in ppb (PCD screening)	PCD range / Normal
PCD_SEVERITY ⚠️	LCI z-score + FEV1 z-score + nNO (PCD monitoring)	Mild / Moderate / Severe / Inconclusive

⚠️ PCD_SEVERITY is experimental. No PCD-specific severity staging system is currently endorsed by the ERS, ATS, or any other professional society. The combination logic and the specific thresholds used here (LCI z > 3.0 for severe; FEV1 z < −2.5 for severe) are custom-designed and have not been externally validated or peer-reviewed as a staging instrument. The cited references (Ramsey 2024, Wodehouse 2003) support the individual input parameters but do not define this combined staging scheme. Use for exploratory research only.

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Outputs

All equation classes expose a consistent scalar API and a vectorised compute() for DataFrames:

Method	Returns
percent(...)	Measured value as % of predicted median
zscore(...)	Z-score relative to the reference distribution
lln(...)	Lower limit of normal (5th percentile)
uln(...)	Upper limit of normal (95th percentile)
lms(...)	Raw (L, M, S) triplet (LMS equations only)
all(...)	Tuple of (% predicted, z-score, LLN, ULN)
compute(df, parameter, ...)	Vectorised apply over a DataFrame; returns a DataFrame of metrics

Conventions: sex as integer (0 = female, 1 = male); age in years; height in cm; measured values in their natural units (L, L/s, mmol/min/kPa, etc.). FEV1/FVC ratios are passed as a fraction (0–1) for LMS-based equations.

compute() column-mapping arguments

Argument	Default	Notes
sex_col	'sex'	Integer column: 0 = female, 1 = male
age_col	'age'	Numeric, years
height_col	'height'	Numeric, cm
value_col	None	Required for percent / zscore
ethnicity_col	None	Required for ethnicity-stratified equations (GLI_2012, HANKINSON_1999)
weight_col	None	Required for SCHULZ_2013 lln / uln
metrics	('percent','zscore','lln','uln')	Any subset

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Installation

pip install pyspiro

For centile chart visualisation, install the optional dependencies:

pip install pyspiro[viz]   # adds matplotlib and scipy

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Usage examples

LMS-based equations (GLI_2012, BOWERMAN_2022, GLI_2017, GLI_2021, SCAPIS_2023, KUBOTA_2014)

from pyspiro import GLI_2012, BOWERMAN_2022

# Multi-ethnic equation — ethnicity_col required
gli = GLI_2012()
results = gli.compute(
    df,
    GLI_2012.Parameters.FEV1,
    value_col='fev1',
    ethnicity_col='eth',     # 1=Caucasian, 2=African-American, 3=NE Asian, 4=SE Asian
)
# DataFrame with columns: percent, zscore, lln, uln — same index as df
df[['fev1_pct', 'fev1_z', 'fev1_lln', 'fev1_uln']] = results

# Race-neutral — no ethnicity_col needed
bow = BOWERMAN_2022()
df[['fvc_pct', 'fvc_z', 'fvc_lln', 'fvc_uln']] = bow.compute(
    df, BOWERMAN_2022.Parameters.FVC, value_col='fvc')

Column name defaults are sex, age, height; override any that differ in your DataFrame:

results = gli.compute(
    df, GLI_2012.Parameters.FEV1,
    sex_col='gender', age_col='age_years', height_col='ht_cm',
    value_col='fev1', ethnicity_col='eth',
    metrics=('percent', 'lln'),
)

Polynomial equations (KUSTER_2008, HANKINSON_1999)

from pyspiro import KUSTER_2008, HANKINSON_1999

# KUSTER_2008: no ethnicity stratification
kus = KUSTER_2008()
df['fev1_pct'] = kus.compute(
    df, KUSTER_2008.Parameters.FEV1,
    value_col='fev1', metrics=('percent',))['percent']
df['fev1_lln'] = kus.compute(
    df, KUSTER_2008.Parameters.FEV1_LLN,
    value_col='fev1', metrics=('lln',))['lln']

# HANKINSON_1999: ethnicity_col required (1=Caucasian, 2=African-American, 3=Mexican-American)
han = HANKINSON_1999()
df[['fvc_pct', 'fvc_z', 'fvc_lln', 'fvc_uln']] = han.compute(
    df, HANKINSON_1999.Parameters.FVC,
    value_col='fvc', ethnicity_col='eth')

Oscillometry (SCHULZ_2013)

SCHULZ_2013 uses direct percentile regression. compute() produces LLN (5th) and ULN (95th); the median (50th) is available via percentiles().

from pyspiro import SCHULZ_2013
import pandas as pd

s = SCHULZ_2013()

# p05 and p95 via compute()
result = s.compute(
    df, SCHULZ_2013.Parameters.R10,
    weight_col='weight', metrics=('lln', 'uln'))
df['R10_p05'] = result['lln']
df['R10_p95'] = result['uln']

# p50 via apply (no single-metric method for the median)
df['R10_p50'] = df.apply(
    lambda r: s.percentiles(r.sex, r.age, r.height, r.weight,
                            SCHULZ_2013.Parameters.R10)[1], axis=1)

Scalar methods (single patient)

All equations still support per-call scalar methods for single-patient use:

gli = GLI_2012()
pct = gli.percent(1, 40, 175, 1, GLI_2012.Parameters.FEV1, 3.2)
z   = gli.zscore (1, 40, 175, 1, GLI_2012.Parameters.FEV1, 3.2)
lln = gli.lln    (1, 40, 175, 1, GLI_2012.Parameters.FEV1, 3.2)

bow = BOWERMAN_2022()
pct = bow.percent(1, 40, 175, BOWERMAN_2022.Parameters.FEV1, 3.2)  # no ethnicity

kus = KUSTER_2008()
pct = kus.percent(1, 40, 175, 1, KUSTER_2008.Parameters.FEV1, 3.2)

NHANES III (polynomial equations)

import pandas as pd
from pyspiro import HANKINSON_1999

h = HANKINSON_1999()

# Ethnicity: 1 = Caucasian, 2 = African-American, 3 = Mexican-American
df[['fvc_pct', 'fvc_z', 'fvc_lln', 'fvc_uln']] = df.apply(
    lambda x: pd.Series(
        h.all(x.sex, x.age, x.height, 1, h.Parameters.FVC, x.fvc)
    ),
    axis=1
)

Pediatric oscillometry (CALOGERO_2013)

from pyspiro import CALOGERO_2013

c = CALOGERO_2013()

# Single patient (female, age 7, height 120 cm)
pred   = c.predicted(sex=0, age=7.0, height=120.0, parameter=CALOGERO_2013.Parameters.Rrs6)
z      = c.zscore   (sex=0, age=7.0, height=120.0, parameter=CALOGERO_2013.Parameters.Rrs6, value=8.5)
uln    = c.uln      (sex=0, age=7.0, height=120.0, parameter=CALOGERO_2013.Parameters.Rrs6)
lln_x  = c.lln      (sex=0, age=7.0, height=120.0, parameter=CALOGERO_2013.Parameters.Xrs6)  # most negative normal

# Fres (no sex term — same result for male/female):
fres_uln = c.uln(sex=0, age=7.0, height=120.0, parameter=CALOGERO_2013.Parameters.Fres)

# Batch via compute()
df[['rrs6_z', 'rrs6_lln', 'rrs6_uln']] = c.compute(
    df, CALOGERO_2013.Parameters.Rrs6,
    value_col='rrs6', metrics=('zscore', 'lln', 'uln'))

# Note: height is the predictor; age is accepted but ignored in the regression

Multiple breath washout — LCI and FRC (RAMSEY_2024)

from pyspiro import RAMSEY_2024

r = RAMSEY_2024()

# LCI: no sex or height dependence (pass dummy height or actual height — ignored)
lci_z   = r.zscore(sex=0, age=12.0, height=145.0, parameter=RAMSEY_2024.Parameters.LCI, value=8.5)
lci_uln = r.uln   (sex=0, age=12.0, height=145.0, parameter=RAMSEY_2024.Parameters.LCI)
lci_pct = r.percent(sex=0, age=12.0, height=145.0, parameter=RAMSEY_2024.Parameters.LCI, value=8.5)

# FRC: sex and height matter
frc_z   = r.zscore(sex=1, age=35.0, height=178.0, parameter=RAMSEY_2024.Parameters.FRC, value=3.8)
frc_lln = r.lln   (sex=1, age=35.0, height=178.0, parameter=RAMSEY_2024.Parameters.FRC)

# Batch via compute()
df[['lci_z', 'lci_lln', 'lci_uln']] = r.compute(
    df, RAMSEY_2024.Parameters.LCI,
    value_col='lci', metrics=('zscore', 'lln', 'uln'))

PCD screening and severity (WODEHOUSE_2003, PCD_SEVERITY)

from pyspiro import RAMSEY_2024, GLI_2012, WODEHOUSE_2003, PCD_SEVERITY

# nNO classification (Wodehouse 2003)
w = WODEHOUSE_2003()
print(w.classify(nno_ppb=65))    # 'PCD range'
print(w.classify(nno_ppb=750))   # 'Normal'
print(w.zscore(65))               # z-score relative to healthy controls (~−4.8)

# PCD severity staging
r   = RAMSEY_2024()
gli = GLI_2012()
sev = PCD_SEVERITY()

lci_zscore  = r.zscore(sex=0, age=12.0, height=145.0,
                        parameter=RAMSEY_2024.Parameters.LCI, value=9.2)
fev1_zscore = gli.zscore(0, 12.0, 145.0, 1, GLI_2012.Parameters.FEV1, 1.4)

stage = sev.classify(
    lci_zscore  = lci_zscore,   # from RAMSEY_2024
    nno_ppb     = 65,           # measured nNO; ≥200 ppb → 'Inconclusive'
    fev1_zscore = fev1_zscore,  # from any spirometry reference
)
# → 'Mild', 'Moderate', 'Severe', or 'Inconclusive'

COPD severity staging

import pandas as pd
from pyspiro import GLI_2012, GOLD

gli  = GLI_2012()
gold = GOLD()

df['fev1_pct'] = df.apply(
    lambda x: gli.percent(x.sex, x.age, x.height, 1, gli.Parameters.FEV1, x.fev1),
    axis=1
)
df['GOLD'] = df.apply(lambda x: gold.classify(FEV1p=x.fev1_pct), axis=1)
df['GOLD'] = pd.Categorical(df['GOLD'], categories=gold.get_order(), ordered=True)

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Cross-equation comparison

compare_equations() returns a DataFrame comparing % predicted and z-score for a single patient across all specified equations. This is useful for sensitivity analyses and understanding how equation choice affects clinical interpretation.

import pandas as pd
from pyspiro import GLI_2012, BOWERMAN_2022, HANKINSON_1999, compare_equations

patient = pd.Series({
    'sex':       1,     # male
    'age':       45.0,
    'height':    175.0,
    'ethnicity': 1,     # Caucasian
    'FEV1':      2.8,
})

result = compare_equations(
    patient,
    GLI_2012.Parameters.FEV1,
    equations=[GLI_2012(), BOWERMAN_2022(), HANKINSON_1999()],
)
print(result)

         equation  percent_predicted    zscore  applicable
0        GLI_2012              71.12 -2.223992        True
1   BOWERMAN_2022              75.41 -1.685500        True
2  HANKINSON_1999              71.13 -2.450593        True

Parameters are matched across equations by name (e.g. FEV1), so the same physiological parameter is compared even though each equation encodes it with a different internal enum value. An equation is marked applicable=False only when it does not define the requested parameter or the inputs fall outside its valid range.

Pass equations=None to compare against all available equations automatically.

Batch processing a cohort

results = []
for _, row in df.iterrows():
    cmp = compare_equations(row, GLI_2012.Parameters.FEV1,
                            equations=[GLI_2012(), BOWERMAN_2022()])
    cmp['patient_id'] = row['patient_id']
    results.append(cmp)

summary = pd.concat(results, ignore_index=True)

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Centile chart generation

plot_centile_curves() draws lung function percentile curves across the age range supported by an equation. The function returns a matplotlib.figure.Figure and can be saved for publication.

Requires pip install pyspiro[viz].

import matplotlib.pyplot as plt
from pyspiro import GLI_2012, plot_centile_curves

gli = GLI_2012()

fig = plot_centile_curves(
    gli,
    sex=1,            # male
    height=175,       # cm
    ethnicity=1,      # Caucasian
    parameter=gli.Parameters.FEV1,
    title="FEV1 reference values — male, 175 cm, Caucasian (GLI 2012)",
)
fig.savefig("fev1_percentiles.png", dpi=300, bbox_inches="tight")
plt.show()

Customise the plotted percentiles, age window, and figure size:

fig = plot_centile_curves(
    gli,
    sex=0,
    height=165,
    ethnicity=1,
    parameter=gli.Parameters.FVC,
    age_range=(18, 80),
    percentiles=[5, 50, 95],
    figsize=(10, 6),
)

Race-neutral equations (e.g. BOWERMAN_2022) do not require an ethnicity argument:

from pyspiro import BOWERMAN_2022, plot_centile_curves

fig = plot_centile_curves(
    BOWERMAN_2022(),
    sex=1,
    height=175,
    parameter=BOWERMAN_2022.Parameters.FEV1,
)

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Citation

If you use pyspiro in your research, please cite:

Hendrik Pott, Roman Martin. pyspiro v0.9.0. doi:10.5281/zenodo.15519193