pygrowthstandards 0.1.1

A Python library for calculating child growth z-scores and percentiles using WHO and INTERGROWTH-21st standards

pygrowthstandards

A Python library for calculating and visualizing child growth standards using data from the World Health Organization (WHO) and the INTERGROWTH-21st Project.

This toolkit provides a simple and flexible API to assess child growth by calculating z-scores and percentiles for common anthropometric measurements, including height, weight, BMI, and head circumference.

Data Sources

This library implements standards from internationally recognized sources:

• WHO Child Growth Standards: For infants and children from birth to 5 years.
• WHO Growth Reference Data for 5-19 years: For school-aged children and adolescents.
• The INTERGROWTH-21st Project: For newborn, preterm, and postnatal growth.

Features

• Calculate z-scores and percentiles for stature (length/height), weight, BMI, and head circumference.
• Support for both WHO and INTERGROWTH-21st growth standards.
• A simple object-oriented Calculator for tracking a Patient's measurements over time.
• A straightforward functional API for one-off calculations.
• Generate and save customizable growth charts.

Installation

To install the latest stable release from PyPI:

pip install pygrowthstandards

Development Installation

To install for development, clone the repository and install in editable mode using uv:

git clone https://github.com/Yannngn/pygrowthstandards.git
cd pygrowthstandards
uv venv --python 3.11
source .venv/bin/activate
uv sync

Quick Start

Object-Oriented Approach

The object-oriented API is ideal for tracking a patient's growth over time. It uses a Patient object to store measurements and a Plotter to visualize them.

# filepath: main.py
import datetime
from pygrowthstandards.oop.patient import Patient
from pygrowthstandards.oop.measurement import MeasurementGroup
from pygrowthstandards.oop.plotter import Plotter

# 1. Create a Patient
patient = Patient(
    sex="M",
    birthday_date=datetime.date(2022, 1, 1),
)

# 2. Add measurements over time
measurements = [
    MeasurementGroup(date=datetime.date(2022, 7, 1), weight=8.6, stature=68.4, head_circumference=44.5),
    MeasurementGroup(date=datetime.date(2023, 1, 1), weight=10.2, stature=75.7, head_circumference=46.5),
    MeasurementGroup(date=datetime.date(2024, 1, 1), weight=12.6, stature=87.8, head_circumference=48.5),
]
for mg in measurements:
    patient.add_measurements(mg)

# 3. Calculate z-scores for all measurements
patient.calculate_all()

# 4. Display a summary table
print(patient.display_measurements())

# 5. Plot the growth charts
plotter = Plotter(patient)
plotter.plot(
    age_group="0-2",
    measurement_type="stature",
    show=False,
    output_path="stature_growth_chart.png"
)

Example Output

After running the above code, you can view the generated growth chart:

Functional Approach

For quick, single, stateless calculations, the functional API provides direct access to the z-score calculation engine. This is useful when you don't need to track a patient's history.

# filepath: main.py
from pygrowthstandards import functional as F

# Calculate z-scores for various measurements and ages
z1 = F.zscore("stature", 50, "F", age_days=0, gestational_age=280)
z2 = F.zscore("weight", 5, "F", age_days=30)
z3 = F.zscore("head_circumference", 40, "F", age_days=180)
z4 = F.zscore("stature", 80, "F", age_days=365)
z5 = F.zscore("weight", 12, "F", age_days=730)
z6 = F.zscore("head_circumference", 48, "F", age_days=1460)

print(f"{z1:.2f}\n{z2:.2f}\n{z3:.2f}\n{z4:.2f}\n{z5:.2f}\n{z6:.2f}")

The output of this script is:

0.45
1.34
-1.64
2.33
0.36
-0.94

Contributing

Contributions are welcome! Please feel free to open an issue to report a bug or suggest a feature, or submit a pull request with your improvements.

Before contributing, please set up the development environment and run the pre-commit hooks and tests.

# Install hooks
pre-commit install

# Run tests
pytest

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

This package is built upon the publicly available data provided by the World Health Organization (WHO) and The INTERGROWTH-21st Project. We are grateful for their commitment to open data and global health.