The pythermalcomfort Python package is a comprehensive toolkit for calculating thermal comfort indices, heat/cold stress metrics, and thermophysiological responses based on international standards and peer-reviewed research. Designed for researchers, engineers, and building-science professionals, it simplifies complex calculations while promoting accuracy and standards compliance.

If you use pythermalcomfort in your research, please cite it as follows:

Tartarini, F., Schiavon, S., 2020.
pythermalcomfort: A Python package for thermal comfort research.
SoftwareX 12, 100578.
https://doi.org/10.1016/j.softx.2020.100578

• Thermal Comfort Calculations: Supports multiple models, including PMV, PPD, adaptive comfort, and SET.
• Heat and Cold Stress Indices: Calculate UTCI, Heat Index, Wind Chill Index, and Humidex.
• Thermophysiological Models: Includes the two-node (Gagge) and multinode (JOS-3) models to estimate physiological responses such as core temperature, skin temperature, and skin wettedness.
• Standards Compliance: Implements calculations based on ASHRAE 55, ISO 7730, EN 16798, and more.
• Ease of Use: Intuitive API for seamless integration into Python projects.
• Extensive Documentation: Detailed guides, examples, and tutorials.
• Active Development: Regularly updated with new features, improvements, and bug fixes.
• Open Source: Licensed under the MIT License.

• Accurate Assessments: Reliable thermal comfort and stress evaluations for diverse environments.
• Time-Saving: Automates complex calculations, saving time and effort.
• Versatility: Useful for building science, HVAC design, environmental design, thermal physiology, sports science, and biometeorology.
• Enhanced Decision-Making: Supports data-driven decisions for HVAC systems, building performance, and occupant comfort.

Install pythermalcomfort via pip:

pip install pythermalcomfort

For advanced installation options, refer to the Installation Instructions.

A typical environment includes:

• Python 3.9+ (or the minimum version used in CI)
• NumPy, SciPy, pandas (installed automatically as package dependencies)
• Optional: Matplotlib / other plotting libraries for examples and visualizations

Get started with pythermalcomfort in just a few lines of code:

from pythermalcomfort.models import pmv_ppd_iso, utci
# Calculate PMV and PPD using ISO 7730 standard
result = pmv_ppd_iso(
    tdb=25,  # Dry Bulb Temperature in °C
    tr=25,  # Mean Radiant Temperature in °C
    vr=0.1,  # Relative air speed in m/s
    rh=50,  # Relative Humidity in %
    met=1.4,  # Metabolic rate in met
    clo=0.5,  # Clothing insulation in clo
    model="7730-2005"  # Year of the ISO standard
)
print(f"PMV: {result.pmv}, PPD: {result.ppd}")
# Calculate UTCI for heat stress assessment
utci_value = utci(tdb=30, tr=30, v=0.5, rh=50)
print(utci_value)

For more examples and detailed usage, check out models and indices in the models section of the documentation.

If you find this project useful, consider supporting the maintainers. Maintaining an open-source scientific package requires time for development, review, CI, and user support.

• Sponsor via GitHub: https://github.com/sponsors/FedericoTartarini
• Contribute code, tests, or documentation: open a PR against pythermalcomfort
• Report bugs or request features with a minimal reproduction in issues
• Help with testing, translations, or reviewing pull requests
• Star or share the project to increase visibility

Any support—financial or contribution-based—is appreciated and helps keep the project healthy.

We welcome contributions! Whether reporting a bug, suggesting a feature, or submitting a pull request, your input helps make pythermalcomfort better for everyone. See the contributing guide for full contribution instructions.

• Open an issue first for larger features to discuss scope and design.
• Fork the repo and create a feature branch for your work.
• Add tests for new behavior and run the test suite locally.
• Run linters and formatters and fix reported issues.
• Update documentation and changelog entries for public API changes.
• Submit a clear, focused pull request referencing any related issues.

# clone your fork and add upstream remote
git clone git@github.com:your-username/pythermalcomfort.git
cd pythermalcomfort
git remote add upstream git@github.com:CenterForTheBuiltEnvironment/pythermalcomfort.git
git fetch upstream
# create a branch and work on it
git checkout -b Feature/awesome-feature
# run the full test matrix (may be slow)
tox
# run a single test env locally (replace py312 with the env you want)
tox -e py312
# run a subset of pytest tests
pytest -k test_name_fragment
# fix linting/formatting
ruff check --fix
ruff format
docformatter --in-place --wrap-summaries 88 --wrap-descriptions 88 pythermalcomfort/*.py
# commit and push
git add .
git commit -m "feat: short description of change"
git push origin Feature/awesome-feature

• Open an issue on GitHub with a minimal reproduction for bugs.
• Ask questions in PR comments for implementation guidance.
• See the contribution guidelines for detailed guidance on testing, documentation, and changelog expectations.
• For API reference and examples, consult the online docs: https://pythermalcomfort.readthedocs.io/en/latest/

Detailed docs, examples and API references are available at: https://pythermalcomfort.readthedocs.io/en/latest/

pythermalcomfort is released under the MIT License.

Documentation
License
Downloads
Tests
Package