This release is a pre-release and may not be stable for production use.
Wavefunction analysis, made reproducible.
A unified post-processing toolkit for turning quantum-chemistry calculations into traceable, validated, publication-ready results.
Get started · CLI reference · Validation · Citation
openWFN connects scientific analysis, automation, validation evidence, and research output through one typed calculation model. It works locally through the command line or Python, from individual calculations and high-throughput collections.
Analyze · Automate · Validate · Publish
- Analyze molecular structure, orbitals, electron density, atomic populations, electrostatic potential, and derived properties.
- Automate with stable CLI commands, a typed Python API, structured results, resumable batches, and JSON/CSV exports.
- Validate using explicit capability status, parser provenance, transformations, numerical controls, and fixture-backed evidence.
- Publish durable reports, figures, cube files, structures, batch manifests, and portable offline workbenches.
Install
openWFN supports Python 3.10–3.13.
python -m pip install openwfn
openwfn --version
First analysis
openwfn molecule.fchk doctor
openwfn molecule.fchk summary
openwfn molecule.fchk orbitals frontier
openwfn molecule.fchk population mulliken
openwfn molecule.fchk report build report.html
Run a collection of calculations with resumable, structured output:
openwfn batch ./calculations \
--analysis summary \
--analysis frontier \
--output-dir ./results \
--resume
Use JSON when another program will consume the result:
openwfn --format json --output summary.json molecule.fchk summary
Capability map
| Area | Current capability | Status |
|---|---|---|
| Parsing and structure | FCHK records, molecular state, geometry, topology | Stable |
| Orbitals | Alpha/beta frontier energies and HOMO–LUMO gap | Stable |
| Population | Mulliken and symmetric Löwdin populations and charges | Stable |
| Density | Total, alpha, beta, and spin integration and cube export | Validated for active fixtures |
| Electrostatic potential | Nuclear and charge-model point ESP | Stable |
| Grid electronic/total ESP | Numerical Coulomb evaluation | Experimental |
| Automation | Versioned results, batch manifests, discovery, resume, JSON and CSV | Stable |
| Research output | HTML/Markdown reports, tables, figures, structures, and workbench | Stable |
“Validated” is deliberately scoped. Review the validation evidence and limitations before research use.
Input support
Gaussian formatted-checkpoint (.fchk) data currently provides the full electronic wavefunction record used by openWFN analyses. Gaussian .chk is a proprietary binary format; openWFN calls Gaussian's separately installed formchk utility and does not decode it directly.
XYZ, MOL/SDF, and PDB inputs provide structure-only records. Additional quantum-chemistry parsers will be listed as supported only after their implementation and validation evidence are available.
openwfn calculation.chk formchk calculation.fchk
openwfn calculation.fchk convert --to sdf --output molecule.sdf
openwfn calculation.fchk density cube density.cube
Documentation
- First analysis
- CLI workflows
- Complete CLI reference
- Python API
- Scientific methods
- Validation status
- Troubleshooting
Security and privacy
Computations are local, but input and generated artifacts can contain unpublished research. Never post private checkpoint files, credentials, personal filesystem paths, or license details in public issues. Read the security and data-privacy guide.
Development
python -m pip install -e ".[test,docs]"
python -m pytest
python -m ruff check .
python scripts/run_validation.py
python scripts/check_docs.py --root .
python -m mkdocs build --strict
Contributions should include tests, documented units and assumptions, and validation evidence appropriate to the claim. See Contributing.
Citation and license
Cite the exact version used and the project repository; see the citation guide. openWFN is released under the MIT License.
Author: Muhammed Shah Shaji, University of Louisville
Metadata
Release files for openwfn 0.8.0a1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| openwfn-0.8.0a1.tar.gz | 235.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| openwfn-0.8.0a1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 479.6 kB
Release files / openwfn-0.8.0a1.tar.gz
| Download URL | openwfn-0.8.0a1.tar.gz |
|---|---|
| Size | 235.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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PyPI Publish Attestation
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Transparency logRelease files / openwfn-0.8.0a1-py3-none-any.whl
| Download URL | openwfn-0.8.0a1-py3-none-any.whl |
|---|---|
| Size | 244.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 21, 2026.
Transparency log