ABMPTools (ABINIT-MP Tools)

A Python toolkit for pre-processing, post-processing, and analysis of Fragment Molecular Orbital (FMO) calculations with ABINIT-MP.

Features

IFIE/PIEDA Analysis (getifiepieda, anlfmo, cpf2ifielist, getcharge)

• Distance-filtered IFIE tables for target fragments or molecules
• Fragment–fragment interaction matrices (1:1, 1:N, N:1, N:N)
• Time-series IFIE from MD-FMO trajectory snapshots
• SVD-based interaction decomposition
• Charge extraction from ABINIT-MP logs

CPF Management (cpfmanager, convertcpf, generate_difie, log2cpf)

• Parse and write CPF files (versions 4.201, 7.0 MIZUHO, 10, 23)
• Version conversion between CPF formats
• Residue-based CPF extraction
• Dynamic IFIE (DIFIE) averaging across MD snapshots with mean/σ statistics
• Generate CPF from ABINIT-MP log files

FMO Input Generation (generateajf, pdb2fmo, udf2fmo, setfmo, addsolvfrag)

• Auto-generate AJF input files from PDB structures
• Fragment assignment for proteins and molecular assemblies
• Solvation fragment addition
• Support for sp2 fragmentation and various basis sets

File Format Conversion

• CIF → PDB/XYZ (readcif) with symmetry operations
• ABINIT-MP log → fragment config (log2config, ajf2config)
• PDB editing and serial AJF generation (pdbmodify, ajfserial)

GROMACS ↔ OCTA COGNAC Conversion

• udf2gro: Convert OCTA UDF files to GROMACS format (.gro, .top, .mdp, .itp)
• gro2udf: Convert GROMACS files to OCTA UDF format (supports --from-top mode)
• udfcharge: Transfer per-atom partial charges from a single-molecule UDF to bulk molecules (transfer), or restore a neutralized UDF's charges to a target integer formal charge (restore)

Geometry Optimization (geomopt)

• MacePdbOptimizer: MACE/ASE-based PDB structure optimization
• OpenFFOpenMMMinimizer: OpenFF force-field minimization via OpenMM
• QMOptimizerPySCF: Quantum chemistry optimization with PySCF

Amorphous Structure Building (amorphous, build_amorphous.py)

• Multi-component amorphous system construction (API + polymer / API + API / binary mixture)
• Initial structures from either SMILES (OpenFF conformer generation), external 3D SDF/MOL files (--mol), or PubChem CID / name (--pubchem_cid / --pubchem_name, auto-downloads MMFF94 3D SDF; raises PubChemNo3DError when no 3D conformer exists)
• Packmol-based packing + OpenFF force field parameterization + AM1-BCC charges
• Auto-generates GROMACS inputs and a 5-stage annealing protocol (EM → high-T NVT → high-T NPT → simulated annealing → low-T NPT equilibration)
• Bundled md/run_all.sh drives the MD run; md/wrap_pbc.sh post-processes trajectories with gmx trjconv -pbc mol -ur compact for VMD-friendly *_pbc.xtc / _pbc.gro outputs

Supported ABINIT-MP Versions

• ABINIT-MP v1: Rev.10–23
• ABINIT-MP v2: Rev.4–8

Installation

From PyPI (normal use)

pip install abmptools

Updating:

pip install --upgrade abmptools

Checking what you actually got — run this outside the repository, since a source tree in the current directory shadows the installed package:

cd /tmp
pip show abmptools | head -2
python -c "import abmptools; print(abmptools.__file__)"

If Location and __file__ disagree, another copy is winning.

Optional extras pull in the plotting and chemistry dependencies a given subpackage needs, e.g. pip install 'abmptools[amorphous]'.

From source (development)

Editable install is recommended for day-to-day use and development:

pip install -e .

Non-editable install (e.g. for production deployment):

pip install .

--user is usually unnecessary; pip handles both virtual environments and system Python appropriately.

Installation runs make to compile the optional Fortran shared library for accelerated IFIE/PIEDA reading. If gfortran is not available, the install still succeeds without Fortran acceleration.

Requirements

• Required: Python 3.8+, numpy, pandas
• Optional: UDFManager (OCTA COGNAC), gfortran, OpenBabel, PySCF, ASE, OpenMM, Packmol

Testing

pytest tests/ -v                     # 1013 tests collected (2.11.0 時点)
pytest tests/ -v -k molcalc          # specific module
pytest tests/test_regression.py -v   # regression tests (60 bundled + 16 gated)

See tests/TEST_COVERAGE.md for details.

Regression Tests

tests/test_regression.py compares current CLI output against reference fixtures stored in tests/regression/reference/ (generated from the pre-refactor state). This guards against behavior drift during refactoring.

Covered tools: generateajf, log2cpf, convertcpf, udf2gro, gro2udf, and getifiepieda.

Developer-only tests: the 16 getifiepieda regression cases require external sample data (the internal abmptools-sample repository) at:

../abmptools-sample/sample/getifiepieda/
├── 6lu7-multi-fmolog/    (extracted from abmptools-fmolog-sample.tar.bz2)
├── cd7-fmolog/
├── 6m0j-pb-fmolog/
└── xyzfile/

These tests are automatically skipped when the data is not available, so public CI runs are unaffected.

Samples

Each sample/ subdirectory contains input data and a run.sh / run_sample.sh script:

# FMO / IFIE / CPF samples
cd sample/generateajf            && bash run.sh
cd sample/log2cpf                && bash run.sh
cd sample/generate_difie/TrpCage && bash run.sh
cd sample/convertcpf             && bash run.sh

# Amorphous structure builder samples — see sample/amorphous/README.md for the full index
cd sample/amorphous/pentane_benzene     && bash run_sample.sh   # pentane / benzene mixture (SMILES)
cd sample/amorphous/ketoprofen          && bash run_sample.sh   # ketoprofen (SMILES)
cd sample/amorphous/ketoprofen_pubchem  && bash run_sample.sh   # ketoprofen via PubChem 3D SDF (CID 3825)
cd sample/amorphous/mixture_json        && bash run_sample.sh   # multi-component via JSON config

See docs/amorphous_tutorial.md for the hands-on walk-through and sample/amorphous/ketoprofen/README.md for an annotated run log of the ketoprofen build.

Beyond this package

abmptools covers the generic layer: file formats, established-method utilities, and the analysis and conversion tools around ABINIT-MP. Workflows for methods still under active development are maintained separately, in a private package (moldeck) that builds on this one. It currently covers:

The dependency runs one way — those workflows import abmptools, never the reverse — so nothing here depends on having them.

It is not publicly distributed: it is proprietary and handed out individually, on request and subject to approval. Contact the author below if you have a use for it.

How to cite

If you use ABMPTools in academic or scientific work, please cite the project. GitHub's "Cite this repository" button on the repo home page generates BibTeX / APA / etc. from CITATION.cff.

A peer-reviewed publication and Zenodo DOI will be added on the first release tag; until then, use:

Okuwaki, K. (2026). ABMPTools: a Python toolkit for ABINIT-MP Fragment Molecular Orbital pre/post-processing. https://github.com/kojioku/abmptools

Author

Koji Okuwaki