diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md new file mode 100644 index 0000000..6a14a9f --- /dev/null +++ b/.github/copilot-instructions.md @@ -0,0 +1,198 @@ +# HECSS - GitHub Copilot Instructions + +## Project Overview + +HECSS (High Efficiency Configuration Space Sampler) is a Python library for Markov chain Monte Carlo configuration space sampling using the Metropolis-Hastings algorithm. It provides an efficient alternative to molecular dynamics simulations for creating thermal equilibrium representations of physical systems. + +**Primary Use Case**: Sampling thermodynamic distributions of crystal structures at various temperatures without expensive MD simulations. + +## Technology Stack + +- **Language**: Python 3.6+ +- **Development Framework**: nbdev (notebook-based development) +- **Core Dependencies**: + - ASE (Atomic Simulation Environment) - structure manipulation and calculator interface + - VASP & LAMMPS - DFT and classical MD calculators + - NumPy, SciPy - numerical computing + - spglib - symmetry operations + - tqdm - progress bars + - matplotlib - visualization + - click - CLI interface + +## Development Workflow + +### Notebook-First Development + +**CRITICAL**: This project uses nbdev - all source code is generated from Jupyter notebooks. + +- **DO NOT** edit Python files in `hecss/` directory directly (they are auto-generated) +- **ALWAYS** edit the corresponding `.ipynb` notebook files in the root directory +- Files have header comments indicating which notebook generates them (e.g., `# AUTOGENERATED! DO NOT EDIT! File to edit: 11_core.ipynb`) + +### Notebook to Module Mapping + +- `11_core.ipynb` → `hecss/core.py` (main HECSS sampler logic) +- `12_monitor.ipynb` → `hecss/monitor.py` (monitoring and visualization) +- `02_CLI.ipynb` → `hecss/cli.py` (command-line interface) +- `00_Background.ipynb`, `00_Setup.ipynb` → documentation +- `01_LAMMPS_Tutorial.ipynb`, `01_VASP_Tutorial.ipynb` → tutorials + +### Build Commands + +```bash +# Build library from notebooks +make hecss +# or +nbdev_build_lib + +# Build documentation +make docs +# or +nbdev_build_docs + +# Run tests +make test +# or +nbdev_test_nbs + +# Run tests with specific flags +make test_asap # for ASAP3/OpenKIM tests +make test_vasp # for VASP tests +``` + +## Code Style & Conventions + +### Python Style + +- Follow standard Python conventions (PEP 8) +- Use type hints where appropriate +- Preserve existing code structure and formatting +- Use NumPy-style docstrings + +### Naming Conventions + +- Functions: `snake_case` +- Classes: `PascalCase` (e.g., `HECSS_Sampler`, `HECSS`) +- Constants: `UPPER_CASE` +- Private functions: prefix with `_` + +### Physics & Scientific Computing + +- Use ASE units (`ase.units`) for physical quantities +- Temperature in Kelvin +- Energy in eV (electron volts) +- Forces in eV/Angstrom +- Use `un` as alias for `ase.units` (existing convention) +- Preserve scientific notation and physical constants + +### Key Abstractions + +- **HECSS**: Main user-facing class for running sampling +- **HECSS_Sampler**: Lower-level sampler implementation +- Structures are ASE `Atoms` objects +- Calculators follow ASE calculator interface +- Samples are tuples: `(n, i, displacement, forces, energy)` + +## Testing + +- Tests are embedded in notebooks with `#slow`, `#vasp`, `#asap`, `#interactive` flags +- Use `nbdev_test_nbs` with appropriate flags +- Tests require external calculators (VASP, LAMMPS/ASAP3) - not all tests may run in CI +- Focus on testing sampling convergence and statistical properties + +## Documentation + +- Generated automatically from notebooks using nbdev +- Hosted at: https://jochym.gitlab.io/hecss/ +- API documentation is in notebook cells with `#export` directive +- Examples and tutorials are in dedicated notebook files + +## Repository Structure + +``` +. +├── .github/ # GitHub configuration +├── hecss/ # Auto-generated Python package (DO NOT EDIT) +│ ├── core.py # Main sampling logic +│ ├── monitor.py # Monitoring and plotting +│ └── cli.py # Command-line tools +├── *.ipynb # Source notebooks (EDIT THESE) +├── docs/ # Generated documentation +├── example/ # Example calculations and data +├── data/ # Data files +├── settings.ini # Project configuration +├── setup.py # Package setup +└── Makefile # Build automation +``` + +## Contributing Guidelines + +### Making Changes + +1. Edit the appropriate `.ipynb` notebook file, not the generated `.py` files +2. Run `make hecss` to regenerate Python modules +3. Run `make test` (or targeted tests) to verify changes +4. Run `make docs` to update documentation +5. Keep PRs focused on a single feature or fix +6. Do not mix style changes with functional changes + +### PR Requirements + +- Include tests that demonstrate the fix or feature +- Ensure tests pass +- Update documentation in notebooks if needed +- Clear description of problem and solution +- Reference related issue numbers + +## Special Considerations + +### Calculator Integration + +- VASP calculator requires proper POTCAR files and working VASP installation +- LAMMPS/ASAP3 calculators need OpenKIM models +- Not all calculator tests can run in standard CI environments + +### Performance + +- HECSS is designed for efficiency - avoid unnecessary calculations +- Sampling can be long-running - use progress bars (tqdm) +- Support for parallel sampling may be added in future + +### File Formats + +- Supports ALAMODE DFSET format for displacement-force data +- Uses standard ASE structure formats +- Configuration files use Python ConfigParser format (settings.ini) + +## Common Tasks + +### Adding a New Feature to Core Sampling + +1. Edit `11_core.ipynb` +2. Add function/class with `#export` directive +3. Add to `__all__` list +4. Include docstring and example +5. Run `make hecss && make test` + +### Adding a CLI Command + +1. Edit `02_CLI.ipynb` +2. Use `@click.command()` decorator +3. Add to `console_scripts` in `settings.ini` +4. Run `make hecss` + +### Updating Documentation + +1. Edit the relevant tutorial or documentation notebook +2. Run `make docs` to regenerate +3. Check output in `docs/` directory + +## License + +GPL v3 or later - ensure any contributions are compatible with this license. + +## Contact + +- Author: Paweł T. Jochym (pawel.jochym@ifj.edu.pl) +- Primary Repository: https://gitlab.com/jochym/hecss +- Mirror: https://github.com/jochym/hecss diff --git a/Makefile b/Makefile index 70f58bc..d4a7e98 100644 --- a/Makefile +++ b/Makefile @@ -23,7 +23,7 @@ test_vasp: nbdev_test_nbs --flags vasp release: pypi # conda_release - nbdev_bump_version --part 2 + nbdev_bump_version --part 3 conda_meta: fastrelease_conda_package --do_build false @@ -36,7 +36,7 @@ conda_release: conda_meta conda mambabuild --python 3.8 conda/hecss pypi: dist - twine upload --repository test_hecss dist/* + twine upload --repository hecss dist/* dist: clean python setup.py sdist bdist_wheel diff --git a/README.md b/README.md index a484099..93199e3 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,9 @@ [![Downloads Badge](https://anaconda.org/conda-forge/hecss/badges/downloads.svg)](https://anaconda.org/conda-forge/hecss) [![License Badge](https://anaconda.org/jochym/hecss/badges/license.svg)](https://anaconda.org/jochym/hecss) -HECSS is a Markow chain Monte-Carlo, configuration space sampler using Metropolis-Hastings algorithm for probablity distribution sampling. It provides an alternative way to create representations of systems at thermal equilibrium without running a very expensive molecular dynamics simulation. The theoretical foundation of the code are presented in the section [Background](https://jochym.gitlab.io/hecss/Background) in the [Documentation](https://jochym.gitlab.io/hecss/). More detailed examples are included in the [LAMMPS](https://jochym.gitlab.io/hecss/LAMMPS_Tutorial) and [VASP](https://jochym.gitlab.io/hecss/VASP_Tutorial) tutorials. +HECSS is a Markow chain Monte-Carlo, configuration space sampler using Metropolis-Hastings algorithm for probablity distribution sampling. It provides an alternative way to create representations of systems at thermal equilibrium without running a very expensive molecular dynamics simulation. The theoretical foundation of the code are presented in [SciPost Phys. 10, 129 (2021)](https://scipost.org/SciPostPhys.10.6.129) (short excerpt in [Background](https://jochym.gitlab.io/hecss/Background) in the [Documentation](https://jochym.gitlab.io/hecss/)). More detailed examples are included in the [LAMMPS](https://jochym.gitlab.io/hecss/LAMMPS_Tutorial) and [VASP](https://jochym.gitlab.io/hecss/VASP_Tutorial) tutorials. + +If you use this software in published research please cite the above paper ([BibTeX](https://gitlab.com/jochym/hecss/-/raw/master/scipost.bib)) in your publication. ## A very short example diff --git a/docs/images/output_9_0.png b/docs/images/output_9_0.png index 21b3ab8..d034ff2 100644 Binary files a/docs/images/output_9_0.png and b/docs/images/output_9_0.png differ diff --git a/docs/index.html b/docs/index.html index c3375fc..f98b4a6 100644 --- a/docs/index.html +++ b/docs/index.html @@ -36,7 +36,8 @@ CVersion Badge Downloads Badge License Badge

-

HECSS is a Markow chain Monte-Carlo, configuration space sampler using Metropolis-Hastings algorithm for probablity distribution sampling. It provides an alternative way to create representations of systems at thermal equilibrium without running a very expensive molecular dynamics simulation. The theoretical foundation of the code are presented in the section Background in the Documentation. More detailed examples are included in the LAMMPS and VASP tutorials.

+

HECSS is a Markow chain Monte-Carlo, configuration space sampler using Metropolis-Hastings algorithm for probablity distribution sampling. It provides an alternative way to create representations of systems at thermal equilibrium without running a very expensive molecular dynamics simulation. The theoretical foundation of the code are presented in SciPost Phys. 10, 129 (2021) (short excerpt in Background in the Documentation). More detailed examples are included in the LAMMPS and VASP tutorials.

+

If you use this software in published research please cite the above paper (BibTeX) in your publication.

@@ -156,7 +157,7 @@

A very short example - diff --git a/hecss/__init__.py b/hecss/__init__.py index 98a433b..1ca9536 100644 --- a/hecss/__init__.py +++ b/hecss/__init__.py @@ -1 +1 @@ -__version__ = "0.4.5" +__version__ = "0.5.0.2" diff --git a/index.ipynb b/index.ipynb index 1f57e63..4d6f593 100644 --- a/index.ipynb +++ b/index.ipynb @@ -29,7 +29,9 @@ "[![Downloads Badge](https://anaconda.org/conda-forge/hecss/badges/downloads.svg)](https://anaconda.org/conda-forge/hecss)\n", "[![License Badge](https://anaconda.org/jochym/hecss/badges/license.svg)](https://anaconda.org/jochym/hecss)\n", "\n", - "HECSS is a Markow chain Monte-Carlo, configuration space sampler using Metropolis-Hastings algorithm for probablity distribution sampling. It provides an alternative way to create representations of systems at thermal equilibrium without running a very expensive molecular dynamics simulation. The theoretical foundation of the code are presented in the section [Background](https://jochym.gitlab.io/hecss/Background) in the [Documentation](https://jochym.gitlab.io/hecss/). More detailed examples are included in the [LAMMPS](https://jochym.gitlab.io/hecss/LAMMPS_Tutorial) and [VASP](https://jochym.gitlab.io/hecss/VASP_Tutorial) tutorials." + "HECSS is a Markow chain Monte-Carlo, configuration space sampler using Metropolis-Hastings algorithm for probablity distribution sampling. It provides an alternative way to create representations of systems at thermal equilibrium without running a very expensive molecular dynamics simulation. The theoretical foundation of the code are presented in [SciPost Phys. 10, 129 (2021)](https://scipost.org/SciPostPhys.10.6.129) (short excerpt in [Background](https://jochym.gitlab.io/hecss/Background) in the [Documentation](https://jochym.gitlab.io/hecss/)). More detailed examples are included in the [LAMMPS](https://jochym.gitlab.io/hecss/LAMMPS_Tutorial) and [VASP](https://jochym.gitlab.io/hecss/VASP_Tutorial) tutorials.\n", + "\n", + "If you use this software in published research please cite the above paper ([BibTeX](https://gitlab.com/jochym/hecss/-/raw/master/scipost.bib)) in your publication." ] }, { @@ -94,7 +96,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b6c493e316684902852116384cde6bdf", + "model_id": "61013ee687254a48a1abc136307e025d", "version_major": 2, "version_minor": 0 }, @@ -127,7 +129,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] diff --git a/settings.ini b/settings.ini index b6c1caa..4a79392 100644 --- a/settings.ini +++ b/settings.ini @@ -7,15 +7,15 @@ keywords = physics temperature configurations author = Paweł T. Jochym author_email = pawel.jochym@ifj.edu.pl copyright = Paweł T. Jochym -branch = devel -version = 0.4.5 +branch = master +version = 0.5.0.2 min_python = 3.6 audience = Science/Research language = English custom_sidebar = False license = GPL3 status = 4 -requirements = ase spglib tqdm click matplotlib numpy scipy +requirements = ase spglib tqdm click matplotlib numpy scipy ipython console_scripts = hecss_sampler=hecss.cli:hecss_sampler plot_stats=hecss.cli:plot_stats plot_bands=hecss.cli:plot_bands calculate_xscale=hecss.cli:calculate_xscale nbs_path = . doc_path = docs