cli_ofdft

class OFDFTRunResult(xyz_path: Path, energies: Energies, converged: bool, coeffs: Tensor | None, sample: OFData, sample_path: Path | None)

Container summarizing the outcome of a single OFDFT calculation.

__eq__(other)

Return self==value.

__init__(xyz_path: Path, energies: Energies, converged: bool, coeffs: Tensor | None, sample: OFData, sample_path: Path | None) → None

__repr__()

Return repr(self).

_config_to_log_dict(config: Any) → Mapping[str, Any] | None

Convert config-like objects into a mapping suitable for logging.

add_sad_kwargs(ofdft_kwargs: dict, basis_info: BasisInfo) → None

Inject SAD initialization options into the OFDFT configuration.

console_filter(record: dict) → bool

Filter to exclude messages marked as file-only from console output.

Parameters:

record (dict) – Log record.

Returns:

True if the message should be logged to console, False otherwise.

Return type:

bool

extract_group(prefix: str, args: Namespace, cls: type | None = None)

Extract args starting with “prefix_” and optionally instantiate cls.

log_config(base_args, model_args, optimizer_args, logger)

Log the configuration to the logger.

main() → None

Main function to run OFDFT calculations from the command line.

run_ofdft(base_config: BaseConfig, model_config: ModelConfig, optimizer_config: OptimizerConfig, logger_format: str | None = None) → list[OFDFTRunResult]

Run OFDFT calculations using dataclass-based CLI configuration.

This helper instantiates the sample generator, optimizer, and set of input structures from the provided configuration dataclasses before delegating to run_ofdft_from_components(). It is the high-level entry point exposed to the command line interface but remains usable programmatically for automated workflows.

Parameters:

• base_config – Settings describing the molecular inputs and run options.

• model_config – Parameters identifying and configuring the trained model.

• optimizer_config – Hyperparameters controlling the density optimizer.

• logger_format – Optional loguru format string for auxiliary log files.

Returns:

A list of OFDFTRunResult objects, one for each processed XYZ structure.

run_ofdft_from_components(*, base_config: BaseConfig, sample_generator: SampleGenerator, optimizer: Optimizer, xyzfiles: Sequence[Path], model_config: ModelConfig | Mapping[str, Any] | None = None, optimizer_config: OptimizerConfig | Mapping[str, Any] | None = None, logger_format: str | None = None) → list[OFDFTRunResult]

Run OFDFT calculations using pre-instantiated dependencies.

This function executes the end-to-end OFDFT workflow given a set of already constructed components. It is suitable for library use where callers manage model loading, optimizer creation, or input discovery themselves while reusing the orchestration logic implemented for the CLI.

Parameters:

• base_config – Run configuration that supplies shared OFDFT options.

• sample_generator – Prepared SampleGenerator instance.

• optimizer – Optimizer responsible for electron density updates.

• xyzfiles – Sequence of XYZ input paths to be processed.

• model_config – Optional model configuration for logging purposes.

• optimizer_config – Optional optimizer configuration for logging.

• logger_format – Optional loguru format string for per-molecule logs.

Returns:

A list of OFDFTRunResult objects containing energies, density coefficients, and bookkeeping data for each input structure.

save_sample(sample: OFData, energies: Energies, samplefile: Path) → None

Persist the optimized sample to disk after removing intermediate data.