Source code for physicsnemo_curator.run.sequential

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"""Sequential execution backend.

Processes pipeline items one at a time in a simple for-loop.
This is the default when ``n_jobs=1``.
"""

from __future__ import annotations

from typing import TYPE_CHECKING, Any, ClassVar

from physicsnemo_curator.run.base import (
    RunBackend,
    RunConfig,
    WorkerProgressDisplay,
    _flush_filters,
)

if TYPE_CHECKING:
    from physicsnemo_curator.core.base import Pipeline




class SequentialBackend(RunBackend):
    """Execute pipeline items sequentially in a for-loop.

    This backend has no dependencies and is always available. It's the
    safest choice for pipelines with stateful filters that need to
    accumulate results across all items.
    """

    name: ClassVar[str] = "sequential"
    description: ClassVar[str] = "Sequential for-loop execution (no parallelism)"
    requires: ClassVar[tuple[str, ...]] = ()



    def run(
        self,
        pipeline: Pipeline[Any],
        config: RunConfig,
    ) -> list[list[str]]:
        """Execute pipeline indices sequentially.

        Parameters
        ----------
        pipeline : Pipeline
            The pipeline to execute.
        config : RunConfig
            Execution configuration.

        Returns
        -------
        list[list[str]]
            Sink outputs, one list per index.
        """
        indices = config.indices if config.indices is not None else list(range(len(pipeline)))

        display = WorkerProgressDisplay(
            total=len(indices),
            n_workers=1,
            enabled=config.progress,
        )

        results: list[list[str]] = []
        try:
            for idx in indices:
                display.worker_start(0, idx)
                results.append(pipeline[idx])
                _flush_filters(pipeline, idx)
                display.worker_done(0)
        finally:
            display.close()

        return results