# manifest_extractor

## nominal.experimental.extractor.decorators.manifest_extractor(fn: Callable[[...], None], *, default_timestamp_column: str | None = None, default_timestamp_type: Iso8601 | Epoch | Relative | Custom | Literal['iso_8601', 'epoch_picoseconds', 'epoch_nanoseconds', 'epoch_microseconds', 'epoch_milliseconds', 'epoch_seconds', 'epoch_minutes', 'epoch_hours', 'epoch_days'] | None = None) → Extractor[ManifestExtractorContext]

## nominal.experimental.extractor.decorators.manifest_extractor(*, default_timestamp_column: str | None = None, default_timestamp_type: Iso8601 | Epoch | Relative | Custom | Literal['iso_8601', 'epoch_picoseconds', 'epoch_nanoseconds', 'epoch_microseconds', 'epoch_milliseconds', 'epoch_seconds', 'epoch_minutes', 'epoch_hours', 'epoch_days'] | None = None) → Callable[[Callable[[...], None]], Extractor[ManifestExtractorContext]]

Turn a parsing function into a manifest extractor, recommended for new images.

Declare the callback’s files and settings with `@input` and `@parameter` below
this decorator. The framework resolves them before invoking the callback; `ctx`
supplies output paths and job metadata. Add `@error` for expected failure mappings.

Write files under `ctx.output_dir` and declare each with
`ManifestExtractorContext.add_tabular()`,
`add_avro_stream()`,
`add_journal_json()`, or
`add_video()`. One run can produce several files with
different timestamps, tags, and channel prefixes. `Extractor.run()` writes
`manifest.json` after the callback returns.

Supply both timestamp defaults here to reuse the declarations through
`Extractor.registration_kwargs()` or `Extractor.catalog_manifest()`.
They describe image defaults; per-output timestamp settings belong on the context’s
output methods. Register the image with `MANIFEST`; the runner rejects a different
injected output format at startup.

Example

```python
from pathlib import Path

from nominal.experimental.extractor import ManifestExtractorContext, input, manifest_extractor, parameter

@manifest_extractor(
    default_timestamp_column="time_us",
    default_timestamp_type="epoch_microseconds",
)
@input("source")
@parameter("parts", type=int, default=2)
def split(ctx: ManifestExtractorContext, *, source: Path, parts: int) -> None:
    table = read_parquet(source)
    for i, chunk in enumerate(chunks_of(table, parts)):
        out = ctx.output_dir / f"part_{i}.parquet"
        write_parquet(chunk, out)
        ctx.add_tabular(out, timestamp_column="time_us", timestamp_type="epoch_microseconds")

    footage = ctx.output_dir / "camera.mp4"
    write_video(footage)
    ctx.add_video(footage, channel="camera/front", start=recording_started_at)

if __name__ == "__main__":
    split.run()
```

Return type:
:   [*Extractor*](https://dev.nominal.io/python/ref/generated/nominal.experimental.extractor.runner.Extractor.md#nominal.experimental.extractor.runner.Extractor "nominal.experimental.extractor.runner.Extractor")[[*ManifestExtractorContext*](https://dev.nominal.io/python/ref/generated/nominal.experimental.extractor.context.ManifestExtractorContext.md#nominal.experimental.extractor.context.ManifestExtractorContext "nominal.experimental.extractor.context.ManifestExtractorContext")] | [*Callable*](https://docs.python.org/3/library/typing.html#typing.Callable "(in Python v3.15)")[[[*Callable*](https://docs.python.org/3/library/typing.html#typing.Callable "(in Python v3.15)")[[…], *None*]], [*Extractor*](https://dev.nominal.io/python/ref/generated/nominal.experimental.extractor.runner.Extractor.md#nominal.experimental.extractor.runner.Extractor "nominal.experimental.extractor.runner.Extractor")[[*ManifestExtractorContext*](https://dev.nominal.io/python/ref/generated/nominal.experimental.extractor.context.ManifestExtractorContext.md#nominal.experimental.extractor.context.ManifestExtractorContext "nominal.experimental.extractor.context.ManifestExtractorContext")]]