Parallelize coherence-based bad channel detection

[JESUSROYETH]

Description

The default coherence+psd method first materialises 100 scaled chunks and then processes them in serial. On a 384-channel recording, profiling shows that the Welch and median computations dominate the call, while extracting the random traces takes a small part of the total time.

This change sends the same random slices through TimeSeriesChunkExecutor. The default stays at n_jobs=1, but the chunks are streamed instead of kept together in memory. A final job_kwargs parameter lets callers select a thread or process pool without changing positional arguments.

I measured the default 100 × 0.3 s chunk path on an Intel Core i9-13900HX, with numerical thread pools pinned to one thread. The generated row uses the 30 s / 384-channel shape reported in the issue. The second row uses real 384-channel SpikeGLX traces from the public Noise4Sam fixture; the public file is short, it was repeated in memory to cover the same chunk path.

Workload	Before	After (n_jobs=8, processes)	Improvement
Generated 30 s recording	8.454 s (8.384–8.479)	2.193 s (2.118–2.236)	3.86x
Noise4Sam real traces	12.836 s (12.749–12.964)	2.984 s (2.973–3.014)	4.30x

The channel ids and labels matched exactly between both implementations in each run. On the serial real-trace path, three fresh-process peak RSS measurements gave a median of 1,688,604 KiB before (range 1,688,500-1,689,352) and 353,452 KiB after (range 352,984-354,896), around 79% less. This comes from processing one random slice at a time instead of retaining the scaled chunks.

Validation:

• pytest -q src/spikeinterface/preprocessing/tests/test_detect_bad_channels.py: 11 passed.
• The regression tests cover a thread pool, a spawned process pool, and a two-segment recording.
• A second spawned-process test uses a recording that is not pre-marked as filtered, so the
  highpass_filter wrapper detect_bad_channels builds internally also gets exercised across a process boundary; it matches the serial result on the same data.
• Both new tests fail on the matching main revision and pass with this patch.
• black and the trailing-whitespace/end-of-file checks are clean.

Fixes #2869.

[alejoe91]

Suggested change

	if job_kwargs is None:
	job_kwargs = {"progress_bar": False}
	job_kwargs = fix_job_kwargs(job_kwargs)
	executor_job_kwargs = {
	key: job_kwargs[key]
	for key in ("pool_engine", "n_jobs", "progress_bar", "mp_context", "max_threads_per_worker")
	}
	job_kwargs = {} if job_kwargs is None else job_kwargs
	job_kwargs = fix_job_kwargs(job_kwargs)

@JESUSROYETH why isn't this enough to propagate to the TimeSeriesChunkExecutor?

[alejoe91]

from spikeinterface.core.job_tools import fix_job_kwargs

job_kwargs = dict(n_jobs=4)

print(fix_job_kwargs(job_kwargs)

>>> {'pool_engine': 'process',
 'n_jobs': 4,
 'chunk_duration': '1s',
 'progress_bar': True,
 'mp_context': None,
 'max_threads_per_worker': 1}

[alejoe91]

Thanks @JESUSROYETH

I just have one comment but this looks great! :)

[alejoe91]

Suggested change

	**executor_job_kwargs,
	**job_kwargs,

See https://github.com/SpikeInterface/spikeinterface/pull/4750/changes#r3923673650