Add STALTA characteristic-function atom

[amaggi]

Add an STALTA characteristic-function atom

Xdas has a threshold detector (Trigger) but nothing to feed it apart from a
SeisBench model. This adds the classic amplitude detector — the ratio of a
short-term to a long-term average of the squared signal — as an atom, so
detection no longer needs a network of weights:

picks = xd.trigger(xd.stalta(da, sta=0.5, lta=10.0), thresh=8.0)

or, as a pipeline that streams:

pipeline = xd.stalta(..., sta=0.5, lta=10.0) >> xd.trigger(..., thresh=8.0)
pipeline.process(source, out="picks.csv")

What it does

• Windows in physical units. sta and lta are given in the units of
  dim, and the sample counts are derived in initialize from the measured
  sampling rate, the way operations.Filter does — so a parameter survives a
  change of rate.
• Two placements. mode="causal" (default) averages the samples up to and
  including the one it produces, as a real-time detector must; mode="centered"
  averages symmetrically about it, which sharpens the onset but reaches into the
  future and so is offline only. The choice moves pick times, so it is a
  property of the detector rather than a tuning knob.
• window and pad_mode for the taper applied to both means and for how
  the two ends of the record are extended. Chunk seams are never padded: they
  use the real neighbouring samples.
• Chunk-invariant. State carries the halo the next window needs, so a
  chunked run returns exactly what a single call on the whole record returns —
  tested over 2, 5, 17 and 200 chunks in both modes, and for a record shorter
  than the halo. In "centered" mode the samples whose window is not yet
  complete are held back and flush() releases them; in "causal" mode nothing
  is held back and flush() has nothing to return.

Notes for review

• Nothing is added to Trigger. It already turns a characteristic function
  into picks, statefully, and does it well; this only supplies another kind of
  input to it.
• recursive_sta_lta is deliberately left out. The recursive form is a
  different (and genuinely streaming-native) estimator, and belongs in its own
  PR rather than as a method= flag bolted on here.
• STALTA does not survive save_state/load_state. Its buffer is a
  DataArray, and Atom.set_state converts a DataArray state to a bare
  ndarray (xdas/atoms/core.py, the # TODO: shouldn't need __array__ line),
  so the coordinates are lost. This is the same limitation DownSample,
  Rechunk and Polyphase already have — the two commented-out round-trip
  tests in tests/test_atoms.py are about exactly this — so I have not worked
  around it here. Happy to take it on as a follow-up if you want the state
  machinery finished.

Checks

• ruff check and ruff format clean.
• xdas/atoms/detect.py at 100% coverage bar one pre-existing line
  (Trigger.merge's empty-table branch, covered by test_atoms_ml.py).
• Docs build with no new warnings; the new user-guide section executes.
• Full suite green, with two caveats that are not from this branch and
  reproduce on a clean dev on macOS arm64:
  • tests/test_signal.py::TestSTFT::test_parrallel and
    tests/virtual/test_tiles.py::TestStoredForm::test_non_utf8_root_survives_the_header
    fail on dev as they do here.
  • The interpreter segfaults when tests/test_picking.py runs in the same
    process as anything that has imported torch (tests/test_atoms_ml.py, but
    also dascore/seisbench pulled in elsewhere) — torch's OpenMP against
    numba's @njit(parallel=True) in xdas/picking.py. Neither
    KMP_DUPLICATE_LIB_OK=TRUE nor NUMBA_NUM_THREADS=1 helps. Running
    tests/test_picking.py in its own process is the workaround; worth a
    separate issue.