Replace zoom FFT peak search with a budget-bounded PyFFTW direct transform

Drop the coarse+fine zoom refinement, the SciPy FFT backend, and the
exact= audit path in favor of a single always-on full-transform peak
search (_peak_bins). Block size is now derived from a per-thread memory
budget (_fft_block_for/SRAS_FFT_PLAN_BUDGET_MB) instead of a fixed
constant, so the existing block-parallel PyFFTW pool stays memory-safe
at high pad factors without the zoom algorithm's bookkeeping. Also
removes the now-unused threadpoolctl dependency and the FFT backend
selector from the UI.

Also includes a pre-existing min_freq_mhz peak-search floor (excludes
bins below a caller-supplied frequency from the argmax) that was
already implemented and tested in the working tree.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

pyproject.toml

@@ -14,10 +14,8 @@ dependencies = [
     "scipy==1.18.0",
     # Angle alignment only: masked FFT phase correlation (skimage.registration).
     "scikit-image==0.26.0",
-    # Faster rfft backend; the viewer falls back to scipy.fft without it.
+    # Mandatory rfft backend for the peak search (no SciPy fallback).
     "pyFFTW==0.15.1",
-    # Clamps BLAS threading under the FFT worker pool.
-    "threadpoolctl==3.6.0",
 ]
 
 [project.optional-dependencies]