P3: normalization fit (1.2.0 and 1.1.2), apply, polar, simurg normalize

- signal/normalization/numerics.py: vendor percentile, trimmean, MAD sigma
  (old/new), bisquare robust fits, float32/float64 typing and sequential
  sums reproduced exactly (ported from gtc2vcf, MIT)
- signal/normalization/fit.py: outlier removal, offset, rotation, shear,
  scale, nn12/rr12, single-channel model for small Infinium I bins,
  exact and binned nearest-neighbour sweeps; vendor quirks marked QUIRK
- signal/normalization/apply.py: affine transform and theta/R
- pipeline/normalize.py + CLI `simurg normalize`
- oracle GTCs rebuilt with -ffp-contract=off (aarch64 gcc fuses a*b+c by
  default, shifting the last bits); the old FMA oracle differed by <=4 ULP

Result: transforms bit-identical to the oracle for all 36 GSA samples x
48 bins, for both normalization versions (3456/3456 bins, 0 ULP).

tests/oracle/test_p2_raw_xy.py

@@ -6,12 +6,12 @@ from __future__ import annotations
 
 import numpy as np
 import pytest
+from tests.conftest import GSA_BPM, GSA_IDATS, ORACLE_GTCS, need
 
 from simurg.formats.bpm import read_bpm
 from simurg.formats.gtc import read_gtc
 from simurg.formats.idat import read_idat
 from simurg.signal.raw_xy import LocusMapCache, assemble_raw_xy, check_coverage
-from tests.conftest import GSA_BPM, GSA_IDATS, ORACLE_GTCS, need
 
 pytestmark = pytest.mark.data