- vendor comparison (1000 Genomes Omni2.5, AutoCall 1.6.2.2): every
computed field identical in 2138/2141 samples; the 3 others trace back
to the single-locus raw-intensity rule (K-009); sex rule 1 matches all
- AutoCall 1.6.2.x leaves GC10/GC50/#calls/#no-calls at 0 in GTC v3:
new `--preset autocall-1.6.2` (1.1.2 normalization, sex rule 1, GTC v3,
legacy statistics); the sample summary keeps the real statistics
- NaN sign canonicalised to the x86 pattern (K-014); report P4.md
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01814GKVzZVL3FuF3DeiZXhv
- tests/unit/test_genotype.py: fuzzy memberships incl. NaN rules, calls
at cluster centres, intensity threshold, BAF/LRR interpolation and
NaN pattern, LogR-dev locus set, sex rules v1/v2, TOP-strand base
calls, invariant dates, end-to-end `simurg genotype` (summary schema,
pass-through columns, thread/layout determinism, overwrite refusal)
- tests/oracle/test_p4_genotype.py: 36 GSA samples x 2 versions
byte-identical to the oracle
- scripts/validate_p4_large.py + cloud task p4: compare every vendor
GTC field by field and as whole files; reports which sex rule matches
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01814GKVzZVL3FuF3DeiZXhv
- genotype/clusters.py: EGT aligned to manifest order (float32 mean R)
- genotype/gencall.py: flair, six-case likelihood, priors/posteriors,
fuzzy sub-scores and score map, vectorised with the reference's
float/double evaluation order and libm transcendentals
- genotype/baf_lrr.py, sex.py (rules v1/v2), sample.py (IDAT -> Gtc)
- pipeline/inputs.py shared by ingest and genotype
- pipeline/genotype.py + CLI: GTC per sample, sample_summary.tsv/json
(OUT-060..063), run manifest
- GTCs from IDATs are byte-identical to the oracle for 36 GSA samples x
2 normalization versions (NaN sign canonicalised to the x86 pattern)
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01814GKVzZVL3FuF3DeiZXhv
- 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).
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01814GKVzZVL3FuF3DeiZXhv
- tests/oracle/test_p2_raw_xy.py: IDAT-assembled raw X/Y and controls
equal the arrays in oracle GTCs
- validate_p1_large.py: phase-P2 comparison against vendor GTCs with
diagnostics (channel swap, bead-count disagreements)
- cloud trial: also runs `simurg ingest` on all samples (time/memory),
disk 250 GB
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01814GKVzZVL3FuF3DeiZXhv
kemalandClaude Opus 5.5