scripts/build_dist.sh: sdist + pure wheel + manylinux_2_28 kernel wheels (maturin --zig,
both architectures). Dockerfile (multi-stage, kernels included, non-root user).
packaging/conda/{simurg,simurg-core}. scripts/p10/digest.py: platform-independent content
digests of GTC/VCF/summary outputs. Cloud task p10 installs the artifacts on a fresh
Ubuntu VM and runs the tutorial with pip, Docker and conda installs. trial.sh picks the
arm64/amd64 image from the instance type and can run instances in parallel (PARALLEL=1).
Licence files ship in both wheels; Rust crate notices added.
Rust ports of the hot paths found by profiling: fused per-locus GenCall calling
and scoring, BAF/LRR, libm maps with CPython math semantics, both nearest-neighbour
searches of the normalization fit, and the BPM LocusEntry parser. Address
coverage is memoised per IDAT layout. SIMURG_PURE_PYTHON=1 forces the pure paths.
On the Pi: genotype_sample 3.4 s -> 1.1 s, 36-sample GSA run 131 s -> 46 s, BPM
read 4x faster; 36 GSA + 6 Omni2.5 GTCs and QC tables byte-identical to the pure
path; tests/unit/test_core_kernels.py checks every kernel on edge cases.
bench/profile_genotype.py, bench/scaling.py, cloud task p9 (thread scaling).
The download-only phase (EBI ~15 MB/s) left the CPU idle long enough for the
idle alarm to terminate the first run. Streams are now cut to the manifest
positions (±100 bp) and the 120 individuals while genotyping runs.
scripts/p8/prepare.py lays out the 360 GEO IDATs under their header barcode/position;
scripts/p8/validate_p8.py computes concordance, NRD, no-call rate, per-class and per-MAF
sensitivity/specificity/precision/F1 vs NYGC 30x and GIAB HG001, replicate concordance,
systematic/allele-swap site errors, per-chip batch table and duplicate detection.
Cloud userdata can skip the Omni2.5 archive (OMNI=0).
- 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
- fit.py: exact and binned nearest-neighbour searches use one distance
matrix per searched bin (first-minimum rule kept); fit 2.6 s -> 1.4 s
per GSA sample, still bit-identical to the oracle
- scripts/validate_p3_large.py: refits 1.2.0 and 1.1.2 transforms for
every vendor GTC and reports ULP distances per field
- cloud trial runs scripts/cloud/tasks/<TASK>.sh (TASK=p3 default;
p1p2 = previous validation + ingest)
kemal