* simurg/core/brand.py: single source of the mark geometry, colours, favicon
and the report header band (scripts/brand now load it from there).
* HTML reports (QC report, compare dashboard, methylation QC): offline SVG
favicon, indigo header band with the mark, version and a sha256 of the data
shown; Noto Sans font stack; methylation plots in Okabe-Ito colours.
* README banner, guide logo, brand/blog links; branding brief updated to the
final identity; Noto (OFL) and Okabe-Ito entries in THIRD_PARTY_NOTICES;
CHANGELOG (Unreleased).
Unit tests + P5 QC oracle: 285 passed; ruff and mypy clean.
Frontal dotted simurg on a bead grid: gold body and wings, three-dot crown,
red / gold / green channel plumes with peacock eyes. Detailed (31x27), simple
(11x10) and micro (7x7) drawings; dark, light, gold, black, white versions;
outlined Noto Sans wordmark; favicon and app icons; README banner, social
preview, avatar, featured-image template, report corner mark, CLI banner.
Checks: WCAG contrast table, protan/deutan simulation, icon sizes, family
sheet. Blog featured images regenerated with the new mark for consistency.
Geometry shared in scripts/brand/simurg_mark.py.
Dotted simurg on a bead grid in the channel colors (red/green, gold bird,
indigo), one motif per post; generated by scripts/blog/make_featured_images.py.
- scripts/pgx/wgs_star_truth.py: star alleles from the 1000G 30x phased panel with the DB definitions
(wildcard-aware haplotype assignment; checked against GeT-RM on the demo CEPH individuals).
- validate_star_getrm.py: --all-genes, --individuals, --coverage (projection onto what the array can see);
replicate_concordance.py.
- 120 individuals x 3 GSA v1 arrays: array-resolvable 1637/1710 (95.7 %), top 1223/1800 (chip lacks many
defining variants), GeT-RM subset 28/35, replicates 99.2 % without the failed array (V2_pgx_star.md, K-029).
fetch_sources.sh downloads PharmVar per-gene zips; pgx db build adds core alleles for genes CPIC does not
define (normalised against the FASTA, *1 = reference). Masks re-derived with the new variants. GeT-RM:
CYP3A4 15/15, CYP2A6 7/9; all comparable genes 154/168 (CYP1A2/CYP2C8 definitions differ from GeT-RM's).
Sub-region CNs (p5, intron 2, exon 9) resolve full copies and *36/*68/*13 units; candidates carry the
tandem names, background alleles, and CPIC activity sums. GeT-RM: CYP2D6 13/15 top (NA12878 *3/*68+*4 and
NA11832 *1/*68+*4 exact), 15/15 within feasible; all genes 132/144.
- DB build resolves the PGX-007 tag variants from the manifest's probes; coverage over all gene kinds.
- Variant-based genes reported per variant (rs.. REF/ALT | Reference) with the CPIC lowest-activity diplotype;
tagging genes as positive/negative with the population caveat; GSTM1/GSTT1/UGT2B17 *0 from targeted CN.
- Case-insensitive phenotype keys (MT-RNR1). GeT-RM check extended to VKORC1 and the CN genes:
131/144 gene calls (docs/validation/V2_pgx_star.md).
- scripts/pgx/fetch_sources.sh: CPIC API tables (CC0) into a dated folder with version and hashes.
- simurg/pgx/db.py: CPIC HGVS locations and values -> left-normalised VCF variants (SNV, del, ins, dup,
repeats, delins, chrM); IUPAC values are core-allele wildcards; alleles without definitions are flagged.
- pipeline/pgx_db.py: deterministic SQLite (content-hash name), probe_map via the SNV VCF site mapper;
GSA v4 PGx: 22 genes, 1354 alleles, 5002 probe links; two builds byte-identical.
Mosaic fraction from the BAF split now subtracts the heterozygous BAF noise (median |BAF-0.5| of unsplit
hets = 0.6745 sigma_b), and a mosaic loss lacking its expected heterozygotes gets MF 1 and is promoted to a
constitutional DEL (CYTO-020). Adds scripts/v2/cn_tune.sh + cn_tune_report.py (GSE165845 rounds on the Pi).
The Pi now boots with cgroup_enable=memory cgroup_memory=1; the MemAvailable guard stays as a
second layer and as the fallback on systems without the memory controller.
scripts/pi_run.sh runs heavy jobs in their own process group and stops them when
MemAvailable drops below a floor (Raspberry Pi OS boots with cgroup_disable=memory, so a
systemd MemoryMax cap is not available); a 360-sample genotype run with cohort QC had
exhausted memory and the OOM killer took down the desktop session.
GSE165845 360 arrays: constitutional DEL/DUP calls 77/74 % in the NYGC SV truth, LOH
reproduced across replicates 74 %; mosaic calls are mostly false positives (12-23 %),
sensitivity for 25 kb-1 Mb events 7-13 % at QUAL >= 20. All samples had been flagged
SampleQuality by the summary logr_dev (cluster-file mismatch); the gate now uses the SD
of the LRR after the batch reference. Validation script: --ignore-filter.
cn call now records per-chromosome and sample statistics (percent het, LRR stats, BAF
deviations, robust LogR dev, call rate) in the sidecar; cn annotate writes
<sample>.cyto.json (CYTO-033) with ISCN microarray/traditional strings (CYTO-034), bands
and RefSeq gene lists (CYTO-035), LOH summaries, weighted median CN (CYTO-026); annotation
bundle from UCSC cytoBand + ncbiRefSeq (CYTO-036, IN-054). Tests for ISCN strings, gene
overlap, sidecar-missing error and an end-to-end call+annotate.
216 implanted events in 22 synthetic GSA samples (4 replicates per kind x fraction x
size; reference model from 30 other samples): constitutional DEL/DUP 16/16 at 5-20 Mb,
mosaic loss/gain/CN-LOH at 15 % / 5 Mb 12/12.
Found by the P10 fresh-VM run: `simurg inspect … | head` raised BrokenPipeError, reported as
an internal error, and stopped examples/gsa_demo.sh (set -o pipefail). run_guarded now flushes
stdout inside the guard and maps BrokenPipeError to ExitCode.SIGPIPE (141) without a message.
p10.sh: SKIP_DOCKER/SKIP_CONDA, clearer digest report; p10t.sh runs build+pip+tutorial only.
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).
- compare/core: EXACT/NEAR/DISCREPANCY/UNKNOWN with the plan §16.3 tolerances (abs, float32 ULP), NaN = NaN,
locus classes (assay, variant type, chromosome group, intensity-only); fields absent on both sides are
not applicable
- compare/records: GTC vs GTC (per-locus arrays, transforms per bin, controls, sample statistics; metadata
listed, not counted) and VCF vs VCF (records keyed incl. ID; SNV/MNV/indel, merged, multi-allelic classes)
- simurg compare A B (files or folders paired by name): summary.json, discrepancies.tsv.gz (never truncated),
self-contained dashboard.html; --tolerance, --fail-on-discrepancy (exit 3)
- scripts/nightly.py + install_nightly.sh (crontab 02:30): unit tests, GSA vs gtc2vcf (GTC + VCF), VCF
regression vs golden, Omni2.5 subset vs vendor GTCs; first run PASS on every step
- tests/unit/test_compare.py; docs/validation/P7.md, README
- 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
- 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)
- 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: 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
scripts/cloud/trial.sh launches one spot instance that validates the
parsers on the 1000 Genomes Omni2.5 set (2141 samples, vendor GTCs)
streamed from the public S3 bucket, uploads the summary and terminates
itself. Cost guards: spot price cap, hard time limit (shutdown =
terminate), EXIT-trap shutdown, CloudWatch idle alarm that terminates
from outside, disk deleted with the instance, monthly budget alert, and
an `audit` command listing anything still billable.
scripts/validate_p1_large.py compares every GTC with BeadArrayFiles,
checks byte-identical round-trips, IDAT pairs and BPM/EGT/CSV.
- pyproject (uv/hatch, ruff, mypy strict, pytest markers), MIT LICENSE,
THIRD_PARTY_NOTICES with verbatim BSD-2 (BeadArrayFiles) and MIT (gtc2vcf)
- CI workflow for Gitea/GitHub Actions: lint, type check, unit tests
- fetch_data.sh p0 now fetches the GSA v4 PGx demo IDATs (the GDA "Demo 36"
bundle is a GenomeStudio project), unpacks archives, fixes pipefail bug
- make_oracle_gtcs.sh builds bcftools + gtc2vcf and writes oracle GTCs
- plan §17: corrected P0 dataset table
kemal