diff --git a/scripts/cloud/tasks/p4.sh b/scripts/cloud/tasks/p4.sh
new file mode 100755
index 0000000..1e67025
--- /dev/null
+++ b/scripts/cloud/tasks/p4.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+# Phase P4: genotype every Omni2.5 IDAT pair and compare the GTC with the vendor's (fields + whole file).
+# Args:
+set -uo pipefail
+D=$1; OUT=$2
+SRC=s3://1000genomes/release/20130502/supporting/hd_genotype_chip/broad_intensities
+mkdir -p $D/egt
+aws s3 cp --no-sign-request "$SRC/HumanOmni2.5-4v1-Multi_H.egt" $D/egt/ --only-show-errors
+cp $D/HumanOmni2.5-4v1-Multi_B.egt $D/egt/
+/usr/bin/time -v .venv/bin/python scripts/validate_p4_large.py --root $D/data --bpm $D/HumanOmni2.5-4v1-Multi_B.bpm \
+ --egt-dir $D/egt --out $OUT/summary.json --norm-version 1.1.2 --sex-version 1 --workers "$(nproc)" \
+ 2> $OUT/time.txt || echo "P4 validation reported differences"
+tail -25 $OUT/time.txt
diff --git a/scripts/validate_p4_large.py b/scripts/validate_p4_large.py
new file mode 100644
index 0000000..f2ccf63
--- /dev/null
+++ b/scripts/validate_p4_large.py
@@ -0,0 +1,248 @@
+#!/usr/bin/env python3
+"""Phase P4 validation against vendor GTCs: genotype every IDAT pair and compare the produced GTC with the vendor's,
+field by field and as whole files (after aligning free-text metadata that cannot match).
+
+Usage: validate_p4_large.py --root DIR --bpm F --egt-dir DIR --out summary.json
+ [--norm-version 1.1.2] [--sex-version 1] [--workers N] [--limit N]
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import multiprocessing as mp
+import os
+import re
+import sys
+import time
+import traceback
+from concurrent.futures import ProcessPoolExecutor
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+NEG_NAN = np.frombuffer(np.uint32(0xFFC00000).tobytes(), dtype=np.float32)[0]
+ALIGN = (
+ "cluster_file",
+ "snp_manifest",
+ "autocall_date",
+ "autocall_version",
+ "imaging_date",
+ "sample_name",
+ "sample_plate",
+ "sample_well",
+ "scanner_data",
+)
+_W: dict[str, Any] = {}
+
+
+def _init(bpm: str, egt_dir: str, norm: str, sex_version: int) -> None:
+ _W.update(bpm=bpm, egt_dir=egt_dir, norm=norm, sex_version=sex_version, ctx={})
+
+
+def _ctx(cluster_field: str) -> Any:
+ from simurg.formats.bpm import read_bpm
+ from simurg.formats.egt import read_egt
+ from simurg.genotype.clusters import align_clusters
+ from simurg.genotype.sample import manifest_context
+
+ name = re.sub(r"\(MD5Checksum=.*\)$", "", cluster_field).strip()
+ if name not in _W["ctx"]:
+ if "manifest" not in _W:
+ _W["manifest"] = read_bpm(_W["bpm"])
+ path = Path(_W["egt_dir"]) / name
+ if not path.exists():
+ raise FileNotFoundError(f"cluster file {name!r} named in the GTC is not in {_W['egt_dir']}")
+ m = _W["manifest"]
+ _W["ctx"][name] = manifest_context(m, align_clusters(read_egt(path), m), cluster_file_name=name)
+ return _W["ctx"][name]
+
+
+def _canon(a: np.ndarray) -> np.ndarray: # type: ignore[type-arg]
+ a = np.asarray(a).copy()
+ if a.dtype.kind == "f":
+ a[np.isnan(a)] = NEG_NAN
+ return a
+
+
+def check(args: tuple[str, str, str]) -> dict[str, Any]:
+ from simurg.formats.gtc import encode_gtc, read_gtc
+ from simurg.formats.idat import read_idat
+ from simurg.genotype.sample import GenotypeParams, genotype_sample
+ from simurg.genotype.sex import SexParams
+
+ gtc, grn, red = args
+ res: dict[str, Any] = {"gtc": gtc}
+ try:
+ ref = read_gtc(gtc, check_write_complete=False)
+ ctx = _ctx(ref.cluster_file)
+ params = GenotypeParams(
+ norm_version=_W["norm"], sex=SexParams(version=_W["sex_version"]), gtc_version=ref.version
+ )
+ g = genotype_sample(read_idat(grn), read_idat(red), ctx, params)
+ res["cluster_file"] = ctx.cluster_file_name
+ diffs: dict[str, Any] = {}
+ for k in ("raw_x", "raw_y", "genotypes", "genotype_scores", "base_calls", "controls_x", "controls_y"):
+ a, b = _canon(getattr(g, k)), _canon(getattr(ref, k))
+ if a.shape != b.shape:
+ diffs[k] = f"shape {a.shape} vs {b.shape}"
+ elif a.tobytes() != b.tobytes():
+ n = int((a.reshape(len(a), -1) != b.reshape(len(b), -1)).any(axis=1).sum())
+ diffs[k] = n
+ if g.normalization_transforms.tobytes() != ref.normalization_transforms.tobytes():
+ diffs["normalization_transforms"] = True
+ if ref.version >= 4:
+ for k in ("b_allele_freqs", "logr_ratios"):
+ if _canon(getattr(g, k)).tobytes() != _canon(getattr(ref, k)).tobytes():
+ diffs[k] = int(
+ (_canon(getattr(g, k)).view(np.uint32) != _canon(getattr(ref, k)).view(np.uint32)).sum()
+ )
+ for k in (
+ "call_rate",
+ "gender",
+ "logr_dev",
+ "gc10",
+ "gc50",
+ "num_calls",
+ "num_no_calls",
+ "num_intensity_only",
+ "ploidy",
+ "ploidy_type",
+ "dx",
+ "slide_identifier",
+ ):
+ a, b = getattr(g, k), getattr(ref, k)
+ same = a == b or (isinstance(a, float) and np.isnan(a) and np.isnan(b))
+ if not same:
+ diffs[k] = [repr(a), repr(b)]
+ if "genotype_scores" in diffs:
+ d = np.abs(g.genotype_scores.astype(np.float64) - ref.genotype_scores.astype(np.float64))
+ res["max_score_abs_diff"] = float(np.nanmax(d))
+ res["field_diffs"] = diffs
+ # also evaluate the other sex-estimate generation on the same calls (cheap), to identify the vendor's rule
+ from simurg.genotype.sex import estimate_sex
+ from simurg.signal.normalization.apply import normalize, polar
+
+ nx, ny = normalize(g.raw_x, g.raw_y, ctx.lookup, g.normalization_transforms)
+ _, r_all = polar(nx, ny)
+ res["sex_by_version"] = {
+ v: estimate_sex(
+ g.genotypes, r_all, ctx.clusters.gentrain, ctx.clusters.present, ctx.sex_classes, SexParams(version=v)
+ )
+ for v in (1, 2)
+ }
+ res["sex_ref"] = ref.gender_char
+ for k in ALIGN:
+ setattr(g, k, getattr(ref, k))
+ for k in ("b_allele_freqs", "logr_ratios", "genotype_scores"):
+ setattr(g, k, _canon(getattr(g, k)))
+ setattr(ref, k, _canon(getattr(ref, k)))
+ res["byte_identical"] = encode_gtc(g) == encode_gtc(ref)
+ res["ref_layout_canonical"] = encode_gtc(ref) == Path(gtc).read_bytes() or None
+ res["metadata"] = {k: str(getattr(ref, k))[:80] for k in ("autocall_version", "cluster_file", "imaging_date")}
+ except Exception as e:
+ res["error"] = f"{type(e).__name__}: {e}"
+ res["trace"] = traceback.format_exc(limit=4)
+ return res
+
+
+def main() -> int:
+ ap = argparse.ArgumentParser()
+ ap.add_argument("--root", required=True, type=Path)
+ ap.add_argument("--bpm", required=True, type=Path)
+ ap.add_argument("--egt-dir", required=True, type=Path)
+ ap.add_argument("--out", required=True, type=Path)
+ ap.add_argument("--norm-version", default="1.1.2")
+ ap.add_argument("--sex-version", type=int, default=1)
+ ap.add_argument("--workers", type=int, default=os.cpu_count() or 1)
+ ap.add_argument("--limit", type=int, default=0)
+ a = ap.parse_args()
+
+ from simurg.formats.discovery import walk_files
+
+ t0 = time.time()
+ files = walk_files(a.root, (".gtc", ".idat"))
+ key_re = re.compile(r"(\d+_r\d{2}c\d{2})", re.IGNORECASE)
+ grn, red, gtcs = {}, {}, []
+ for p in files:
+ low = p.name.lower()
+ m = key_re.search(p.name)
+ if not m:
+ continue
+ k = m.group(1).lower()
+ if low.endswith("_grn.idat"):
+ grn[k] = str(p)
+ elif low.endswith("_red.idat"):
+ red[k] = str(p)
+ elif low.endswith(".gtc"):
+ gtcs.append((k, str(p)))
+ triples = [(g, grn[k], red[k]) for k, g in gtcs if k in grn and k in red]
+ if a.limit:
+ triples = triples[: a.limit]
+ with ProcessPoolExecutor(
+ a.workers,
+ mp_context=mp.get_context("spawn"),
+ initializer=_init,
+ initargs=(str(a.bpm), str(a.egt_dir), a.norm_version, a.sex_version),
+ ) as ex:
+ results = list(ex.map(check, triples, chunksize=2))
+
+ field_counts: dict[str, int] = {}
+ for r in results:
+ for k in r.get("field_diffs", {}):
+ field_counts[k] = field_counts.get(k, 0) + 1
+ summary: dict[str, Any] = {
+ "n_gtc": len(gtcs),
+ "n_compared": len(results),
+ "errors": [r for r in results if "error" in r][:20],
+ "byte_identical": sum(bool(r.get("byte_identical")) for r in results),
+ "samples_with_no_field_diff": sum(not r.get("field_diffs") and "error" not in r for r in results),
+ "samples_with_field_diff": field_counts,
+ "genotype_diff_loci_total": sum(
+ int(r.get("field_diffs", {}).get("genotypes", 0) or 0)
+ if isinstance(r.get("field_diffs", {}).get("genotypes", 0), int)
+ else 0
+ for r in results
+ ),
+ "score_diff_loci_total": sum(
+ int(r.get("field_diffs", {}).get("genotype_scores", 0) or 0)
+ if isinstance(r.get("field_diffs", {}).get("genotype_scores", 0), int)
+ else 0
+ for r in results
+ ),
+ "max_score_abs_diff": max((r.get("max_score_abs_diff", 0.0) for r in results), default=0.0),
+ "cluster_files": sorted({r.get("cluster_file", "") for r in results}),
+ "sex_agreement_by_version": {
+ str(v): sum(r.get("sex_by_version", {}).get(v) == r.get("sex_ref") for r in results) for v in (1, 2)
+ },
+ "ref_sex_counts": {s: sum(r.get("sex_ref") == s for r in results) for s in ("M", "F", "U")},
+ "examples": [r for r in results if r.get("field_diffs")][:15],
+ "params": {"norm_version": a.norm_version, "sex_version": a.sex_version},
+ "seconds": round(time.time() - t0, 1),
+ }
+ summary["passed"] = summary["byte_identical"] == len(results) > 0
+ a.out.parent.mkdir(parents=True, exist_ok=True)
+ a.out.write_text(json.dumps(summary, indent=2, default=str) + "\n")
+ print(
+ json.dumps(
+ {
+ k: summary[k]
+ for k in (
+ "n_compared",
+ "byte_identical",
+ "samples_with_no_field_diff",
+ "samples_with_field_diff",
+ "genotype_diff_loci_total",
+ "score_diff_loci_total",
+ "passed",
+ "seconds",
+ )
+ }
+ )
+ )
+ return 0 if summary["passed"] else 1
+
+
+if __name__ == "__main__":
+ sys.exit(main())
diff --git a/tests/unit/test_genotype.py b/tests/unit/test_genotype.py
new file mode 100644
index 0000000..3e3ac16
--- /dev/null
+++ b/tests/unit/test_genotype.py
@@ -0,0 +1,235 @@
+"""Phase P4: calling, BAF/LRR, sex, base calls and `simurg genotype` on synthetic data (plan §7.5–§7.8)."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import numpy as np
+import pytest
+from typer.testing import CliRunner
+
+from simurg.formats.bpm import read_bpm
+from simurg.formats.egt import read_egt
+from simurg.formats.gtc import read_gtc
+from simurg.genotype.baf_lrr import NEG_NAN, autosomal_mask, baf_lrr, logr_dev
+from simurg.genotype.clusters import ClusterArrays, align_clusters
+from simurg.genotype.gencall import call_genotypes, call_rate, smf, zmf
+from simurg.genotype.sample import base_calls, invariant_datetime, top_alleles
+from simurg.genotype.sex import SexParams, chromosome_classes, estimate_sex
+from simurg.synth.files import SynthIdat, SynthLocus, encode_bpm, encode_egt, encode_idat
+
+F32 = np.float32
+
+
+def clusters(n: int, *, gentrain: float = 0.8, threshold: float = np.nan) -> ClusterArrays:
+ tm = np.tile(np.array([0.05, 0.5, 0.95], np.float32), (n, 1))
+ rm = np.ones((n, 3), np.float32)
+ return ClusterArrays(
+ present=np.ones(n, bool),
+ n=np.full((n, 3), 100, np.int32),
+ theta_mean=tm,
+ theta_dev=np.full((n, 3), 0.03, np.float32),
+ r_mean=rm,
+ r_dev=np.full((n, 3), 0.1, np.float32),
+ intensity_threshold=np.full(n, threshold, np.float32),
+ gentrain=np.full(n, gentrain, np.float32),
+ mean_r=np.ones(n, np.float32),
+ )
+
+
+def test_fuzzy_memberships() -> None:
+ x = np.array([-1, 0, 0.25, 0.5, 0.75, 1, 2, np.nan], np.float32)
+ s = smf(x, F32(0), F32(1))
+ assert s.tolist()[:7] == [0.0, 0.0, 0.125, 0.5, 0.875, 1.0, 1.0]
+ assert s[7] == 1.0 # the reference returns 1 for NaN (all comparisons false)
+ z = zmf(x, F32(0), F32(1))
+ assert z.tolist()[:7] == [1.0, 1.0, 0.875, 0.5, 0.125, 0.0, 0.0] and z[7] == 0.0
+ assert smf(np.array([0.4, 0.6], np.float32), F32(1), F32(0)).tolist() == [0.0, 1.0] # a >= b rule
+
+
+def test_calls_at_cluster_centres() -> None:
+ n = 6
+ t = np.array([0.05, 0.5, 0.95, 0.5, 0.5, 0.5], np.float32)
+ r = np.ones(n, np.float32)
+ io = np.array([0, 0, 0, 1, 0, 0], bool)
+ raw_zero = np.array([0, 0, 0, 0, 1, 0], bool)
+ c = clusters(n)
+ c.intensity_threshold[5] = 2.0 # R below the cluster file's intensity threshold -> no-call
+ calls = call_genotypes(t, r, raw_zero, c, io)
+ assert calls.genotypes.tolist() == [1, 2, 3, 0, 0, 0]
+ assert (calls.scores[:3] > 0.5).all() and calls.scores[4] == 0 and calls.scores[5] == 0
+ assert calls.num_calls == 3 and calls.num_intensity_only == 1
+ assert call_rate(calls, n) == F32(3 / 5)
+
+
+def test_low_gentrain_and_outliers_score_low() -> None:
+ n = 2
+ t = np.array([0.5, 0.3], np.float32) # second point between clusters
+ calls_good = call_genotypes(t, np.ones(n, np.float32), np.zeros(n, bool), clusters(n), np.zeros(n, bool))
+ calls_bad = call_genotypes(
+ t, np.ones(n, np.float32), np.zeros(n, bool), clusters(n, gentrain=0.05), np.zeros(n, bool)
+ )
+ assert calls_good.scores[0] > calls_good.scores[1]
+ assert (calls_bad.scores < 0.15).all() and (calls_bad.genotypes == 0).all()
+
+
+def test_baf_lrr_interpolation() -> None:
+ n = 6
+ t = np.array([0.05, 0.5, 0.95, 0.275, 1.2, np.nan], np.float32)
+ r = np.array([1, 1, 2, 1, 1, np.nan], np.float32)
+ c = clusters(n)
+ io = np.array([0, 0, 0, 0, 0, 0], bool)
+ baf, lrr = baf_lrr(t, r, np.zeros(n, bool), c, io)
+ assert baf[:5].tolist() == pytest.approx([0.0, 0.5, 1.0, 0.25, 1.0]) # clamped at 1
+ assert lrr[:4].tolist() == pytest.approx([0.0, 0.0, 1.0, 0.0])
+ assert baf.view(np.uint32)[5] == 0xFFC00000 and lrr.view(np.uint32)[5] == 0xFFC00000
+ io[1] = True
+ c.mean_r[1] = 2.0
+ _, lrr2 = baf_lrr(t, r, np.zeros(n, bool), c, io)
+ assert lrr2[1] == pytest.approx(-1.0) # intensity-only loci use the count-weighted mean R
+ assert np.isnan(NEG_NAN)
+
+
+def test_logr_dev_locus_set() -> None:
+ lrr = np.array([1, -1, 1, -1, 50, 50, np.nan], np.float32)
+ chroms = ["1", "2", "0", "22_KI270879v1_alt", "X", "MT", "3"]
+ auto = autosomal_mask(chroms)
+ assert auto.tolist() == [True, True, True, True, False, False, True]
+ assert logr_dev(lrr, auto, np.zeros(7, bool), np.ones(7, bool)) == pytest.approx(1.0)
+
+
+def test_sex_rules() -> None:
+ chroms = ["X"] * 30 + ["Y"] * 25 + ["1"] * 150
+ classes = chromosome_classes(chroms)
+ n = len(chroms)
+ gt = np.ones(n, np.uint8)
+ present = np.ones(n, bool)
+ gtrain = np.full(n, 0.8, np.float32)
+ r = np.ones(n, np.float32)
+ assert estimate_sex(gt, r, gtrain, present, classes) == "M" # median Y R 1.0 > 0.3
+ r_f = r.copy()
+ r_f[30:55] = 0.05
+ assert estimate_sex(gt, r_f, gtrain, present, classes) == "F" # X R 1.0 > 0.9
+ r_u = r_f.copy()
+ r_u[:30] = 0.5
+ assert estimate_sex(gt, r_u, gtrain, present, classes) == "U"
+ het = gt.copy()
+ het[:10] = 2 # X het rate 10/30 > 0.1
+ assert estimate_sex(het, r, gtrain, present, classes, SexParams(version=1)) == "F"
+ assert estimate_sex(gt, r, gtrain, present, classes, SexParams(version=1)) == "M"
+
+
+def test_base_calls_and_top_strand(tmp_path: Path) -> None:
+ loci = [
+ SynthLocus("a", snp="[A/G]", ilmn_strand="TOP"),
+ SynthLocus("b", address_a=2, snp="[T/C]", ilmn_strand="BOT"),
+ SynthLocus("c", address_a=3, snp="[N/A]", ilmn_strand="BOT"),
+ ]
+ (tmp_path / "m.bpm").write_bytes(encode_bpm(loci))
+ a, b = top_alleles(read_bpm(tmp_path / "m.bpm"))
+ assert bytes(a) == b"AA\x00" and bytes(b) == b"GGT"
+ bc = base_calls(np.array([1, 2, 0], np.uint8), a, b)
+ assert [bytes(x) for x in bc] == [b"AA", b"AG", b"--"]
+
+
+def test_invariant_datetime() -> None:
+ import datetime as dt
+
+ assert invariant_datetime(dt.datetime(2026, 1, 2, 0, 5, 9)) == "1/2/2026 12:05:09 AM"
+ assert invariant_datetime(dt.datetime(2026, 12, 31, 13, 0, 0)) == "12/31/2026 1:00:00 PM"
+
+
+def _synthetic_run(tmp_path: Path) -> dict[str, Path]:
+ """Two samples on a 600-locus synthetic array with a matching cluster file."""
+ rng = np.random.default_rng(4)
+ n = 600
+ chroms = ["1"] * 500 + ["X"] * 60 + ["Y"] * 40
+ loci = [SynthLocus(f"rs{i}", chrom=chroms[i], address_a=i + 1, pos=i + 1) for i in range(n)]
+ loci.append(SynthLocus("TGA_Ctrl_5716", address_a=n + 1))
+ (tmp_path / "m.bpm").write_bytes(encode_bpm(loci))
+ names = [lc.name for lc in loci]
+ tm = np.tile([0.03, 0.5, 0.97], (len(loci), 1))
+ rm = np.tile([1.0, 1.0, 1.0], (len(loci), 1))
+ (tmp_path / "c.egt").write_bytes(encode_egt(names, theta_mean=tm, r_mean=rm))
+ idats = tmp_path / "idats"
+ idats.mkdir()
+ addr = np.arange(1, len(loci) + 1, dtype=np.int32)
+ for pos in ("R01C01", "R02C01"):
+ g = rng.choice(3, len(loci))
+ inten = rng.uniform(0.6, 1.4, len(loci))
+ x = inten * np.select([g == 0, g == 1, g == 2], [1.0, 0.5, 0.02]) * 8000 + 300
+ y = inten * np.select([g == 0, g == 1, g == 2], [0.02, 0.5, 1.0]) * 9000 + 400
+ for ch, mean in (("Red", x), ("Grn", y)):
+ s = SynthIdat(
+ address_ids=addr,
+ mean=np.clip(mean, 0, 65535).astype(np.uint16),
+ n_beads=np.full(len(loci), 12, np.uint8),
+ barcode="200000000001",
+ position=pos,
+ )
+ (idats / f"200000000001_{pos}_{ch}.idat").write_bytes(encode_idat(s))
+ sheet = tmp_path / "s.csv"
+ sheet.write_text(
+ "[Data]\nSentrixBarcode_A,SentrixPosition_A,Sample_Name,Gender,Tissue\n"
+ "200000000001,R01C01,alpha,Male,blood\n200000000001,R02C01,beta,Female,saliva\n"
+ "200000000001,R09C01,ghost,Female,blood\n"
+ )
+ return {"bpm": tmp_path / "m.bpm", "egt": tmp_path / "c.egt", "idats": idats, "sheet": sheet}
+
+
+def test_genotype_cli_end_to_end(tmp_path: Path) -> None:
+ from simurg.cli.main import app
+
+ f = _synthetic_run(tmp_path)
+ runner = CliRunner()
+ args = [
+ "genotype",
+ "--quiet",
+ "--bpm",
+ str(f["bpm"]),
+ "--egt",
+ str(f["egt"]),
+ "--idats",
+ str(f["idats"]),
+ "--sheet",
+ str(f["sheet"]),
+ "--autocall-date",
+ "1/1/2026 12:00:00 PM",
+ ]
+ r1 = runner.invoke(app, [*args, "--out", str(tmp_path / "o1"), "--threads", "1"])
+ assert r1.exit_code == 3, r1.output # 'ghost' has no IDATs
+ r2 = runner.invoke(app, [*args, "--out", str(tmp_path / "o2"), "--threads", "2", "--layout", "flat"])
+ assert r2.exit_code == 3
+ for stem in ("alpha", "beta"):
+ a = (tmp_path / "o1" / "samples" / stem / f"{stem}.gtc").read_bytes()
+ assert a == (tmp_path / "o2" / f"{stem}.gtc").read_bytes() # thread count / layout never change bytes
+ g = read_gtc(tmp_path / "o1" / "samples" / stem / f"{stem}.gtc")
+ assert g.version == 5 and g.num_snps == 601 and g.autocall_version.startswith("SimurgArray ")
+ assert g.cluster_file == "c.egt" and g.snp_manifest == "m.bpm"
+ assert g.call_rate > 0.9
+ summary = json.loads((tmp_path / "o1" / "sample_summary.json").read_text())
+ assert summary["schema"] == "simurg.sample_summary/1"
+ cols = summary["columns"]
+ assert cols[:5] == ["sample_id", "sample_name", "sample_folder", "status", "fail_reason"]
+ assert "TGA_Ctrl_5716_norm_r" in cols and cols[-2:] == ["Gender", "Tissue"]
+ by = {s["sample_name"]: s for s in summary["samples"]}
+ assert by["ghost"]["status"] == "MISSING_INPUT" and by["alpha"]["status"] == "OK"
+ assert by["alpha"]["sex_reported"] == "M" and by["alpha"]["Tissue"] == "blood"
+ assert by["alpha"]["sex_check"] in ("match", "mismatch", "unknown")
+ tsv = (tmp_path / "o1" / "sample_summary.tsv").read_text().splitlines()
+ assert tsv[0].split("\t") == cols and len(tsv) == 4
+ r3 = runner.invoke(app, [*args, "--out", str(tmp_path / "o1")])
+ assert r3.exit_code == 2 and "E_OUTPUT_EXISTS" in r3.output
+
+
+def test_cluster_alignment_errors(tmp_path: Path) -> None:
+ f = _synthetic_run(tmp_path)
+ (tmp_path / "small.egt").write_bytes(encode_egt(["rs1"], theta_mean=[[0.1, 0.5, 0.9]], r_mean=[[1, 1, 1]]))
+ from simurg.core.errors import SimurgError
+
+ with pytest.raises(SimurgError) as e:
+ align_clusters(read_egt(tmp_path / "small.egt"), read_bpm(f["bpm"]))
+ assert e.value.code == "E_EGT_MANIFEST_MISMATCH"
+ c = align_clusters(read_egt(tmp_path / "small.egt"), read_bpm(f["bpm"]), allow_missing=True)
+ assert int(c.present.sum()) == 1