P4: unit tests for calling/BAF/LRR/sex/CLI; vendor validation script
- 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
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#!/bin/bash
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# Phase P4: genotype every Omni2.5 IDAT pair and compare the GTC with the vendor's (fields + whole file).
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# Args: <data dir with bpm/egt and data/> <results dir>
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set -uo pipefail
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D=$1; OUT=$2
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SRC=s3://1000genomes/release/20130502/supporting/hd_genotype_chip/broad_intensities
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mkdir -p $D/egt
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aws s3 cp --no-sign-request "$SRC/HumanOmni2.5-4v1-Multi_H.egt" $D/egt/ --only-show-errors
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cp $D/HumanOmni2.5-4v1-Multi_B.egt $D/egt/
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/usr/bin/time -v .venv/bin/python scripts/validate_p4_large.py --root $D/data --bpm $D/HumanOmni2.5-4v1-Multi_B.bpm \
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--egt-dir $D/egt --out $OUT/summary.json --norm-version 1.1.2 --sex-version 1 --workers "$(nproc)" \
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2> $OUT/time.txt || echo "P4 validation reported differences"
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tail -25 $OUT/time.txt
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@@ -0,0 +1,248 @@
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#!/usr/bin/env python3
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"""Phase P4 validation against vendor GTCs: genotype every IDAT pair and compare the produced GTC with the vendor's,
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field by field and as whole files (after aligning free-text metadata that cannot match).
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Usage: validate_p4_large.py --root DIR --bpm F --egt-dir DIR --out summary.json
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[--norm-version 1.1.2] [--sex-version 1] [--workers N] [--limit N]
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"""
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from __future__ import annotations
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import argparse
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import json
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import multiprocessing as mp
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import os
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import re
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import sys
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import time
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import traceback
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from concurrent.futures import ProcessPoolExecutor
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from pathlib import Path
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from typing import Any
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import numpy as np
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NEG_NAN = np.frombuffer(np.uint32(0xFFC00000).tobytes(), dtype=np.float32)[0]
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ALIGN = (
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"cluster_file",
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"snp_manifest",
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"autocall_date",
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"autocall_version",
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"imaging_date",
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"sample_name",
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"sample_plate",
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"sample_well",
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"scanner_data",
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)
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_W: dict[str, Any] = {}
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def _init(bpm: str, egt_dir: str, norm: str, sex_version: int) -> None:
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_W.update(bpm=bpm, egt_dir=egt_dir, norm=norm, sex_version=sex_version, ctx={})
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def _ctx(cluster_field: str) -> Any:
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from simurg.formats.bpm import read_bpm
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from simurg.formats.egt import read_egt
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from simurg.genotype.clusters import align_clusters
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from simurg.genotype.sample import manifest_context
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name = re.sub(r"\(MD5Checksum=.*\)$", "", cluster_field).strip()
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if name not in _W["ctx"]:
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if "manifest" not in _W:
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_W["manifest"] = read_bpm(_W["bpm"])
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path = Path(_W["egt_dir"]) / name
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if not path.exists():
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raise FileNotFoundError(f"cluster file {name!r} named in the GTC is not in {_W['egt_dir']}")
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m = _W["manifest"]
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_W["ctx"][name] = manifest_context(m, align_clusters(read_egt(path), m), cluster_file_name=name)
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return _W["ctx"][name]
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def _canon(a: np.ndarray) -> np.ndarray: # type: ignore[type-arg]
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a = np.asarray(a).copy()
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if a.dtype.kind == "f":
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a[np.isnan(a)] = NEG_NAN
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return a
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def check(args: tuple[str, str, str]) -> dict[str, Any]:
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from simurg.formats.gtc import encode_gtc, read_gtc
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from simurg.formats.idat import read_idat
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from simurg.genotype.sample import GenotypeParams, genotype_sample
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from simurg.genotype.sex import SexParams
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gtc, grn, red = args
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res: dict[str, Any] = {"gtc": gtc}
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try:
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ref = read_gtc(gtc, check_write_complete=False)
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ctx = _ctx(ref.cluster_file)
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params = GenotypeParams(
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norm_version=_W["norm"], sex=SexParams(version=_W["sex_version"]), gtc_version=ref.version
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)
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g = genotype_sample(read_idat(grn), read_idat(red), ctx, params)
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res["cluster_file"] = ctx.cluster_file_name
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diffs: dict[str, Any] = {}
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for k in ("raw_x", "raw_y", "genotypes", "genotype_scores", "base_calls", "controls_x", "controls_y"):
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a, b = _canon(getattr(g, k)), _canon(getattr(ref, k))
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if a.shape != b.shape:
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diffs[k] = f"shape {a.shape} vs {b.shape}"
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elif a.tobytes() != b.tobytes():
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n = int((a.reshape(len(a), -1) != b.reshape(len(b), -1)).any(axis=1).sum())
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diffs[k] = n
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if g.normalization_transforms.tobytes() != ref.normalization_transforms.tobytes():
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diffs["normalization_transforms"] = True
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if ref.version >= 4:
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for k in ("b_allele_freqs", "logr_ratios"):
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if _canon(getattr(g, k)).tobytes() != _canon(getattr(ref, k)).tobytes():
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diffs[k] = int(
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(_canon(getattr(g, k)).view(np.uint32) != _canon(getattr(ref, k)).view(np.uint32)).sum()
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)
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for k in (
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"call_rate",
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"gender",
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"logr_dev",
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"gc10",
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"gc50",
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"num_calls",
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"num_no_calls",
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"num_intensity_only",
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"ploidy",
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"ploidy_type",
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"dx",
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"slide_identifier",
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):
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a, b = getattr(g, k), getattr(ref, k)
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same = a == b or (isinstance(a, float) and np.isnan(a) and np.isnan(b))
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if not same:
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diffs[k] = [repr(a), repr(b)]
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if "genotype_scores" in diffs:
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d = np.abs(g.genotype_scores.astype(np.float64) - ref.genotype_scores.astype(np.float64))
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res["max_score_abs_diff"] = float(np.nanmax(d))
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res["field_diffs"] = diffs
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# also evaluate the other sex-estimate generation on the same calls (cheap), to identify the vendor's rule
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from simurg.genotype.sex import estimate_sex
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from simurg.signal.normalization.apply import normalize, polar
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nx, ny = normalize(g.raw_x, g.raw_y, ctx.lookup, g.normalization_transforms)
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_, r_all = polar(nx, ny)
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res["sex_by_version"] = {
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v: estimate_sex(
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g.genotypes, r_all, ctx.clusters.gentrain, ctx.clusters.present, ctx.sex_classes, SexParams(version=v)
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)
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for v in (1, 2)
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}
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res["sex_ref"] = ref.gender_char
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for k in ALIGN:
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setattr(g, k, getattr(ref, k))
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for k in ("b_allele_freqs", "logr_ratios", "genotype_scores"):
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setattr(g, k, _canon(getattr(g, k)))
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setattr(ref, k, _canon(getattr(ref, k)))
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res["byte_identical"] = encode_gtc(g) == encode_gtc(ref)
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res["ref_layout_canonical"] = encode_gtc(ref) == Path(gtc).read_bytes() or None
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res["metadata"] = {k: str(getattr(ref, k))[:80] for k in ("autocall_version", "cluster_file", "imaging_date")}
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except Exception as e:
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res["error"] = f"{type(e).__name__}: {e}"
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res["trace"] = traceback.format_exc(limit=4)
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return res
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def main() -> int:
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ap = argparse.ArgumentParser()
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ap.add_argument("--root", required=True, type=Path)
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ap.add_argument("--bpm", required=True, type=Path)
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ap.add_argument("--egt-dir", required=True, type=Path)
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ap.add_argument("--out", required=True, type=Path)
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ap.add_argument("--norm-version", default="1.1.2")
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ap.add_argument("--sex-version", type=int, default=1)
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ap.add_argument("--workers", type=int, default=os.cpu_count() or 1)
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ap.add_argument("--limit", type=int, default=0)
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a = ap.parse_args()
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from simurg.formats.discovery import walk_files
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t0 = time.time()
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files = walk_files(a.root, (".gtc", ".idat"))
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key_re = re.compile(r"(\d+_r\d{2}c\d{2})", re.IGNORECASE)
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grn, red, gtcs = {}, {}, []
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for p in files:
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low = p.name.lower()
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m = key_re.search(p.name)
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if not m:
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continue
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k = m.group(1).lower()
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if low.endswith("_grn.idat"):
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grn[k] = str(p)
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elif low.endswith("_red.idat"):
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red[k] = str(p)
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elif low.endswith(".gtc"):
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gtcs.append((k, str(p)))
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triples = [(g, grn[k], red[k]) for k, g in gtcs if k in grn and k in red]
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if a.limit:
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triples = triples[: a.limit]
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with ProcessPoolExecutor(
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a.workers,
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mp_context=mp.get_context("spawn"),
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initializer=_init,
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initargs=(str(a.bpm), str(a.egt_dir), a.norm_version, a.sex_version),
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) as ex:
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results = list(ex.map(check, triples, chunksize=2))
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field_counts: dict[str, int] = {}
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for r in results:
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for k in r.get("field_diffs", {}):
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field_counts[k] = field_counts.get(k, 0) + 1
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summary: dict[str, Any] = {
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"n_gtc": len(gtcs),
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"n_compared": len(results),
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"errors": [r for r in results if "error" in r][:20],
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"byte_identical": sum(bool(r.get("byte_identical")) for r in results),
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"samples_with_no_field_diff": sum(not r.get("field_diffs") and "error" not in r for r in results),
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"samples_with_field_diff": field_counts,
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"genotype_diff_loci_total": sum(
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int(r.get("field_diffs", {}).get("genotypes", 0) or 0)
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if isinstance(r.get("field_diffs", {}).get("genotypes", 0), int)
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else 0
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for r in results
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),
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"score_diff_loci_total": sum(
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int(r.get("field_diffs", {}).get("genotype_scores", 0) or 0)
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if isinstance(r.get("field_diffs", {}).get("genotype_scores", 0), int)
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else 0
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for r in results
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),
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"max_score_abs_diff": max((r.get("max_score_abs_diff", 0.0) for r in results), default=0.0),
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"cluster_files": sorted({r.get("cluster_file", "") for r in results}),
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"sex_agreement_by_version": {
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str(v): sum(r.get("sex_by_version", {}).get(v) == r.get("sex_ref") for r in results) for v in (1, 2)
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},
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"ref_sex_counts": {s: sum(r.get("sex_ref") == s for r in results) for s in ("M", "F", "U")},
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"examples": [r for r in results if r.get("field_diffs")][:15],
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"params": {"norm_version": a.norm_version, "sex_version": a.sex_version},
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"seconds": round(time.time() - t0, 1),
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}
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summary["passed"] = summary["byte_identical"] == len(results) > 0
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a.out.parent.mkdir(parents=True, exist_ok=True)
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a.out.write_text(json.dumps(summary, indent=2, default=str) + "\n")
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print(
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json.dumps(
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{
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k: summary[k]
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for k in (
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"n_compared",
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"byte_identical",
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"samples_with_no_field_diff",
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"samples_with_field_diff",
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"genotype_diff_loci_total",
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"score_diff_loci_total",
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"passed",
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"seconds",
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)
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}
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)
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)
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return 0 if summary["passed"] else 1
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if __name__ == "__main__":
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sys.exit(main())
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@@ -0,0 +1,235 @@
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"""Phase P4: calling, BAF/LRR, sex, base calls and `simurg genotype` on synthetic data (plan §7.5–§7.8)."""
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from __future__ import annotations
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import json
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from pathlib import Path
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import numpy as np
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import pytest
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from typer.testing import CliRunner
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from simurg.formats.bpm import read_bpm
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from simurg.formats.egt import read_egt
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from simurg.formats.gtc import read_gtc
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from simurg.genotype.baf_lrr import NEG_NAN, autosomal_mask, baf_lrr, logr_dev
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from simurg.genotype.clusters import ClusterArrays, align_clusters
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from simurg.genotype.gencall import call_genotypes, call_rate, smf, zmf
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from simurg.genotype.sample import base_calls, invariant_datetime, top_alleles
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from simurg.genotype.sex import SexParams, chromosome_classes, estimate_sex
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from simurg.synth.files import SynthIdat, SynthLocus, encode_bpm, encode_egt, encode_idat
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F32 = np.float32
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def clusters(n: int, *, gentrain: float = 0.8, threshold: float = np.nan) -> ClusterArrays:
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tm = np.tile(np.array([0.05, 0.5, 0.95], np.float32), (n, 1))
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rm = np.ones((n, 3), np.float32)
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return ClusterArrays(
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present=np.ones(n, bool),
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n=np.full((n, 3), 100, np.int32),
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theta_mean=tm,
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theta_dev=np.full((n, 3), 0.03, np.float32),
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r_mean=rm,
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r_dev=np.full((n, 3), 0.1, np.float32),
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intensity_threshold=np.full(n, threshold, np.float32),
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gentrain=np.full(n, gentrain, np.float32),
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mean_r=np.ones(n, np.float32),
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)
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def test_fuzzy_memberships() -> None:
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x = np.array([-1, 0, 0.25, 0.5, 0.75, 1, 2, np.nan], np.float32)
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s = smf(x, F32(0), F32(1))
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assert s.tolist()[:7] == [0.0, 0.0, 0.125, 0.5, 0.875, 1.0, 1.0]
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assert s[7] == 1.0 # the reference returns 1 for NaN (all comparisons false)
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z = zmf(x, F32(0), F32(1))
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assert z.tolist()[:7] == [1.0, 1.0, 0.875, 0.5, 0.125, 0.0, 0.0] and z[7] == 0.0
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assert smf(np.array([0.4, 0.6], np.float32), F32(1), F32(0)).tolist() == [0.0, 1.0] # a >= b rule
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def test_calls_at_cluster_centres() -> None:
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n = 6
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t = np.array([0.05, 0.5, 0.95, 0.5, 0.5, 0.5], np.float32)
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r = np.ones(n, np.float32)
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io = np.array([0, 0, 0, 1, 0, 0], bool)
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raw_zero = np.array([0, 0, 0, 0, 1, 0], bool)
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c = clusters(n)
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c.intensity_threshold[5] = 2.0 # R below the cluster file's intensity threshold -> no-call
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calls = call_genotypes(t, r, raw_zero, c, io)
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assert calls.genotypes.tolist() == [1, 2, 3, 0, 0, 0]
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assert (calls.scores[:3] > 0.5).all() and calls.scores[4] == 0 and calls.scores[5] == 0
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assert calls.num_calls == 3 and calls.num_intensity_only == 1
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assert call_rate(calls, n) == F32(3 / 5)
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def test_low_gentrain_and_outliers_score_low() -> None:
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n = 2
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t = np.array([0.5, 0.3], np.float32) # second point between clusters
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calls_good = call_genotypes(t, np.ones(n, np.float32), np.zeros(n, bool), clusters(n), np.zeros(n, bool))
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calls_bad = call_genotypes(
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t, np.ones(n, np.float32), np.zeros(n, bool), clusters(n, gentrain=0.05), np.zeros(n, bool)
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)
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assert calls_good.scores[0] > calls_good.scores[1]
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assert (calls_bad.scores < 0.15).all() and (calls_bad.genotypes == 0).all()
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def test_baf_lrr_interpolation() -> None:
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n = 6
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t = np.array([0.05, 0.5, 0.95, 0.275, 1.2, np.nan], np.float32)
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r = np.array([1, 1, 2, 1, 1, np.nan], np.float32)
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c = clusters(n)
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io = np.array([0, 0, 0, 0, 0, 0], bool)
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baf, lrr = baf_lrr(t, r, np.zeros(n, bool), c, io)
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assert baf[:5].tolist() == pytest.approx([0.0, 0.5, 1.0, 0.25, 1.0]) # clamped at 1
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assert lrr[:4].tolist() == pytest.approx([0.0, 0.0, 1.0, 0.0])
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assert baf.view(np.uint32)[5] == 0xFFC00000 and lrr.view(np.uint32)[5] == 0xFFC00000
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io[1] = True
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c.mean_r[1] = 2.0
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_, lrr2 = baf_lrr(t, r, np.zeros(n, bool), c, io)
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assert lrr2[1] == pytest.approx(-1.0) # intensity-only loci use the count-weighted mean R
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assert np.isnan(NEG_NAN)
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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
|
||||
Reference in new issue
Block a user