P7: differential-testing harness (simurg compare) and nightly run on the Pi
- 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
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@@ -444,6 +444,61 @@ def gtc_to_vcf_cmd(
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run_guarded(rep, lambda: run_gtc_to_vcf(opts, rep))
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# --------------------------------------------------------------------------------------------------------
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@app.command("compare")
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def compare_cmd(
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a: Annotated[Path, typer.Argument(help="GTC/VCF file or folder (A).")],
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b: Annotated[Path, typer.Argument(help="GTC/VCF file or folder (B), paired with A by relative path.")],
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out: Annotated[Path, typer.Option("--out", help="Output folder (summary.json, discrepancies, dashboard).")],
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bpm: Annotated[Path | None, typer.Option("--bpm", help="Manifest, for per-locus-class counts of GTCs.")] = None,
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tolerance: Annotated[
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list[str] | None,
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typer.Option("--tolerance", help="FIELD=exact|abs=X|ulp=N (repeatable); VCF floats: float=abs=X"),
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] = None,
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sample: Annotated[
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list[str] | None, typer.Option("--sample", help="VCF sample(s) to compare (default: all shared).")
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] = None,
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title: Annotated[str, typer.Option("--title", help="Dashboard title.")] = "",
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threads: Annotated[int, typer.Option("--threads", min=0, max=1024, help="Worker processes (0 = auto).")] = 0,
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fail_on_discrepancy: Annotated[
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bool, typer.Option("--fail-on-discrepancy", help="Exit 3 when any DISCREPANCY is found.")
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] = False,
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force: Annotated[bool, typer.Option("--force", help="Replace existing outputs.")] = False,
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log_json: Annotated[bool, typer.Option("--log-json", "--json-log")] = False,
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debug: Annotated[bool, typer.Option("--debug")] = False,
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quiet: Annotated[bool, typer.Option("--quiet", "--no-progress")] = False,
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) -> None:
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"""Field-level differential test of two GTCs/VCFs (or folders): EXACT/NEAR/DISCREPANCY/UNKNOWN (plan §16.3)."""
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from simurg.pipeline.compare import CompareOptions, run_compare
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rep = Reporter(
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command="compare", level="debug" if debug else "info", json_stderr=log_json, debug=debug, quiet=quiet
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)
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def body() -> int:
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tol: dict[str, str] = {}
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for item in tolerance or []:
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key, sep, val = item.partition("=")
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if not sep:
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raise SimurgError("E_CONFIG_KEY", f"--tolerance expects FIELD=VALUE, got {item!r}")
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tol[key.strip()] = val.strip()
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opts = CompareOptions(
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a=a,
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b=b,
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out=out,
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bpm=bpm,
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tolerances=tol,
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samples=list(sample) if sample else None,
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threads=threads,
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fail_on_discrepancy=fail_on_discrepancy,
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title=title,
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force=force,
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)
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return run_compare(opts, rep)
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run_guarded(rep, body)
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# --------------------------------------------------------------------------------------------------------
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export_app = typer.Typer(help="Exports from GTCs or VCFs: bedgraph, finalreport, plink, locus-summary.")
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app.add_typer(export_app, name="export")
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Whitespace-only changes.
@@ -0,0 +1,223 @@
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"""Field-level comparison with tolerance categories (spec CLI-038; plan §16.3).
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Every compared value falls in exactly one category:
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* **EXACT** — bitwise equal (NaN equals NaN whatever its sign bit, K-014);
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* **NEAR** — within the field's tolerance;
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* **DISCREPANCY** — outside it (or NaN on one side only);
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* **UNKNOWN** — one side lacks the field or the record.
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Counts are kept per locus class (assay, variant type, chromosome group, intensity-only), and every discrepancy is
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kept with its identifiers — nothing is hidden (plan §16.3).
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"""
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from __future__ import annotations
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from collections.abc import Mapping, Sequence
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from dataclasses import dataclass
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from dataclasses import field as dc_field
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from typing import Any
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import numpy as np
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import numpy.typing as npt
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EXACT, NEAR, DISCREPANCY, UNKNOWN = 0, 1, 2, 3
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CATEGORY_NAMES = ("EXACT", "NEAR", "DISCREPANCY", "UNKNOWN")
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@dataclass(frozen=True)
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class Tolerance:
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abs: float | None = None # absolute difference allowed
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ulp: int | None = None # float32 units in the last place allowed
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@property
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def exact(self) -> bool:
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return self.abs is None and self.ulp is None
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EXACT_TOL = Tolerance()
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# plan §16.3 (bit-exact mode targets)
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GTC_TOLERANCES: dict[str, Tolerance] = {
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"raw_x": EXACT_TOL,
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"raw_y": EXACT_TOL,
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"controls_x": EXACT_TOL,
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"controls_y": EXACT_TOL,
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"genotypes": EXACT_TOL,
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"base_calls": EXACT_TOL,
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"normalization_transforms": Tolerance(ulp=2),
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"genotype_scores": Tolerance(abs=1e-5),
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"b_allele_freqs": Tolerance(abs=1e-5),
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"logr_ratios": Tolerance(abs=1e-5),
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"call_rate": Tolerance(abs=1e-6),
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"logr_dev": Tolerance(abs=1e-6),
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"gc10": Tolerance(abs=1e-6),
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"gc50": Tolerance(abs=1e-6),
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"gender": EXACT_TOL,
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"num_calls": EXACT_TOL,
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"num_no_calls": EXACT_TOL,
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"num_intensity_only": EXACT_TOL,
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"percentiles_x": EXACT_TOL,
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"percentiles_y": EXACT_TOL,
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}
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VCF_FLOAT_TOL = Tolerance(abs=1e-5) # GS, BAF, LRR, NORMX, NORMY, THETA, R
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VCF_EXACT_FIELDS = {"GT", "X", "Y"}
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def _float_ulps(a: npt.NDArray[np.float32], b: npt.NDArray[np.float32]) -> npt.NDArray[np.int64]:
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"""Distance in float32 ULPs (monotone integer mapping of the bit patterns)."""
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def key(x: npt.NDArray[np.float32]) -> npt.NDArray[np.int64]:
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i = x.astype(np.float32).view(np.int32).astype(np.int64)
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return np.where(i < 0, -(i & 0x7FFFFFFF), i)
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return np.abs(key(a) - key(b))
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def categorize(a: npt.ArrayLike, b: npt.ArrayLike, tol: Tolerance) -> npt.NDArray[np.int8]:
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"""Element-wise category of two equally shaped arrays (numbers or strings)."""
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a, b = np.asarray(a), np.asarray(b)
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if a.shape != b.shape:
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raise ValueError(f"shape {a.shape} vs {b.shape}")
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out = np.full(a.shape, DISCREPANCY, np.int8)
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if a.dtype.kind in "fc" or b.dtype.kind in "fc":
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fa, fb = a.astype(np.float64), b.astype(np.float64)
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na, nb = np.isnan(fa), np.isnan(fb)
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same = (fa == fb) | (na & nb)
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if a.dtype == np.float32 and b.dtype == np.float32:
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same &= (a.view(np.uint32) == b.view(np.uint32)) | (na & nb) | ((fa == 0) & (fb == 0))
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out[same] = EXACT
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near = np.zeros(a.shape, np.bool_)
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valid = ~(na | nb) & ~same
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if tol.abs is not None:
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near |= valid & (np.abs(fa - fb) <= tol.abs)
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if tol.ulp is not None:
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near |= valid & (_float_ulps(a.astype(np.float32), b.astype(np.float32)) <= tol.ulp)
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out[near] = NEAR
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return out
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out[a == b] = EXACT
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return out
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@dataclass
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class FieldResult:
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"""Category counts of one field: overall and per locus class, plus the discrepancies themselves."""
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field: str
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tolerance: str
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counts: dict[str, list[int]] = dc_field(default_factory=dict) # class -> [EXACT, NEAR, DISCREPANCY, UNKNOWN]
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discrepancies: list[dict[str, Any]] = dc_field(default_factory=list)
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def add(self, cls: str, cats: npt.NDArray[np.int8]) -> None:
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c = self.counts.setdefault(cls, [0, 0, 0, 0])
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binc = np.bincount(cats.ravel().astype(np.int64), minlength=4)
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for k in range(4):
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c[k] += int(binc[k])
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@property
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def total(self) -> list[int]:
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return self.counts.get("all", [0, 0, 0, 0])
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def tol_text(t: Tolerance) -> str:
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if t.exact:
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return "exact"
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parts = []
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if t.abs is not None:
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parts.append(f"abs<={t.abs:g}")
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if t.ulp is not None:
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parts.append(f"ulp<={t.ulp}")
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return ",".join(parts)
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def locus_classes(
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chroms: Sequence[str], snps: Sequence[str], assay_types: npt.ArrayLike, intensity_only: npt.ArrayLike
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) -> dict[str, npt.NDArray[np.bool_]]:
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"""Locus class masks (plan §16.3): assay, variant type, chromosome group, intensity-only."""
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at = np.asarray(assay_types)
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io = np.asarray(intensity_only, dtype=np.bool_)
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def group(c: str) -> str:
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c = c[3:] if c.lower().startswith("chr") else c
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if c in ("0", ""):
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return "unmapped"
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if c == "XY":
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return "PAR"
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if c in ("X", "Y"):
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return c
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if c in ("MT", "M"):
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return "MT"
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return "autosome" if c.isdigit() else "other"
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groups = np.array([group(c) for c in chroms])
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indel = np.array([("D" in s or "I" in s) for s in snps])
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out = {"all": np.ones(len(groups), np.bool_), "assay=InfII": at == 0, "assay=InfI": at != 0,
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"type=SNV": ~indel, "type=indel": indel, "intensity_only": io} # fmt: skip
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for gname in ("autosome", "X", "Y", "PAR", "MT", "unmapped", "other"):
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m = groups == gname
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if m.any():
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out[f"chrom={gname}"] = m
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return out
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def compare_locus_field(
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name: str,
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a: npt.ArrayLike | None,
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b: npt.ArrayLike | None,
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tol: Tolerance,
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classes: Mapping[str, npt.NDArray[np.bool_]],
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ids: Sequence[str],
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context: Mapping[str, str],
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) -> FieldResult:
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"""Per-locus arrays (first axis = locus). Missing or empty on one side → UNKNOWN for every locus."""
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res = FieldResult(name, tol_text(tol))
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n = len(classes["all"])
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a_ok = a is not None and len(np.asarray(a)) == n
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b_ok = b is not None and len(np.asarray(b)) == n
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if not (a_ok and b_ok):
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cats = np.full(n, UNKNOWN, np.int8)
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else:
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el = categorize(np.asarray(a), np.asarray(b), tol)
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cats = el.max(axis=tuple(range(1, el.ndim))) if el.ndim > 1 else el # worst element of the locus
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for i in np.flatnonzero(cats == DISCREPANCY).tolist():
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res.discrepancies.append(
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{**context, "field": name, "locus": ids[i], "a": _fmt(np.asarray(a)[i]), "b": _fmt(np.asarray(b)[i])}
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)
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for cls, mask in classes.items():
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res.add(cls, cats[mask])
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return res
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def compare_scalar(name: str, a: Any, b: Any, tol: Tolerance, context: Mapping[str, str]) -> FieldResult:
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res = FieldResult(name, tol_text(tol))
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if a is None or b is None:
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cat = np.array([UNKNOWN], np.int8)
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else:
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aa, bb = np.asarray([a]), np.asarray([b])
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if aa.dtype.kind == "f" or bb.dtype.kind == "f":
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aa, bb = aa.astype(np.float64), bb.astype(np.float64)
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cat = categorize(aa, bb, tol)
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res.add("sample", cat)
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if cat[0] == DISCREPANCY:
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res.discrepancies.append({**context, "field": name, "locus": "", "a": _fmt(a), "b": _fmt(b)})
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return res
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def _fmt(v: Any) -> str:
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arr = np.asarray(v)
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if arr.ndim == 0:
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x = arr.item()
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return repr(x) if isinstance(x, float) else str(x)
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return "[" + ",".join(_fmt(x) for x in arr.ravel().tolist()) + "]"
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def merge_results(results: Sequence[FieldResult]) -> dict[str, FieldResult]:
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"""Sum results of the same field (e.g. over samples)."""
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out: dict[str, FieldResult] = {}
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for r in results:
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m = out.setdefault(r.field, FieldResult(r.field, r.tolerance))
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for cls, c in r.counts.items():
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cur = m.counts.setdefault(cls, [0, 0, 0, 0])
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for k in range(4):
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cur[k] += c[k]
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m.discrepancies.extend(r.discrepancies)
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return out
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@@ -0,0 +1,223 @@
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"""GTC-vs-GTC and VCF-vs-VCF comparison on parsed data (spec CLI-038; plan §16.3)."""
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from __future__ import annotations
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import math
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from collections.abc import Mapping, Sequence
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from dataclasses import dataclass, field
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from typing import Any
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import numpy as np
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import numpy.typing as npt
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from numpy.lib import recfunctions as rfn
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from simurg.compare.core import (
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DISCREPANCY,
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EXACT,
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GTC_TOLERANCES,
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UNKNOWN,
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VCF_EXACT_FIELDS,
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VCF_FLOAT_TOL,
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FieldResult,
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Tolerance,
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categorize,
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compare_locus_field,
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compare_scalar,
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tol_text,
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)
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GTC_LOCUS_FIELDS = ("raw_x", "raw_y", "genotypes", "base_calls", "genotype_scores", "b_allele_freqs", "logr_ratios")
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GTC_SCALARS = ("call_rate", "logr_dev", "gc10", "gc50", "gender", "num_calls", "num_no_calls", "num_intensity_only")
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GTC_METADATA = ("sample_name", "sample_plate", "sample_well", "cluster_file", "snp_manifest", "imaging_date",
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"autocall_date", "autocall_version", "slide_identifier") # fmt: skip
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@dataclass
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class Comparison:
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"""Results of one pair (one sample)."""
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label: str
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kind: str # "gtc" | "vcf"
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fields: list[FieldResult] = field(default_factory=list)
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metadata: dict[str, list[str]] = field(default_factory=dict) # field -> [a, b] when they differ
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notes: list[str] = field(default_factory=list)
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def compare_gtc(
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a: Any,
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b: Any,
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label: str,
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classes: Mapping[str, npt.NDArray[np.bool_]],
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names: Sequence[str],
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tolerances: Mapping[str, Tolerance] = GTC_TOLERANCES,
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) -> Comparison:
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"""Two parsed GTCs of the same manifest."""
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res = Comparison(label, "gtc")
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ctx = {"pair": label}
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if a.num_snps != b.num_snps:
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res.notes.append(f"locus count differs: {a.num_snps} vs {b.num_snps}")
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return res
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for f in GTC_LOCUS_FIELDS:
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va, vb = getattr(a, f, None), getattr(b, f, None)
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if (va is None or len(va) == 0) and (vb is None or len(vb) == 0):
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continue # absent on both sides (e.g. BAF/LRR in GTC v3): not applicable
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res.fields.append(compare_locus_field(f, va, vb, tolerances[f], classes, names, ctx))
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# normalization transforms: one row per bin
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ta, tb = a.normalization_transforms, b.normalization_transforms
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fr = FieldResult("normalization_transforms", tol_text(tolerances["normalization_transforms"]))
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if len(ta) != len(tb) or not len(ta):
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fr.add("bins", np.full(max(len(ta), len(tb), 1), UNKNOWN, np.int8))
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else:
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ua = rfn.structured_to_unstructured(ta).astype(np.float32)
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ub = rfn.structured_to_unstructured(tb).astype(np.float32)
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cats = categorize(ua, ub, tolerances["normalization_transforms"]).max(axis=1)
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fr.add("bins", cats)
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for i in np.flatnonzero(cats == DISCREPANCY).tolist():
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fr.discrepancies.append({**ctx, "field": "normalization_transforms", "locus": f"bin{i}",
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"a": str(ua[i].tolist()), "b": str(ub[i].tolist())}) # fmt: skip
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res.fields.append(fr)
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# controls: one row per control bead
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for f in ("controls_x", "controls_y"):
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ca, cb = getattr(a, f), getattr(b, f)
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fr = FieldResult(f, tol_text(tolerances[f]))
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fr.add("controls", categorize(ca, cb, tolerances[f]) if len(ca) == len(cb) else np.array([UNKNOWN], np.int8))
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res.fields.append(fr)
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for f in GTC_SCALARS:
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res.fields.append(compare_scalar(f, _scalar(a, f), _scalar(b, f), tolerances[f], ctx))
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for f in ("percentiles_x", "percentiles_y"):
|
||||
pa, pb = getattr(a, f), getattr(b, f)
|
||||
fr = FieldResult(f, tol_text(tolerances[f]))
|
||||
fr.add("sample", categorize(pa, pb, tolerances[f]) if len(pa) == len(pb) else np.array([UNKNOWN], np.int8))
|
||||
res.fields.append(fr)
|
||||
for f in GTC_METADATA:
|
||||
va, vb = str(getattr(a, f, "")), str(getattr(b, f, ""))
|
||||
if va != vb:
|
||||
res.metadata[f] = [va, vb]
|
||||
if a.version != b.version:
|
||||
res.notes.append(f"GTC versions differ: {a.version} vs {b.version} (fields absent in one are UNKNOWN)")
|
||||
return res
|
||||
|
||||
|
||||
def _scalar(g: Any, f: str) -> Any:
|
||||
if f == "gender":
|
||||
return g.gender_char
|
||||
v = getattr(g, f, None)
|
||||
if isinstance(v, float) and math.isnan(v):
|
||||
return v
|
||||
return v
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------------ VCF
|
||||
@dataclass
|
||||
class VcfRecord:
|
||||
chrom: str
|
||||
pos: int
|
||||
vid: str
|
||||
ref: str
|
||||
alt: str
|
||||
fields: dict[str, str] # FORMAT key -> value of the compared sample
|
||||
|
||||
|
||||
def vcf_classes(r: VcfRecord) -> list[str]:
|
||||
c = r.chrom[3:] if r.chrom.lower().startswith("chr") else r.chrom
|
||||
grp = "autosome" if c.isdigit() else {"X": "X", "Y": "Y", "MT": "MT", "M": "MT"}.get(c, "other")
|
||||
alts = r.alt.split(",")
|
||||
if all(len(x) == len(r.ref) == 1 for x in alts):
|
||||
vtype = "SNV"
|
||||
elif all(len(x) == len(r.ref) for x in alts):
|
||||
vtype = "MNV"
|
||||
else:
|
||||
vtype = "indel"
|
||||
out = ["all", f"chrom={grp}", f"type={vtype}"]
|
||||
if "," in r.vid or ";" in r.vid:
|
||||
out.append("merged_site")
|
||||
if len(alts) > 1:
|
||||
out.append("multiallelic")
|
||||
return out
|
||||
|
||||
|
||||
def _gt_norm(gt: str) -> str:
|
||||
parts = gt.replace("|", "/").split("/")
|
||||
if any(p == "." for p in parts):
|
||||
return "." if len(parts) == 1 else "./."
|
||||
return "/".join(sorted(parts, key=int))
|
||||
|
||||
|
||||
def _cat_scalar(va: str, vb: str, tol: Tolerance) -> int:
|
||||
if va == vb:
|
||||
return EXACT
|
||||
x, y = _num(va), _num(vb)
|
||||
if math.isnan(x) and math.isnan(y):
|
||||
return EXACT
|
||||
if math.isnan(x) or math.isnan(y):
|
||||
return DISCREPANCY
|
||||
if x == y:
|
||||
return EXACT
|
||||
if tol.abs is not None and abs(x - y) <= tol.abs:
|
||||
return 1 # NEAR
|
||||
return DISCREPANCY
|
||||
|
||||
|
||||
def compare_vcf_records(
|
||||
a: Mapping[tuple[str, int, str, str, str], VcfRecord],
|
||||
b: Mapping[tuple[str, int, str, str, str], VcfRecord],
|
||||
label: str,
|
||||
float_tol: Tolerance = VCF_FLOAT_TOL,
|
||||
) -> Comparison:
|
||||
"""Records keyed by (CHROM, POS, REF, ALT, ID); FORMAT fields of one sample on each side."""
|
||||
res = Comparison(label, "vcf")
|
||||
ctx = {"pair": label}
|
||||
keys = list(a) + [k for k in b if k not in a]
|
||||
fields = sorted({f for r in a.values() for f in r.fields} | {f for r in b.values() for f in r.fields})
|
||||
counts: dict[str, dict[str, list[int]]] = {f: {} for f in ["record", *fields]}
|
||||
disc: dict[str, list[dict[str, Any]]] = {f: [] for f in ["record", *fields]}
|
||||
tols = {f: (Tolerance() if f in VCF_EXACT_FIELDS else float_tol) for f in fields}
|
||||
|
||||
def bump(f: str, classes: list[str], cat: int) -> None:
|
||||
per = counts[f]
|
||||
for cls in classes:
|
||||
c = per.get(cls)
|
||||
if c is None:
|
||||
c = per[cls] = [0, 0, 0, 0]
|
||||
c[cat] += 1
|
||||
|
||||
for k in keys:
|
||||
ra, rb = a.get(k), b.get(k)
|
||||
r = ra or rb
|
||||
assert r is not None
|
||||
classes = vcf_classes(r)
|
||||
where = f"{r.chrom}:{r.pos}:{r.ref}>{r.alt} {r.vid}"
|
||||
if ra is None or rb is None:
|
||||
bump("record", classes, UNKNOWN)
|
||||
disc["record"].append({**ctx, "field": "record", "locus": where,
|
||||
"a": "present" if ra else "absent", "b": "present" if rb else "absent"}) # fmt: skip
|
||||
continue
|
||||
bump("record", classes, EXACT)
|
||||
for f in fields:
|
||||
va, vb = ra.fields.get(f), rb.fields.get(f)
|
||||
if va is None and vb is None:
|
||||
continue # the field does not apply to this record on either side (e.g. BAF of merged sites)
|
||||
if va is None or vb is None:
|
||||
cat = UNKNOWN
|
||||
elif f == "GT":
|
||||
cat = EXACT if _gt_norm(va) == _gt_norm(vb) else DISCREPANCY
|
||||
else:
|
||||
cat = _cat_scalar(va, vb, tols[f])
|
||||
bump(f, classes, cat)
|
||||
if cat == DISCREPANCY:
|
||||
disc[f].append({**ctx, "field": f, "locus": where, "a": va, "b": vb})
|
||||
for f in ["record", *fields]:
|
||||
fr = FieldResult(
|
||||
f, "present in both" if f == "record" else ("exact" if f in VCF_EXACT_FIELDS else tol_text(float_tol))
|
||||
)
|
||||
fr.counts = counts[f]
|
||||
fr.discrepancies = disc[f]
|
||||
res.fields.append(fr)
|
||||
return res
|
||||
|
||||
|
||||
def _num(v: str) -> float:
|
||||
try:
|
||||
return float(v)
|
||||
except ValueError:
|
||||
return math.nan
|
||||
@@ -0,0 +1,359 @@
|
||||
"""``simurg compare A B``: field-level differential testing of GTCs or VCFs (spec CLI-038; plan §14, §16.3, §18 P7).
|
||||
|
||||
A and B are two files or two folders; folders are paired by relative path. Outputs: ``summary.json``,
|
||||
``discrepancies.tsv.gz`` (every discrepancy, never truncated) and ``dashboard.html``.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import gzip
|
||||
import html
|
||||
import io
|
||||
import json
|
||||
import multiprocessing as mp
|
||||
import os
|
||||
from collections.abc import Iterator, Sequence
|
||||
from concurrent.futures import ProcessPoolExecutor
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
from typing import IO, Any, cast
|
||||
|
||||
import numpy as np
|
||||
|
||||
from simurg import PRODUCT_NAME, __version__
|
||||
from simurg.compare.core import CATEGORY_NAMES, GTC_TOLERANCES, FieldResult, Tolerance, locus_classes, merge_results
|
||||
from simurg.compare.records import Comparison, VcfRecord, compare_gtc, compare_vcf_records
|
||||
from simurg.core.atomic import atomic_open, atomic_write_text, check_outputs_absent
|
||||
from simurg.core.errors import ExitCode, SimurgError
|
||||
from simurg.core.log import Reporter
|
||||
|
||||
GTC_EXT = (".gtc",)
|
||||
VCF_EXT = (".vcf", ".vcf.gz")
|
||||
|
||||
|
||||
@dataclass
|
||||
class CompareOptions:
|
||||
a: Path
|
||||
b: Path
|
||||
out: Path
|
||||
bpm: Path | None = None
|
||||
tolerances: dict[str, str] = field(default_factory=dict) # field -> "abs=1e-5" | "ulp=2" | "exact"
|
||||
samples: list[str] | None = None
|
||||
threads: int = 0
|
||||
fail_on_discrepancy: bool = False
|
||||
title: str = ""
|
||||
force: bool = False
|
||||
|
||||
|
||||
def parse_tolerance(text: str) -> Tolerance:
|
||||
text = text.strip().lower()
|
||||
if text in ("exact", "0"):
|
||||
return Tolerance()
|
||||
kv = dict(part.split("=", 1) for part in text.split(","))
|
||||
try:
|
||||
return Tolerance(abs=float(kv["abs"]) if "abs" in kv else None, ulp=int(kv["ulp"]) if "ulp" in kv else None)
|
||||
except (KeyError, ValueError):
|
||||
raise SimurgError("E_CONFIG_KEY", f"bad tolerance {text!r}; use exact, abs=X, ulp=N or both") from None
|
||||
|
||||
|
||||
def _kind(p: Path) -> str:
|
||||
n = p.name.lower()
|
||||
if n.endswith(GTC_EXT):
|
||||
return "gtc"
|
||||
if n.endswith(VCF_EXT):
|
||||
return "vcf"
|
||||
raise SimurgError("E_FILE_UNREADABLE", f"{p}: compare accepts .gtc and .vcf(.gz) files (convert BCF with bcftools)")
|
||||
|
||||
|
||||
def pair_inputs(a: Path, b: Path) -> tuple[list[tuple[str, Path, Path]], list[str]]:
|
||||
"""((label, file_a, file_b)…, unmatched notes). Folders are paired by file name (or relative path)."""
|
||||
for p in (a, b):
|
||||
if not p.exists():
|
||||
raise SimurgError("E_FILE_NOT_FOUND", f"not found: {p}", path=str(p))
|
||||
if a.is_file() and b.is_file():
|
||||
if _kind(a) != _kind(b):
|
||||
raise SimurgError("E_FILE_UNREADABLE", f"cannot compare a {_kind(a)} with a {_kind(b)}")
|
||||
return [(a.name, a, b)], []
|
||||
if not (a.is_dir() and b.is_dir()):
|
||||
raise SimurgError("E_FILE_UNREADABLE", "give two files or two folders")
|
||||
|
||||
def listing(root: Path) -> dict[str, Path]:
|
||||
"""Key files by name when names are unique in the tree (so nested and flat layouts pair up),
|
||||
otherwise by relative path."""
|
||||
files = [p for p in sorted(root.rglob("*")) if p.is_file() and p.name.lower().endswith(GTC_EXT + VCF_EXT)]
|
||||
names = [p.name for p in files]
|
||||
if len(set(names)) == len(names):
|
||||
return {p.name: p for p in files}
|
||||
return {str(p.relative_to(root)): p for p in files}
|
||||
|
||||
la, lb = listing(a), listing(b)
|
||||
pairs = [(k, la[k], lb[k]) for k in sorted(la) if k in lb]
|
||||
notes = [f"only in A: {k}" for k in sorted(set(la) - set(lb))] + [
|
||||
f"only in B: {k}" for k in sorted(set(lb) - set(la))
|
||||
]
|
||||
if not pairs:
|
||||
raise SimurgError("E_NO_INPUT", f"no files with the same relative path under {a} and {b}")
|
||||
return pairs, notes
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------------ workers
|
||||
_W: dict[str, Any] = {}
|
||||
|
||||
|
||||
def _init(bpm: str | None, tolerances: dict[str, Tolerance], samples: list[str] | None) -> None:
|
||||
_W.update(bpm=bpm, tol=tolerances, samples=samples)
|
||||
|
||||
|
||||
def _manifest() -> tuple[Any, Any]:
|
||||
if "classes" not in _W:
|
||||
if _W["bpm"]:
|
||||
from simurg.formats.bpm import read_bpm
|
||||
|
||||
m = read_bpm(_W["bpm"])
|
||||
_W["classes"] = locus_classes(m.chroms, m.snps, m.assay_types, m.intensity_only)
|
||||
_W["names"] = list(m.names)
|
||||
else:
|
||||
_W["classes"], _W["names"] = None, None
|
||||
return _W["classes"], _W["names"]
|
||||
|
||||
|
||||
VcfKey = tuple[str, int, str, str, str]
|
||||
|
||||
|
||||
def read_vcf(path: Path, sample: str | None = None) -> tuple[list[str], dict[str, dict[VcfKey, VcfRecord]], int]:
|
||||
"""Records per sample (all samples, or one), keyed by (CHROM, POS, REF, ALT, ID). Repeated keys are kept under
|
||||
a numbered ID so nothing is silently overwritten; their count is returned."""
|
||||
opener = gzip.open if str(path).endswith(".gz") else open
|
||||
samples: list[str] = []
|
||||
out: dict[str, dict[VcfKey, VcfRecord]] = {}
|
||||
seen: set[VcfKey] = set()
|
||||
dups = 0
|
||||
with opener(path, "rt") as fh:
|
||||
for line in fh:
|
||||
if line.startswith("##"):
|
||||
continue
|
||||
f = line.rstrip("\n").split("\t")
|
||||
if line.startswith("#"):
|
||||
samples = f[9:]
|
||||
keep = [i for i, s in enumerate(samples) if sample is None or s == sample]
|
||||
out = {samples[i]: {} for i in keep}
|
||||
continue
|
||||
keys = f[8].split(":")
|
||||
k = (f[0], int(f[1]), f[3], f[4], f[2])
|
||||
if k in seen:
|
||||
dups += 1
|
||||
k = (f[0], int(f[1]), f[3], f[4], f"{f[2]}#{dups}")
|
||||
seen.add(k)
|
||||
for i in keep:
|
||||
vals = f[9 + i].split(":")
|
||||
out[samples[i]][k] = VcfRecord(f[0], int(f[1]), f[2], f[3], f[4], dict(zip(keys, vals, strict=False)))
|
||||
return samples, out, dups
|
||||
|
||||
|
||||
def _compare_pair(task: tuple[str, str, str]) -> list[Comparison]:
|
||||
label, pa, pb = task
|
||||
kind = _kind(Path(pa))
|
||||
try:
|
||||
if kind == "gtc":
|
||||
from simurg.formats.gtc import read_gtc
|
||||
|
||||
ga, gb = read_gtc(pa, check_write_complete=False), read_gtc(pb, check_write_complete=False)
|
||||
classes, names = _manifest()
|
||||
if classes is None or len(names) != ga.num_snps:
|
||||
classes = locus_classes(["1"] * ga.num_snps, ["[A/B]"] * ga.num_snps, np.zeros(ga.num_snps),
|
||||
np.zeros(ga.num_snps, np.bool_)) # fmt: skip
|
||||
classes = {"all": classes["all"]}
|
||||
names = [str(i) for i in range(ga.num_snps)]
|
||||
tol = {**GTC_TOLERANCES, **_W["tol"]}
|
||||
return [compare_gtc(ga, gb, label, classes, names, tol)]
|
||||
sa, ra, da = read_vcf(Path(pa))
|
||||
sb, rb, db = read_vcf(Path(pb))
|
||||
wanted = _W["samples"] or [s for s in sa if s in sb]
|
||||
out = []
|
||||
for s in wanted:
|
||||
if s not in ra or s not in rb:
|
||||
c = Comparison(f"{label}:{s}", "vcf")
|
||||
c.notes.append(f"sample {s} missing in {'A' if s not in ra else 'B'}")
|
||||
out.append(c)
|
||||
continue
|
||||
ftol = _W["tol"].get("float")
|
||||
if ftol is None:
|
||||
out.append(compare_vcf_records(ra[s], rb[s], f"{label}:{s}"))
|
||||
else:
|
||||
out.append(compare_vcf_records(ra[s], rb[s], f"{label}:{s}", ftol))
|
||||
if not wanted:
|
||||
c = Comparison(label, "vcf")
|
||||
c.notes.append("no sample in common")
|
||||
out.append(c)
|
||||
if da or db:
|
||||
out[0].notes.append(f"duplicate record keys: {da} in A, {db} in B")
|
||||
return out
|
||||
except SimurgError as e:
|
||||
c = Comparison(label, kind)
|
||||
c.notes.append(f"{e.code}: {e.message}")
|
||||
return [c]
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------------ outputs
|
||||
def _totals(fields: dict[str, FieldResult]) -> dict[str, Any]:
|
||||
out = {}
|
||||
for f, r in fields.items():
|
||||
out[f] = {
|
||||
"tolerance": r.tolerance,
|
||||
"counts": {cls: dict(zip(CATEGORY_NAMES, c, strict=True)) for cls, c in sorted(r.counts.items())},
|
||||
"discrepancies": sum(c[2] for k, c in r.counts.items() if k in ("all", "sample", "bins", "controls")),
|
||||
"listed": len(r.discrepancies),
|
||||
}
|
||||
return out
|
||||
|
||||
|
||||
def write_discrepancies(path: Path, rows: Iterator[dict[str, Any]]) -> int:
|
||||
n = 0
|
||||
with atomic_open(path, "wb") as raw:
|
||||
gz = gzip.GzipFile(fileobj=cast(IO[bytes], raw), mode="wb", mtime=0)
|
||||
fh = io.TextIOWrapper(gz, encoding="utf-8", newline="\n")
|
||||
fh.write("#simurg_schema=simurg.compare_discrepancies/1\npair\tfield\tlocus\ta\tb\n")
|
||||
for r in rows:
|
||||
fh.write(
|
||||
"\t".join(str(r.get(k, "")).replace("\t", " ") for k in ("pair", "field", "locus", "a", "b")) + "\n"
|
||||
)
|
||||
n += 1
|
||||
fh.flush()
|
||||
fh.detach()
|
||||
gz.close()
|
||||
return n
|
||||
|
||||
|
||||
DASH_CSS = """
|
||||
:root{--bg:#fafafa;--fg:#222;--muted:#666;--line:#ddd;--ok:#d6e6f5;--near:#e8f2d6;--bad:#f9d3ac;--unk:#e6e6e6}
|
||||
@media (prefers-color-scheme: dark){:root{--bg:#1d1f21;--fg:#e6e6e6;--muted:#aaa;--line:#444;
|
||||
--ok:#23405c;--near:#35451f;--bad:#6b3d10;--unk:#3a3a3a}}
|
||||
body{margin:0;padding:16px;background:var(--bg);color:var(--fg);font:14px/1.4 system-ui,sans-serif}
|
||||
table{border-collapse:collapse;margin:6px 0}
|
||||
td,th{border:1px solid var(--line);padding:3px 8px;text-align:right}
|
||||
td:first-child,th:first-child{text-align:left}
|
||||
.cEXACT{background:var(--ok)} .cNEAR{background:var(--near)} .cUNKNOWN{background:var(--unk)}
|
||||
tr.bad .cDISCREPANCY{background:var(--bad)}
|
||||
.verdict{font-size:20px;font-weight:600} .muted{color:var(--muted)} .wrap{overflow-x:auto}
|
||||
"""
|
||||
|
||||
|
||||
def _table(head: Sequence[str], rows: Sequence[Sequence[str]], cls: str = "") -> str:
|
||||
th = "".join(f"<th>{h}</th>" for h in head)
|
||||
body = "".join("<tr>" + "".join(f"<td>{c}</td>" for c in r) + "</tr>" for r in rows)
|
||||
return f'<div class="wrap"><table class="{cls}"><tr>{th}</tr>{body}</table></div>'
|
||||
|
||||
|
||||
def _counts_cells(c: dict[str, int] | list[int]) -> str:
|
||||
vals = c if isinstance(c, list) else [c.get(k, 0) for k in CATEGORY_NAMES]
|
||||
return "".join(f'<td class="c{k}">{v:,}</td>' for k, v in zip(CATEGORY_NAMES, vals, strict=True))
|
||||
|
||||
|
||||
def render_dashboard(summary: dict[str, Any], examples: dict[str, list[dict[str, Any]]]) -> str:
|
||||
esc = html.escape
|
||||
title = esc(summary["title"] or "Differential test")
|
||||
verdict = summary["verdict"]
|
||||
head = "".join(f"<th>{k}</th>" for k in CATEGORY_NAMES)
|
||||
rows = []
|
||||
for f, d in summary["fields"].items():
|
||||
counts = d["counts"]
|
||||
main: dict[str, int] = next((counts[k] for k in ("all", "sample", "bins", "controls") if k in counts), {})
|
||||
per = "".join(f"<tr><td>{esc(cls)}</td>{_counts_cells(c)}</tr>" for cls, c in counts.items())
|
||||
inner = f"<table><tr><th>class</th>{head}</tr>{per}</table>"
|
||||
bad = ' class="bad"' if d["discrepancies"] else ""
|
||||
rows.append(
|
||||
f"<tr{bad}><td><details><summary>{esc(f)}</summary>{inner}</details></td>"
|
||||
f"<td>{esc(d['tolerance'])}</td>{_counts_cells(main)}</tr>"
|
||||
)
|
||||
fields_html = (
|
||||
f'<div class="wrap"><table><tr><th>field (click for classes)</th><th>tolerance</th>{head}</tr>'
|
||||
+ "".join(rows)
|
||||
+ "</table></div>"
|
||||
)
|
||||
parts = [
|
||||
f'<h1>{title}</h1><div class="muted">{PRODUCT_NAME} {__version__} · {summary["pairs"]} pair(s)</div>',
|
||||
f'<div class="muted">A = {esc(summary["a"])}<br>B = {esc(summary["b"])}</div>',
|
||||
f'<p class="verdict">{verdict}: {summary["discrepancies"]:,} discrepancies, {summary["unknown"]:,} unknown</p>',
|
||||
"<h2>Fields</h2>" + fields_html,
|
||||
]
|
||||
meta = [
|
||||
[esc(p), esc(k), esc(v[0]), esc(v[1])]
|
||||
for p, md in summary["metadata_differences"].items()
|
||||
for k, v in md.items()
|
||||
]
|
||||
if meta:
|
||||
parts.append("<h2>Metadata differences (not counted)</h2>" + _table(["pair", "field", "A", "B"], meta))
|
||||
if summary["notes"]:
|
||||
parts.append("<h2>Notes</h2><ul>" + "".join(f"<li>{esc(n)}</li>" for n in summary["notes"]) + "</ul>")
|
||||
for f, lst in examples.items():
|
||||
if lst:
|
||||
n = summary["fields"][f]["listed"]
|
||||
body = [[esc(str(r.get(k, "")))[:200] for k in ("pair", "locus", "a", "b")] for r in lst]
|
||||
parts.append(
|
||||
f"<h3>{esc(f)}: first {len(lst)} of {n:,} discrepancies</h3>"
|
||||
+ _table(["pair", "locus", "A", "B"], body)
|
||||
)
|
||||
parts.append('<p class="muted">Every discrepancy and every unmatched record is listed in discrepancies.tsv.gz.</p>')
|
||||
return (
|
||||
'<!doctype html><html lang="en"><head><meta charset="utf-8">'
|
||||
'<meta name="viewport" content="width=device-width, initial-scale=1">'
|
||||
f"<title>{title} — {PRODUCT_NAME}</title><style>{DASH_CSS}</style></head><body>"
|
||||
+ "\n".join(parts)
|
||||
+ "</body></html>\n"
|
||||
)
|
||||
|
||||
|
||||
def run_compare(opts: CompareOptions, rep: Reporter) -> int:
|
||||
targets = [opts.out / "summary.json", opts.out / "discrepancies.tsv.gz", opts.out / "dashboard.html"]
|
||||
check_outputs_absent(targets, force=opts.force)
|
||||
tol = {k: parse_tolerance(v) for k, v in opts.tolerances.items()}
|
||||
unknown = [k for k in tol if k not in GTC_TOLERANCES and k != "float"]
|
||||
if unknown:
|
||||
valid = ", ".join(sorted(GTC_TOLERANCES))
|
||||
raise SimurgError("E_CONFIG_KEY", f"unknown tolerance field(s) {unknown}; GTC fields: {valid}; VCF: float")
|
||||
pairs, notes = pair_inputs(opts.a, opts.b)
|
||||
tasks = [(label, str(pa), str(pb)) for label, pa, pb in pairs]
|
||||
workers = max(1, min(opts.threads or (os.cpu_count() or 1), len(tasks)))
|
||||
rep.info(f"comparing {len(tasks)} pair(s) with {workers} worker(s)")
|
||||
if workers == 1:
|
||||
_init(str(opts.bpm) if opts.bpm else None, tol, opts.samples)
|
||||
results = [c for t in tasks for c in _compare_pair(t)]
|
||||
else:
|
||||
with ProcessPoolExecutor(workers, mp_context=mp.get_context("spawn"), initializer=_init,
|
||||
initargs=(str(opts.bpm) if opts.bpm else None, tol, opts.samples)) as ex: # fmt: skip
|
||||
results = [c for batch in ex.map(_compare_pair, tasks) for c in batch]
|
||||
merged = merge_results([f for c in results for f in c.fields])
|
||||
for c in results:
|
||||
notes += [f"{c.label}: {n}" for n in c.notes]
|
||||
|
||||
def main_counts(r: FieldResult) -> list[int]:
|
||||
for k in ("all", "sample", "bins", "controls"):
|
||||
if k in r.counts:
|
||||
return r.counts[k]
|
||||
return [0, 0, 0, 0]
|
||||
|
||||
total_disc = sum(main_counts(r)[2] for r in merged.values())
|
||||
total_unknown = sum(main_counts(r)[3] for r in merged.values())
|
||||
summary = {
|
||||
"schema": "simurg.compare/1",
|
||||
"title": opts.title,
|
||||
"a": str(opts.a),
|
||||
"b": str(opts.b),
|
||||
"pairs": len(results),
|
||||
"discrepancies": total_disc,
|
||||
"unknown": total_unknown,
|
||||
"verdict": "DISCREPANCIES" if total_disc else ("UNKNOWNS" if total_unknown else "PASS"),
|
||||
"fields": _totals(merged),
|
||||
"metadata_differences": {c.label: c.metadata for c in results if c.metadata},
|
||||
"notes": notes,
|
||||
}
|
||||
opts.out.mkdir(parents=True, exist_ok=True)
|
||||
write_discrepancies(opts.out / "discrepancies.tsv.gz", (d for r in merged.values() for d in r.discrepancies))
|
||||
atomic_write_text(opts.out / "summary.json", json.dumps(summary, indent=2) + "\n")
|
||||
examples = {f: r.discrepancies[:50] for f, r in merged.items()}
|
||||
atomic_write_text(opts.out / "dashboard.html", render_dashboard(summary, examples))
|
||||
rep.info(f"{summary['verdict']}: {total_disc} discrepancies, {total_unknown} unknown in {len(results)} pair(s); "
|
||||
f"report {opts.out / 'dashboard.html'}") # fmt: skip
|
||||
if total_disc and opts.fail_on_discrepancy:
|
||||
return ExitCode.PARTIAL
|
||||
return ExitCode.OK
|
||||
Reference in new issue
Block a user