CN: noise-corrected BAF split and complete-loss MF (CYTO-041); Pi tuning harness

Mosaic fraction from the BAF split now subtracts the heterozygous BAF noise (median |BAF-0.5| of unsplit
hets = 0.6745 sigma_b), and a mosaic loss lacking its expected heterozygotes gets MF 1 and is promoted to a
constitutional DEL (CYTO-020). Adds scripts/v2/cn_tune.sh + cn_tune_report.py (GSE165845 rounds on the Pi).

scripts/v2/cn_tune_report.py

@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+"""One-line-per-call-type summary of a tuning round: calls, fraction in NYGC truth, fraction seen in the other
+replicate; plus truth sensitivity by size bin (QUAL >= 20, PASS or SampleQuality)."""
+
+from __future__ import annotations
+
+import csv
+import json
+import sys
+from collections import defaultdict
+from pathlib import Path
+
+
+def ro(a: tuple[int, int], b: tuple[int, int]) -> float:
+    ov = min(a[1], b[1]) - max(a[0], b[0]) + 1
+    return 0.0 if ov <= 0 else min(ov / (a[1] - a[0] + 1), ov / (b[1] - b[0] + 1))
+
+
+def main() -> int:
+    run, mapf = Path(sys.argv[1]), Path(sys.argv[2])
+    with mapf.open() as fh:
+        smap = {r["sample_id"]: r["individual"] for r in csv.DictReader(fh, delimiter="\t")}
+    truth: dict[str, list[tuple[str, int, int, str]]] = defaultdict(list)
+    with open("data/v2cn/cloud1/truth.tsv") as fh:
+        for line in fh:
+            c, s, e, t, i = line.rstrip("\n").split("\t")
+            truth[i].append((c.removeprefix("chr"), int(s), int(e), t))
+    side = json.loads((run / "cn_call_manifest.json").read_text())
+    calls = {s["sample_id"]: [e for e in s["events"] if e["quality"] >= 20 and set(e["filters"]) <= {"PASS",
+             "SampleQuality"}] for s in side["samples"] if s.get("status") == "OK"}  # fmt: skip
+    cls = {"DEL": "DEL", "MDEL": "DEL", "DUP": "DUP", "MDUP": "DUP"}
+    by_ind: dict[str, list[str]] = defaultdict(list)
+    for sid in calls:
+        by_ind[smap[sid]].append(sid)
+    st: dict[str, list[int]] = defaultdict(lambda: [0, 0, 0, 0])
+    for sid, evs in calls.items():
+        ind = smap[sid]
+        others = [calls[x] for x in by_ind[ind] if x != sid]
+        for e in evs:
+            k = e["call"]
+            v = st[k]
+            v[0] += 1
+            if k in cls:
+                v[1] += any(t == cls[k] and c == e["chrom"] and ro((s, en), (e["start"], e["end"])) >= 0.5
+                            for c, s, en, t in truth[ind])  # fmt: skip
+            for oc in others:
+                v[2] += any(o["chrom"] == e["chrom"] and o["call"] == k and ro((o["start"], o["end"]),
+                            (e["start"], e["end"])) >= 0.5 for o in oc)  # fmt: skip
+                v[3] += 1
+    print(f"{run.name}: {len(calls)} samples")
+    for k, v in sorted(st.items()):
+        tr = f"{v[1] / v[0]:.2f}" if k in cls and v[0] else "  - "
+        rp = f"{v[2] / v[3]:.2f}" if v[3] else "  - "
+        print(f"  {k:8s} calls {v[0]:5d}  in-truth {tr}  replicated {rp}")
+    val = json.loads((run / "val" / "summary.json").read_text())
+    for t, bins in val["sensitivity"].items():
+        print(f"  sensitivity {t}: " + ", ".join(f"{b} {x['found']}/{x['truth']}" for b, x in sorted(bins.items())))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())