#!/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())))
    for t, bins in val.get("sensitivity_by_carrier_frequency", {}).items():
        print(f"  by frequency {t}: " + ", ".join(f"{b} {x['found']}/{x['truth']}" for b, x in sorted(bins.items())))
    return 0


if __name__ == "__main__":
    raise SystemExit(main())