"""CN/LOH event extraction, statistics and filters — spec CYTO-006 … CYTO-021, CYTO-027. Pure functions, no I/O.

From a per-chromosome Viterbi path: runs of non-baseline states become events; statistics per CYTO-027; effective
size per CYTO-010 (gaps > 150 × the chromosome's median spacing replaced by the median); QUAL from the mean posterior
of the called state class (CYTO-012); mosaic fraction from the BAF split or, failing that, LRR (CYTO-018/019/042);
post-filters in the order of CYTO-015; per-type thresholds (CYTO-007/008) and promotion/low fraction (CYTO-020/021).
"""

from __future__ import annotations

import math
from dataclasses import dataclass, field

import numpy as np
import numpy.typing as npt

from simurg.cn.hmm import State

F64 = npt.NDArray[np.float64]
I64 = npt.NDArray[np.int64]
GAP_FACTOR = 150
MIN_MF_PROBES = 20
LOH_EXPECTED_HETS = 5.0  # CYTO-041: expected heterozygous probes needed to call a loss complete
LOH_HET_RATIO = 0.25  # CYTO-041: observed / expected heterozygotes below which a loss is complete
TYPES = ("del", "dup", "loh", "gainloh", "mdel", "mdup", "mloh")
DEFAULTS = {"del": (10, 25_000), "dup": (10, 50_000), "loh": (500, 3_000_000), "gainloh": (300, 500_000),
            "mdel": (10, 25_000), "mdup": (10, 50_000), "mloh": (500, 3_000_000)}  # fmt: skip
CALL_KEY = {"DEL": "del", "DUP": "dup", "LOH": "loh", "GAINLOH": "gainloh", "MDEL": "mdel", "MDUP": "mdup",
            "MLOH": "mloh"}  # fmt: skip


@dataclass
class Event:
    chrom: str
    start: int  # first affected base S (1-based) = first probe position
    end: int  # last affected base E = last probe position
    i0: int  # probe index range in the chromosome arrays [i0, i1)
    i1: int
    call: str  # DEL | DUP | LOH | GAINLOH | MDEL | MDUP | MLOH
    cn: int
    state: str
    quality: float = 0.0
    effective_size: int = 0
    nr: float = math.nan
    lrd: float = math.nan
    baf_dev: float = math.nan
    percent_het: float = math.nan
    lrr_median: float = math.nan
    mosaic_fraction: float | None = None
    high_fraction: bool = False
    filters: list[str] = field(default_factory=list)
    flags: list[str] = field(default_factory=list)

    @property
    def mosaic(self) -> bool:
        return self.call.startswith("M")

    @property
    def probes(self) -> int:
        return self.i1 - self.i0

    @property
    def size(self) -> int:
        return self.end - self.start + 1

    @property
    def svtype(self) -> str:
        return {"MDEL": "DEL", "MDUP": "DUP", "MLOH": "LOH", "GAINLOH": "LOH"}.get(self.call, self.call)

    @property
    def loh_type(self) -> str | None:
        if self.call == "LOH":
            return "AOH"
        if self.call == "GAINLOH":
            return "AOH"
        if self.call == "MLOH":
            return "CNLOH"
        return None

    @property
    def id_prefix(self) -> str:
        return {"DEL": "DEL", "DUP": "DUP", "LOH": "AOH", "GAINLOH": "AOH", "MDEL": "MosaicDEL", "MDUP": "MosaicDUP",
                "MLOH": "CNLOH"}[self.call]  # fmt: skip


def median_spacing(pos: I64) -> float:
    d = np.diff(np.sort(pos))
    d = d[d > 0]
    return float(np.median(d)) if len(d) else 1.0


def effective_size(pos: I64, m: float) -> int:
    """CYTO-010 for the probes of one event (sorted positions)."""
    if len(pos) < 2:
        return 1
    s = np.diff(pos).astype(np.float64)
    gap = s > GAP_FACTOR * m
    return int((pos[-1] - pos[0] + 1) - float((s[gap] - m).sum()))


def extract(chrom: str, path: npt.NDArray[np.int32], states: list[State], pos: I64) -> list[Event]:
    """Runs of non-baseline states, merged across runs of the same call type (mosaic fraction steps)."""
    out: list[Event] = []
    if len(path) == 0:
        return out
    br = np.flatnonzero(np.diff(path) != 0) + 1
    starts, ends = np.r_[0, br], np.r_[br, len(path)]
    for s, e in zip(starts.tolist(), ends.tolist(), strict=True):
        st = states[int(path[s])]
        if not st.call:
            continue
        if out and out[-1].i1 == s and out[-1].call == st.call and out[-1].cn == st.cn:
            out[-1].i1 = e
            out[-1].end = int(pos[e - 1])
            continue
        out.append(Event(chrom, int(pos[s]), int(pos[e - 1]), s, e, st.call, st.cn, st.name))
    return out


def _mf_from_baf(call: str, baf: F64, baf_sd: float = 0.0, pfb: F64 | None = None) -> float | None:
    """CYTO-018 with the CYTO-041 refinements [OURS]: the split is corrected for BAF noise (the median |BAF − 0.5| of
    unsplit heterozygotes is 0.6745·σ_b), and a loss whose expected heterozygotes are absent is complete (MF 1)."""
    fin = np.isfinite(baf)
    mid = fin & (baf > 0.1) & (baf < 0.9)
    b = baf[mid]
    if call == "MDEL" and pfb is not None and fin.sum() >= MIN_MF_PROBES:
        p = np.clip(np.where(np.isfinite(pfb), pfb, 0.5), 0.0, 1.0)[fin]
        expected = float((2 * p * (1 - p)).sum())
        if expected >= LOH_EXPECTED_HETS and len(b) < LOH_HET_RATIO * expected:
            return 1.0
    if len(b) < MIN_MF_PROBES:
        return None
    d_obs = float(np.median(np.abs(b - 0.5)))
    d = math.sqrt(max(0.0, d_obs**2 - (0.6745 * baf_sd) ** 2))
    if call == "MDEL":
        f = 2 * d / (0.5 + d)
    elif call == "MDUP":
        f = 2 * d / (0.5 - d) if d < 0.5 else 1.0
    else:
        f = 2 * d
    return float(min(1.0, max(0.0, f)))


def _mf_from_lrr(call: str, lrr: F64, gamma: float) -> float | None:
    x = lrr[np.isfinite(lrr)]
    if len(x) < MIN_MF_PROBES or gamma <= 0:
        return None
    f = abs(2 ** (float(np.median(x)) / gamma) - 1)
    return float(min(1.0, max(0.0, f)))


def annotate_stats(
    ev: Event, pos: I64, lrr: F64, baf: F64, geno: npt.NDArray[np.uint8], post: F64, states: list[State], m: float,
    gamma: float, haploid: bool, baf_sd: float = 0.0, pfb: F64 | None = None,
) -> None:  # fmt: skip
    """CYTO-010/012/018/027 statistics in place (``lrr`` unsmoothed)."""
    sl = slice(ev.i0, ev.i1)
    x = lrr[sl]
    fx = x[np.isfinite(x)]
    ev.nr = float(np.median(2.0**fx)) if len(fx) else math.nan
    ev.lrd = float(np.std(fx)) if len(fx) > 1 else math.nan
    ev.lrr_median = float(np.median(fx)) if len(fx) else math.nan
    g = geno[sl]
    called = g > 0
    ev.percent_het = float(100.0 * (g == 2).sum() / called.sum()) if called.any() else math.nan
    b = baf[sl][(g == 2) & np.isfinite(baf[sl])]
    ev.baf_dev = float(np.std(b)) if len(b) > 1 else math.nan
    ev.effective_size = effective_size(pos[sl], m)
    cls = [j for j, st in enumerate(states) if st.call == ev.call]
    p = float(post[sl][:, cls].sum(axis=1).mean()) if cls else 0.0
    ev.quality = 60.0 if p >= 1.0 else float(min(60, max(0, round(-10 * math.log10(max(1e-300, 1 - p))))))
    if ev.mosaic:
        mf = None if haploid else _mf_from_baf(ev.call, baf[sl], baf_sd, None if pfb is None else pfb[sl])
        ev.mosaic_fraction = mf if mf is not None else _mf_from_lrr(ev.call, x, gamma)


@dataclass
class Thresholds:
    probes: dict[str, int]
    size_bp: dict[str, int]
    max_mosaic_fraction: float = 1.0
    min_mosaic_fraction: float = 0.10
    filter_qual: int = 10
    lrr_direction_min_frac: float = 0.6

    @classmethod
    def defaults(cls) -> Thresholds:
        return cls({k: v[0] for k, v in DEFAULTS.items()}, {k: v[1] for k, v in DEFAULTS.items()})

    def as_header(self, extra: dict[str, object]) -> str:
        parts = []
        for k in ("del", "dup", "loh", "gainloh", "mdel", "mdup", "mloh"):
            parts += [f"min_{k}_probes={self.probes[k]}", f"min_{k}_size_bp={self.size_bp[k]}"]
        parts += [f"{k}={v}" for k, v in extra.items()]
        return ";".join(parts)


def post_filter(events: list[Event], lrr_by_event: list[F64], th: Thresholds, counts: dict[str, int]) -> list[Event]:
    """CYTO-015 steps 1–3 and CYTO-020 promotion; per-type thresholds are applied by :func:`apply_thresholds`."""
    kept: list[Event] = []
    for ev, x in zip(events, lrr_by_event, strict=True):
        fx = x[np.isfinite(x)]
        if ev.call in ("DEL", "MDEL") and len(fx) and (fx < 0).mean() < th.lrr_direction_min_frac:
            counts["lrr_direction"] = counts.get("lrr_direction", 0) + 1
            continue
        if ev.call in ("DUP", "MDUP", "GAINLOH") and len(fx) and (fx > 0).mean() < th.lrr_direction_min_frac:
            counts["lrr_direction"] = counts.get("lrr_direction", 0) + 1
            continue
        kept.append(ev)
    # short LOH between copy-neutral neighbours (no other event adjacent on either side)
    out: list[Event] = []
    for i, ev in enumerate(kept):
        if ev.call in ("LOH", "MLOH") and ev.size < 500_000:
            left = kept[i - 1] if i > 0 and kept[i - 1].chrom == ev.chrom else None
            right = kept[i + 1] if i + 1 < len(kept) and kept[i + 1].chrom == ev.chrom else None
            if not (left and left.i1 == ev.i0) and not (right and right.i0 == ev.i1):
                counts["short_loh"] = counts.get("short_loh", 0) + 1
                continue
        out.append(ev)
    merged: list[Event] = []
    for ev in out:
        prev = merged[-1] if merged else None
        if (prev and prev.chrom == ev.chrom and prev.call == ev.call and prev.cn == ev.cn
                and ev.i0 - prev.i1 <= 3 and (ev.start - prev.end) <= 0.1 * (ev.end - prev.start + 1)):  # fmt: skip
            prev.i1, prev.end = ev.i1, ev.end
            counts["merged"] = counts.get("merged", 0) + 1
            continue
        merged.append(ev)
    for ev in merged:  # CYTO-020 promotion
        if ev.mosaic and ev.mosaic_fraction is not None and ev.mosaic_fraction >= th.max_mosaic_fraction:
            ev.call = {"MDEL": "DEL", "MDUP": "DUP", "MLOH": "LOH"}[ev.call]
            ev.high_fraction = True
    return merged


def apply_thresholds(events: list[Event], th: Thresholds, counts: dict[str, int]) -> list[Event]:
    out = []
    for ev in events:
        k = CALL_KEY[ev.call]
        if ev.probes < th.probes[k] or ev.effective_size < th.size_bp[k]:
            counts["below_threshold"] = counts.get("below_threshold", 0) + 1
            continue
        out.append(ev)
    return out


def set_filters(events: list[Event], th: Thresholds, sample_noisy: bool) -> None:
    """CYTO-013 FILTER values."""
    for ev in events:
        f = []
        if ev.quality < th.filter_qual:
            f.append(f"Q{th.filter_qual}")
        if sample_noisy:
            f.append("SampleQuality")
        if ev.mosaic and ev.mosaic_fraction is not None and ev.mosaic_fraction < th.min_mosaic_fraction:
            f.append("LowFraction")
        ev.filters = f or ["PASS"]