#!/usr/bin/env python3
"""Per-stage timing of `simurg genotype` for one sample (plan §19 measurements).

python bench/profile_genotype.py BPM EGT GRN_IDAT [--repeat N]
"""

from __future__ import annotations

import argparse
import datetime
import time
from dataclasses import replace
from pathlib import Path

import numpy as np

from simurg.formats.bpm import read_bpm
from simurg.formats.egt import read_egt
from simurg.formats.gtc import write_gtc
from simurg.formats.idat import read_idat
from simurg.genotype.baf_lrr import baf_lrr, logr_dev
from simurg.genotype.clusters import align_clusters
from simurg.genotype.gencall import call_genotypes
from simurg.genotype.sample import genotype_sample, manifest_context, params_for_preset
from simurg.genotype.sex import estimate_sex
from simurg.signal.normalization.apply import normalize, polar
from simurg.signal.normalization.fit import fit_transforms
from simurg.signal.raw_xy import LocusMapCache, assemble_raw_xy


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("bpm")
    ap.add_argument("egt")
    ap.add_argument("grn")
    ap.add_argument("--repeat", type=int, default=3)
    ap.add_argument("--out", default="/tmp/claude-1000/profile.gtc")
    a = ap.parse_args()
    m = read_bpm(a.bpm)
    ctx = manifest_context(m, align_clusters(read_egt(a.egt), m))
    lite = m.lite()
    ctx = replace(ctx, manifest=lite, cache=LocusMapCache(lite))
    params = params_for_preset("current")
    red_path = a.grn.replace("_Grn", "_Red")
    t: dict[str, list[float]] = {}

    def lap(name: str, t0: float) -> float:
        now = time.perf_counter()
        t.setdefault(name, []).append(now - t0)
        return now

    for _ in range(a.repeat):
        t0 = time.perf_counter()
        grn, red = read_idat(a.grn), read_idat(red_path)
        t0 = lap("read_idat x2", t0)
        raw = assemble_raw_xy(grn, red, lite, cache=ctx.cache)
        t0 = lap("assemble_raw_xy", t0)
        xf = fit_transforms(raw.raw_x, raw.raw_y, ctx.norm_bins, params.norm_version)
        t0 = lap("fit_transforms", t0)
        nx, ny = normalize(raw.raw_x, raw.raw_y, ctx.lookup, xf)
        th, r = polar(nx, ny)
        t0 = lap("normalize+polar", t0)
        z = (raw.raw_x == 0) & (raw.raw_y == 0)
        thc = np.where(z, np.float32(np.nan), th).astype(np.float32)
        rc = np.where(z, np.float32(np.nan), r).astype(np.float32)
        calls = call_genotypes(thc, rc, z, ctx.clusters, lite.intensity_only, params.cutoff)
        t0 = lap("call_genotypes", t0)
        estimate_sex(calls.genotypes, r, ctx.clusters.gentrain, ctx.clusters.present, ctx.sex_classes, params.sex)
        t0 = lap("estimate_sex", t0)
        _, lrr = baf_lrr(thc, rc, z, ctx.clusters, lite.intensity_only)
        logr_dev(lrr, ctx.autosomal, lite.intensity_only, ctx.clusters.present)
        t0 = lap("baf_lrr+logr_dev", t0)
        g = genotype_sample(grn, red, ctx, params, datetime.datetime(2026, 1, 1))
        t0 = lap("genotype_sample (whole)", t0)
        write_gtc(g, Path(a.out))
        t0 = lap("write_gtc", t0)
    print(f"{'stage':28s} {'median s':>9s}")
    for k, v in t.items():
        print(f"{k:28s} {sorted(v)[len(v) // 2]:9.3f}")


if __name__ == "__main__":
    main()