"""CPU-only cycle extraction. All criteria frozen before any Re100 GPU result."""

import numpy as np

GATES = dict(
    min_tstar=250.0,
    cycles=5,
    min_period=2.0,
    max_period=5.0,
    min_lift_amplitude=0.2,
    period_relative_range=0.005,
    extrema_absolute_range=0.005,
    mass_relative_range=1e-4,
    cycle_mean_flux_relative=0.005,
)
REFERENCE_GATES = dict(
    cd_relative=0.02, cl_absolute=0.03, dp_relative=0.02, st_relative=0.02
)


def minima(t, y):
    out = []
    for i in range(1, len(y) - 1):
        if y[i] < y[i - 1] and y[i] <= y[i + 1]:
            # Quadratic sub-sample location; only separated physical cycles accepted.
            coef = np.polyfit(t[i - 1 : i + 2] - t[i], y[i - 1 : i + 2], 2)
            v = t[i] - coef[1] / (2 * coef[0]) if coef[0] > 0 else t[i]
            if t[i - 1] <= v <= t[i + 1] and (not out or v - out[-1] > 1.0):
                out.append(float(v))
    return out


def extract(rows, min_time=250.0):
    if len(rows) < 3:
        return []
    t = np.array([r["tstar_force"] for r in rows])
    cl = np.array([r["cl"] for r in rows])
    ends = minima(t, cl)
    cycles = []
    for a, b in zip(ends, ends[1:]):
        if a < min_time or not GATES["min_period"] < b - a < GATES["max_period"]:
            continue
        grid = np.linspace(a, b, 257)
        c = dict(start=a, end=b, period=b - a, st=1 / (b - a))
        for key in ("cd", "cl", "dp", "mass", "flux_in", "flux_out"):
            times = np.array(
                [r["tstar" if key not in ("cd", "cl") else "tstar_force"] for r in rows]
            )
            # No endpoint extrapolation: pressure/mass must cover the whole cycle.
            if a < times[0] or b > times[-1]:
                break
            v = np.interp(grid, times, [r[key] for r in rows])
            c[key + "_min"] = float(v.min())
            c[key + "_max"] = float(v.max())
            c[key + "_midrange"] = float((v.min() + v.max()) / 2)
            c[key + "_amplitude"] = float(v.max() - v.min())
            c[key + "_time_mean"] = float(
                np.sum((v[1:] + v[:-1]) * np.diff(grid) / 2) / (b - a)
            )
            if key in ("cd", "cl", "dp"):
                c[key + "_waveform"] = v.tolist()
        else:
            c["flux_relative"] = abs(
                c["flux_in_time_mean"] - c["flux_out_time_mean"]
            ) / max(abs(c["flux_in_time_mean"]), 1e-30)
            cycles.append(c)
    return cycles


def assess(rows):
    cycles = extract(rows, min_time=GATES["min_tstar"])
    tail = cycles[-GATES["cycles"] :]
    checks = {}
    checks["enough_cycles"] = len(tail) == GATES["cycles"]
    if checks["enough_cycles"]:
        checks["consecutive"] = all(
            abs(a["end"] - b["start"]) < 1e-8 for a, b in zip(tail, tail[1:])
        )
        checks["recent"] = (
            0 <= rows[-1]["tstar_force"] - tail[-1]["end"] <= GATES["max_period"]
        )
        checks["shedding"] = (
            min(c["cl_amplitude"] for c in tail) >= GATES["min_lift_amplitude"]
        )
        periods = [c["period"] for c in tail]
        checks["period"] = (
            np.ptp(periods) / np.mean(periods) <= GATES["period_relative_range"]
        )
        checks["extrema"] = all(
            np.ptp([c[k + "_" + e] for c in tail]) <= GATES["extrema_absolute_range"]
            for k in ("cd", "cl", "dp")
            for e in ("min", "max")
        )
        mass = [c["mass_time_mean"] for c in tail]
        checks["mass"] = (
            np.ptp(mass) / abs(np.mean(mass)) <= GATES["mass_relative_range"]
        )
        checks["flux"] = (
            max(c["flux_relative"] for c in tail) <= GATES["cycle_mean_flux_relative"]
        )
    return dict(
        periodic=bool(checks["enough_cycles"] and all(checks.values())),
        checks={k: bool(v) for k, v in checks.items()},
        cycles=cycles,
    )


def compare(cycle, ref):
    errors = {}
    passed = True
    for key in ("cd", "cl", "dp"):
        for ext in ("min", "max"):
            k = key + "_" + ext
            e = abs(cycle[k] - ref[k])
            e = e if key == "cl" else e / abs(ref[k])
            errors[k] = e
            passed &= (
                e
                <= REFERENCE_GATES[key + ("_absolute" if key == "cl" else "_relative")]
            )
        errors[key + "_waveform_rms"] = float(
            np.sqrt(
                np.mean(
                    (np.array(cycle[key + "_waveform"]) - ref[key + "_waveform"]) ** 2
                )
            )
        )
    errors["st_relative"] = abs(cycle["st"] - ref["st"]) / ref["st"]
    passed &= errors["st_relative"] <= REFERENCE_GATES["st_relative"]
    return dict(reference_agreement=bool(passed), errors=errors)