"""
CsI(Tl) detector response continuum for Radiacode 103.

Models ONLY the detector's noise continuum. Photopeaks from environmental
isotopes depend on measurement location and are NOT included.

Auto-calibrated from measured background using smoothing spline (GCV)
when available. Falls back to a simple parametric model otherwise.
"""

import numpy as np
from app.config import ENERGY_OFFSET, ENERGY_SLOPE, NUM_CHANNELS


def _get_continuum_cps():
    """Try to load calibrated spline continuum from measured data."""
    try:
        from app.bg_calibration import load_or_calibrate
        calibrated = load_or_calibrate()
        if calibrated and "continuum_cps" in calibrated:
            return np.array(calibrated["continuum_cps"])
    except Exception:
        pass
    return None


def generate_continuum_only(cps: float = 6.0, live_time_s: float = 3600.0):
    """Detector response continuum only (no photopeaks, no noise)."""
    channels = np.arange(NUM_CHANNELS, dtype=np.float64)
    energy_axis = ENERGY_OFFSET + ENERGY_SLOPE * channels
    total_counts = cps * live_time_s

    # Try calibrated spline first (fits measured background)
    continuum_cps = _get_continuum_cps()

    if continuum_cps is not None and len(continuum_cps) == NUM_CHANNELS:
        # Scale calibrated CPS to match requested total counts
        continuum = continuum_cps.copy()
        if continuum.sum() > 0:
            continuum *= total_counts / continuum.sum()
    else:
        # Fallback: parametric model
        continuum = _fallback_continuum(energy_axis, total_counts)

    return {
        "energy_kev": [round(float(E), 2) for E in energy_axis],
        "counts": [round(float(c), 1) for c in continuum],
        "cps": round(cps, 2),
        "live_time_s": round(live_time_s, 1),
    }


def _fallback_continuum(energy_axis, total_counts):
    """Simple parametric fallback when no measured data available."""
    E = energy_axis

    # Asymmetric hump
    hump_center, sigma_left, tail_decay_right = 110.0, 40.0, 100.0
    left = np.exp(-0.5 * ((E - hump_center) / sigma_left) ** 2)
    right = np.exp(-(E - hump_center) / tail_decay_right)
    hump = np.where(E <= hump_center, left, right)

    # Housing absorption
    absorption = 1.0 * (1.0 - np.exp(-E / 20.0))

    # Compton tail
    compton = 0.5 / (np.maximum(E, 1.0) + 15.0) ** 1.3

    continuum = (hump + compton) * absorption

    if continuum.sum() > 0 and total_counts > 0:
        continuum *= total_counts / continuum.sum()

    return continuum