from __future__ import annotations

from math import log10, sqrt

from app.core.fresnel import wavelength
from app.core.surface import SurfaceProfile


def knife_edge_loss(v: float) -> float:
    if v <= -0.78:
        return 0.0
    return 6.9 + 20 * log10(sqrt((v - 0.1) ** 2 + 1) + v - 0.1)


def knife_edge_v(h: float, d1: float, d2: float, freq_hz: float) -> float:
    if d1 <= 0 or d2 <= 0:
        return float("-inf")
    lmbda = wavelength(freq_hz)
    return h * sqrt(2 * (d1 + d2) / (lmbda * d1 * d2))


def deygout(
    profile: SurfaceProfile,
    tx_height_agl: float,
    rx_height_agl: float,
    freq_hz: float,
) -> float:
    """Recursive Deygout diffraction loss using the dominant edge and subprofiles."""
    if len(profile.samples) < 3:
        return 0.0

    endpoint_heights = {
        0: profile.samples[0].ground_m + tx_height_agl,
        len(profile.samples) - 1: profile.samples[-1].ground_m + rx_height_agl,
    }

    def sample_height(index: int) -> float:
        return endpoint_heights.get(index, profile.samples[index].surface_m)

    def solve(left: int, right: int) -> float:
        if right - left < 2:
            return 0.0

        left_sample = profile.samples[left]
        right_sample = profile.samples[right]
        span_m = right_sample.distance_m - left_sample.distance_m
        left_height = sample_height(left)
        right_height = sample_height(right)
        max_v = float("-inf")
        max_index: int | None = None

        for index in range(left + 1, right):
            sample = profile.samples[index]
            d1 = sample.distance_m - left_sample.distance_m
            d2 = right_sample.distance_m - sample.distance_m
            path_height = left_height + (right_height - left_height) * (d1 / span_m)
            h = sample.surface_m - path_height
            v = knife_edge_v(h, d1, d2, freq_hz)
            if v > max_v:
                max_v = v
                max_index = index

        if max_index is None:
            return 0.0

        main_loss = knife_edge_loss(max_v)
        if main_loss == 0.0:
            return 0.0
        return main_loss + solve(left, max_index) + solve(max_index, right)

    return solve(0, len(profile.samples) - 1)