"""Single-carrier QPSK/QAM with root-raised-cosine pulse shaping.

Frame: 16 pilot symbols (orthogonal per-user preamble) + 144 data symbols
= 160 symbols, oversampled by 2 -> 320 samples.  Roll-off beta = 0.5 with
symbol rate 0.5 Fs gives occupied bandwidth (1+beta)/2 = 0.75 Fs, matching
the multicarrier formats' 48/64 subcarrier occupancy.
"""

from __future__ import annotations

import numpy as np

from ..pilots import pilot_matrix
from .base import Waveform


def rrc_taps(oversample: int, beta: float, span: int) -> np.ndarray:
    """Unit-energy root-raised-cosine filter, `span` symbols long."""
    n = np.arange(-span * oversample // 2, span * oversample // 2 + 1)
    t = n / oversample  # in symbol durations
    h = np.empty(t.shape, dtype=float)
    for i, ti in enumerate(t):
        if abs(ti) < 1e-12:
            h[i] = 1.0 - beta + 4.0 * beta / np.pi
        elif abs(abs(ti) - 1.0 / (4.0 * beta)) < 1e-9:
            h[i] = (beta / np.sqrt(2.0)) * (
                (1.0 + 2.0 / np.pi) * np.sin(np.pi / (4.0 * beta))
                + (1.0 - 2.0 / np.pi) * np.cos(np.pi / (4.0 * beta))
            )
        else:
            num = np.sin(np.pi * ti * (1.0 - beta)) + 4.0 * beta * ti * np.cos(np.pi * ti * (1.0 + beta))
            den = np.pi * ti * (1.0 - (4.0 * beta * ti) ** 2)
            h[i] = num / den
    return h / np.linalg.norm(h)


def conv_same(x: np.ndarray, h: np.ndarray) -> np.ndarray:
    """FFT-based 'same' convolution along the last axis (batched)."""
    t, l = x.shape[-1], len(h)
    nfft = int(2 ** np.ceil(np.log2(t + l - 1)))
    y = np.fft.ifft(np.fft.fft(x, nfft, axis=-1) * np.fft.fft(h, nfft), axis=-1)
    lo = (l - 1) // 2
    return y[..., lo : lo + t]


class SingleCarrier(Waveform):
    name = "sc"
    oversample = 2
    beta = 0.5
    span = 8
    n_pilot_syms = 16

    def __init__(self, cfg):
        assert cfg.n_samples == self.oversample * (self.n_pilot_syms + cfg.n_data)
        self.taps = rrc_taps(self.oversample, self.beta, self.span)
        self.pilots = pilot_matrix(cfg.pilot_scheme, cfg.n_tx, self.n_pilot_syms)
        super().__init__(cfg)

    @property
    def n_pilot_re(self) -> int:
        return self.n_pilot_syms

    def _modulate_raw(self, s):
        pil = np.broadcast_to(self.pilots, s.shape[:-1] + (self.n_pilot_syms,))
        syms = np.concatenate([pil, s], axis=-1)  # (..., M, 160)
        up = np.zeros(syms.shape[:-1] + (self.cfg.n_samples,), dtype=np.complex128)
        up[..., :: self.oversample] = syms
        return conv_same(up, self.taps)

    def demodulate(self, u):
        # Matched filter (overall raised-cosine => ~ISI-free at symbol instants)
        v = conv_same(np.asarray(u, dtype=np.complex128), self.taps)
        return v[..., :: self.oversample][..., self.n_pilot_syms :]