src/covcollab/generate.py

"""Dataset generation for both pipeline consumers (blueprint steps 2 and 3).

* ``sample_components`` / ``synthesize_h1``: raw random components
  (messages X, collaboration noise A, channels, receiver noise) and the
  linear synthesis Y_E = G (F_q(W X + A)) + N.  Policy optimization (PSGD /
  GA-PSGD) calls these with fresh RNGs per minibatch and with frozen
  common-random-number sets for held-out evaluation.

* ``eve_dataset``: balanced, shuffled H0/H1 received blocks under a frozen
  policy W for training/evaluating the transformer Eve.  H0 blocks contain
  receiver noise only; H1 blocks contain pilots, prefixes, data, and
  collaboration noise (blueprint Section 4).

Seeding: every shard derives its own ``np.random.SeedSequence`` from
(master_seed, format_id, snr_mdB, split_id, role), so train/val/test and all
shards are mutually independent and each shard is reproducible in isolation.
"""

from __future__ import annotations

import numpy as np

from .affine import AffineOperators, scramble_phases
from .channel import (
    block_channels,
    block_doppler_coef,
    block_taps,
    cn,
    exp_pdp,
    jakes_doppler,
    receive,
    receive_doubly_dispersive,
    receive_multipath,
    sigma2_from_snr,
)
from .config import SystemConfig
from .constellations import draw_symbols
from .formats.base import Waveform

SPLIT_IDS = {"train": 0, "val": 1, "test": 2}

# role tags inside a shard's seed tree
_ROLE_H1, _ROLE_H0, _ROLE_SHUFFLE, _ROLE_COMPONENTS, _ROLE_SCRAMBLE = 0, 1, 2, 3, 4


def shard_seed(master_seed: int, format_id: int, snr_db: float, split: str, role: int) -> np.random.SeedSequence:
    snr_key = int(round(snr_db * 1000)) % (2**32)
    return np.random.SeedSequence([master_seed, format_id, snr_key, SPLIT_IDS[split], role])


# --------------------------------------------------------------------------
# Raw components (policy-design stage)
# --------------------------------------------------------------------------

def sample_components(cfg: SystemConfig, rng: np.random.Generator, n_blocks: int,
                     bob_gains=None, eve_gains=None) -> dict:
    """Draw all H1 randomness except the policy: X, A, G_eve, H_bob, noises.

    bob_gains / eve_gains: optional (M,) per-user large-scale power gains (the
    selected users' path-loss/shadowing to Bob / Eve).  Small-scale fading stays
    CN(0,1); the gains scale each user's channel column by sqrt(gain).  Used by
    subset selection, where users are heterogeneous.
    """
    # draw in the original order (X, A, G_eve, H_bob, N_eve, N_bob) so gains=None
    # reproduces pre-existing data bit-exactly; apply gains only afterward.
    x = draw_symbols(rng, cfg.constellation, (n_blocks, cfg.kd, cfg.n_data))
    a = np.sqrt(cfg.sigma_a2) * cn(rng, (n_blocks, cfg.n_tx, cfg.n_data))
    if cfg.nu_max > 0:  # doubly-dispersive: (b, R, M, L, D) Doppler coefficients
        pdp = exp_pdp(cfg.n_taps, cfg.pdp_decay)
        _, q = jakes_doppler(cfg.n_doppler, cfg.nu_max)
        g_eve = block_doppler_coef(rng, n_blocks, cfg.n_rx_eve, cfg.n_tx, pdp, q)
        h_bob = block_doppler_coef(rng, n_blocks, cfg.n_rx_bob, cfg.n_tx, pdp, q)
    elif cfg.n_taps > 1:  # frequency-selective: channels are (b, R, M, L) taps
        pdp = exp_pdp(cfg.n_taps, cfg.pdp_decay)
        g_eve = block_taps(rng, n_blocks, cfg.n_rx_eve, cfg.n_tx, pdp)
        h_bob = block_taps(rng, n_blocks, cfg.n_rx_bob, cfg.n_tx, pdp)
    else:  # flat single-tap (bit-exact legacy)
        g_eve = block_channels(rng, n_blocks, cfg.n_rx_eve, cfg.n_tx)
        h_bob = block_channels(rng, n_blocks, cfg.n_rx_bob, cfg.n_tx)
    n_eve = cn(rng, (n_blocks, cfg.n_rx_eve, cfg.n_samples))  # unit variance; scale by sigma
    n_bob = cn(rng, (n_blocks, cfg.n_rx_bob, cfg.n_samples))
    # per-user gains scale each Tx column (axis 2); trailing tap/Doppler axes broadcast
    xdim = g_eve.ndim - 3  # 0 (flat), 1 (multipath), or 2 (doubly-dispersive)
    if eve_gains is not None:
        eg = np.sqrt(np.asarray(eve_gains)).reshape((1, 1, cfg.n_tx) + (1,) * xdim)
        g_eve = g_eve * eg
    if bob_gains is not None:
        bg = np.sqrt(np.asarray(bob_gains)).reshape((1, 1, cfg.n_tx) + (1,) * xdim)
        h_bob = h_bob * bg
    return {"X": x, "A": a, "G_eve": g_eve, "H_bob": h_bob, "N_eve": n_eve, "N_bob": n_bob}


def collaborate(w: np.ndarray, x: np.ndarray, a: np.ndarray) -> np.ndarray:
    """S = W X + A for batched blocks: (m,kd) @ (b,kd,n) + (b,m,n)."""
    return np.einsum("mk,bkn->bmn", w, x) + a


def _through_channel(h: np.ndarray, u: np.ndarray, noise: np.ndarray, cfg=None) -> np.ndarray:
    """Flat (3-D h), multipath (4-D taps), or doubly-dispersive (5-D Doppler coef)."""
    if h.ndim == 5:
        nu, _ = jakes_doppler(cfg.n_doppler, cfg.nu_max)
        return receive_doubly_dispersive(h, nu, u, noise)
    return receive_multipath(h, u, noise) if h.ndim == 4 else receive(h, u, noise)


def synthesize_h1(fmt: Waveform, w: np.ndarray, comp: dict, sigma2_eve: float) -> np.ndarray:
    """Eve's H1 received blocks from components: G (F_q(W X + A)) + sigma N."""
    u = fmt.modulate(collaborate(w, comp["X"], comp["A"]))
    return _through_channel(comp["G_eve"], u, np.sqrt(sigma2_eve) * comp["N_eve"], fmt.cfg)


def synthesize_h1_ctrl(ops, w, comp, sigma2_eve, beta, scramble) -> np.ndarray:
    """H1 blocks with pilot power/scramble control (ops: AffineOperators)."""
    u = ops.modulate_ctrl(collaborate(w, comp["X"], comp["A"]), beta, scramble)
    return _through_channel(comp["G_eve"], u, np.sqrt(sigma2_eve) * comp["N_eve"], ops.cfg)


def synthesize_bob(fmt: Waveform, w: np.ndarray, comp: dict, sigma2_bob: float) -> np.ndarray:
    u = fmt.modulate(collaborate(w, comp["X"], comp["A"]))
    return _through_channel(comp["H_bob"], u, np.sqrt(sigma2_bob) * comp["N_bob"], fmt.cfg)


# --------------------------------------------------------------------------
# Frozen-policy H0/H1 dataset (Eve-training stage)
# --------------------------------------------------------------------------

def eve_dataset(
    cfg: SystemConfig,
    fmt: Waveform,
    w: np.ndarray,
    n_samples: int,
    snr_db: float,
    master_seed: int,
    format_id: int,
    split: str,
    batch: int = 256,
    pilot_beta=None,
    pilot_scramble: bool = False,
    eve_gains=None,
) -> dict:
    """Balanced, shuffled H0/H1 received blocks for Eve (complex64).

    pilot_beta (None -> 1.0) scales pilot power; pilot_scramble applies a per-block
    Tx/Bob-shared pilot scramble (not exposed to Eve).  Both only affect H1.
    eve_gains: optional (M,) per-user Eve-channel gains (subset selection).
    """
    sigma2 = sigma2_from_snr(cfg, snr_db)
    n1 = n_samples // 2
    n0 = n_samples - n1
    ctrl = pilot_beta is not None or pilot_scramble
    ops = AffineOperators(fmt) if ctrl else None
    beta = 1.0 if pilot_beta is None else float(pilot_beta)

    rng1 = np.random.default_rng(shard_seed(master_seed, format_id, snr_db, split, _ROLE_H1))
    rng0 = np.random.default_rng(shard_seed(master_seed, format_id, snr_db, split, _ROLE_H0))
    rngs = np.random.default_rng(shard_seed(master_seed, format_id, snr_db, split, _ROLE_SHUFFLE))
    rngscr = np.random.default_rng(shard_seed(master_seed, format_id, snr_db, split, _ROLE_SCRAMBLE))

    y1 = np.empty((n1, cfg.n_rx_eve, cfg.n_samples), dtype=np.complex64)
    for lo in range(0, n1, batch):
        hi = min(lo + batch, n1)
        comp = sample_components(cfg, rng1, hi - lo, eve_gains=eve_gains)
        if ctrl:
            scr = scramble_phases(rngscr, hi - lo, ops.n_pilot_re) if pilot_scramble else None
            y1[lo:hi] = synthesize_h1_ctrl(ops, w, comp, sigma2, beta, scr).astype(np.complex64)
        else:
            y1[lo:hi] = synthesize_h1(fmt, w, comp, sigma2).astype(np.complex64)

    y0 = (np.sqrt(sigma2) * cn(rng0, (n0, cfg.n_rx_eve, cfg.n_samples))).astype(np.complex64)

    y = np.concatenate([y0, y1], axis=0)
    labels = np.concatenate([np.zeros(n0, np.uint8), np.ones(n1, np.uint8)])
    perm = rngs.permutation(n_samples)
    return {
        "Y": y[perm],
        "labels": labels[perm],
        "snr_db": np.float32(snr_db),
        "sigma2": np.float32(sigma2),
        "W": w.astype(np.complex64),
        "format": fmt.name,
        "split": split,
        "pilot_beta": np.float32(beta),
        "pilot_scramble": bool(pilot_scramble),
        "config_json": cfg.to_json(),
    }


def component_set(cfg: SystemConfig, master_seed: int, split: str, n_blocks: int) -> dict:
    """Frozen common-random-number component set for held-out policy evaluation."""
    rng = np.random.default_rng(shard_seed(master_seed, 0, 0.0, split, _ROLE_COMPONENTS))
    comp = sample_components(cfg, rng, n_blocks)
    comp = {k: v.astype(np.complex64) for k, v in comp.items()}
    comp["split"] = split
    comp["config_json"] = cfg.to_json()
    return comp


def save_shard(path, arrays: dict) -> None:
    import os

    os.makedirs(os.path.dirname(str(path)), exist_ok=True)
    np.savez_compressed(path, **arrays)