cross_model/scripts/ddpm_triangle_integration.py

#!/usr/bin/env python3
"""
Surrogate posterior on $(\\Omega_m, \\sigma_8)$ → triangle/MCMC-style chains for one test map.

Loads the same surrogate likelihood used in ``ddpm_posterior_six_anchors``, resamples discrete
posterior masses to ``--n-hist`` correlated $(\\Omega_m,\\sigma_8)$ pairs, and writes ``.npz``.

DDPM-2: sweeps $(\\Omega_m,\\sigma_8)$.
DDPM-6: dims 2–5 fixed per ``--six-tail-mode`` (``truth`` uses the test-map labels 2–5; ``min``/``max``
use LHS extrema from training labels).

If you replace this file with a copy from your machine (Downloads), keep argparse compatible or wrap it.
"""

from __future__ import annotations

import argparse
import sys
from pathlib import Path

import numpy as np
import torch

_SCRIPTS = Path(__file__).resolve().parent
if str(_SCRIPTS) not in sys.path:
    sys.path.insert(0, str(_SCRIPTS))

import ddpm_posterior_six_anchors as dps  # noqa: E402

MODELS_ROOT = Path(__file__).resolve().parents[1]
CODE_6 = MODELS_ROOT / "6param_ddpm_hi_lh6"
if str(CODE_6.resolve()) not in sys.path:
    sys.path.insert(0, str(CODE_6.resolve()))

import evaluate_conditional as ec  # noqa: E402


def _tail_vec(
    mode: str,
    lab_full: np.ndarray,
    data6: Path,
) -> np.ndarray | None:
    if lab_full.size <= 2:
        return None
    if mode == "truth":
        return lab_full[2:6].astype(np.float32)
    low, hi = dps.tail_lhs_bounds(data6)
    if mode == "min":
        return low
    if mode == "max":
        return hi
    raise ValueError("six-tail-mode must be truth|min|max")


def main() -> None:
    p = argparse.ArgumentParser(description="DDPM surrogate posterior → resampled Ωm σ8 chains (.npz).")
    p.add_argument(
        "--label-dim",
        type=int,
        choices=[2, 6],
        required=True,
        help="Which model to use.",
    )
    p.add_argument(
        "--bundle",
        type=Path,
        default=None,
        help="Checkpoint bundle dir with args.json (default: notebook_model_weights/<2|6>).",
    )
    p.add_argument(
        "--checkpoint-name",
        type=str,
        default=None,
        help="Checkpoint file under bundle (defaults: DDPM2 epoch200, DDPM6 best_model).",
    )
    p.add_argument(
        "--data-dir",
        type=Path,
        default=None,
        help="LH data dir matching label_dim (default: params_2 vs params_6).",
    )
    p.add_argument("--split", type=str, default="test", choices=["train", "val", "test"])
    p.add_argument("--test-index", type=int, default=56, help="Index into split for CAMELS observation.")
    p.add_argument("--grid", type=int, default=14)
    p.add_argument("--ddim-steps", type=int, default=50)
    p.add_argument("--batch-size", type=int, default=8)
    p.add_argument(
        "--n-hist",
        type=int,
        default=10_000,
        help="Resampled posterior pairs (with replacement).",
    )
    p.add_argument(
        "--six-tail-mode",
        type=str,
        default="truth",
        choices=["truth", "min", "max"],
        help="Applies only to label_dim==6 — how dims 2–5 are fixed.",
    )
    p.add_argument(
        "--output",
        "-o",
        type=Path,
        required=True,
        help="Output .npz path.",
    )
    p.add_argument("--seed", type=int, default=42)
    args = p.parse_args()

    ld = args.label_dim
    if ld == 2:
        data_dir = args.data_dir or Path("<DDPM_ROOT>/data/LH_data/params_2")
        bundle = args.bundle or MODELS_ROOT / "notebook_model_weights" / "2param_epoch200"
        ck_name = args.checkpoint_name or "checkpoint_epoch_200.pt"
    else:
        data_dir = args.data_dir or Path("<DDPM_ROOT>/data/LH_data/params_6")
        bundle = args.bundle or MODELS_ROOT / "notebook_model_weights" / "6param_best"
        ck_name = args.checkpoint_name or "best_model.pt"

    rng = np.random.default_rng(args.seed)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    imgs, labs = ec.load_split(data_dir, args.split)
    ix = int(args.test_index)
    if not (0 <= ix < len(labs)):
        raise SystemExit(f"test-index {ix} out of range for split ({len(labs)} rows)")
    lab_t = labs[ix].astype(np.float64)
    obs = imgs[ix]
    ckpt = bundle / ck_name
    args_json = bundle / "args.json"
    mean, std = ec.load_label_stats(data_dir)

    tail = None
    if ld == 6:
        lab6 = labs[ix].astype(np.float64)
        if lab6.shape[0] != 6:
            raise SystemExit("--label-dim 6 requires labels with 6 columns in data-dir")
        tail = _tail_vec(args.six_tail_mode, lab6, Path(data_dir))
    model, cfg = dps.load_model(args_json, ckpt, device)
    normalize = bool(cfg.get("normalize_labels", True))
    H = int(obs.shape[-2])
    W = int(obs.shape[-1])
    gsz = args.grid

    full, om_ax, s8_ax = dps.build_full_grid_2d(labs, gsz, tail=tail, lab_dim=ld)
    Wmap, OM, S8 = dps.posterior_weights(
        obs,
        full,
        om_ax,
        s8_ax,
        mean,
        std,
        normalize,
        model,
        H=H,
        W=W,
        device=device,
        grid=gsz,
        batch_sz=args.batch_size,
        ddim_steps=args.ddim_steps,
    )

    wflat = np.clip(Wmap.ravel().astype(np.float64), 0.0, None)
    if wflat.sum() <= 0:
        raise RuntimeError("Posterior masses collapsed to zero.")
    wflat /= wflat.sum()
    omapflat = OM.ravel()
    s8flat = S8.ravel()
    draws = rng.choice(np.arange(len(wflat)), size=args.n_hist, replace=True, p=wflat)
    samp_om = omapflat[draws].astype(np.float64)
    samp_s8 = s8flat[draws].astype(np.float64)

    out = Path(args.output).resolve()
    out.parent.mkdir(parents=True, exist_ok=True)
    tag = f"ddpm{ld}_{args.six_tail_mode}" if ld == 6 else "ddpm2"
    np.savez_compressed(
        out,
        omega_m=samp_om,
        sigma_8=samp_s8,
        samples=np.column_stack([samp_om, samp_s8]),
        truth_Omega_m=float(lab_t[0]),
        truth_sigma_8=float(lab_t[1]),
        posterior_map=Wmap,
        OM=OM,
        S8=S8,
        index=np.array(ix, dtype=np.int32),
        label_dim=np.array(ld, dtype=np.int16),
        meta_tag=np.array(tag, dtype="U128"),
        six_tail_mode=np.array(args.six_tail_mode if ld == 6 else "", dtype="U16"),
    )
    print("Saved", out, "pairs:", args.n_hist, "device:", device)


if __name__ == "__main__":
    main()