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eb725f8 1f3e7a2 eb725f8 1f3e7a2 eb725f8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | #!/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()
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