Datasets:
Formats:
parquet
Size:
100K - 1M
Tags:
population-genetics
demographic-inference
effective-population-size
coalescent
msprime
simulation
License:
| """Rebuild a converged-sidecar variant on any host from patch.npz + the production sidecar. | |
| The production file is read only. The output is written into a NEW directory and is always | |
| named chain0_level32.npz, because the frozen runner refuses any other basename | |
| (training/fixed_view.py: SIDECAR_BASENAME = f"chain{VIEW_CHAIN}_level{VIEW_DIPLOIDS}.npz"). | |
| Variants (see RECEIPT.json for why meanfill exists): | |
| dropin production values on every un-rebuilt row, converged refit on the rebuilt ones | |
| meanfill converged refit on the rebuilt rows; every other row = the rebuilt-fit mean of | |
| the converged vectors | |
| meanfill_baseline the matched control: stored values on the rebuilt rows; every other row = the | |
| rebuilt-fit mean of the stored vectors | |
| Usage: | |
| python apply_patch.py --stored /cloud/cloud-ssd1/dlcoal/sidecar109k/chain0_level32.npz \ | |
| --patch patch.npz \ | |
| --out /cloud/cloud-ssd1/dlcoal/sidecar109k_converged_20260916 \ | |
| --variant dropin --variant meanfill --variant meanfill_baseline | |
| """ | |
| import argparse, hashlib, json, os | |
| import numpy as np | |
| FEAT = 256 | |
| BASENAME = "chain0_level32.npz" | |
| def sha256_file(path, chunk=1 << 22): | |
| h = hashlib.sha256() | |
| with open(path, "rb") as f: | |
| for block in iter(lambda: f.read(chunk), b""): | |
| h.update(block) | |
| return h.hexdigest() | |
| ap = argparse.ArgumentParser() | |
| ap.add_argument("--stored", required=True) | |
| ap.add_argument("--patch", required=True) | |
| ap.add_argument("--out", required=True) | |
| ap.add_argument("--variant", action="append", default=None, | |
| choices=["dropin", "meanfill", "meanfill_baseline"]) | |
| A = ap.parse_args() | |
| variants = A.variant or ["dropin"] | |
| store = np.load(A.stored, allow_pickle=False) | |
| stored = store["sidecar"].astype(np.float32) | |
| present = store["sidecar_present"] | |
| patch = np.load(A.patch, allow_pickle=False) | |
| reg = patch["registry_index"].astype(np.int64) | |
| feats = patch["features"].astype(np.float32) | |
| pilot_fit = patch["pilot_fit"].astype(np.int64) | |
| assert stored.ndim == 2 and stored.shape[1] == FEAT, stored.shape | |
| assert feats.shape == (len(reg), FEAT) | |
| assert reg.min() >= 0 and reg.max() < len(stored), "the patch does not fit this cohort" | |
| assert len(np.unique(reg)) == len(reg) | |
| # the patch leaves the plain half of every rebuilt row exactly as production had it | |
| assert np.array_equal(feats[:, 0:128], stored[reg][:, 0:128]), \ | |
| "patch plain block differs from the stored file: wrong production sidecar" | |
| built_fit = np.asarray(sorted(set(pilot_fit.tolist()) & set(reg.tolist())), dtype=np.int64) | |
| os.makedirs(A.out, exist_ok=True) | |
| report = {"stored": A.stored, "stored_sha256": sha256_file(A.stored), "patch": A.patch, | |
| "patch_sha256": sha256_file(A.patch), "rows": int(len(stored)), | |
| "rows_patched": int(len(reg)), "built_fit_rows": int(len(built_fit)), "written": {}} | |
| for name in variants: | |
| if name == "dropin": | |
| values = stored.copy(); values[reg] = feats | |
| elif name == "meanfill": | |
| fill = feats[np.isin(reg, built_fit)].mean(axis=0) | |
| values = np.repeat(fill[None, :], len(stored), axis=0).astype(np.float32); values[reg] = feats | |
| else: | |
| fill = stored[built_fit].mean(axis=0) | |
| values = np.repeat(fill[None, :], len(stored), axis=0).astype(np.float32) | |
| values[reg] = stored[reg] | |
| assert np.isfinite(values).all() | |
| d = os.path.join(A.out, name); os.makedirs(d, exist_ok=True) | |
| p = os.path.join(d, BASENAME) | |
| np.savez(p, sidecar=values, sidecar_present=np.asarray(present).astype(np.uint8)) | |
| report["written"][name] = {"path": p, "sha256": sha256_file(p), "bytes": os.path.getsize(p)} | |
| print("wrote", p) | |
| json.dump(report, open(os.path.join(A.out, "APPLY_PATCH.json"), "w"), indent=1, sort_keys=True) | |
| print(json.dumps(report, indent=1, sort_keys=True)) | |