| """Pin official source metadata without bulk acquisition.""" |
|
|
| from __future__ import annotations |
|
|
| import json |
| from pathlib import Path |
|
|
| import requests |
|
|
| DANDI_COHORTS = { |
| "000541": "EY", |
| "000692": "KK", |
| "000565": "SK1", |
| "000472": "SK2", |
| "000776": "SF", |
| "000715": "NP", |
| "000714": "HL", |
| "001623": "Dunn", |
| "000981": "Chemosensory", |
| } |
| ZENODO = {"17561700": "ASCENT", "10818810": "ZephIR", "10008744": "Targettrack"} |
| WORMND_COMMIT = "0c74e9ddffee138cfc6330ab65c0e690e8e199dd" |
|
|
|
|
| def inventory(workspace): |
| """Save release metadata, immutable asset IDs/checksums, and the original split CSV. |
| |
| Re-running an inventory captures the current latest upstream release. Assembly |
| uses saved snapshots, never silently updates an existing experiment. Original |
| data licenses are taken from release metadata, separately from code licenses. |
| """ |
| out = Path(workspace) / "audit" |
| out.mkdir(parents=True, exist_ok=True) |
| asset_provenance = {} |
| for identifier in DANDI_COHORTS: |
| base = f"https://api.dandiarchive.org/api/dandisets/{identifier}/versions/" |
| versions = requests.get(base, timeout=60) |
| versions.raise_for_status() |
| versions = versions.json() |
| versions = versions.get("results", versions) if isinstance(versions, dict) else versions |
| published = [v["version"] for v in versions if v["version"] != "draft"] |
| version = max(published) if published else "draft" |
| metadata = requests.get(base + version + "/", timeout=60) |
| metadata.raise_for_status() |
| (out / f"dandi-{identifier}-pinned.json").write_text(json.dumps(metadata.json(), indent=2)) |
| assets = [] |
| url = base + version + "/assets/?page_size=1000" |
| while url: |
| response = requests.get(url, timeout=60) |
| response.raise_for_status() |
| page = response.json() |
| assets.extend(page["results"]) |
| url = page.get("next") |
| (out / f"dandi-{identifier}-assets.json").write_text( |
| json.dumps({"version": version, "assets": assets}, indent=2) |
| ) |
| for asset in assets: |
| response = requests.get( |
| f"https://api.dandiarchive.org/api/assets/{asset['asset_id']}/", timeout=60 |
| ) |
| response.raise_for_status() |
| value = response.json() |
| asset_provenance[asset["asset_id"]] = { |
| key: value.get(key) |
| for key in ["digest", "wasAttributedTo", "wasGeneratedBy", "datePublished"] |
| } |
| (out / "dandi-asset-provenance.json").write_text(json.dumps(asset_provenance, indent=2)) |
| for identifier in ZENODO: |
| response = requests.get(f"https://zenodo.org/api/records/{identifier}", timeout=60) |
| response.raise_for_status() |
| (out / f"zenodo-{identifier}.json").write_text(json.dumps(response.json(), indent=2)) |
| url = ( |
| f"https://raw.githubusercontent.com/focolab/WormND/{WORMND_COMMIT}" |
| "/neuron_tracking/3deecelltracker/dataset_split.csv" |
| ) |
| response = requests.get(url, timeout=60) |
| response.raise_for_status() |
| (out / "wormid-tracking-split.csv").write_text(response.text) |
|
|
|
|
| def snapshot_hub(workspace, repo_id="pytc/trackingBench"): |
| """Record the immutable Hub state before edits; does not publish or expose tokens.""" |
| from huggingface_hub import HfApi, hf_hub_download |
|
|
| api = HfApi() |
| info = api.dataset_info(repo_id, files_metadata=True) |
| readme = Path( |
| hf_hub_download(repo_id, "README.md", repo_type="dataset", revision=info.sha) |
| ).read_text() |
| snapshot = { |
| "repo_id": repo_id, |
| "sha": info.sha, |
| "private": info.private, |
| "card": info.card_data.to_dict() if info.card_data else {}, |
| "readme": readme, |
| "files": [ |
| {"path": f.rfilename, "size": f.size, "blob_id": f.blob_id} for f in info.siblings |
| ], |
| } |
| Path(workspace).mkdir(parents=True, exist_ok=True) |
| (Path(workspace) / "hub-before.json").write_text(json.dumps(snapshot, indent=2)) |
| return snapshot |
|
|
