Super-squash branch 'main' using huggingface_hub

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README.md

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+---
+language:
+- en
+tags:
+- neuroscience
+---
+
+# Omnimouse dataset
+
+Repository for the complete Omnimouse dataset.
+
+The Omnimouse dataset comprises **>3 million single-neuron recordings** from visual cortex in **78 mice** across **323 sessions**, capturing **>150 billion neural tokens** during presentations of **natural movies**, **natural images**, **parametric stimuli**, and aligned **behavioral measurements**.
+
+For more information, see the [OmniMouse paper](https://openreview.net/forum?id=mEw4lhAn0F).
+
+
+## Additional resources
+
+We provide [Experanto](https://github.com/sensorium-competition/experanto), a Python package for working with neuroscience experiments and loading them into torch
+(including interpolation utilities for recordings and stimuli). Experanto is fully compatible with the Omnimouse data format out of the box and is highly recommended.
+
+We also provide the [Sensorium 2023 codebase](https://github.com/ecker-lab/sensorium_2023) and the OmniMouse codebase (TBA),
+which include tutorial notebooks and baseline models to help you get started.
+
+## Sample download
+
+The following Python script demonstrates how to download a single experiment from the dataset.
+
+1. Set the environment variable `HF_TOKEN` to your Hugging Face access token.
+2. Run the script in a suitable Python environment (with `huggingface_hub` installed).
+
+```python
+import os
+import tarfile
+from pathlib import Path
+from huggingface_hub import hf_hub_download
+
+# =========================
+# Configuration
+# =========================
+HF_TOKEN = os.environ.get("HF_TOKEN")
+REPO_ID = "the-enigma-project/omnimouse-dataset"
+
+# Where you want the extracted experiment locally
+DOWNLOAD_DIR = Path("data")
+
+# Path of the tar file inside the HF repo
+REMOTE_TAR_PATH = "experiments/dynamic17797-4-7-Video-021a75e56847d574b9acbcc06c675055_30hz.tar"
+
+
+def test_download_and_extract():
+    print(f"--- Starting download test from {REPO_ID} ---")
+
+    try:
+        DOWNLOAD_DIR.mkdir(parents=True, exist_ok=True)
+
+        tar_local_path = hf_hub_download(
+            repo_id=REPO_ID,
+            repo_type="dataset",
+            filename=REMOTE_TAR_PATH,
+            token=HF_TOKEN,
+            local_dir=DOWNLOAD_DIR,
+        )
+
+        tar_local_path = Path(tar_local_path)
+        print(f"Downloaded tar to: {tar_local_path}")
+
+        print("Extracting archive...")
+        with tarfile.open(tar_local_path, "r") as tar:
+            tar.extractall(path=DOWNLOAD_DIR)
+
+        extracted_root = DOWNLOAD_DIR / tar_local_path.stem
+        print(f"Extraction complete.")
+        print(f"Experiment folder available at: {extracted_root}")
+
+    except Exception as e:
+        print(f"An error occurred during download or extraction: {e}")
+
+
+if __name__ == "__main__":
+    test_download_and_extract()
+```
+
+For a more detailed and customizable download script checkout the download_dataset.py file uploaded in this repo.
+For further guidance on downloading larger subsets (or the full dataset), see the [Hugging Face Hub download documentation](https://huggingface.co/docs/huggingface_hub/guides/download).
+
+## Data sources and attribution
+The experiments in the Omnimouse dataset are aggregated from multiple projects and original sources.
+Please consult the [metadata table](https://enigma-brain.github.io/omnimouse-data-release/dataset_details.html) to identify the originating project(s) for any experiment you use, and cite the corresponding paper(s) accordingly

download_dataset.py

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+import argparse
+import os
+import random
+import tarfile
+from pathlib import Path
+from typing import List
+
+from huggingface_hub import hf_hub_download, list_repo_files
+
+
+HF_TOKEN = os.environ.get("HF_TOKEN")
+DEFAULT_REPO_ID = "the-enigma-project/omnimouse-dataset"
+DEFAULT_DOWNLOAD_DIR = Path("data")
+DEFAULT_PARENT_FOLDER = "experiments"
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Download experiment tar files from a Hugging Face dataset repo, extract them, and delete the tar files."
+    )
+    parser.add_argument(
+        "--repo-id",
+        type=str,
+        default=DEFAULT_REPO_ID,
+        help=f"Hugging Face dataset repo id. Default: {DEFAULT_REPO_ID}",
+    )
+    parser.add_argument(
+        "--download-dir",
+        type=Path,
+        default=DEFAULT_DOWNLOAD_DIR,
+        help=f"Local root directory for downloaded and extracted data. Default: {DEFAULT_DOWNLOAD_DIR}",
+    )
+    parser.add_argument(
+        "--parent-folder",
+        type=str,
+        default=DEFAULT_PARENT_FOLDER,
+        help=f"Top-level folder inside the repo that contains the tar files. Default: {DEFAULT_PARENT_FOLDER}",
+    )
+    parser.add_argument(
+        "--num-files",
+        type=int,
+        default=1,
+        help="Number of experiment tar files to download.",
+    )
+    parser.add_argument(
+        "--experiments",
+        nargs="*",
+        default=[],
+        help="Specific experiment names to prioritize, without .tar suffix.",
+    )
+    parser.add_argument(
+        "--download-all",
+        action="store_true",
+        help="Download all available experiment tar files. Overrides --num-files and --experiments.",
+    )
+    parser.add_argument(
+        "--token",
+        type=str,
+        default=HF_TOKEN,
+        help="Hugging Face token. Defaults to HF_TOKEN environment variable.",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=0,
+        help="Random seed for filling remaining experiments. Default: 0",
+    )
+    return parser.parse_args()
+
+
+def get_available_experiment_names(repo_id: str, parent_folder: str, token: str | None) -> List[str]:
+    repo_files = list_repo_files(
+        repo_id=repo_id,
+        repo_type="dataset",
+        token=token,
+    )
+
+    prefix = parent_folder.rstrip("/") + "/"
+    experiment_names = []
+
+    for path in repo_files:
+        if not path.startswith(prefix):
+            continue
+        if not path.endswith(".tar"):
+            continue
+
+        filename = Path(path).name
+        experiment_names.append(filename[:-4])
+
+    return sorted(experiment_names)
+
+
+def choose_experiments(
+    requested: List[str],
+    available: List[str],
+    num_files: int,
+    seed: int,
+) -> List[str]:
+    available_set = set(available)
+
+    valid_requested = []
+    invalid_requested = []
+
+    for name in requested:
+        if name in available_set:
+            if name not in valid_requested:
+                valid_requested.append(name)
+        else:
+            invalid_requested.append(name)
+
+    if invalid_requested:
+        print(
+            f"Warning: {len(invalid_requested)} requested experiment(s) were not found in the repo and will be ignored:\n"
+            + "\n".join(f"  - {name}" for name in invalid_requested)
+        )
+
+    if len(valid_requested) > num_files:
+        print(
+            f"Warning: you requested {len(valid_requested)} valid experiment(s) but --num-files={num_files}. "
+            f"Only the first {num_files} requested experiment(s) will be used."
+        )
+        return valid_requested[:num_files]
+
+    chosen = list(valid_requested)
+
+    if len(chosen) < num_files:
+        remaining_pool = [name for name in available if name not in chosen]
+        random.Random(seed).shuffle(remaining_pool)
+
+        needed = num_files - len(chosen)
+        fillers = remaining_pool[:needed]
+
+        if fillers:
+            print(
+                f"Warning: only {len(valid_requested)} requested experiment(s) were selected, "
+                f"so the remaining {len(fillers)} slot(s) will be filled with random experiment(s)."
+            )
+
+        chosen.extend(fillers)
+
+    if len(chosen) < num_files:
+        print(
+            f"Warning: only {len(chosen)} experiment(s) are available in total, "
+            f"which is fewer than requested --num-files={num_files}."
+        )
+
+    return chosen
+
+
+def safe_extract_tar(tar_path: Path, extract_root: Path) -> List[Path]:
+    """
+    Extract tar safely, preventing path traversal.
+    Returns the list of top-level extracted paths.
+    """
+    top_level_names = []
+
+    with tarfile.open(tar_path, "r") as tar:
+        members = tar.getmembers()
+
+        resolved_extract_root = extract_root.resolve()
+
+        for member in members:
+            member_path = extract_root / member.name
+            resolved_member_path = member_path.resolve()
+
+            if not str(resolved_member_path).startswith(str(resolved_extract_root)):
+                raise RuntimeError(f"Unsafe path in tar archive: {member.name}")
+
+        for member in members:
+            parts = Path(member.name).parts
+            if parts:
+                top_level_names.append(parts[0])
+
+        tar.extractall(path=extract_root)
+
+    unique_top_paths = []
+    seen = set()
+    for name in top_level_names:
+        if name not in seen:
+            seen.add(name)
+            unique_top_paths.append(extract_root / name)
+
+    return unique_top_paths
+
+
+def download_and_extract_experiment(
+    repo_id: str,
+    parent_folder: str,
+    experiment_name: str,
+    download_dir: Path,
+    token: str | None,
+):
+    remote_tar_path = f"{parent_folder.rstrip('/')}/{experiment_name}.tar"
+
+    # download_dir is the local top-level target already
+    local_parent_dir = download_dir
+
+    print(f"\n--- Downloading {experiment_name} ---")
+    local_parent_dir.mkdir(parents=True, exist_ok=True)
+
+    tar_local_path = hf_hub_download(
+        repo_id=repo_id,
+        repo_type="dataset",
+        filename=remote_tar_path,
+        token=token,
+        local_dir=local_parent_dir,
+        local_dir_use_symlinks=False,
+    )
+
+    tar_local_path = Path(tar_local_path)
+    print(f"Downloaded tar to: {tar_local_path}")
+
+    print("Extracting archive...")
+    extracted_paths = safe_extract_tar(tar_local_path, local_parent_dir)
+
+    if tar_local_path.exists():
+        tar_local_path.unlink()
+        print(f"Deleted tar file: {tar_local_path}")
+
+    if extracted_paths:
+        print("Extraction complete. Extracted top-level path(s):")
+        for path in extracted_paths:
+            print(f"  - {path}")
+    else:
+        print("Extraction complete, but no extracted paths were detected.")
+
+
+def main():
+    args = parse_args()
+
+    if args.num_files < 1 and not args.download_all:
+        raise ValueError("--num-files must be at least 1 unless --download-all is used")
+
+    download_dir = args.download_dir.expanduser().resolve()
+    download_dir.mkdir(parents=True, exist_ok=True)
+
+    print(f"--- Inspecting repo: {args.repo_id} ---")
+    available = get_available_experiment_names(
+        repo_id=args.repo_id,
+        parent_folder=args.parent_folder,
+        token=args.token,
+    )
+
+    if not available:
+        raise RuntimeError(
+            f"No .tar files found under '{args.parent_folder}' in dataset repo '{args.repo_id}'."
+        )
+
+    print(f"Found {len(available)} available experiment tar file(s).")
+
+    if args.download_all:
+        if args.experiments:
+            print("Warning: --download-all was set, so --experiments will be ignored.")
+        if args.num_files != 1:
+            print("Warning: --download-all was set, so --num-files will be ignored.")
+        selected = available
+    else:
+        selected = choose_experiments(
+            requested=args.experiments,
+            available=available,
+            num_files=args.num_files,
+            seed=args.seed,
+        )
+
+    if not selected:
+        raise RuntimeError("No experiments were selected for download.")
+
+    print("\nExperiments selected for download:")
+    for name in selected:
+        print(f"  - {name}")
+
+    for experiment_name in selected:
+        try:
+            download_and_extract_experiment(
+                repo_id=args.repo_id,
+                parent_folder=args.parent_folder,
+                experiment_name=experiment_name,
+                download_dir=download_dir,
+                token=args.token,
+            )
+        except Exception as e:
+            print(f"Error while processing {experiment_name}: {e}")
+
+    print("\nDone.")
+
+
+if __name__ == "__main__":
+    main()

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