Add default Parquet file index for MGnify
Browse files- README.md +178 -51
- _MANIFEST.json +341 -0
- data/test-00000-of-00001.parquet +3 -0
- data/train-00000-of-00001.parquet +3 -0
- dataset_summary.json +341 -0
- metadata/source_files.parquet +3 -0
- scripts/prepare_mgnify_dataset.py +408 -0
README.md
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license: cc-by-4.0
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pretty_name: MGnify Protein Catalogues
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size_categories:
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task_categories:
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- other
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language:
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- fasta
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- mgnify
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- metagenomics
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---
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# MGnify Protein Catalogues
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(internal repo). Original source: <https://www.ebi.ac.uk/metagenomics/>.
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```
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```
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interest under `sequences/`):
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```
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```
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```python
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from huggingface_hub import snapshot_download
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from pathlib import Path
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import zstandard as zstd
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repo_id="LiteFold/Mgnify",
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repo_type="dataset",
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allow_patterns=[
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)
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dctx = zstd.ZstdDecompressor()
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while chunk := reader.read(1 << 20):
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buf += chunk
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*lines, buf = buf.split(b"\n")
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for line in lines:
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... # naive splitter; swap in your FASTA parser
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```
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##
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Pipeline source: `megadata-post normalize --dataset mgnify_proteins`.
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license: cc-by-4.0
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pretty_name: MGnify Protein Catalogues
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size_categories:
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- 1B<n<10B
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task_categories:
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- other
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language:
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- fasta
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- mgnify
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- metagenomics
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- protein-catalogue
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- train-test-split
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- parquet
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configs:
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- config_name: default
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data_files:
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- split: train
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path:
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- data/train-*.parquet
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- split: test
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path:
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- data/test-*.parquet
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---
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# MGnify Protein Catalogues
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This repository contains the LiteFold/Mgnify MGnify protein catalogue payload plus a compact default index for the Hugging Face Dataset Viewer.
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The raw sequence and table payload is TB-scale, so the `default` config is intentionally a file/shard index rather than a duplicate of every raw row. The raw files remain in `sequences/` and `tables/`; use the index to discover sources, shards, part files, sizes, and download patterns, then stream or download only the payload files you need.
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## Dataset Summary
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| Metric | Value |
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|---|---:|
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| Default index rows | 3,226 |
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| Default index columns | 34 |
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| Repository files indexed | 3,226 |
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| Repository bytes indexed | 3,156,136,546,084 |
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| Sequence source files | 26 |
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| Sequence shards | 3,148 |
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| Sequence shard bytes | 342,815,994,580 |
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| Logical table sources | 28 |
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| Logical table bytes | 2,380,980,172,257 |
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| Table files in repo | 76 |
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| Table repo bytes | 2,813,320,536,987 |
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The table repo bytes include both top-level table JSONL files and split part files where both are present. The logical table bytes count each upstream table once.
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## Default Splits
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The default index split is deterministic by file id:
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`sha256(file_id) % 10`
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Bucket `0` is `test`; buckets `1` through `9` are `train`.
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| Split | Rows |
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|---|---:|
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| `train` | 2,902 |
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| `test` | 324 |
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These are file-index splits, not biological train/test sequence splits. For model training, create sequence-level or cluster-level splits appropriate to your task after loading the relevant MGnify payload.
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## Loading With `datasets`
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Load the default file/shard index:
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```python
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from datasets import load_dataset
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index = load_dataset("LiteFold/Mgnify")
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print(index)
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print(index["train"][0])
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```
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Load one split directly:
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```python
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from datasets import load_dataset
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train_index = load_dataset("LiteFold/Mgnify", split="train")
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```
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Find sequence shards for one source family:
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```python
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from datasets import load_dataset
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index = load_dataset("LiteFold/Mgnify", split="train")
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mgy_clusters = index.filter(
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lambda row: row["role"] == "sequence_shard"
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and row["source_family"] == "mgy_clusters"
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)
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print(mgy_clusters[0]["download_pattern"])
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```
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Find split table parts:
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```python
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from datasets import load_dataset
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index = load_dataset("LiteFold/Mgnify", split="train")
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parts = index.filter(lambda row: row["role"] == "table_split_part")
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print(parts[0]["path"], parts[0]["size_bytes"])
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```
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## Streaming Raw FASTA Shards
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Download one source family with the Hub client:
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```python
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from huggingface_hub import snapshot_download
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local_dir = snapshot_download(
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repo_id="LiteFold/Mgnify",
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repo_type="dataset",
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allow_patterns=[
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"sequences/sequence_mgnify_current_release_mgy_clusters.fa.gz/shard-*.fasta.zst"
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],
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)
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print(local_dir)
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```
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Stream a shard without downloading the whole source family:
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```python
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from huggingface_hub import HfFileSystem
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import zstandard as zstd
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fs = HfFileSystem()
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path = (
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"datasets/LiteFold/Mgnify/"
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"sequences/sequence_mgnify_current_release_mgy_clusters.fa.gz/"
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"shard-000001.fasta.zst"
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)
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dctx = zstd.ZstdDecompressor()
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with fs.open(path, "rb") as f, dctx.stream_reader(f) as reader:
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chunk = reader.read(1 << 20)
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print(chunk[:200])
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```
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## Downloading Raw Table Parts
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For split tables, use the `download_pattern` column or a direct include pattern:
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```bash
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hf download LiteFold/Mgnify --repo-type dataset \
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--include 'tables/sequence_mgnify_current_release_mgy_proteins_pfam.tsv.gz.jsonl.parts/part-*.jsonl' \
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--local-dir ./mgnify
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```
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For unsplit tables:
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```bash
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hf download LiteFold/Mgnify --repo-type dataset \
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--include 'tables/sequence_mgnify_current_release_mgy_seq_metadata_2.tsv.gz.jsonl' \
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--local-dir ./mgnify
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```
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The raw table files are not registered as `datasets` configs because they are multi-TB nested JSONL payloads. Keeping the default config as a compact Parquet index prevents accidental full-repo scans and keeps the Dataset Viewer responsive.
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## Default Columns
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| Column | Description |
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|---|---|
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| `file_id` | Stable file identifier, currently the repository path. |
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| `repo_id` | Hugging Face dataset repository id. |
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| `source_sha` | Source commit used to build the index. |
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| `dataset_id` | `mgnify_proteins`. |
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| `source_family` | Parsed source family such as `mgy_clusters`, `mgy_proteins_1`, or `mgy_seq_metadata_2`. |
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| `source_slug` | Source slug/path component used by the repository. |
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| `source_file` | Original MGnify source path when derivable. |
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| `path` | File path in the repository. |
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| `role` | File role: `sequence_shard`, `table_jsonl`, `table_split_part`, `table_split_manifest`, `readme`, or `git_attributes`. |
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| `shard_index` | FASTA shard index, otherwise `-1`. |
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| `part_index` | Split table part index, otherwise `-1`. |
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| `size_bytes` | File size in bytes. |
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| `compression` | File/container format. |
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| `logical_table_size_bytes` | Logical source table size when applicable, otherwise `-1`. |
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| `split_part_count` | Number of table split parts when applicable, otherwise `-1`. |
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| `split_chunk_bytes` | Target split chunk size when applicable, otherwise `-1`. |
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| `sequence_source_shard_count` | Number of shards in the sequence source, otherwise `-1`. |
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| `sequence_source_bytes` | Total bytes for that sequence source, otherwise `-1`. |
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| `repo_file_count` | Total repository files indexed. |
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| `repo_total_bytes` | Total indexed repository bytes. |
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| `sequence_shard_count_total` | Total sequence shards. |
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| `sequence_shard_bytes_total` | Total sequence shard bytes. |
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| `table_repo_file_count_total` | Total table files in the repo, including split manifests and parts. |
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| `table_repo_bytes_total` | Total table repo bytes, including duplicated top-level and split files where both exist. |
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| `logical_table_count_total` | Logical upstream table count. |
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| `logical_table_bytes_total` | Logical upstream table bytes. |
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| `is_sequence_shard` | Whether the row is a FASTA shard. |
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| `is_table_file` | Whether the row is a table file or table manifest. |
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| `is_split_part` | Whether the row is a split table part. |
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| `is_split_manifest` | Whether the row is a split manifest. |
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| `is_original_table_copy` | Whether a top-level table also has split parts. |
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| `download_pattern` | Glob or exact path for downloading related payload files. |
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| `access_note` | Short usage note. |
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| `split_bucket` | Deterministic bucket used for the default train/test split. |
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## Files
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- `data/*.parquet`: default file/shard index for Dataset Viewer.
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- `metadata/source_files.parquet`: full index copy.
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- `sequences/*/shard-*.fasta.zst`: raw compressed MGnify FASTA shards.
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- `tables/**/*.jsonl`: raw normalized table payloads and split parts.
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- `_MANIFEST.json`: index build summary.
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- `dataset_summary.json`: same summary in a Dataset Viewer-adjacent file.
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- `scripts/prepare_mgnify_dataset.py`: script used to build the default index.
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## Source
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Derived from LiteFold/Mgnify, originally sourced from EMBL-EBI MGnify.
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## License
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CC BY 4.0.
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## Citation
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If you use the MGnify data, cite MGnify:
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Mitchell AL, et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Research, 48(D1):D570-D578, 2020.
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_MANIFEST.json
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| 1 |
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{
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| 2 |
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| 3 |
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| 8 |
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| 10 |
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| 29 |
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| 1 |
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{
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"source": "LiteFold/Mgnify",
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|
metadata/source_files.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:34d589581c5e9a811006c99d4862144b26db96af8328bb1107cecd4bd10a0df2
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size 52607
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scripts/prepare_mgnify_dataset.py
ADDED
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@@ -0,0 +1,408 @@
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|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
"""Build a viewer-friendly file/shard index for LiteFold/Mgnify."""
|
| 3 |
+
|
| 4 |
+
from __future__ import annotations
|
| 5 |
+
|
| 6 |
+
import argparse
|
| 7 |
+
import hashlib
|
| 8 |
+
import json
|
| 9 |
+
import os
|
| 10 |
+
import re
|
| 11 |
+
import shutil
|
| 12 |
+
from collections import Counter, defaultdict
|
| 13 |
+
from pathlib import Path
|
| 14 |
+
from typing import Any
|
| 15 |
+
|
| 16 |
+
import pyarrow as pa
|
| 17 |
+
import pyarrow.parquet as pq
|
| 18 |
+
from huggingface_hub import HfApi, hf_hub_download
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
DATASET_ID = "mgnify_proteins"
|
| 22 |
+
PREFIX = "sequence_mgnify_current_release_"
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
INDEX_COLUMNS = [
|
| 26 |
+
"file_id",
|
| 27 |
+
"repo_id",
|
| 28 |
+
"source_sha",
|
| 29 |
+
"dataset_id",
|
| 30 |
+
"source_family",
|
| 31 |
+
"source_slug",
|
| 32 |
+
"source_file",
|
| 33 |
+
"path",
|
| 34 |
+
"role",
|
| 35 |
+
"shard_index",
|
| 36 |
+
"part_index",
|
| 37 |
+
"size_bytes",
|
| 38 |
+
"compression",
|
| 39 |
+
"logical_table_size_bytes",
|
| 40 |
+
"split_part_count",
|
| 41 |
+
"split_chunk_bytes",
|
| 42 |
+
"sequence_source_shard_count",
|
| 43 |
+
"sequence_source_bytes",
|
| 44 |
+
"repo_file_count",
|
| 45 |
+
"repo_total_bytes",
|
| 46 |
+
"sequence_shard_count_total",
|
| 47 |
+
"sequence_shard_bytes_total",
|
| 48 |
+
"table_repo_file_count_total",
|
| 49 |
+
"table_repo_bytes_total",
|
| 50 |
+
"logical_table_count_total",
|
| 51 |
+
"logical_table_bytes_total",
|
| 52 |
+
"is_sequence_shard",
|
| 53 |
+
"is_table_file",
|
| 54 |
+
"is_split_part",
|
| 55 |
+
"is_split_manifest",
|
| 56 |
+
"is_original_table_copy",
|
| 57 |
+
"download_pattern",
|
| 58 |
+
"access_note",
|
| 59 |
+
"split_bucket",
|
| 60 |
+
]
|
| 61 |
+
|
| 62 |
+
|
| 63 |
+
SCHEMA = pa.schema(
|
| 64 |
+
[
|
| 65 |
+
pa.field("file_id", pa.string()),
|
| 66 |
+
pa.field("repo_id", pa.string()),
|
| 67 |
+
pa.field("source_sha", pa.string()),
|
| 68 |
+
pa.field("dataset_id", pa.string()),
|
| 69 |
+
pa.field("source_family", pa.string()),
|
| 70 |
+
pa.field("source_slug", pa.string()),
|
| 71 |
+
pa.field("source_file", pa.string()),
|
| 72 |
+
pa.field("path", pa.string()),
|
| 73 |
+
pa.field("role", pa.string()),
|
| 74 |
+
pa.field("shard_index", pa.int64()),
|
| 75 |
+
pa.field("part_index", pa.int64()),
|
| 76 |
+
pa.field("size_bytes", pa.int64()),
|
| 77 |
+
pa.field("compression", pa.string()),
|
| 78 |
+
pa.field("logical_table_size_bytes", pa.int64()),
|
| 79 |
+
pa.field("split_part_count", pa.int64()),
|
| 80 |
+
pa.field("split_chunk_bytes", pa.int64()),
|
| 81 |
+
pa.field("sequence_source_shard_count", pa.int64()),
|
| 82 |
+
pa.field("sequence_source_bytes", pa.int64()),
|
| 83 |
+
pa.field("repo_file_count", pa.int64()),
|
| 84 |
+
pa.field("repo_total_bytes", pa.int64()),
|
| 85 |
+
pa.field("sequence_shard_count_total", pa.int64()),
|
| 86 |
+
pa.field("sequence_shard_bytes_total", pa.int64()),
|
| 87 |
+
pa.field("table_repo_file_count_total", pa.int64()),
|
| 88 |
+
pa.field("table_repo_bytes_total", pa.int64()),
|
| 89 |
+
pa.field("logical_table_count_total", pa.int64()),
|
| 90 |
+
pa.field("logical_table_bytes_total", pa.int64()),
|
| 91 |
+
pa.field("is_sequence_shard", pa.bool_()),
|
| 92 |
+
pa.field("is_table_file", pa.bool_()),
|
| 93 |
+
pa.field("is_split_part", pa.bool_()),
|
| 94 |
+
pa.field("is_split_manifest", pa.bool_()),
|
| 95 |
+
pa.field("is_original_table_copy", pa.bool_()),
|
| 96 |
+
pa.field("download_pattern", pa.string()),
|
| 97 |
+
pa.field("access_note", pa.string()),
|
| 98 |
+
pa.field("split_bucket", pa.int64()),
|
| 99 |
+
]
|
| 100 |
+
)
|
| 101 |
+
|
| 102 |
+
|
| 103 |
+
def load_token() -> str | None:
|
| 104 |
+
for key in ("HF_TOKEN", "HUGGINGFACE_HUB_TOKEN"):
|
| 105 |
+
value = os.environ.get(key)
|
| 106 |
+
if value:
|
| 107 |
+
return value
|
| 108 |
+
env_path = Path(".env")
|
| 109 |
+
if env_path.exists():
|
| 110 |
+
for line in env_path.read_text().splitlines():
|
| 111 |
+
stripped = line.strip()
|
| 112 |
+
if not stripped or stripped.startswith("#") or "=" not in stripped:
|
| 113 |
+
continue
|
| 114 |
+
key, value = stripped.split("=", 1)
|
| 115 |
+
if key.strip() in {"HF_TOKEN", "HUGGINGFACE_HUB_TOKEN"}:
|
| 116 |
+
value = value.strip().strip('"').strip("'")
|
| 117 |
+
if value:
|
| 118 |
+
return value
|
| 119 |
+
return None
|
| 120 |
+
|
| 121 |
+
|
| 122 |
+
def stable_bucket(value: str, buckets: int = 10) -> int:
|
| 123 |
+
digest = hashlib.sha256(value.encode("utf-8")).hexdigest()[:16]
|
| 124 |
+
return int(digest, 16) % buckets
|
| 125 |
+
|
| 126 |
+
|
| 127 |
+
def source_file_from_slug(slug: str) -> str:
|
| 128 |
+
if slug.startswith(PREFIX):
|
| 129 |
+
return "sequence/mgnify/current_release/" + slug[len(PREFIX) :]
|
| 130 |
+
return ""
|
| 131 |
+
|
| 132 |
+
|
| 133 |
+
def source_family_from_slug(slug: str) -> str:
|
| 134 |
+
if slug.startswith(PREFIX):
|
| 135 |
+
slug = slug[len(PREFIX) :]
|
| 136 |
+
for suffix in (".fasta.zst", ".fa.gz", ".tsv.gz.jsonl", ".tsv.gz"):
|
| 137 |
+
if slug.endswith(suffix):
|
| 138 |
+
slug = slug[: -len(suffix)]
|
| 139 |
+
return slug
|
| 140 |
+
|
| 141 |
+
|
| 142 |
+
def compression_for_path(path: str) -> str:
|
| 143 |
+
if path.endswith(".fasta.zst"):
|
| 144 |
+
return "zstd"
|
| 145 |
+
if path.endswith(".jsonl"):
|
| 146 |
+
return "jsonl"
|
| 147 |
+
if path.endswith(".json"):
|
| 148 |
+
return "json"
|
| 149 |
+
return ""
|
| 150 |
+
|
| 151 |
+
|
| 152 |
+
def parse_path(path: str) -> dict[str, Any]:
|
| 153 |
+
sequence_match = re.fullmatch(r"sequences/([^/]+)/shard-(\d+)\.fasta\.zst", path)
|
| 154 |
+
if sequence_match:
|
| 155 |
+
source_slug = sequence_match.group(1)
|
| 156 |
+
return {
|
| 157 |
+
"role": "sequence_shard",
|
| 158 |
+
"source_slug": source_slug,
|
| 159 |
+
"source_family": source_family_from_slug(source_slug),
|
| 160 |
+
"source_file": source_file_from_slug(source_slug),
|
| 161 |
+
"shard_index": int(sequence_match.group(2)),
|
| 162 |
+
"part_index": -1,
|
| 163 |
+
"is_sequence_shard": True,
|
| 164 |
+
"is_table_file": False,
|
| 165 |
+
"is_split_part": False,
|
| 166 |
+
"is_split_manifest": False,
|
| 167 |
+
"is_original_table_copy": False,
|
| 168 |
+
}
|
| 169 |
+
|
| 170 |
+
split_manifest_match = re.fullmatch(r"tables/(.+\.jsonl)\.parts/_SPLIT_MANIFEST\.json", path)
|
| 171 |
+
if split_manifest_match:
|
| 172 |
+
source_slug = split_manifest_match.group(1)
|
| 173 |
+
return {
|
| 174 |
+
"role": "table_split_manifest",
|
| 175 |
+
"source_slug": source_slug,
|
| 176 |
+
"source_family": source_family_from_slug(source_slug),
|
| 177 |
+
"source_file": source_file_from_slug(source_slug.removesuffix(".jsonl")),
|
| 178 |
+
"shard_index": -1,
|
| 179 |
+
"part_index": -1,
|
| 180 |
+
"is_sequence_shard": False,
|
| 181 |
+
"is_table_file": True,
|
| 182 |
+
"is_split_part": False,
|
| 183 |
+
"is_split_manifest": True,
|
| 184 |
+
"is_original_table_copy": False,
|
| 185 |
+
}
|
| 186 |
+
|
| 187 |
+
split_part_match = re.fullmatch(r"tables/(.+\.jsonl)\.parts/part-(\d+)\.jsonl", path)
|
| 188 |
+
if split_part_match:
|
| 189 |
+
source_slug = split_part_match.group(1)
|
| 190 |
+
return {
|
| 191 |
+
"role": "table_split_part",
|
| 192 |
+
"source_slug": source_slug,
|
| 193 |
+
"source_family": source_family_from_slug(source_slug),
|
| 194 |
+
"source_file": source_file_from_slug(source_slug.removesuffix(".jsonl")),
|
| 195 |
+
"shard_index": -1,
|
| 196 |
+
"part_index": int(split_part_match.group(2)),
|
| 197 |
+
"is_sequence_shard": False,
|
| 198 |
+
"is_table_file": True,
|
| 199 |
+
"is_split_part": True,
|
| 200 |
+
"is_split_manifest": False,
|
| 201 |
+
"is_original_table_copy": False,
|
| 202 |
+
}
|
| 203 |
+
|
| 204 |
+
table_match = re.fullmatch(r"tables/(.+\.jsonl)", path)
|
| 205 |
+
if table_match:
|
| 206 |
+
source_slug = table_match.group(1)
|
| 207 |
+
return {
|
| 208 |
+
"role": "table_jsonl",
|
| 209 |
+
"source_slug": source_slug,
|
| 210 |
+
"source_family": source_family_from_slug(source_slug),
|
| 211 |
+
"source_file": source_file_from_slug(source_slug.removesuffix(".jsonl")),
|
| 212 |
+
"shard_index": -1,
|
| 213 |
+
"part_index": -1,
|
| 214 |
+
"is_sequence_shard": False,
|
| 215 |
+
"is_table_file": True,
|
| 216 |
+
"is_split_part": False,
|
| 217 |
+
"is_split_manifest": False,
|
| 218 |
+
"is_original_table_copy": False,
|
| 219 |
+
}
|
| 220 |
+
|
| 221 |
+
role = {".gitattributes": "git_attributes", "README.md": "readme"}.get(path, "other")
|
| 222 |
+
return {
|
| 223 |
+
"role": role,
|
| 224 |
+
"source_slug": "",
|
| 225 |
+
"source_family": "",
|
| 226 |
+
"source_file": "",
|
| 227 |
+
"shard_index": -1,
|
| 228 |
+
"part_index": -1,
|
| 229 |
+
"is_sequence_shard": False,
|
| 230 |
+
"is_table_file": False,
|
| 231 |
+
"is_split_part": False,
|
| 232 |
+
"is_split_manifest": False,
|
| 233 |
+
"is_original_table_copy": False,
|
| 234 |
+
}
|
| 235 |
+
|
| 236 |
+
|
| 237 |
+
def load_split_manifests(raw_dir: Path) -> dict[str, dict[str, Any]]:
|
| 238 |
+
manifests: dict[str, dict[str, Any]] = {}
|
| 239 |
+
for path in raw_dir.glob("tables/*.parts/_SPLIT_MANIFEST.json"):
|
| 240 |
+
manifest = json.loads(path.read_text())
|
| 241 |
+
base_path = "tables/" + path.parent.name.removesuffix(".parts")
|
| 242 |
+
manifests[base_path] = manifest
|
| 243 |
+
return manifests
|
| 244 |
+
|
| 245 |
+
|
| 246 |
+
def build_dataset(repo_id: str, raw_dir: Path, out_dir: Path) -> dict[str, Any]:
|
| 247 |
+
token = load_token()
|
| 248 |
+
api = HfApi(token=token)
|
| 249 |
+
info = api.dataset_info(repo_id, files_metadata=True)
|
| 250 |
+
raw_dir.mkdir(parents=True, exist_ok=True)
|
| 251 |
+
|
| 252 |
+
for sibling in info.siblings or []:
|
| 253 |
+
path = sibling.rfilename
|
| 254 |
+
if path == "README.md" or path.endswith("_SPLIT_MANIFEST.json"):
|
| 255 |
+
hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=path, local_dir=raw_dir, token=token)
|
| 256 |
+
|
| 257 |
+
split_manifests = load_split_manifests(raw_dir)
|
| 258 |
+
sizes = {s.rfilename: int(getattr(s, "size", 0) or 0) for s in info.siblings or []}
|
| 259 |
+
source_sequence_counts: dict[str, int] = defaultdict(int)
|
| 260 |
+
source_sequence_bytes: dict[str, int] = defaultdict(int)
|
| 261 |
+
for path, size in sizes.items():
|
| 262 |
+
parsed = parse_path(path)
|
| 263 |
+
if parsed["is_sequence_shard"]:
|
| 264 |
+
source_sequence_counts[parsed["source_slug"]] += 1
|
| 265 |
+
source_sequence_bytes[parsed["source_slug"]] += size
|
| 266 |
+
|
| 267 |
+
top_level_tables = {
|
| 268 |
+
path: size for path, size in sizes.items() if path.startswith("tables/") and path.endswith(".jsonl") and ".parts/" not in path
|
| 269 |
+
}
|
| 270 |
+
logical_tables = dict(top_level_tables)
|
| 271 |
+
for base_path, manifest in split_manifests.items():
|
| 272 |
+
if base_path not in logical_tables:
|
| 273 |
+
logical_tables[base_path] = int(manifest["original_size"])
|
| 274 |
+
|
| 275 |
+
repo_file_count = len(sizes)
|
| 276 |
+
repo_total_bytes = sum(sizes.values())
|
| 277 |
+
sequence_shard_count_total = sum(1 for path in sizes if path.startswith("sequences/"))
|
| 278 |
+
sequence_shard_bytes_total = sum(size for path, size in sizes.items() if path.startswith("sequences/"))
|
| 279 |
+
table_repo_file_count_total = sum(1 for path in sizes if path.startswith("tables/"))
|
| 280 |
+
table_repo_bytes_total = sum(size for path, size in sizes.items() if path.startswith("tables/"))
|
| 281 |
+
logical_table_count_total = len(logical_tables)
|
| 282 |
+
logical_table_bytes_total = sum(logical_tables.values())
|
| 283 |
+
|
| 284 |
+
rows = []
|
| 285 |
+
for path in sorted(sizes):
|
| 286 |
+
if (
|
| 287 |
+
path.startswith("data/")
|
| 288 |
+
or path.startswith("metadata/")
|
| 289 |
+
or path.startswith("scripts/")
|
| 290 |
+
or path in {"_MANIFEST.json", "dataset_summary.json"}
|
| 291 |
+
):
|
| 292 |
+
continue
|
| 293 |
+
size = sizes[path]
|
| 294 |
+
parsed = parse_path(path)
|
| 295 |
+
source_slug = parsed["source_slug"]
|
| 296 |
+
base_table_path = f"tables/{source_slug}" if source_slug.endswith(".jsonl") else ""
|
| 297 |
+
manifest = split_manifests.get(base_table_path) or {}
|
| 298 |
+
file_id = path
|
| 299 |
+
download_pattern = path
|
| 300 |
+
if parsed["role"] == "sequence_shard" and source_slug:
|
| 301 |
+
download_pattern = f"sequences/{source_slug}/shard-*.fasta.zst"
|
| 302 |
+
elif parsed["role"] in {"table_split_part", "table_split_manifest"} and source_slug:
|
| 303 |
+
download_pattern = f"tables/{source_slug}.parts/part-*.jsonl"
|
| 304 |
+
elif parsed["role"] == "table_jsonl" and source_slug:
|
| 305 |
+
download_pattern = f"tables/{source_slug}"
|
| 306 |
+
rows.append(
|
| 307 |
+
{
|
| 308 |
+
"file_id": file_id,
|
| 309 |
+
"repo_id": repo_id,
|
| 310 |
+
"source_sha": info.sha,
|
| 311 |
+
"dataset_id": DATASET_ID,
|
| 312 |
+
"source_family": parsed["source_family"],
|
| 313 |
+
"source_slug": source_slug,
|
| 314 |
+
"source_file": parsed["source_file"],
|
| 315 |
+
"path": path,
|
| 316 |
+
"role": parsed["role"],
|
| 317 |
+
"shard_index": parsed["shard_index"],
|
| 318 |
+
"part_index": parsed["part_index"],
|
| 319 |
+
"size_bytes": size,
|
| 320 |
+
"compression": compression_for_path(path),
|
| 321 |
+
"logical_table_size_bytes": int(logical_tables.get(base_table_path, -1)) if parsed["is_table_file"] else -1,
|
| 322 |
+
"split_part_count": len(manifest.get("parts", [])) if manifest else -1,
|
| 323 |
+
"split_chunk_bytes": int(manifest.get("chunk_bytes", -1)) if manifest else -1,
|
| 324 |
+
"sequence_source_shard_count": source_sequence_counts.get(source_slug, -1) if parsed["is_sequence_shard"] else -1,
|
| 325 |
+
"sequence_source_bytes": source_sequence_bytes.get(source_slug, -1) if parsed["is_sequence_shard"] else -1,
|
| 326 |
+
"repo_file_count": repo_file_count,
|
| 327 |
+
"repo_total_bytes": repo_total_bytes,
|
| 328 |
+
"sequence_shard_count_total": sequence_shard_count_total,
|
| 329 |
+
"sequence_shard_bytes_total": sequence_shard_bytes_total,
|
| 330 |
+
"table_repo_file_count_total": table_repo_file_count_total,
|
| 331 |
+
"table_repo_bytes_total": table_repo_bytes_total,
|
| 332 |
+
"logical_table_count_total": logical_table_count_total,
|
| 333 |
+
"logical_table_bytes_total": logical_table_bytes_total,
|
| 334 |
+
"is_sequence_shard": parsed["is_sequence_shard"],
|
| 335 |
+
"is_table_file": parsed["is_table_file"],
|
| 336 |
+
"is_split_part": parsed["is_split_part"],
|
| 337 |
+
"is_split_manifest": parsed["is_split_manifest"],
|
| 338 |
+
"is_original_table_copy": parsed["role"] == "table_jsonl" and base_table_path in split_manifests,
|
| 339 |
+
"download_pattern": download_pattern,
|
| 340 |
+
"access_note": "Default config indexes Mgnify files. Stream raw FASTA/table payloads from sequences/ and tables/ with huggingface_hub.",
|
| 341 |
+
"split_bucket": stable_bucket(file_id),
|
| 342 |
+
}
|
| 343 |
+
)
|
| 344 |
+
|
| 345 |
+
if out_dir.exists():
|
| 346 |
+
shutil.rmtree(out_dir)
|
| 347 |
+
data_dir = out_dir / "data"
|
| 348 |
+
metadata_dir = out_dir / "metadata"
|
| 349 |
+
data_dir.mkdir(parents=True, exist_ok=True)
|
| 350 |
+
metadata_dir.mkdir(parents=True, exist_ok=True)
|
| 351 |
+
|
| 352 |
+
train_rows = [row for row in rows if row["split_bucket"] != 0]
|
| 353 |
+
test_rows = [row for row in rows if row["split_bucket"] == 0]
|
| 354 |
+
pq.write_table(pa.Table.from_pylist(train_rows, schema=SCHEMA), data_dir / "train-00000-of-00001.parquet", compression="zstd")
|
| 355 |
+
pq.write_table(pa.Table.from_pylist(test_rows, schema=SCHEMA), data_dir / "test-00000-of-00001.parquet", compression="zstd")
|
| 356 |
+
pq.write_table(pa.Table.from_pylist(rows, schema=SCHEMA), metadata_dir / "source_files.parquet", compression="zstd")
|
| 357 |
+
|
| 358 |
+
role_counts = Counter(row["role"] for row in rows)
|
| 359 |
+
source_family_counts = Counter(row["source_family"] for row in rows if row["source_family"])
|
| 360 |
+
sequence_sources = {
|
| 361 |
+
source_slug: {"shards": source_sequence_counts[source_slug], "bytes": source_sequence_bytes[source_slug]}
|
| 362 |
+
for source_slug in sorted(source_sequence_counts)
|
| 363 |
+
}
|
| 364 |
+
logical_table_sources = {
|
| 365 |
+
path: {"bytes": int(size), "has_split_parts": path in split_manifests}
|
| 366 |
+
for path, size in sorted(logical_tables.items())
|
| 367 |
+
}
|
| 368 |
+
summary = {
|
| 369 |
+
"source": repo_id,
|
| 370 |
+
"source_sha": info.sha,
|
| 371 |
+
"viewer_table_scope": "file/shard index",
|
| 372 |
+
"data_format": "parquet",
|
| 373 |
+
"dataset_id": DATASET_ID,
|
| 374 |
+
"index_rows": len(rows),
|
| 375 |
+
"splits": {"train": len(train_rows), "test": len(test_rows)},
|
| 376 |
+
"split_strategy": "default file index uses deterministic sha256(file_id) % 10; bucket 0 is test, buckets 1-9 are train",
|
| 377 |
+
"repo_file_count": repo_file_count,
|
| 378 |
+
"repo_total_bytes": repo_total_bytes,
|
| 379 |
+
"sequence_source_count": len(sequence_sources),
|
| 380 |
+
"sequence_shard_count_total": sequence_shard_count_total,
|
| 381 |
+
"sequence_shard_bytes_total": sequence_shard_bytes_total,
|
| 382 |
+
"table_repo_file_count_total": table_repo_file_count_total,
|
| 383 |
+
"table_repo_bytes_total": table_repo_bytes_total,
|
| 384 |
+
"logical_table_count_total": logical_table_count_total,
|
| 385 |
+
"logical_table_bytes_total": logical_table_bytes_total,
|
| 386 |
+
"role_counts": dict(sorted(role_counts.items())),
|
| 387 |
+
"source_family_index_counts": dict(sorted(source_family_counts.items())),
|
| 388 |
+
"sequence_sources": sequence_sources,
|
| 389 |
+
"logical_table_sources": logical_table_sources,
|
| 390 |
+
"columns": INDEX_COLUMNS,
|
| 391 |
+
}
|
| 392 |
+
(out_dir / "_MANIFEST.json").write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8")
|
| 393 |
+
(out_dir / "dataset_summary.json").write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8")
|
| 394 |
+
return summary
|
| 395 |
+
|
| 396 |
+
|
| 397 |
+
def main() -> None:
|
| 398 |
+
parser = argparse.ArgumentParser()
|
| 399 |
+
parser.add_argument("--repo-id", default="LiteFold/Mgnify")
|
| 400 |
+
parser.add_argument("--raw-dir", type=Path, default=Path("LiteFold_Mgnify_raw"))
|
| 401 |
+
parser.add_argument("--out-dir", type=Path, default=Path("LiteFold_Mgnify_processed"))
|
| 402 |
+
args = parser.parse_args()
|
| 403 |
+
summary = build_dataset(args.repo_id, args.raw_dir, args.out_dir)
|
| 404 |
+
print(json.dumps(summary, indent=2))
|
| 405 |
+
|
| 406 |
+
|
| 407 |
+
if __name__ == "__main__":
|
| 408 |
+
main()
|