# NAIAD Dataset Package

This package contains the train/valid/test CSV index files used immediately before NAIAD training, plus every referenced `.cif` source structure.

## Contents

- `data/*.csv`: model input index files. `structure_path` has been rewritten to package-relative paths such as `structures/101d.cif`.
- `structures/*.cif`: all 3725 unique source structures referenced by the packaged CSV files.
- `splits/*.json`: released split ID lists used to derive train/valid/test sets.
- `scripts/`: preprocessing, filtering, CSV-generation, and split/preparation scripts from the training repository.
- `configs/`: NAIAD training config.

The `.cif` files were reconstructed locally from the official wwPDB/RCSB mmCIF mirrors using the PDB IDs in `manifest.csv`.
Use `scripts/download_structures_from_manifest.py` to reproduce this step.

The parser still expects the normal NAIAD chemical component cache (`ligands.json.gz` and `elements.txt`) under the repository's `data/datasets/rcsb_cif/`, or via `NAIAD_RCSB_CIF_DIR`.

For the reported NAIAD training-data release, use `data/train.csv` as the training index, `data/valid.csv` as validation, and `data/test.csv` as the held-out test index. 

To use after extraction, point `DF_PATH_TRAIN` and `DF_PATH_VALID` to `data/train.csv` and `data/valid.csv` from this package, or copy the package contents under the repository root so the relative `structure_path` values resolve.

## Reproducing The Split CSVs

There are two different workflows in `scripts/`.

### Exact packaged train/valid/test CSVs

Use this route to reproduce the three split CSVs bundled in `data/` from the released split ID files and the packaged CIF files:

```bash
python scripts/generate_training_csv_from_splits.py \
  --mmcif_dir structures \
  --splits_dir splits \
  --output_dir reproduced_data \
  --split_type design
```

Expected result when run against this package:

- `reproduced_data/train.csv`: 3096 rows.
- `reproduced_data/valid.csv`: 308 rows.
- `reproduced_data/test.csv`: 321 rows.

The row IDs match the shipped `data/train.csv`, `data/valid.csv`, and `data/test.csv`. This is because the script reads the full split ID lists and keeps only IDs whose CIF file is present under `structures/`. 
The regenerated CSVs will contain absolute `structure_path` values because that script calls `os.path.abspath`; the shipped CSVs use package-relative paths such as `structures/101d.cif` for portability.

`generate_training_csv_from_splits.py` requires only normal tabular Python dependencies (`pandas`, `tqdm`) and does not import the NAIAD model/parser code.

### Full rescan/filter/preprocess/split from a mmCIF mirror

Use this route only if you want to create a new dataset split from an external mmCIF mirror rather than reproduce the packaged split:

```bash
python scripts/prepare_diffusion_dataset_full.py scan \
  --mmcif_dir /path/to/mmcif_files \
  --output_dir new_dataset \
  --num_workers 16 \
  --require_na

python scripts/prepare_diffusion_dataset_full.py preprocess \
  --output_dir new_dataset \
  --num_workers 16

# Optional, if CD-HIT is installed.
python scripts/prepare_diffusion_dataset_full.py cluster \
  --output_dir new_dataset \
  --cdhit_path /path/to/cdhit

python scripts/prepare_diffusion_dataset_full.py split \
  --output_dir new_dataset \
  --valid_fraction 0.1 \
  --test_fraction 0.1 \
  --use_clustering
```

Equivalent all-in-one form:

```bash
python scripts/prepare_diffusion_dataset_full.py all \
  --mmcif_dir /path/to/mmcif_files \
  --output_dir new_dataset \
  --num_workers 16 \
  --require_na \
  --valid_fraction 0.1 \
  --test_fraction 0.1
```

This full workflow performs scanning, quality filtering, per-structure preprocessing, optional CD-HIT sequence clustering, then writes `train.csv`, `valid.csv`, and optionally `test.csv`. 
It is not the command used to reproduce the fixed manuscript split files in this package.

`prepare_diffusion_dataset_full.py` imports NAIAD parser/data modules (`cifutils.py`, `pdbutils.py`, `na_data_utils.py`) and expects the chemical component cache (`ligands.json.gz`, `elements.txt`). Run it from a NAIAD source checkout or set `PYTHONPATH` so those modules are visible, and set `NAIAD_RCSB_CIF_DIR` if the cache is not under the repository's `data/datasets/rcsb_cif/`.

### Optional preprocessing of an existing CSV

`preprocess_dataset.py` and `preprocess_dataset.sh` do not create train/valid/test splits. They consume an existing CSV, such as `data/train.csv`, and write per-structure sequence and NumPy preprocessing artifacts:

```bash
python scripts/preprocess_dataset.py \
  data/train.csv \
  preprocessed/train \
  1 \
  0
```

The last two arguments are `modulo` and `remainder`, used for array-job sharding. 
For example, an HPC array with 100 tasks would run each shard with `modulo=100` and `remainder` equal to the task index. `preprocess_dataset.sh` is an example SLURM/Apptainer wrapper for that sharded mode and may need local cluster/container paths edited before use.