modrec-workflow/scripts/dataset_building/produce_dataset.py

import os, h5py, numpy as np
from typing import List
from utils.io import from_npy
from split_dataset import split, split_recording
from helpers.app_settings import DataSetConfig, get_app_settings

meta_dtype = np.dtype(
    [
        ("rec_id", "S256"),
        ("snippet_idx", np.int32),
        ("modulation", "S32"),
        ("snr", np.int32),
        ("beta", np.float32),
        ("sps", np.int32),
    ]
)

info_dtype = np.dtype(
    [
        ("num_records", np.int32),
        ("dataset_name", "S64"),  # up to 64‐byte UTF-8 strings
        ("creator", "S64"),
    ]
)


def write_hdf5_file(records: List, output_path: str, dataset_name: str = "data") -> str:
    """
    Writes a list of records to an HDF5 file.
    Parameters:
        records (list): List of records to be written to the file
        output_path (str): Path to the output HDF5 file
        dataset_name (str): Name of the dataset in the HDF5 file (default: "data")
    Returns:
        str: Path to the created HDF5 file
    """
    meta_arr = np.empty(len(records), dtype=meta_dtype)
    for i, (_, md) in enumerate(records):
        meta_arr[i] = (
            md["rec_id"].encode("utf-8"),
            md["snippet_idx"],
            md["modulation"].encode("utf-8"),
            int(md["snr"]),
            float(md["beta"]),
            int(md["sps"]),
        )

    first_rec, _ = records[0]  # records[0] is a tuple of (data, md)
    sample = first_rec
    shape, dtype = sample.shape, sample.dtype

    with h5py.File(output_path, "w") as hf:
        data_arr = np.stack([rec[0] for rec in records])
        dset = hf.create_dataset(dataset_name, data=data_arr, compression="gzip")

        mg = hf.create_group("metadata")
        mg.create_dataset("metadata", data=meta_arr, compression="gzip")

        print(dset.shape, f"snippets created in {dataset_name}")

        info_arr = np.array(
            [
                (
                    len(records),
                    dataset_name.encode("utf-8"),
                    b"generate_dataset.py",  # already bytes
                )
            ],
            dtype=info_dtype,
        )

        mg.create_dataset("dataset_info", data=info_arr)

    return output_path


def complex_to_channel(data: np.ndarray) -> np.ndarray:
    """
    Converts complex-valued IQ data of shape (1, N) to a 2-channel real array of shape (2, N).

    Parameters:
        data (np.ndarray): Complex-valued array of shape (1, N)

    Returns:
        np.ndarray: Real-valued array of shape (2, N) with separate real and imaginary channels
    """
    assert np.iscomplexobj(data)  # check if the data is in the form a+bi
    real = np.real(data[0])  # (N,)
    imag = np.imag(data[0])  # (N,)
    stacked = np.stack([real, imag], axis=0)  # shape (2, N)
    return stacked.astype(np.float32)


def generate_datasets(cfg: DataSetConfig) -> tuple:
    """
    Generates a dataset from a folder of .npy files and saves it to an HDF5 file

    Parameters:
        cfg (DataSetConfig): Dataset configuration loaded from app.yaml

    Returns:
        dset (h5py.Dataset): The created dataset object
    """

    parent = os.path.dirname("data/dataset")
    if not parent:
        os.makedirs("data/dataset", exist_ok=True)

    # we assume the recordings are in .npy format
    files = os.listdir("data/recordings")
    if not files:
        raise ValueError("No files found in the specified directory.")

    records = []
    for fname in files:
        rec = from_npy(os.path.join("data/recordings", fname))

        data = rec.data  # here data is a numpy array with the shape (1, N)

        data = complex_to_channel(data)  # convert to 2-channel real array

        md = rec.metadata  # pull metadata from the recording
        md.setdefault("recid", len(records))
        records.append((data, md))

    # split each recording into <num_slices> snippets each

    records = split_recording(records, cfg.num_slices)

    train_records, val_records = split(records, cfg.train_split, cfg.seed)

    train_path = os.path.join("data/dataset", "train.h5")
    val_path = os.path.join("data/dataset", "val.h5")

    write_hdf5_file(train_records, train_path, "training_data")
    write_hdf5_file(val_records, val_path, "validation_data")

    return train_path, val_path


def main():
    settings = get_app_settings()
    dataset_cfg = settings.dataset

    print("📦 Generating training and validation datasets...")
    print(f"   ➤ Slicing each recording into {dataset_cfg.num_slices} snippets")
    print(f"   ➤ Train/Val split: {int(dataset_cfg.train_split * 100)}% / {int((1 - dataset_cfg.train_split) * 100)}%")
    print(f"   ➤ Output directory: data/dataset\n")

    train_path, val_path = generate_datasets(dataset_cfg)

    # Count number of samples in each file
    with h5py.File(train_path, "r") as f:
        num_train = f["training_data"].shape[0]
    with h5py.File(val_path, "r") as f:
        num_val = f["validation_data"].shape[0]

    print("✅ Dataset generation complete!")
    print(f"   🔹 Training samples saved to: {train_path} ({num_train} samples)")
    print(f"   🔸 Validation samples saved to: {val_path} ({num_val} samples)")



if __name__ == "__main__":
    main()