modrec-workflow/scripts/dataset_building/split_dataset.py

import random
from collections import defaultdict
from typing import List, Tuple, Dict
import numpy as np

def split(
    dataset: List[Tuple[np.ndarray, Dict[str, any]]],
    train_frac: float,
    seed: int,
    label_key: str = "modulation"
    ) -> Tuple[List[Tuple[np.ndarray, Dict[str, any]]], List[Tuple[np.ndarray, Dict[str, any]]]]:
    """
    Splits a dataset of modulated IQ signal recordings into training and validation subsets.

    Parameters:
        dataset (list): List of tuples where each tuple contains:
            - np.ndarray: 2xN real array (channels x samples)
            - dict: Metadata for the sample
        train_frac (float): Fraction of the dataset to use for training (default: 0.8)
        seed (int): Random seed for reproducibility (default: 42)
        label_key (str): Metadata key to group by during splitting (default: "modulation")

    Returns:
        tuple: Two lists of (np.ndarray, dict) pairs — (train_records, val_records)
    """
    rec_buckets = defaultdict(list)
    for data, md in dataset:
        rec_buckets[md["recid"]].append((data, md))

    rec_labels = {}  # store labels for each recording
    for rec_id, group in rec_buckets.items():
        label = group[0][1][label_key]
        if isinstance(label, bytes):  # if the label is a byte string
            label = label.decode("utf-8")
        rec_labels[rec_id] = label

    label_rec_ids = defaultdict(list)  # group rec_ids by label
    for rec_id, label in rec_labels.items():
        label_rec_ids[label].append(rec_id)

    random.seed(seed)
    train_recs, val_recs = set(), set()

    for label, rec_ids in label_rec_ids.items():
        random.shuffle(rec_ids)
        split_idx = int(len(rec_ids) * train_frac)
        train_recs.update(
            rec_ids[:split_idx]
        )  # pulls train_frac or rec_ids per label, guarantees all modulations are represented
        val_recs.update(rec_ids[split_idx:])

    # add the assigned recordings to the train and val datasets
    train_dataset, val_dataset = [], []
    for rec_id, group in rec_buckets.items():
        if rec_id in train_recs:
            train_dataset.extend(group)
        elif rec_id in val_recs:
            val_dataset.extend(group)

    return train_dataset, val_dataset


def split_recording(
    recording_list: List[Tuple[np.ndarray, Dict[str, any]]],
    num_snippets: int
    ) -> List[Tuple[np.ndarray, Dict[str, any]]]:
    """
    Splits each full recording into a specified number of smaller snippets.

    Each recording is a tuple of:
        - data (np.ndarray): A 2xN real-valued array representing I/Q signal data.
        - metadata (dict): Metadata describing the recording (e.g., modulation, SNR, etc.)

    The split is typically done along the time axis (axis=1), dividing each (2, N)
    array into `num_snippets` contiguous chunks of shape (2, N // num_snippets).

    Parameters:
        recording_list (List[Tuple[np.ndarray, dict]]): 
            List of (data, metadata) tuples to be split.
        num_snippets (int): 
            Number of equal-length segments to divide each recording into.

    Returns:
        List[Tuple[np.ndarray, dict]]: 
            A flat list containing all resulting (snippet, metadata) pairs.
    """
    snippet_list = []

    for data, md in recording_list:
        C, N = data.shape
        L = N // num_snippets
        for i in range(num_snippets):
            start = i * L
            end = (i + 1) * L
            snippet = data[:, start:end]

            # copy the metadata, adding a snippet index
            snippet_md = md.copy()
            snippet_md["snippet_idx"] = i
            snippet_list.append((snippet, snippet_md))
    return snippet_list