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example_load.py

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+#!/usr/bin/env python3
+"""
+Example script for loading preprocessed LibriBrain MEG data.
+
+This script demonstrates how to load and use the preprocessed MEG data
+with the pnpl library for training machine learning models.
+"""
+
+import numpy as np
+from pnpl.datasets import GroupedDataset
+from torch.utils.data import DataLoader
+import torch
+
+def load_preprocessed_data(grouping_level=100, load_to_memory=True):
+    """
+    Load preprocessed LibriBrain MEG data.
+
+    Args:
+        grouping_level: Number of samples grouped together (5, 10, 15, 20, 25, 30, 45, 50, 55, 60, or 100)
+        load_to_memory: If True, loads entire dataset to memory for faster access
+
+    Returns:
+        Tuple of (train_dataset, val_dataset, test_dataset)
+    """
+    base_path = f"data/grouped_{grouping_level}"
+
+    # Load training data
+    train_dataset = GroupedDataset(
+        preprocessed_path=f"{base_path}/train_grouped.h5",
+        load_to_memory=load_to_memory
+    )
+
+    # Load validation data
+    val_dataset = GroupedDataset(
+        preprocessed_path=f"{base_path}/validation_grouped.h5",
+        load_to_memory=load_to_memory
+    )
+
+    # Load test data
+    test_dataset = GroupedDataset(
+        preprocessed_path=f"{base_path}/test_grouped.h5",
+        load_to_memory=load_to_memory
+    )
+
+    return train_dataset, val_dataset, test_dataset
+
+
+def main():
+    # Example 1: Load data with 100-sample grouping
+    print("Loading preprocessed MEG data with 100-sample grouping...")
+    train_dataset, val_dataset, test_dataset = load_preprocessed_data(
+        grouping_level=100,
+        load_to_memory=True
+    )
+
+    print(f"Dataset sizes:")
+    print(f"  Train: {len(train_dataset)} samples")
+    print(f"  Validation: {len(val_dataset)} samples")
+    print(f"  Test: {len(test_dataset)} samples")
+
+    # Example 2: Get a single sample
+    sample = train_dataset[0]
+    meg_data = sample['meg']  # MEG signals: (306 channels, time_points)
+    phoneme_label = sample['phoneme']  # Phoneme class index
+
+    print(f"\nSample structure:")
+    print(f"  MEG shape: {meg_data.shape}")
+    print(f"  Phoneme label: {phoneme_label}")
+
+    # Example 3: Use with PyTorch DataLoader
+    print("\nCreating PyTorch DataLoader...")
+    dataloader = DataLoader(
+        train_dataset,
+        batch_size=32,
+        shuffle=True,
+        num_workers=4,
+        pin_memory=True  # For GPU training
+    )
+
+    # Example 4: Iterate through a batch
+    print("\nExample batch:")
+    for batch_idx, batch in enumerate(dataloader):
+        meg_batch = batch['meg']  # Shape: (batch_size, 306, time_points)
+        phoneme_batch = batch['phoneme']  # Shape: (batch_size,)
+
+        print(f"  Batch {batch_idx}:")
+        print(f"    MEG batch shape: {meg_batch.shape}")
+        print(f"    Phoneme batch shape: {phoneme_batch.shape}")
+
+        if batch_idx >= 2:  # Show only first 3 batches
+            break
+
+    # Example 5: Different grouping levels for different speed/accuracy trade-offs
+    print("\n" + "="*50)
+    print("Available grouping levels:")
+    print("  - grouped_5:   Highest fidelity, largest files")
+    print("  - grouped_10:  High fidelity")
+    print("  - grouped_20:  Good balance")
+    print("  - grouped_50:  Faster loading, moderate averaging")
+    print("  - grouped_100: Fastest loading, most averaging")
+    print("\nChoose based on your requirements:")
+    print("  - For maximum accuracy: use lower grouping (5-20)")
+    print("  - For faster experimentation: use higher grouping (50-100)")
+    print("  - For production models: start with high grouping for prototyping,")
+    print("    then switch to lower grouping for final training")
+
+
+if __name__ == "__main__":
+    main()