example_usage.py

"""
Example usage of the DLSCA Test Dataset with streaming zarr support.

This example demonstrates how to use the custom dataset for both local
development and production with streaming capabilities.

Note: You may see "Repo card metadata block was not found" warnings - 
these are harmless and expected for local datasets without published cards.
"""

import os
from test import TestDataset, TestDownloadManager
import numpy as np

def example_local_usage():
    """Example of using the dataset locally for development."""
    print("=== Local Development Usage ===")
    
    # Load dataset locally
    dataset = TestDataset()
    dataset.download_and_prepare()
    dataset_dict = dataset.as_dataset(split="train")
    
    print(f"Dataset size: {len(dataset_dict)}")
    print(f"Features: {list(dataset_dict.features.keys())}")
    
    # Access a few examples
    for i in range(3):
        example = dataset_dict[i]
        print(f"Example {i}: labels={example['labels'][:2]}..., traces_len={len(example['traces'])}")
    
    return dataset_dict

def example_streaming_usage():
    """Example of using the dataset with streaming zarr support."""
    print("\n=== Streaming Usage ===")
    
    # Initialize custom download manager
    dl_manager = TestDownloadManager(dataset_name="dlsca_test")
    
    # Convert traces to zarr format and cache
    traces_path = os.path.join(os.path.dirname(__file__), "data", "traces.npy")
    zarr_zip_path = dl_manager.download_zarr_chunks(traces_path, chunk_size=100)
    print(f"Zarr chunks cached at: {zarr_zip_path}")
    
    # Load dataset with streaming
    dataset = TestDataset()
    
    # Test streaming access to zarr data
    zarr_array = dataset._load_zarr_from_zip(zarr_zip_path)
    print(f"Zarr array shape: {zarr_array.shape}")
    print(f"Zarr array chunks: {zarr_array.chunks}")
    
    # Demonstrate chunk-based access (simulating streaming)
    chunk_size = 100
    num_chunks = (zarr_array.shape[0] + chunk_size - 1) // chunk_size
    print(f"Total chunks: {num_chunks}")
    
    # Access data by chunks (this would be efficient for large datasets)
    for chunk_idx in range(min(3, num_chunks)):  # Just show first 3 chunks
        start_idx = chunk_idx * chunk_size
        end_idx = min(start_idx + chunk_size, zarr_array.shape[0])
        chunk_data = zarr_array[start_idx:end_idx]
        print(f"Chunk {chunk_idx}: shape={chunk_data.shape}, range=[{start_idx}:{end_idx}]")
    
    return zarr_array

def example_chunk_selection():
    """Example of selecting specific chunks for training."""
    print("\n=== Chunk Selection Example ===")
    
    dl_manager = TestDownloadManager()
    traces_path = os.path.join(os.path.dirname(__file__), "data", "traces.npy")
    zarr_zip_path = dl_manager.download_zarr_chunks(traces_path, chunk_size=100)
    
    dataset = TestDataset()
    zarr_array = dataset._load_zarr_from_zip(zarr_zip_path)
    labels = np.load(os.path.join(os.path.dirname(__file__), "data", "labels.npy"))
    
    # Example: Select specific samples for training (e.g., samples 200-299)
    selected_range = slice(200, 300)
    selected_traces = zarr_array[selected_range]
    selected_labels = labels[selected_range]
    
    print(f"Selected traces shape: {selected_traces.shape}")
    print(f"Selected labels shape: {selected_labels.shape}")
    print(f"Sample labels: {selected_labels[:3]}")
    
    return selected_traces, selected_labels

def benchmark_access_patterns():
    """Benchmark different access patterns."""
    print("\n=== Access Pattern Benchmark ===")
    
    import time
    
    # Load both numpy and zarr versions
    traces_np = np.load(os.path.join(os.path.dirname(__file__), "data", "traces.npy"))
    
    dl_manager = TestDownloadManager()
    traces_path = os.path.join(os.path.dirname(__file__), "data", "traces.npy")
    zarr_zip_path = dl_manager.download_zarr_chunks(traces_path, chunk_size=100)
    dataset = TestDataset()
    traces_zarr = dataset._load_zarr_from_zip(zarr_zip_path)
    
    # Benchmark sequential access
    print("Sequential access (first 300 samples):")
    
    # NumPy
    start = time.time()
    np_data = traces_np[:300]
    np_time = time.time() - start
    print(f"  NumPy: {np_time:.4f}s")
    
    # Zarr
    start = time.time()
    zarr_data = traces_zarr[:300]
    zarr_time = time.time() - start
    print(f"  Zarr:  {zarr_time:.4f}s")
    
    # Verify same data
    print(f"  Data identical: {np.array_equal(np_data, zarr_data)}")
    
    # Benchmark random access
    print("\nRandom chunk access (3 chunks):")
    indices = [50, 250, 450]
    
    # NumPy
    start = time.time()
    for idx in indices:
        _ = traces_np[idx:idx+50]
    np_random_time = time.time() - start
    print(f"  NumPy: {np_random_time:.4f}s")
    
    # Zarr
    start = time.time()
    for idx in indices:
        _ = traces_zarr[idx:idx+50]
    zarr_random_time = time.time() - start
    print(f"  Zarr:  {zarr_random_time:.4f}s")

if __name__ == "__main__":
    # Run all examples
    local_dataset = example_local_usage()
    zarr_array = example_streaming_usage()
    selected_data = example_chunk_selection()
    benchmark_access_patterns()
    
    print("\n=== Summary ===")
    print("✅ Local dataset loading works")
    print("✅ Zarr conversion and streaming works")
    print("✅ Chunk selection works")
    print("✅ Access pattern benchmarking works")
    print("\nThe dataset is ready for use with Hugging Face Hub!")
    print("Next steps:")
    print("1. Push this dataset to Hugging Face Hub")
    print("2. Use datasets.load_dataset('DLSCA/test') to access it")
    print("3. The streaming will automatically use zarr chunks for large traces")