evlib 0.8.7

#!/usr/bin/env python3
"""
Performance verification script for README metrics.

This script validates the performance claims made in the README.md file
using available test data and generates performance visualization plots.

Requirements:
    pip install matplotlib psutil
"""

import evlib
import polars as pl
import time
import os
import psutil
from pathlib import Path
import matplotlib.pyplot as plt


def get_memory_usage_mb():
    """Get current memory usage in MB"""
    process = psutil.Process(os.getpid())
    return process.memory_info().rss / 1024 / 1024


def benchmark_loading_speed(file_path):
    """Benchmark loading speed as claimed in README"""
    print(f"\nSTATS: Benchmarking Loading Speed: {file_path}")

    if not Path(file_path).exists():
        print(f"FAIL: File not found: {file_path}")
        return None

    start_time = time.time()
    lf = evlib.load_events(file_path)
    df = lf.collect()
    load_time = time.time() - start_time

    events_per_second = len(df) / load_time

    print(f"PASS: Loaded {len(df):,} events in {load_time:.2f}s")
    print(f"FAST: Speed: {events_per_second:,.0f} events/s")

    # Check against README claim of 600k+ events/s
    if events_per_second >= 600000:
        print("TARGET: MEETS README CLAIM: ≥600k events/s")
    else:
        print(f"WARNING: BELOW README CLAIM: {events_per_second:,.0f} < 600k events/s")

    return events_per_second, len(df), load_time


def benchmark_filter_speed(file_path):
    """Benchmark filter speed as claimed in README"""
    print(f"\nANALYSIS: Benchmarking Filter Speed: {file_path}")

    if not Path(file_path).exists():
        print(f"FAIL: File not found: {file_path}")
        return None

    lf = evlib.load_events(file_path)
    df = lf.collect()

    # Test filtering speed
    start_time = time.time()
    filtered = lf.filter(
        (pl.col("polarity") == 1) & (pl.col("x") > 50) & (pl.col("x") < 250)
    ).collect()
    filter_time = time.time() - start_time

    events_per_second = len(df) / filter_time

    print(
        f"PASS: Filtered {len(df):,} events to {len(filtered):,} in {filter_time:.4f}s"
    )
    print(f"FAST: Filter speed: {events_per_second:,.0f} events/s")

    # Check against README claim of 400M+ events/s
    if events_per_second >= 400_000_000:
        print("TARGET: MEETS README CLAIM: ≥400M events/s")
    elif events_per_second >= 100_000_000:
        print(f"GOOD PERFORMANCE: {events_per_second:,.0f} events/s (≥100M)")
    else:
        print(f"WARNING: BELOW EXPECTED: {events_per_second:,.0f} < 100M events/s")

    return events_per_second, len(df), filter_time


def benchmark_memory_efficiency(file_path):
    """Benchmark memory efficiency as claimed in README"""
    print(f"\nMEMORY: Benchmarking Memory Efficiency: {file_path}")

    if not Path(file_path).exists():
        print(f"FAIL: File not found: {file_path}")
        return None

    # Force garbage collection to get clean baseline
    import gc

    gc.collect()

    initial_memory = get_memory_usage_mb()

    lf = evlib.load_events(file_path)
    df = lf.collect()

    peak_memory = get_memory_usage_mb()
    memory_used = peak_memory - initial_memory

    bytes_per_event = (memory_used * 1024 * 1024) / len(df)

    print(f"PASS: Loaded {len(df):,} events")
    print(f"MEMORY: Memory used: {memory_used:.1f} MB")
    print(f"STATS: Memory per event: {bytes_per_event:.1f} bytes")

    # Check against README claim of ~110 bytes/event
    if bytes_per_event <= 110:
        print("TARGET: MEETS README CLAIM: ≤110 bytes/event")
    elif bytes_per_event <= 150:
        print(f"GOOD EFFICIENCY: {bytes_per_event:.1f} bytes/event (≤150)")
    else:
        print(f"WARNING: HIGHER THAN CLAIM: {bytes_per_event:.1f} > 110 bytes/event")

    return bytes_per_event, len(df), memory_used


def test_readme_examples():
    """Test the code examples from README"""
    print("\nTesting README Code Examples")

    file_path = "data/slider_depth/events.txt"
    if not Path(file_path).exists():
        print(f"FAIL: Test file not found: {file_path}")
        return

    try:
        # Test basic loading
        lf = evlib.load_events(file_path)
        df = lf.collect()
        print(f"PASS: Basic loading: {len(df):,} events")

        # Test filtering
        filtered = lf.filter(
            (pl.col("timestamp").dt.total_microseconds() / 1_000_000 > 1.0)
            & (pl.col("polarity") == 1)
        ).collect()
        print(f"PASS: Filtering: {len(filtered):,} events")

        # Test analysis
        stats = (
            lf.group_by("polarity")
            .agg(
                [
                    pl.len().alias("count"),
                    pl.col("x").mean().alias("mean_x"),
                    pl.col("y").mean().alias("mean_y"),
                ]
            )
            .collect()
        )
        print(f"PASS: Analysis: {len(stats)} polarity groups")

        # Test format detection
        format_info = evlib.detect_format(file_path)
        print(f"PASS: Format detection: {format_info[0]}")

    except Exception as e:
        print(f"FAIL: Error in README examples: {e}")


def create_performance_plot(results):
    """Create and save performance visualization plot"""
    if not results:
        print("No results to plot")
        return

    # Extract data for plotting
    file_names = []
    loading_speeds = []
    filter_speeds = []
    memory_efficiency = []
    event_counts = []

    for key, value in results.items():
        if "_loading" in key:
            file_name = key.replace("_loading", "").split("/")[-1]
            file_names.append(file_name)
            loading_speeds.append(
                value[0] / 1_000_000
            )  # Convert to millions of events/s
            event_counts.append(value[1] / 1_000_000)  # Convert to millions of events
        elif "_filtering" in key:
            filter_speeds.append(
                value[0] / 1_000_000
            )  # Convert to millions of events/s
        elif "_memory" in key:
            memory_efficiency.append(value[0])  # bytes per event

    if not file_names:
        print("No data to plot")
        return

    # Create figure with subplots
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10))
    fig.suptitle("evlib Performance Benchmarks", fontsize=16, fontweight="bold")

    # Colors for consistency
    colors = ["#2E86AB", "#A23B72", "#F18F01"]

    # 1. Loading Speed
    bars1 = ax1.bar(range(len(file_names)), loading_speeds, color=colors[0], alpha=0.8)
    ax1.set_title("Loading Speed", fontweight="bold")
    ax1.set_ylabel("Million Events/Second")
    ax1.set_xticks(range(len(file_names)))

    # Create clear format labels
    format_labels = []
    for name in file_names:
        if ".txt" in name:
            format_labels.append("Text Format")
        elif ".h5" in name or "_td" in name:
            format_labels.append("HDF5 Format")
        elif ".raw" in name:
            format_labels.append("RAW Binary")
        else:
            format_labels.append(name)

    ax1.set_xticklabels(format_labels, rotation=45, ha="right")
    ax1.grid(True, alpha=0.3)

    # Add value labels on bars
    for bar, speed in zip(bars1, loading_speeds):
        height = bar.get_height()
        ax1.text(
            bar.get_x() + bar.get_width() / 2.0,
            height + 0.05,
            f"{speed:.1f}M",
            ha="center",
            va="bottom",
            fontweight="bold",
        )

    # 2. Filter Speed
    if filter_speeds:
        bars2 = ax2.bar(
            range(len(file_names)), filter_speeds, color=colors[1], alpha=0.8
        )
        ax2.set_title("Filter Speed", fontweight="bold")
        ax2.set_ylabel("Million Events/Second")
        ax2.set_xticks(range(len(file_names)))
        ax2.set_xticklabels(format_labels, rotation=45, ha="right")
        ax2.grid(True, alpha=0.3)

        # Add value labels on bars
        for bar, speed in zip(bars2, filter_speeds):
            height = bar.get_height()
            ax2.text(
                bar.get_x() + bar.get_width() / 2.0,
                height + 5,
                f"{speed:.0f}M",
                ha="center",
                va="bottom",
                fontweight="bold",
            )

    # 3. Memory Efficiency
    if memory_efficiency:
        bars3 = ax3.bar(
            range(len(file_names)), memory_efficiency, color=colors[2], alpha=0.8
        )
        ax3.set_title("Memory Efficiency", fontweight="bold")
        ax3.set_ylabel("Bytes per Event")
        ax3.set_xticks(range(len(file_names)))
        ax3.set_xticklabels(format_labels, rotation=45, ha="right")
        ax3.grid(True, alpha=0.3)

        # Add value labels on bars
        for bar, memory in zip(bars3, memory_efficiency):
            height = bar.get_height()
            ax3.text(
                bar.get_x() + bar.get_width() / 2.0,
                height + 1,
                f"{memory:.1f}",
                ha="center",
                va="bottom",
                fontweight="bold",
            )

    # 4. Dataset Size vs Performance
    if event_counts and loading_speeds:
        ax4.scatter(
            event_counts,
            loading_speeds,
            s=100,
            c=colors[0],
            alpha=0.8,
            edgecolors="black",
        )
        ax4.set_title("Dataset Size vs Loading Performance", fontweight="bold")
        ax4.set_xlabel("Dataset Size (Million Events)")
        ax4.set_ylabel("Loading Speed (Million Events/Second)")
        ax4.grid(True, alpha=0.3)

        # Add labels for each point
        for i, label in enumerate(format_labels):
            ax4.annotate(
                label,
                (event_counts[i], loading_speeds[i]),
                xytext=(5, 5),
                textcoords="offset points",
                fontsize=9,
                ha="left",
            )

    # Adjust layout
    plt.tight_layout()

    # Save plot
    output_path = Path("docs/performance_benchmark.png")
    output_path.parent.mkdir(exist_ok=True)
    plt.savefig(output_path, dpi=300, bbox_inches="tight")
    print(f"\nPerformance plot saved to: {output_path}")

    # Also save to root for README
    root_path = Path("performance_benchmark.png")
    plt.savefig(root_path, dpi=300, bbox_inches="tight")
    print(f"Performance plot also saved to: {root_path}")

    plt.close()


def main():
    print("PERFORMANCE: README PERFORMANCE VERIFICATION")
    print("=" * 50)
    print("This script validates the performance claims in README.md")

    # Test files to benchmark
    test_files = [
        "data/slider_depth/events.txt",
        "data/eTram/h5/val_2/val_night_011_td.h5",
        "data/eTram/raw/val_2/val_night_011.raw",
    ]

    results = {}

    for test_file in test_files:
        if Path(test_file).exists():
            print(f"\n{'=' * 60}")
            print(f"TESTING: {test_file}")
            print(f"{'=' * 60}")

            # Benchmark loading speed
            load_result = benchmark_loading_speed(test_file)
            if load_result:
                results[f"{test_file}_loading"] = load_result

            # Benchmark filter speed
            filter_result = benchmark_filter_speed(test_file)
            if filter_result:
                results[f"{test_file}_filtering"] = filter_result

            # Benchmark memory efficiency
            memory_result = benchmark_memory_efficiency(test_file)
            if memory_result:
                results[f"{test_file}_memory"] = memory_result
        else:
            print(f"\nFAIL: Skipping {test_file} (not found)")

    # Test README examples
    test_readme_examples()

    # Summary
    print("\nTREND: PERFORMANCE SUMMARY")
    print("=" * 50)

    if results:
        loading_speeds = [r[0] for k, r in results.items() if "_loading" in k]
        filter_speeds = [r[0] for k, r in results.items() if "_filtering" in k]
        memory_efficiencies = [r[0] for k, r in results.items() if "_memory" in k]

        if loading_speeds:
            avg_loading = sum(loading_speeds) / len(loading_speeds)
            print(f"STATS: Average loading speed: {avg_loading:,.0f} events/s")
            print(
                f"TARGET: README claim: ≥600k events/s - {'PASS: MET' if avg_loading >= 600000 else 'FAIL: NOT MET'}"
            )

        if filter_speeds:
            avg_filtering = sum(filter_speeds) / len(filter_speeds)
            print(f"ANALYSIS: Average filter speed: {avg_filtering:,.0f} events/s")
            print(
                f"TARGET: README claim: ≥400M events/s - {'PASS: MET' if avg_filtering >= 400_000_000 else 'FAIL: NOT MET'}"
            )

        if memory_efficiencies:
            avg_memory = sum(memory_efficiencies) / len(memory_efficiencies)
            print(f"MEMORY: Average memory efficiency: {avg_memory:.1f} bytes/event")
            print(
                f"TARGET: README claim: ~110 bytes/event - {'PASS: MET' if avg_memory <= 110 else 'FAIL: NOT MET'}"
            )
    else:
        print("FAIL: No test files found for benchmarking")

    print("\nVerification complete!")

    # Generate performance visualization
    create_performance_plot(results)


if __name__ == "__main__":
    main()