"""
Quick start example demonstrating the simplified transivent API.

This shows the two main entry points:
1. detect() - for custom time-series data
2. detect_from_wfm() - for Wfm files with XML sidecars
"""

import numpy as np
from transivent import detect, detect_from_wfm, EventPlotter

print("=" * 60)
print("TRANSIVENT QUICK START")
print("=" * 60)

# ============================================================================
# EXAMPLE 1: Detect events in custom time-series data
# ============================================================================
print("\n[Example 1] Custom time-series data")
print("-" * 60)

# Generate synthetic data
np.random.seed(42)
duration = 0.05  # 50 ms
sampling_rate = 2_000_000  # 2 MHz
n_points = int(duration * sampling_rate)
t = np.linspace(0, duration, n_points)

# Background noise
x = np.random.randn(n_points) * 0.1

# Add synthetic negative spikes
spike_times = [0.01, 0.023, 0.037, 0.045]
spike_amplitudes = [-1.5, -2.0, -0.8, -1.2]
spike_width = 50

for spike_t, amp in zip(spike_times, spike_amplitudes):
    spike_idx = int(spike_t * sampling_rate)
    spike_start = max(0, spike_idx - spike_width // 2)
    spike_end = min(n_points, spike_idx + spike_width // 2)
    x[spike_start:spike_end] += amp * np.exp(
        -0.5 * ((np.arange(spike_start, spike_end) - spike_idx) / (spike_width / 4))**2
    )

print(f"Generated synthetic data: {n_points} points at {sampling_rate/1e6:.1f} MHz")

# Detect events
results = detect(
    t, x,
    name="Synthetic Data",
    detection_snr=3.0,
    min_event_keep_snr=6.0,
    signal_polarity=-1,  # Negative spikes
    save_plots=False,
)

events = results["events"]
print(f"✓ Found {len(events)} events")
print(f"✓ Noise level: {results['global_noise']:.3e}")

for i, (start, end) in enumerate(events):
    duration_us = (end - start) * 1e6
    print(f"    Event {i+1}: {start:.6f}s to {end:.6f}s ({duration_us:.2f} µs)")

# Access other results
print(f"✓ Results contain: {list(results.keys())}")

# ============================================================================
# EXAMPLE 2: Advanced - Build custom pipeline with building blocks
# ============================================================================
print("\n[Example 2] Custom pipeline with building blocks")
print("-" * 60)

from transivent.analysis import (
    calculate_initial_background,
    estimate_noise,
    detect_initial_events,
)

# Manual pipeline for advanced control
smooth_n = 101
bg_initial = calculate_initial_background(t, x, smooth_n, filter_type="gaussian")
global_noise = estimate_noise(x, bg_initial)
print(f"✓ Calculated background (smooth_n={smooth_n})")
print(f"✓ Estimated noise: {global_noise:.3e}")

# ============================================================================
# EXAMPLE 3: Visualize events
# ============================================================================
print("\n[Example 3] Visualizing events")
print("-" * 60)

if results["plot"] is not None and len(events) > 0:
    event_plotter = EventPlotter(
        results["plot"],
        events,
        bg_clean=results["bg_clean"],
        global_noise=results["global_noise"],
    )
    print(f"✓ Created event plotter for {len(events)} events")
    print(f"✓ Can call event_plotter.plot_events_grid(max_events=16)")
    print(f"✓ Can call event_plotter.save('path.png')")

# ============================================================================
# EXAMPLE 4: Using Wfm files (if you have them)
# ============================================================================
print("\n[Example 4] Wfm file format (for reference)")
print("-" * 60)
print("""
To analyze Wfm files with XML sidecars:

    results = detect_from_wfm(
        name="data.Wfm.bin",
        sampling_interval=5e-7,
        data_path="/path/to/data/",
        detection_snr=3.0,
    )
    
    events = results["events"]
    print(f"Found {len(events)} events")
""")

print("\n" + "=" * 60)
print("QUICK START COMPLETE")
print("=" * 60)
print("\nKey takeaways:")
print("  1. Use detect() for any time-series data")
print("  2. Use detect_from_wfm() for proprietary Wfm files")
print("  3. All results are returned as a dict")
print("  4. Access building blocks via transivent.analysis for custom pipelines")
print("\nFor more info, see the README or run the other examples.")