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"""
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.")
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