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diff --git a/examples/example_custom_data.py b/examples/example_custom_data.py
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+"""
+Example: Using transivent with custom data formats.
+
+This example demonstrates how to use transivent's building blocks
+directly with your own time-series data, without requiring any
+proprietary file formats.
+"""
+
+import numpy as np
+from transivent import (
+    calculate_initial_background,
+    calculate_clean_background,
+    detect_initial_events,
+    detect_final_events,
+    merge_overlapping_events,
+    estimate_noise,
+    analyze_thresholds,
+    create_oscilloscope_plot,
+    EventPlotter,
+)
+
+# Generate synthetic data (replace with your actual data loading)
+print("Generating synthetic data...")
+np.random.seed(42)
+
+# Time array (must be in seconds)
+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)
+
+# Signal with some spikes
+x = np.random.randn(n_points) * 0.1  # Background noise
+
+# Add some synthetic spikes
+spike_times = [0.01, 0.023, 0.037, 0.045]
+spike_amplitudes = [-1.5, -2.0, -0.8, -1.2]  # Negative spikes
+spike_width = 50  # samples
+
+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"Data shape: {t.shape}, Sampling interval: {t[1] - t[0]:.2e} s")
+
+# Analysis parameters
+sampling_interval = t[1] - t[0]
+smooth_n = 101  # Smoothing window in samples
+detection_snr = 3.0
+min_event_keep_snr = 5.0
+signal_polarity = -1  # Negative spikes
+min_event_n = 10  # Minimum event length in samples
+widen_frac = 0.5
+
+# Step 1: Calculate initial background
+print("\nStep 1: Calculating initial background...")
+bg_initial = calculate_initial_background(t, x, smooth_n, filter_type="gaussian")
+
+# Step 2: Estimate noise
+print("Step 2: Estimating noise level...")
+global_noise = estimate_noise(x, bg_initial)
+print(f"Estimated noise: {global_noise:.3f}")
+
+# Step 3: Initial event detection
+print("\nStep 3: Initial event detection...")
+events_initial = detect_initial_events(
+    t, x, bg_initial, global_noise, detection_snr, 
+    min_event_keep_snr, widen_frac=widen_frac, signal_polarity=signal_polarity,
+    min_event_n=min_event_n
+)
+print(f"Found {len(events_initial)} initial events")
+
+# Step 4: Calculate clean background (masking events)
+print("\nStep 4: Calculating clean background...")
+bg_clean = calculate_clean_background(
+    t, x, events_initial, smooth_n, bg_initial, filter_type="gaussian"
+)
+
+# Step 5: Final event detection with clean background
+print("\nStep 5: Final event detection...")
+events = detect_final_events(
+    t, x, bg_clean, global_noise, detection_snr,
+    min_event_keep_snr, widen_frac=widen_frac, signal_polarity=signal_polarity,
+    min_event_n=min_event_n
+)
+
+# Step 6: Merge any overlapping events
+events = merge_overlapping_events(events)
+print(f"Final event count: {len(events)}")
+
+# Print event details
+print("\nDetected events:")
+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: {duration_us:.2f} µs)")
+
+# Step 7: Visualization
+print("\nStep 7: Creating visualizations...")
+
+# Analyze thresholds for plotting
+detection_threshold, keep_threshold = analyze_thresholds(
+    x, bg_clean, global_noise, detection_snr, min_event_keep_snr, signal_polarity
+)
+
+# Create main plot
+plot = create_oscilloscope_plot(
+    t, x, bg_initial, bg_clean, events,
+    detection_threshold, keep_threshold,
+    name="Custom Data Example", detection_snr=detection_snr,
+    min_event_keep_snr=min_event_keep_snr,
+    max_plot_points=10000, envelope_mode_limit=10e-3, smooth_n=smooth_n,
+    global_noise=global_noise
+)
+
+# Create event plots
+if len(events) > 0:
+    event_plotter = EventPlotter(
+        plot, events, bg_clean=bg_clean, global_noise=global_noise
+    )
+    event_plotter.plot_events_grid(max_events=16)
+    event_plotter.save("custom_data_events.png")
+
+# Save plots
+plot.save("custom_data_trace.png")
+print("\nPlots saved:")
+print("  - custom_data_trace.png: Full trace with events")
+if len(events) > 0:
+    print("  - custom_data_events.png: Individual event plots")
+
+# Show plots (uncomment to display)
+# import matplotlib.pyplot as plt
+# plt.show()
+
+print("\nDone! The transivent building blocks work with any time-series data.")
+\ No newline at end of file