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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.")
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