Non-Maximum Suppression for Gaussian Pulse Detection

This example demonstrates the use of the NonMaximumSuppression class to detect Gaussian pulses in a one-dimensional signal. It generates a synthetic dataset of Gaussian pulses, applies the non-maximum suppression algorithm, and plots the results.

from DeepPeak.algorithms import NonMaximumSuppression
from DeepPeak.signals import SignalDatasetGenerator
from DeepPeak.kernel import Gaussian

NUM_PEAKS = 3
SEQUENCE_LENGTH = 400

generator = SignalDatasetGenerator(n_samples=6, sequence_length=SEQUENCE_LENGTH)

kernel = Gaussian(
    amplitude=(10, 300),  # Amplitude range
    position=(0.3, 0.7),  # Peak position range
    width=0.02,
)

dataset = generator.generate(
    kernel=kernel,
    n_peaks=(3, 3),
    noise_std=0,  # Add some noise
    categorical_peak_count=False,
)
print(dataset)

dataset.plot()

DataSet(signals, amplitudes, positions, widths, labels, x_values, num_peaks, region_of_interest)

<Figure size 800x900 with 3 Axes>

Configure and run the detector

peak_locator = NonMaximumSuppression(
    gaussian_sigma=kernel.width,
    threshold="auto",
    maximum_number_of_pulses=5,
    kernel_truncation_radius_in_sigmas=5,
)


result = peak_locator.run(time_samples=dataset.x_values, signal=dataset.signals[0])

batch = peak_locator.run_batch(time_samples=dataset.x_values, signal=dataset.signals)

Plot the results

batch.plot_histogram_counts()

<Figure size 500x340 with 1 Axes>

Plot the results

batch.plot(ncols=3, max_plots=6, true_position=dataset.positions, show_kernel=True)

<Figure size 1950x560 with 6 Axes>