Generating and Visualizing Signal Data

This example demonstrates how to:

1. Generate synthetic signals with up to 3 Gaussian pulses.

2. Compute a Region of Interest (ROI) mask based on pulse positions.

3. Visualize signals with peak positions, amplitudes, and the ROI mask.

Imports

from DeepPeak.signals import SignalDatasetGenerator
from DeepPeak import kernel

Generate Synthetic Signal Dataset

We generate a dataset with NUM_PEAKS Gaussian pulses per signal. The peak amplitudes, positions, and widths are randomly chosen within specified ranges.

NUM_PEAKS = 3
SEQUENCE_LENGTH = 200
sample_count = 12

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

kernel = kernel.Lorentzian(
    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,
)

dataset.plot(number_of_columns=3, number_of_samples=9)

<Figure size 2400x900 with 9 Axes>