SignalForge

SignalForge is an open-source Python package that provides a comprehensive set of tools for analyzing and generating non-Gaussian, non-stationary signals in engineering applications. While traditional signal processing methods often rely on assumptions of Gaussianity and stationarity, real-world signals—especially those found in mechanical systems—frequently deviate from these idealized models. SignalForge bridges this gap by offering advanced, yet user-friendly tools to simulate, inspect, and manipulate signals with complex statistical properties. Whether you're designing loads for testing, exploring time-varying behavior, or developing new algorithms, SignalForge enables you to work with signals that better reflect real operating conditions. Built with extensibility and clarity in mind, the package is suitable for researchers, practitioners, and students working across mechanical engineering, signal processing, and beyond.

See the documentation for a complete overview of the implemented methods for analisys and signal generation.

Example code on how to generate a Stationary Gaussian Signal

Stationary Gaussian signals can be easily generated via the specific class StationaryGaussian. Its usage is briefly shown in the following example code:

from SignalForge import StationaryGaussian

fpsd = [10,20]  # Frequency vector [Hz]
psd = [1,1]     # Power density vecrtor [(m/s^2)^2/Hz]
fs = 200        # Sampling frequency [Hz]
T = 200         # Time duration [s]
gauss_signal = StationaryGaussian(
    fpsd=fpsd, 
    psd=psd, 
    fs=fs, 
    T = T, 
    name = 'Stationary Gaussian', 
    unit='$m/s^2$'
    ) # StationaryGaussian class initialization
print(gauss_signal)
gauss_signal.plot()           # Plotting Timehistory
gauss_signal.plot_psd()       # Plotting Power spectral density 

Implemented non-linear transformartion methods to add non-gaussianity are the following:

• Hermite-Winterstein ('winter')
• Cubic polynomial ('cubic')
• Hyperbolic transform – Zheng (‘zheng’)
• Sarkani ('sarkani')
• Improved nonlinear transform ('zmnl')
• Steinwolf ('steinwolf')
• Smallwoood Poisson shots ('smallwood')
• Van Baren ('vanbaren')

Example code on how to generate a Stationary Non Gaussian Signal

Stationary non Gaussian signals can be easily generated via the specific class StationaryNonGaussian. Its usage is briefly shown in the following example code:

from SignalForge import StationaryNonGaussian

fpsd = [10,20]  # Frequency vector [Hz]
psd = [1,1]     # Power density vecrtor [(m/s^2)^2/Hz]
fs = 200        # Sampling frequency [Hz]
T = 200         # Time duration [s]
input_kurtosis = 6
nongauss_signal = StationaryNonGaussian(
    fpsd = fpsd, 
    psd = psd,
    T = T, 
    kurtosis=input_kurtosis,
    fs = fs, 
    method='zmnl', 
    name = 'Stationary NonGaussian', 
    unit='$m/s^2$'
    ) # StationaryNonGaussian class initalization
print(nongauss_signal)
nongauss_signal.plot()      # Plotting Timehistory
nongauss_signal.plot_psd()  # Plotting Power spectral density 
nongauss_signal.plot_fft()  # Plotting Fourier Transform

Example code on how to generate a Non Stationary Non Gaussian Signal

Non stationary non Gaussian signals can be easily generated via the specific class NonStationaryGaussian. Its usage is briefly shown in the following example code:

from SignalForge import NonStationaryNonGaussian

fpsd = [10,20]  # Frequency vector [Hz]
psd = [1,1]     # Power density vecrtor [(m/s^2)^2/Hz]
fs = 200        # Sampling frequency [Hz]
T = 200         # Time duration [s]
params = {'input_kurtosis': 10}
nonstat_signal = NonStationaryNonGaussian(
    fpsd = fpsd, 
    psd = psd, 
    T = T,
    params = params,
    fs = fs, 
    method='trapp_am', 
    name = 'NonStationary NonGaussian', 
    unit='$m/s^2$'
    ) # NonStationaryNonGaussian class initalization
print(nonstat_signal)
nonstat_signal.plot()      # Plotting Timehistory
nonstat_signal.plot_psd()  # Plotting Timehistory
nonstat_signal.plot_sftf()  # Plotting Short Time Fourier Transform

Implemented transformartion methods to build non-gaussianity signals are the following:

• Beta amplitude modulation ('beta_am')
• Gaussian amplitude modulation ('trapp_am')
• Rayleigh amplitude modulation ('ray_am')
• Frequency modulation via STFT ('fm')