SignalProcessingTools 1.2.1

Signal processing tools

SignalProcessingTools

A comprehensive Python package for time and space domain signal processing operations with a focus on vibration analysis and frequency-domain transformations. The space domain operations focus on railway applications, while the time domain operations are more general.

Overview

SignalProcessingTools provides a suite of tools for analyzing, transforming, and processing data.

Time domain operations:

• Fast Fourier Transforms (FFT) and inverse FFT
• Signal filtering
• Integration
• Power Spectral Density (PSD) using Welch's method
• Spectrogram generation
• Effective velocity calculations using SBR method
• 1/3 octave band analysis
• Windowing functions (Hann, Hamming, Blackman, etc.)

Space domain operations:

• D0, D1, D2, and D3 track longitudinal levels, following EN 13848-1:2006.
• Hmax and Hrms according to Zandberg et al. (2022).

Installation

Install from PyPI

You can install the package directly from PyPI using pip:

pip install SignalProcessingTools

Install from Source

To install the package from the source, clone the repository and run the following commands:

git clone https://github.com/PlatypusBytes/SignalProcessing.git
cd SignalProcessing
pip install -e .

Usage

Basic Example of Time Domain Operations

FFT and signal integration

import numpy as np
from SignalProcessingTools.time_signal import SignalProcessing, Windows

# Create a test signal
t = np.linspace(0, 10, 5001)
y = 1.75 * np.sin(2 * np.pi * 6 * t)

# Initialize the signal processor
sig = SignalProcessing(t, y)

# Perform FFT
sig.fft()

# Integrate the signal
sig.integrate(baseline=True, hp=True, fpass=1, n=6)

Windowed Processing and PSD and spectrogram

# Create a signal processor with Hamming window
sig = SignalProcessing(t, y, window=Windows.HAMMING, window_size=4096)

# Calculate Power Spectral Density
sig.psd()

# Generate a spectrogram
sig.spectrogram()

Signal Filtering

# Apply a low-pass filter to remove high frequency noise
sig.filter(10, 4, type_filter="lowpass")

Effective Velocity Calculation (SBR-B Method)

# Calculate effective velocity using SBR method
sig.v_eff_SBR()

Basic Example of Spatial Domain Operations

D0, D1, D2, and D3 Calculation

import numpy as np
from SignalProcessingTools.space_signal import SpatialSignal
from SignalProcessingTools.space_signal import EN13848

# Create test data
x = np.linspace(0, 100, 50001)
omega = 2 * np.pi * 6
y = 1.75 * np.sin(omega * x)
y_noise = y + 0.01 * np.sin(120 * x)

sig = SpaceSignalProcessing(x, y_noise)
# Compute track longitudinal levels
sig.compute_track_longitudinal_levels()

Hmax and Hrms Calculation

x_track = np.linspace(0, 500, 25001)
track_irregularity = (
    0.002 * np.sin(2 * np.pi * 0.1 * x_track) +
    0.001 * np.sin(2 * np.pi * 0.2 * x_track) +
    0.0005 * np.sin(2 * np.pi * 0.4 * x_track) +
    0.0002 * np.random.randn(len(x_track))
)

sig_hmax = SpaceSignalProcessing(x_track, track_irregularity)
# Compute Hmax parameters
sig_hmax.compute_Hmax(convert_m2mm=True)

Example Files

A comprehensive example demonstrating all features is provided for the time signal and space signal.

License

This project is licensed under the MIT License - see the License file for details.