README.md

SignalProcessor

This repository provides a Python package for generating, filtering, fitting, and analyzing signals. The package includes functionalities for creating noisy signals, applying filters, fitting damped sine waves, and performing statistical analysis.

Overview

• Generate noisy sine wave signals (or import custom signals)
• Apply Butterworth low-pass filters
• Fit damped sine waves to filtered signals
• Perform t-tests between filtered signals and fitted models
• Compute and visualize Fourier Transforms

Installation

1. Create and source virtual environment:

python -m venv env
source env/bin/activate  # On Windows use `env\Scripts\activate`

2. Install the dependencies:

pip install -r requirements.txt

Running Tests

Using unittest

python -m unittest discover -s tests

Example

An example demonstrating generating a signal, applying filters, fitting models, and performing analysis, exists in the main.py.

Note

An example plot has been uploaded to the plots directory.

Example Usage

Generate a Noisy Signal

import numpy as np
from src.signal_processor import SignalProcessor

timeVector = np.linspace(0, 1, 1000, endpoint = False)  # Or consider importing or modifying your time vector

generator = SignalGenerator(timeVector)
   
generator.generateNoisySignal(frequency = 20, noiseStdDev = 0.6)

  # or with defaults:
    processor.generateNoisySignal()   # frequency = 10, noiseStdDev = 0.5

Apply a Filter (butter, bessel, highpass). Default is butter.

from src.signal_filter import SignalFilter

filteredInstance = generator.generateNoisySignal() \
                            .applyFilter(filterType = 'butter', 
                                         filterOrder = 4, 
                                         cutOffFrequency = 0.2, 
                                         bType = 'lowpass')
    # Or with different filter parameters:
      filteredInstance.setFilterParameters('bessel', 5, 0.5, 'highpass').applyFilter()    

Fit a damped sine wave to the filtered signal

from src.signal_fitter import SignalFitter

    # default sine wave parameters: amplitudeParam = 1.0, frequencyParam = 10.0, phaseParam = 0.0, decayRateParam = 0.1
fittedInstance = filteredInstance.fitDampedSineWave()

    # Or with custom parameters:
      fittedInstance.setDampedSineWaveParameters(3.0, 12.0, np.pi / 6, 0.3)
      fittedInstance.setDampedSineWaveBounds([0, 0, -np.pi/2, 0], [10, 20, np.pi/2, 1])
      fittedInstance.fitDampedSineWave()      

Perform a t-test between the filtered signal and the fitted damped sine wave

from src.statistical_analyzer import StatisticalAnalyzer

analyzedInstance = fittedInstance.analyzeFit()
tTestResults = analyzedInstance.getTTestResults()
print(f"T-test result: statistic={tTestResults[0]}, p-value={tTestResults[1]}")

Plot and save the results (will be saved under plots directory)

from src.signal_visualizer import SignalVisualizer

visualizer = SignalVisualizer(timeVector, generator.getNoisySignal(), 
                              filteredInstance.getFilteredSignal(), 
                              fittedInstance.getFittedSignal()
                              )
visualizer.plotResults()
visualizer.plotInteractiveResults()