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

Create and source virtual environment:

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

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)