Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ramy-badr-ahmed/signal-processor
Signal Processor in Python
https://github.com/ramy-badr-ahmed/signal-processor
data-visualization filter-design fourier-transform model-fitting noise-reduction noisy-signals python-library python-oop signal-processing statistical-analysis
Last synced: about 11 hours ago
JSON representation
Signal Processor in Python
- Host: GitHub
- URL: https://github.com/ramy-badr-ahmed/signal-processor
- Owner: Ramy-Badr-Ahmed
- License: apache-2.0
- Created: 2024-07-23T16:06:32.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-08-20T16:29:11.000Z (3 months ago)
- Last Synced: 2024-08-20T18:34:40.108Z (3 months ago)
- Topics: data-visualization, filter-design, fourier-transform, model-fitting, noise-reduction, noisy-signals, python-library, python-oop, signal-processing, statistical-analysis
- Language: Python
- Homepage:
- Size: 1.35 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
     
[](https://doi.org/10.5281/zenodo.13286179)
# 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:
```shell
python -m venv env
source env/bin/activate # On Windows use `env\Scripts\activate`
```
2) Install the dependencies:
```shell
pip install -r requirements.txt
```
### Running Tests
Using unittest
```shell
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
```shell
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`.
```shell
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
```shell
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
```shell
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)
```shell
from src.signal_visualizer import SignalVisualizer
visualizer = SignalVisualizer(timeVector, generator.getNoisySignal(),
filteredInstance.getFilteredSignal(),
fittedInstance.getFittedSignal()
)
visualizer.plotResults()
visualizer.plotInteractiveResults()
```