If you come from a Python background, you undoubtedly know SciPy — the fundamental library for scientific computing. But what if you are building an Android app or a high-performance backend service on the JVM?
jSciPy is my attempt to fill that gap. It is a Java Scientific Computing Library designed for Signal Processing, Machine Learning, and Data Science workflows, providing a familiar API for developers used to NumPy and SciPy.
Key Features
1. Advanced Filtering
Implemented from scratch to match SciPy’s output:
It currently includes modules for:
- Signal Processing: Butterworth filters, Savitzky-Golay smoothing, Peak detection.
- Transformations: FFT (Fast Fourier Transform), Hilbert Transform, Convolution.
- Math & Analysis: RK4 ODE Solver, Interpolation (Linear, Cubic Spline), Resampling.
2. Signal Analysis
- Peak Detection: Find peaks in noisy signals with height, distance, and prominence filters (similar to
scipy.signal.find_peaks). - Spectral Analysis: Welch’s Method for Power Spectral Density (PSD).
- Fast Fourier Transforms: Efficient FFT and RFFT wrappers.
3. Math & Solvers
- RK4 Solver: Runge-Kutta 4th Order method for solving ordinary differential equations (ODEs).
- Interpolation: Linear and Cubic Spline interpolation.
- Smoothing: Savitzky-Golay filters for smoothing noisy data while preserving peak heights.
Example Usage
Here is how you can apply a low-pass filter to a signal, just like in Python:
import com.hissain.jscipy.signal.Signal;
double[] data = { ... }; // Your noisy signal
double sampleRate = 100.0;
double cutoff = 10.0;
int order = 4;
// Apply zero-phase low-pass filter
double[] cleanData = Signal.filtfilt(data, sampleRate, cutoff, order);
Get Started
jSciPy is open source and available on GitHub. You can include it in your project via JitPack.