heropy 0.1.0

A high-performance fundamental package for scientific computing with multi-dimensional arrays, linear algebra, and FFT.

HeroPy: High-Performance Scientific Computing in Python

HeroPy is the fundamental package for scientific computing with Python, meticulously crafted by the Death Legion Team. It provides a powerful N-dimensional array object, sophisticated mathematical functions, and the core data structures necessary for modern data science and machine learning.

Why HeroPy?

In the evolving landscape of numerical computing, HeroPy stands as a robust foundation for handling large, multi-dimensional arrays and matrices. Designed for performance and ease of use, HeroPy enables complex mathematical operations on entire blocks of data without the need for slow Python loops.

Core Features

• N-Dimensional Array (ndarray): A versatile, multi-dimensional array object with support for advanced indexing, slicing, and NumPy-style broadcasting.
• Linear Algebra (linalg): High-level mathematical functions for matrix multiplication (dot), transposition, and vector operations.
• Fourier Transforms (fft): Efficient Fast Fourier Transform (FFT) capabilities for signal processing and spectral analysis.
• Random Number Generation (random): Sophisticated random number generation with support for various distributions like Uniform and Normal (Gaussian).
• Extensible Architecture: Built to be the bedrock of the Python data science ecosystem, compatible with machine learning and data analysis workflows.

Installation

# Clone the repository
git clone <repo-url>
cd heropy
export PYTHONPATH=$PYTHONPATH:.

Usage

import heropy as hp
from heropy.linalg import dot

# Create arrays
a = hp.ndarray([[1, 2], [3, 4]])
b = hp.ndarray([10, 20])

# Arithmetic with broadcasting
c = a + b

# Matrix multiplication
d = dot(a, a)

# FFT
from heropy.fft import fft
freq = fft([1, 0, 1, 0])

Performance Note

HeroPy is currently implemented in pure Python for maximum portability and clarity. While it provides the core data structures and operations, performance for very large datasets may be limited compared to C-accelerated libraries.