Metadata-Version: 2.4
Name: heropy
Version: 0.1.0
Summary: A high-performance fundamental package for scientific computing with multi-dimensional arrays, linear algebra, and FFT.
Author-email: Death Legion Team <contact@deathlegion.team>
Project-URL: Homepage, https://github.com/deathlegion/heropy
Project-URL: Bug Tracker, https://github.com/deathlegion/heropy/issues
Keywords: scientific computing,multi-dimensional arrays,linear algebra,FFT,matrix,numerical computing,data science,machine learning
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Requires-Python: >=3.8
Description-Content-Type: text/markdown

# 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.

[![PyPI version](https://badge.fury.io/py/heropy.svg)](https://badge.fury.io/py/heropy)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## 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

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

## Usage

```python
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.
