kadane-adv 1.1.0
About An advanced Python library implementing Kadane’s Algorithm with support for 1D & 2D arrays, visualization, subarray constraints, and test coverage. Perfect for data analysis, time-series problems, and algorithm enthusiasts.
Requires Python: >=3.6

# kadane-adv: Advanced Subarray Optimization Library for Python

**kadane-adv** is a Python package that extends the classic Kadane’s Algorithm to support advanced use cases in 1D and 2D data analysis. Designed for performance and simplicity, it provides efficient tools to identify optimal subarrays and submatrices across diverse datasets—time series, financial data, sensor logs, or image matrices.

It is an essential utility for **data analysts, researchers, and machine learning developers** seeking meaningful patterns in structured data.

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##  Table of Contents

- [Main Features](#main-features)  
- [Installation](#installation)  
- [Dependencies](#dependencies)  
- [License](#license)  
- [Documentation](#documentation)  
- [Background](#background)  
- [Getting Help](#getting-help)  
- [Discussion & Development](#discussion--development)  
- [Contributing](#contributing-to-kadane-adv)

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##  Main Features

- 🔹 **1D Maximum Subarray Detection**  
  Find the subarray with the maximum sum in linear time.

- 🔹 **2D Maximum Submatrix Detection**  
  Extend Kadane’s algorithm to rectangular regions in 2D matrices.

- 🔹 **Constrained Optimization**  
  Support for constraints such as minimum subarray length.

- 🔹 **Built-in Visualization**  
  Visual representation of the detected optimal regions.

- 🔹 **Integration with NumPy & Pandas**  
  Supports direct use of NumPy arrays and Pandas DataFrames.

- 🔹 **Real-world Applications**  
  Use it in financial trend analysis, signal processing, sensor logs, and image matrix optimization.

---

## Installation

### From PyPI

You can install the library using pip:

```bash
pip install kadane-adv
```

### From Source

1. Download the source code from [PyPI](https://pypi.org/project/kadane-adv/) or GitHub.  
2. Extract the archive and navigate to the folder.

```bash
cd kadane-adv
```

3. Run the setup script:

```bash
python setup.py install
```

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## Dependencies

### Required:
- `numpy` — for matrix and array operations  
- `matplotlib` — for visualizations

### Optional:
- `pandas` — for seamless DataFrame integration

> Exact versions can be found in the `requirements.txt` file.

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## License

Licensed under the **MIT License**.  
See the [`LICENSE`](./LICENSE) file for full text.

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## Documentation

- All functions are documented with Python docstrings  
- Fully commented source code  
- Use the built-in `help()` function or your IDE's documentation viewer

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## Background

While classic Kadane’s algorithm finds the maximum sum subarray in 1D, **kadane-adv** extends this by adding:

-  **Multi-dimensional support** (e.g., 2D submatrices)  
-  **Constraint-based searches** (e.g., min length)  
-  **Visual feedback** to verify or interpret the result

### Typical Use Cases:
-  Stock market and financial data analysis  
-  Time series segmentation  
-  Signal and anomaly detection  
-  Image processing and matrix evaluation  
-  Sensor data analysis for IoT applications

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##  Getting Help

- Use `help(kadane_adv.function_name)` in Python  
- Browse built-in examples (if available)  
- Open an issue on the GitHub repo (link to be added)

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##  Discussion & Development

The project evolves with real-world needs in:

- Data Science & ML workflows  
- Financial and signal analysis  
- Academic & exploratory research

Your suggestions and use-cases are welcome!

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##  Contributing to kadane-adv

We welcome contributions in all forms! You can help by:

- Fixing bugs  
- Adding new features  
- Improving documentation  
- Creating real-world usage examples

###  Guidelines:

- Keep changes focused and lightweight  
- Write clear, readable, and commented code  
- Use docstrings for new functions  
- Submit a pull request (PR) with a clear description

Let’s make this tool better — **together**!

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