Metadata-Version: 2.4
Name: measurekit
Version: 0.0.3.dev0
Summary: A Python package for handling measurement units and conversions
Home-page: https://github.com/irvinrx1996/measurekit
Author: Irvin Torres
Author-email: irvinrx1996@hotmail.com
Project-URL: Bug Reports, https://github.com/irvinrx1996/measurekit/issues
Project-URL: Source, https://github.com/irvinrx1996/measurekit
Project-URL: Documentation, https://measurekit.readthedocs.io/
Keywords: units,measurement,conversion,physics,engineering,science
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: sympy>=1.4
Requires-Dist: typing-extensions>=4.15.0
Provides-Extra: numpy
Requires-Dist: numpy>=1.21.0; extra == "numpy"
Requires-Dist: scipy>=1.5.0; extra == "numpy"
Provides-Extra: torch
Requires-Dist: torch>=2.0.0; extra == "torch"
Provides-Extra: jax
Requires-Dist: jax>=0.4.0; extra == "jax"
Requires-Dist: jaxlib>=0.4.0; extra == "jax"
Provides-Extra: pandas
Requires-Dist: pandas>=1.3.0; extra == "pandas"
Provides-Extra: all
Requires-Dist: numpy>=1.21.0; extra == "all"
Requires-Dist: scipy>=1.5.0; extra == "all"
Requires-Dist: torch>=2.0.0; extra == "all"
Requires-Dist: jax>=0.4.0; extra == "all"
Requires-Dist: jaxlib>=0.4.0; extra == "all"
Requires-Dist: pandas>=1.3.0; extra == "all"
Provides-Extra: dev
Requires-Dist: pytest>=6.0.0; extra == "dev"
Requires-Dist: pytest-cov>=2.12.0; extra == "dev"
Requires-Dist: black>=21.5b2; extra == "dev"
Requires-Dist: isort>=5.9.1; extra == "dev"
Requires-Dist: mypy>=0.812; extra == "dev"
Requires-Dist: flake8>=3.9.2; extra == "dev"
Requires-Dist: numpy>=1.21.0; extra == "dev"
Requires-Dist: scipy>=1.5.0; extra == "dev"
Provides-Extra: docs
Requires-Dist: sphinx>=4.0.2; extra == "docs"
Requires-Dist: sphinx-rtd-theme>=0.5.2; extra == "docs"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license-file
Dynamic: project-url
Dynamic: provides-extra
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

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" alt="MeasureKit Logo" width="400">
</p>

<h1 align="center">MeasureKit</h1>

<p align="center">
  <strong>A powerful Python library for physical computing, unit conversions, and symbolic analysis.</strong>
</p>

<p align="center">
  <a href="https://pypi.org/project/measurekit/" style="text-decoration: none;">
    <img src="https://img.shields.io/pypi/v/measurekit.svg" alt="PyPI version">
  </a>
  <a href="https://opensource.org/licenses/MIT" style="text-decoration: none;">
    <img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT">
  </a>
  <a href="https://www.python.org/" style="text-decoration: none;">
    <img src="https://img.shields.io/badge/python-3.10+-blue.svg" alt="Python 3.10+">
  </a>
</p>

---

## 🚀 Description

**MeasureKit** is designed to make working with physical quantities in Python intuitive, robust, and error-free. Whether you are a scientist, engineer, or student, MeasureKit handles the complexity of unit conversions and dimensional analysis so you can focus on the physics.

## 📦 Installation

Install the package via pip:

```bash
pip install measurekit
```

To use it within a Jupyter Notebook/Lab environment:

```bash
pip install ipykernel
python -m ipykernel install --user --name measurekit
```

## 🛠 Usage

```python
from measurekit import Q_

# 1. Easy Unit Conversions
# Use the Q_ factory to create quantities
feet = Q_(3.28, 'ft')
meters = feet.to('m')
print(f"3.28 feet = {meters:.4f}")

# 2. Quantity Arithmetic
distance = Q_(5, 'km')
time = Q_(2, 'h')

# Automatic unit handling in calculations
speed = distance / time
print(f"Speed: {speed}")  # Output: 2.5 km/h

# 3. Fluid Conversions
speed_in_mph = speed.to('mph')
print(f"Speed in mph: {speed_in_mph}")
```

## ✨ Features

- **Robust Unit System:** Support for SI, Imperial, and custom unit definitions.
- **Dimensional Analysis:** Prevents invalid operations (e.g., adding length to mass) at runtime.
- **Symbolic Math:** Built-in support for symbolic manipulation of physical equations.
- **Extensible:** Easily define your own units and constants via configuration files.
- **Developer Friendly:** Type hints, clean API, and comprehensive error messages.

## ⚙️ Configuration

MeasureKit allows you to customize its behavior, define new units, and add constants through a `measurekit.conf` file.

1.  **Create a `measurekit.conf` file** in your project's root directory.
2.  **Define your custom settings** following the INI format.

**Example `measurekit.conf`:**

```ini
[Settings]
default_system = SI

[Units]
# Format: name = factor_to_base, dimension, [symbol, alias...]
hand = 0.1016, L, [hand, hands]
bitcoin = 95000.0, $, [BTC, bitcoin]

[Constants]
# Format: name = value unit
my_gravity = 9.81 m/s^2
```

## 📋 Requirements

- Python 3.8 or higher
- `sympy`
- `numpy` (optional, for advanced features)
- `scipy` (optional, for simulations)

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## ✍️ Author

**Irvin Torres** - [irvinrx1996@hotmail.com](mailto:irvinrx1996@hotmail.com)
