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# Easy-to-use fixed-point library
**qformatpy** is a lightweight Python library for converting numbers to fixed-point format using the ARM-style Qm.n notation. It is designed to be simple, readable, and easy to integrate into simulation, modeling, or hardware verification pipelines.
The library provides a single function, `qformat`, that lets you control integer and fractional precision, signedness, rounding behavior, and overflow handling — all without the complexity of a full fixed-point arithmetic suite.
Whether you're developing embedded systems, DSP algorithms, or just need fast fixed-point conversion, **qformatpy** keeps things straightforward.
To get started, check the example of a [fixed-point integrator](docs/example_int/example_int.md) implemented
using the library.
- [Documentation](https://qformatpy.readthedocs.io/en/latest/)
- [PyPI](https://pypi.org/project/qformatpy/)
- [Sources](https://github.com/ericsmacedo/qformatpy)
- [Issues](https://github.com/ericsmacedo/qformatpy/issues)
## Features
- **Support for Numba**
Optimized with optional [Numba](https://numba.pydata.org/) acceleration for fast, JIT-compiled conversions.
- **Native NumPy Array Support**
Seamlessly handles `numpy.ndarray` inputs for efficient batch processing.
- **Flexible Rounding Modes**
Choose from 9 rounding modes, including truncation, ceiling, rounding to nearest (with tie-breaking), and more.
- **Customizable Overflow Handling**
Select between wraparound, saturation, or error-on-overflow behavior.
- **Signed and Unsigned Formats**
Easily switch between signed and unsigned representations using the `signed` parameter.
- **Consistent Q-Format Notation**
Follows ARM-style Qm.n notation for compatibility with DSP and embedded tools.
- **Well-Suited for Embedded and DSP Simulation**
Ideal for preparing and validating fixed-point behavior before deploying to hardware.
## Installation
Installing it is pretty easy:
```bash
pip install qformatpy
```
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