---
id: fxpmath
version: "0.4.10"
license: MIT
license_treatment: permissive
maintenance: active
---

# fxpmath — A python library for fractional fixed-point (base 2) arithmetic and binary manipulation with Numpy compatibility.

License: permissive · Maintenance: active · Downloads: 194.2K/mo

## What it is and what it does

Fxpmath is a Python library for simulating fixed-point arithmetic in software, mimicking hardware behavior on FPGAs and DSP systems. It represents numbers with configurable word length and fractional bit positions, supporting signed and unsigned formats, and works seamlessly with numpy arrays and operations.

The library handles the full lifecycle of fixed-point values: creation from multiple input types (int, float, complex, strings in binary/hex/decimal, numpy arrays), arithmetic and bitwise operations, configurable rounding and overflow behaviors, and output in multiple bases. It tracks status flags and precision loss, making it useful for algorithm prototyping before hardware implementation or for educational exploration of fixed-point behavior.

Use it for:

- Prototype DSP algorithms in Python before deploying to fixed-point hardware or FPGA
- Simulate fixed-point quantization effects on signal processing pipelines to predict hardware behavior
- Test arithmetic overflow and underflow handling strategies with configurable saturation and rounding modes
- Verify bit-width requirements for embedded systems by experimenting with different word and fractional sizes
- Teach fixed-point representation and binary arithmetic concepts with interactive examples

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Fxpmath provides fixed-point arithmetic with arbitrary word and fractional sizes, supporting signed and unsigned numbers, numpy arrays, and bitwise operations for DSP and FPGA applications.

Yes. Fxpmath is actively maintained, has no known vulnerabilities, installs with minimal friction (numpy only), and fills a specific niche for fixed-point algorithm development and FPGA prototyping. The MIT license and stable API make it suitable for both research and production use. Install if you need to simulate or verify fixed-point behavior before hardware implementation.

## Install

pip install fxpmath
uv add fxpmath
poetry add fxpmath

## Installing fxpmath

Before you install: Low friction: pure Python wheel with only numpy as a runtime dependency. Active maintenance with recent releases; last commit 2026-04-04 and 206 repository stars indicate ongoing development.

License in practice: MIT license permits unrestricted use, modification, and distribution in both open-source and commercial projects with minimal restrictions.

Quickstart:

pip install fxpmath

from fxpmath import Fxp
import numpy as np

x = Fxp(-7.25, signed=True, n_word=16, n_frac=8)
y = x + 2.5
print(x.bin(frac_dot=True))

Requires Python >= 3.7; numpy must be installed.

Verify before relying:

- Performance characteristics for large arrays or complex operations compared to alternatives
- Numerical accuracy guarantees under various rounding and overflow configurations
- Active user community size and response time for issues beyond the 206 GitHub stars

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 194.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

fixed-point arithmetic, fractional binary math, DSP signal processing, FPGA simulation, numpy fixed-point, arbitrary precision binary, bitwise operations, fpga-simulation, dsp-prototyping, fixed-point

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