fxpmath

library0.4.10pypypi✓ verified 89d ago

A Python library for fractional fixed-point (base 2) arithmetic and binary manipulation with NumPy compatibility. Current version 0.4.10 (latest stable), released roughly every few months. Supports Python >=3.7. Widely used in digital signal processing and hardware emulation contexts.

ai-ml data serialization

pip install fxpmath

github ↗package ↗

INSTALL

IMPORT

SIG · FXPMATH

fxpmath

ai-mlpythonv0.4.10

Install

3.5s avg

Import

243ms

Disk

89MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.4.10 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.236s · 89.8MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 3.5s · import 0.250s · 86MB

89MB installed

● package 89MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

Fxp

✓ from fxpmath import Fxp

✗ import fxpmath; fxpmath.Fxp

While that works, direct import is idiomatic.

Create two fixed-point numbers with 8-bit word length and 7 fractional bits, add them, and show the result.

from fxpmath import Fxp x = Fxp(0.5, signed=True, n_word=8, n_frac=7) y = Fxp(0.75, signed=True, n_word=8, n_frac=7) z = x + y print(z) # Returns 1.25 print(z.bin()) # Binary representation: 01.0100000

Debug

Known issues

gotchaDefault word length overflow wraps silently without warning unless you set config.rounding or check the overflow flag explicitly. Always specify signed, n_word, n_frac to avoid unexpected bits.

fix

Always pass explicit fixed-point format arguments: Fxp(value, signed=True, n_word=16, n_frac=8). Use config.overflow='wrap' or 'saturate' to control behavior.

affects: <=0.4.10

gotchaOperations between Fxp objects with different formats may silently coerce to larger word lengths if config.op_input_size is not set to 'consistent' or 'best'. This can lead to unexpected bit growth.

fix

Set fxpmath.config.op_input_size = 'consistent' to require identical formats, or explicitly manage operand formats.

affects: <=0.4.10

breakingIn v0.4.9, the behavior of the wrap function changed for values exceeding n_word_max (64 bits). Previously it might have truncated; now it raises an error or wraps differently. This can break code that relied on raw binary manipulation beyond 64 bits.

fix

Set config.n_word_max to a higher value if you need >64 bits, or use the `Fxp` constructor with explicit word lengths rather than relying on wrap. Check the wrap utility documentation.

affects: >=0.4.9

deprecatedThe `from_bin` function was introduced in 0.4.9; older code using `Fxp(val, dtype=...)` with binary string might break or produce wrong results.

fix

Use `Fxp.from_bin(binary_string, ...)` for binary string input. Update to fxpmath >=0.4.9.

affects: <0.4.9

Errors

Common errors & fixes

ValueError: invalid literal for int() with base 2: '...'

Passing a binary string with spaces or invalid characters (e.g., '0101 0110') directly to Fxp().

fix

Use `Fxp.from_bin('01010110', ...)` which handles spaces and proper parsing.

OverflowError: number of bits > n_word_max (64 bits) and no config to increase it

Attempting to create an Fxp with a value that requires more than 64 bits without setting config.n_word_max.

fix

Set `fxpmath.config.n_word_max = 128` (or required size) before creation.

TypeError: unsupported operand type(s) for +: 'Fxp' and 'float'

Trying to add a Python float to an Fxp object without conversion.

fix

Convert float to Fxp first: `float_val = Fxp(2.5, signed=True, n_word=8, n_frac=4)` then add.

Upgrade

Version history

0.4.10latest on PyPI · released Mar 24, 2026

Audit

Dependencies

numpyrequiredCore dependency for array operations and numerical precision.

Agent activity

9 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/francof2a/fxpmath ↗

packagehttps://pypi.org/project/fxpmath/ ↗