Registry / serialization / colorspacious

colorspacious

library1.1.2pypypiunverified

Colorspacious is a Python library providing robust and accurate tools for colorspace conversions. It supports a wide range of standard colorspaces (sRGB, CIELab, CIELCh, etc.) and is built to handle numerical data efficiently, especially with NumPy arrays. The current version is 1.1.2, but development has been inactive since 2018, placing it in an abandoned state.

serialization data

pip install colorspacious

github ↗package ↗

INSTALL

IMPORT

SIG · COLORSPACIOUS

colorspacious

serializationpythonv1.1.2

Install

3.6s avg

Import

282ms

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 v1.1.2 · 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 3.10–3.910 runs

installs and imports cleanly · install 0.0s · import 0.275s · 89.6MB

glibc

py 3.10–3.910 runs

installs and imports cleanly · install 3.6s · import 0.288s · 86MB

89MB installed

● package 89MB

Code

Verified usage

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

cspace_convert

✓ import colorspacious as cs cs.cspace_convert(...)

CIELCh_from_CIELab

✓ import colorspacious as cs cs.cspace_convert(data, 'CIELab', 'CIELCh')

✗ from colorspacious import CIELCh_from_CIELab

Direct conversion functions like CIELCh_from_CIELab were removed in v1.0.0. Use cspace_convert instead.

This example demonstrates basic color space conversion using the `cspace_convert` function. It takes a NumPy array representing color data and converts it between specified input and output color spaces.

import colorspacious as cs import numpy as np # A color in sRGB255 (standard 0-255 sRGB) space sRGB_color = np.array([255, 128, 0]) # Orange # Convert to CIELab (D65 illuminant) space LAB_color = cs.cspace_convert(sRGB_color, "sRGB255", "CIELab") print(f"sRGB color (255, 128, 0) in CIELab: {LAB_color}") # Convert to CIELCh space LCH_color = cs.cspace_convert(LAB_color, "CIELab", "CIELCh") print(f"sRGB color (255, 128, 0) in CIELCh: {LCH_color}")

Debug

Known issues

breakingThe argument signature for `colorspacious.cspace_convert` changed significantly in version 1.0.0. Previously, it might have been `cspace_convert(data, start_cspace, target_cspace)`. In v1.0.0 and later, it uses explicit keyword arguments `in_cspace` and `out_cspace`.

fix

Update calls to `cspace_convert` from `cs.cspace_convert(data, 'old_in', 'old_out')` to `cs.cspace_convert(data, in_cspace='new_in', out_cspace='new_out')` or `cs.cspace_convert(data, 'new_in', 'new_out')`.

affects: >=1.0.0

breakingDirect utility functions for specific conversions (e.g., `CIELCh_from_CIELab`, `XYZ_from_sRGB1`) were removed in version 1.0.0. All conversions are now consolidated under the general `colorspacious.cspace_convert` function.

fix

Replace calls like `colorspacious.CIELCh_from_CIELab(data)` with `colorspacious.cspace_convert(data, 'CIELab', 'CIELCh')`.

affects: >=1.0.0

breakingMany colorspace specifier strings changed naming conventions in version 1.0.0 (e.g., `sRGB_linear` became `sRGB1`, `sRGB_255` became `sRGB255`). Using old names will result in `ValueError`.

fix

Refer to the `colorspacious` documentation for the updated list of colorspace names. For common cases, update `sRGB_linear` to `sRGB1` and `sRGB_255` to `sRGB255`.

affects: >=1.0.0

gotchaWhile `colorspacious` can process lists or tuples as input, it is heavily optimized for NumPy arrays. Using non-NumPy inputs, especially for large datasets, can lead to significant performance degradation.

fix

Ensure that your color data is converted to a NumPy array before passing it to `colorspacious` functions, e.g., `np.array(my_list_of_colors)`.

affects: All

Upgrade

Version history

1.1.2latest on PyPI · released Apr 8, 2018

Audit

Dependencies

numpyrequiredRequired for core numerical operations and efficient array processing.

Agent activity

13 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/njsmith/colorspacious ↗

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