Skip to main content

Color palettes for scientific purposes

Project description

ArtSciColor

PyPI version License: GPL v3 Open Source? Yes! DOI

Creating a python package with color palettes and utilities for their use in matplotlib, seaborn, plotly, and others.

:construction: WORK IN PROGRESS :construction:

R users or Python users who don't want to install the package but still want to use the palettes, can download them in CSV form from the dataset's permalink!

Installation

The package is available through pypi, so it can be installed by running:

pip install ArtSciColor

Usage

To use a color palette simply load the package and run:

import ArtSciColor as art

hexPalette = art.getSwatch(SWATCH_ID)

where the SWATCH_ID should match one of the palettes available in our package (see the following section for more info).

Available Swatches

Have a look at currently-available palettes by selecting your favorite artist or category, and use one through its ID!

Art

Miro, Kandinsky, Kirchner, Matisse, Picasso, Signac, Warhol, Nolde, Monet, VanGogh, EdnaAndrade, DarbyBannard, UmbertoBoccioni

Movies

Studio Ghibli, Disney

Gaming

Splatoon1, Splatoon2, Splatoon3

Other

chipdelmal, coolors, lospec

Full dataframe in CSV for available for download here!

How are the palettes generated?

Getting palette colors is a common exercise for people getting started into clustering methods. The most widely-used algorithm for this task is k-means, but in this package the algorithm and its parameters can be provided as long as they adhere to scikit-learn's standards. Most of the curated palettes were calculated through the agglomerative clustering algorithm as follows:

from sklearn.cluster import AgglomerativeClustering
# Read image and setup number of desired clusters
img = art.readCV2Image(fPath)
CLST_NUM=4
# Clustering algorithm
CLUSTERING = {
    'algorithm': AgglomerativeClustering, 
    'params': {'n_clusters': CLST_NUM} 
}
(pixels, labels) = art.calcDominantColors(
    img, 
    cFun=CLUSTERING['algorithm'], 
    cArgs=CLUSTERING['params']
)

Other algorithms such as DBSCAN and HDBSCAN, Spectral Clustering, OPTICS, etc; can also be used.

Notes and Sources

This package was initially inspired by Blake R Mills' R packages (MoMA Colors and MetBrewer). Most palettes or original artworks are sourced from: NGA, wikiart, staedelemuseum, filmartgallery, coolors, inkipedia, lospec; so please visit and support their work!


Coded by: Héctor M. Sánchez C.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ArtSciColor-0.3.0.6.tar.gz (67.8 kB view details)

Uploaded Source

Built Distribution

ArtSciColor-0.3.0.6-py3-none-any.whl (64.8 kB view details)

Uploaded Python 3

File details

Details for the file ArtSciColor-0.3.0.6.tar.gz.

File metadata

  • Download URL: ArtSciColor-0.3.0.6.tar.gz
  • Upload date:
  • Size: 67.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for ArtSciColor-0.3.0.6.tar.gz
Algorithm Hash digest
SHA256 61b6fe427193cbc426f3a0f9ec2097a38806f945cef3b32d2a55c6052a019046
MD5 f2ed3206713167451ca6ead57a2a7411
BLAKE2b-256 4d70bfe0da627728257f0b0594c8379d418e0652a9154db5b35638535ebbe538

See more details on using hashes here.

File details

Details for the file ArtSciColor-0.3.0.6-py3-none-any.whl.

File metadata

File hashes

Hashes for ArtSciColor-0.3.0.6-py3-none-any.whl
Algorithm Hash digest
SHA256 82a6b4399423f8a39274533491e1fb620d6467225d5eb196c4d9cc90ba58f71e
MD5 3e8a8bb7aff7a81b769135e6f5d4aa34
BLAKE2b-256 6117fadfe7ae1a7c36a8213819bf6fe60ed99544a935b8ec1637fc4b7984f33f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page