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, Warhol, Nolde, Monet

Movies

Studio Ghibli

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, 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-0.2.0.0.tar.gz (58.6 kB view details)

Uploaded Source

Built Distribution

ArtSciColor-0.2.0.0-py3-none-any.whl (55.7 kB view details)

Uploaded Python 3

File details

Details for the file ArtSciColor-0.2.0.0.tar.gz.

File metadata

  • Download URL: ArtSciColor-0.2.0.0.tar.gz
  • Upload date:
  • Size: 58.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for ArtSciColor-0.2.0.0.tar.gz
Algorithm Hash digest
SHA256 72a8221e287b03120c1b0706000ce51ad48efbfcd075c5a8c3e9864058abd28f
MD5 8d815674c69de493e57d6749919b5815
BLAKE2b-256 a92f8b6e0581474968a233d4228870df192b05c583e3cdaa4fc0778e1c05482e

See more details on using hashes here.

File details

Details for the file ArtSciColor-0.2.0.0-py3-none-any.whl.

File metadata

File hashes

Hashes for ArtSciColor-0.2.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 9aa42a6f37af3ba79fab79e012de8f77d2bb56b5bc41878887d563fd3450f78d
MD5 460d6eba30498795c1660613efef92d3
BLAKE2b-256 6c439840f35828575c09cd119c47d75b03d8638371525df99772b1bf6660e1f2

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