{"title":"colorspace: A Python Toolbox for Manipulating and Assessing Colors and Palettes","authors":"Reto Stauffer, Achim Zeileis","doi":"arxiv-2407.19921","DOIUrl":null,"url":null,"abstract":"The Python colorspace package provides a toolbox for mapping between\ndifferent color spaces which can then be used to generate a wide range of\nperceptually-based color palettes for qualitative or quantitative (sequential\nor diverging) information. These palettes (as well as any other sets of colors)\ncan be visualized, assessed, and manipulated in various ways, e.g., by color\nswatches, emulating the effects of color vision deficiencies, or depicting the\nperceptual properties. Finally, the color palettes generated by the package can\nbe easily integrated into standard visualization workflows in Python, e.g.,\nusing matplotlib, seaborn, or plotly.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"11 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Graphics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.19921","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The Python colorspace package provides a toolbox for mapping between
different color spaces which can then be used to generate a wide range of
perceptually-based color palettes for qualitative or quantitative (sequential
or diverging) information. These palettes (as well as any other sets of colors)
can be visualized, assessed, and manipulated in various ways, e.g., by color
swatches, emulating the effects of color vision deficiencies, or depicting the
perceptual properties. Finally, the color palettes generated by the package can
be easily integrated into standard visualization workflows in Python, e.g.,
using matplotlib, seaborn, or plotly.