592 lines
2.4 MiB
HTML
592 lines
2.4 MiB
HTML
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8" />
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<meta name="generator" content="pandoc" />
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<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<meta name="author" content="Bob Rudis, Noam Ross and Simon Garnier" />
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<meta name="date" content="2024-01-28" />
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<title>Introduction to the viridis color maps</title>
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<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
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// be compatible with the behavior of Pandoc < 2.8).
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document.addEventListener('DOMContentLoaded', function(e) {
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var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
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var i, h, a;
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for (i = 0; i < hs.length; i++) {
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h = hs[i];
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if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
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a = h.attributes;
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while (a.length > 0) h.removeAttribute(a[0].name);
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}
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});
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</script>
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<style type="text/css">
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code{white-space: pre-wrap;}
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span.smallcaps{font-variant: small-caps;}
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span.underline{text-decoration: underline;}
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div.column{display: inline-block; vertical-align: top; width: 50%;}
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div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
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ul.task-list{list-style: none;}
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</style>
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<style type="text/css">
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code {
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white-space: pre;
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}
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.sourceCode {
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overflow: visible;
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}
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</style>
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<style type="text/css" data-origin="pandoc">
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pre > code.sourceCode { white-space: pre; position: relative; }
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pre > code.sourceCode > span { line-height: 1.25; }
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pre > code.sourceCode > span:empty { height: 1.2em; }
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.sourceCode { overflow: visible; }
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code.sourceCode > span { color: inherit; text-decoration: inherit; }
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div.sourceCode { margin: 1em 0; }
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pre.sourceCode { margin: 0; }
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@media screen {
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div.sourceCode { overflow: auto; }
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}
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@media print {
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pre > code.sourceCode { white-space: pre-wrap; }
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pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
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}
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pre.numberSource code
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{ counter-reset: source-line 0; }
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pre.numberSource code > span
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{ position: relative; left: -4em; counter-increment: source-line; }
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pre.numberSource code > span > a:first-child::before
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{ content: counter(source-line);
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position: relative; left: -1em; text-align: right; vertical-align: baseline;
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border: none; display: inline-block;
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-webkit-touch-callout: none; -webkit-user-select: none;
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-khtml-user-select: none; -moz-user-select: none;
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-ms-user-select: none; user-select: none;
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padding: 0 4px; width: 4em;
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color: #aaaaaa;
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}
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pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
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div.sourceCode
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{ }
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@media screen {
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pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
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}
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code span.al { color: #ff0000; font-weight: bold; }
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code span.an { color: #60a0b0; font-weight: bold; font-style: italic; }
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code span.at { color: #7d9029; }
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code span.bn { color: #40a070; }
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code span.bu { color: #008000; }
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code span.cf { color: #007020; font-weight: bold; }
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code span.ch { color: #4070a0; }
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code span.cn { color: #880000; }
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code span.co { color: #60a0b0; font-style: italic; }
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code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; }
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code span.do { color: #ba2121; font-style: italic; }
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code span.dt { color: #902000; }
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code span.dv { color: #40a070; }
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code span.er { color: #ff0000; font-weight: bold; }
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code span.ex { }
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code span.fl { color: #40a070; }
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code span.fu { color: #06287e; }
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code span.im { color: #008000; font-weight: bold; }
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code span.in { color: #60a0b0; font-weight: bold; font-style: italic; }
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code span.kw { color: #007020; font-weight: bold; }
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code span.op { color: #666666; }
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code span.ot { color: #007020; }
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code span.pp { color: #bc7a00; }
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code span.sc { color: #4070a0; }
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code span.ss { color: #bb6688; }
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code span.st { color: #4070a0; }
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code span.va { color: #19177c; }
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code span.vs { color: #4070a0; }
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code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; }
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</style>
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<script>
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// apply pandoc div.sourceCode style to pre.sourceCode instead
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(function() {
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var sheets = document.styleSheets;
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for (var i = 0; i < sheets.length; i++) {
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if (sheets[i].ownerNode.dataset["origin"] !== "pandoc") continue;
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try { var rules = sheets[i].cssRules; } catch (e) { continue; }
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var j = 0;
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while (j < rules.length) {
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var rule = rules[j];
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// check if there is a div.sourceCode rule
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if (rule.type !== rule.STYLE_RULE || rule.selectorText !== "div.sourceCode") {
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j++;
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continue;
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}
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var style = rule.style.cssText;
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// check if color or background-color is set
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if (rule.style.color === '' && rule.style.backgroundColor === '') {
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j++;
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continue;
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}
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// replace div.sourceCode by a pre.sourceCode rule
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sheets[i].deleteRule(j);
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sheets[i].insertRule('pre.sourceCode{' + style + '}', j);
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}
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}
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})();
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</script>
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<style type="text/css">body {
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background-color: #fff;
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margin: 1em auto;
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max-width: 700px;
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overflow: visible;
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padding-left: 2em;
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padding-right: 2em;
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font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
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font-size: 14px;
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line-height: 1.35;
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}
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#TOC {
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clear: both;
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margin: 0 0 10px 10px;
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padding: 4px;
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width: 400px;
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border: 1px solid #CCCCCC;
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border-radius: 5px;
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background-color: #f6f6f6;
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font-size: 13px;
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line-height: 1.3;
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}
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#TOC .toctitle {
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font-weight: bold;
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font-size: 15px;
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margin-left: 5px;
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}
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#TOC ul {
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padding-left: 40px;
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margin-left: -1.5em;
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margin-top: 5px;
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margin-bottom: 5px;
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}
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#TOC ul ul {
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margin-left: -2em;
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}
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#TOC li {
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line-height: 16px;
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}
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table {
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margin: 1em auto;
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border-width: 1px;
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border-color: #DDDDDD;
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border-style: outset;
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border-collapse: collapse;
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}
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table th {
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border-width: 2px;
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padding: 5px;
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border-style: inset;
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}
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table td {
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border-width: 1px;
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border-style: inset;
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line-height: 18px;
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padding: 5px 5px;
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}
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table, table th, table td {
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border-left-style: none;
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border-right-style: none;
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}
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table thead, table tr.even {
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background-color: #f7f7f7;
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}
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p {
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margin: 0.5em 0;
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}
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blockquote {
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background-color: #f6f6f6;
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padding: 0.25em 0.75em;
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}
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hr {
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border-style: solid;
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border: none;
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border-top: 1px solid #777;
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margin: 28px 0;
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}
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dl {
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margin-left: 0;
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}
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dl dd {
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margin-bottom: 13px;
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margin-left: 13px;
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}
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dl dt {
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font-weight: bold;
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}
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ul {
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margin-top: 0;
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}
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ul li {
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list-style: circle outside;
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}
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ul ul {
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margin-bottom: 0;
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}
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pre, code {
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background-color: #f7f7f7;
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border-radius: 3px;
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color: #333;
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white-space: pre-wrap;
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}
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pre {
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border-radius: 3px;
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margin: 5px 0px 10px 0px;
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padding: 10px;
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}
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pre:not([class]) {
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background-color: #f7f7f7;
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}
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code {
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font-family: Consolas, Monaco, 'Courier New', monospace;
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font-size: 85%;
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}
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p > code, li > code {
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padding: 2px 0px;
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}
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div.figure {
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text-align: center;
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}
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img {
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background-color: #FFFFFF;
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padding: 2px;
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border: 1px solid #DDDDDD;
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border-radius: 3px;
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border: 1px solid #CCCCCC;
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margin: 0 5px;
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}
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h1 {
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margin-top: 0;
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font-size: 35px;
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line-height: 40px;
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}
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h2 {
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border-bottom: 4px solid #f7f7f7;
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padding-top: 10px;
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padding-bottom: 2px;
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font-size: 145%;
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}
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h3 {
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border-bottom: 2px solid #f7f7f7;
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padding-top: 10px;
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font-size: 120%;
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}
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h4 {
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border-bottom: 1px solid #f7f7f7;
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margin-left: 8px;
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font-size: 105%;
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}
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h5, h6 {
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border-bottom: 1px solid #ccc;
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font-size: 105%;
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}
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a {
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color: #0033dd;
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text-decoration: none;
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}
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a:hover {
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color: #6666ff; }
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a:visited {
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|||
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color: #800080; }
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a:visited:hover {
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|||
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color: #BB00BB; }
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|||
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a[href^="http:"] {
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|||
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text-decoration: underline; }
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|||
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a[href^="https:"] {
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|||
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text-decoration: underline; }
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|||
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|||
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code > span.kw { color: #555; font-weight: bold; }
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|||
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code > span.dt { color: #902000; }
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code > span.dv { color: #40a070; }
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code > span.bn { color: #d14; }
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code > span.fl { color: #d14; }
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code > span.ch { color: #d14; }
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code > span.st { color: #d14; }
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code > span.co { color: #888888; font-style: italic; }
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code > span.ot { color: #007020; }
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code > span.al { color: #ff0000; font-weight: bold; }
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code > span.fu { color: #900; font-weight: bold; }
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code > span.er { color: #a61717; background-color: #e3d2d2; }
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</style>
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</head>
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<body>
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<h1 class="title toc-ignore">Introduction to the viridis color maps</h1>
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<h4 class="author">Bob Rudis, Noam Ross and Simon Garnier</h4>
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<h4 class="date">2024-01-28</h4>
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<div id="TOC">
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<ul>
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<li><a href="#tldr" id="toc-tldr">tl;dr</a></li>
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<li><a href="#introduction" id="toc-introduction">Introduction</a></li>
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<li><a href="#the-color-scales" id="toc-the-color-scales">The Color
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Scales</a></li>
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<li><a href="#comparison" id="toc-comparison">Comparison</a></li>
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<li><a href="#usage" id="toc-usage">Usage</a></li>
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|
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<li><a href="#gallery" id="toc-gallery">Gallery</a></li>
|
|||
|
|
</ul>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<style>
|
|||
|
|
img {
|
|||
|
|
max-width: 100%;
|
|||
|
|
max-height: 100%;
|
|||
|
|
}
|
|||
|
|
</style>
|
|||
|
|
<div id="tldr" class="section level1">
|
|||
|
|
<h1>tl;dr</h1>
|
|||
|
|
<p>Use the color scales in this package to make plots that are pretty,
|
|||
|
|
better represent your data, easier to read by those with colorblindness,
|
|||
|
|
and print well in gray scale.</p>
|
|||
|
|
<p>Install <strong>viridis</strong> like any R package:</p>
|
|||
|
|
<pre><code>install.packages("viridis")
|
|||
|
|
library(viridis)</code></pre>
|
|||
|
|
<p>For base plots, use the <code>viridis()</code> function to generate a
|
|||
|
|
palette:</p>
|
|||
|
|
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a>x <span class="ot"><-</span> y <span class="ot"><-</span> <span class="fu">seq</span>(<span class="sc">-</span><span class="dv">8</span><span class="sc">*</span>pi, <span class="dv">8</span><span class="sc">*</span>pi, <span class="at">len =</span> <span class="dv">40</span>)</span>
|
|||
|
|
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a>r <span class="ot"><-</span> <span class="fu">sqrt</span>(<span class="fu">outer</span>(x<span class="sc">^</span><span class="dv">2</span>, y<span class="sc">^</span><span class="dv">2</span>, <span class="st">"+"</span>))</span>
|
|||
|
|
<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a><span class="fu">filled.contour</span>(<span class="fu">cos</span>(r<span class="sc">^</span><span class="dv">2</span>)<span class="sc">*</span><span class="fu">exp</span>(<span class="sc">-</span>r<span class="sc">/</span>(<span class="dv">2</span><span class="sc">*</span>pi)), </span>
|
|||
|
|
<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a> <span class="at">axes=</span><span class="cn">FALSE</span>,</span>
|
|||
|
|
<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a> <span class="at">color.palette=</span>viridis,</span>
|
|||
|
|
<span id="cb2-6"><a href="#cb2-6" tabindex="-1"></a> <span class="at">asp=</span><span class="dv">1</span>)</span></code></pre></div>
|
|||
|
|
<p><img src="data:image/png;base64,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
|
|||
|
|
<p>For ggplot, use <code>scale_color_viridis()</code> and
|
|||
|
|
<code>scale_fill_viridis()</code>:</p>
|
|||
|
|
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
|
|||
|
|
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="fu">data.frame</span>(<span class="at">x =</span> <span class="fu">rnorm</span>(<span class="dv">10000</span>), <span class="at">y =</span> <span class="fu">rnorm</span>(<span class="dv">10000</span>)), <span class="fu">aes</span>(<span class="at">x =</span> x, <span class="at">y =</span> y)) <span class="sc">+</span></span>
|
|||
|
|
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a> <span class="fu">geom_hex</span>() <span class="sc">+</span> <span class="fu">coord_fixed</span>() <span class="sc">+</span></span>
|
|||
|
|
<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a> <span class="fu">scale_fill_viridis</span>() <span class="sc">+</span> <span class="fu">theme_bw</span>()</span></code></pre></div>
|
|||
|
|
<p><img src="data:image/png;base64,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
|
|||
|
|
<hr />
|
|||
|
|
</div>
|
|||
|
|
<div id="introduction" class="section level1">
|
|||
|
|
<h1>Introduction</h1>
|
|||
|
|
<p><a href="https://cran.r-project.org/package=viridis"><code>viridis</code></a>,
|
|||
|
|
and its companion package <a href="https://cran.r-project.org/package=viridisLite"><code>viridisLite</code></a>
|
|||
|
|
provide a series of color maps that are designed to improve graph
|
|||
|
|
readability for readers with common forms of color blindness and/or
|
|||
|
|
color vision deficiency. The color maps are also perceptually-uniform,
|
|||
|
|
both in regular form and also when converted to black-and-white for
|
|||
|
|
printing.</p>
|
|||
|
|
<p>These color maps are designed to be:</p>
|
|||
|
|
<ul>
|
|||
|
|
<li><strong>Colorful</strong>, spanning as wide a palette as possible so
|
|||
|
|
as to make differences easy to see,</li>
|
|||
|
|
<li><strong>Perceptually uniform</strong>, meaning that values close to
|
|||
|
|
each other have similar-appearing colors and values far away from each
|
|||
|
|
other have more different-appearing colors, consistently across the
|
|||
|
|
range of values,</li>
|
|||
|
|
<li><strong>Robust to colorblindness</strong>, so that the above
|
|||
|
|
properties hold true for people with common forms of colorblindness, as
|
|||
|
|
well as in grey scale printing, and</li>
|
|||
|
|
<li><strong>Pretty</strong>, oh so pretty</li>
|
|||
|
|
</ul>
|
|||
|
|
<p><code>viridisLite</code> provides the base functions for generating
|
|||
|
|
the color maps in base <code>R</code>. The package is meant to be as
|
|||
|
|
lightweight and dependency-free as possible for maximum compatibility
|
|||
|
|
with all the <code>R</code> ecosystem. <a href="https://cran.r-project.org/package=viridis"><code>viridis</code></a>
|
|||
|
|
provides additional functionalities, in particular bindings for
|
|||
|
|
<code>ggplot2</code>.</p>
|
|||
|
|
<hr />
|
|||
|
|
</div>
|
|||
|
|
<div id="the-color-scales" class="section level1">
|
|||
|
|
<h1>The Color Scales</h1>
|
|||
|
|
<p>The package contains eight color scales: “viridis”, the primary
|
|||
|
|
choice, and five alternatives with similar properties - “magma”,
|
|||
|
|
“plasma”, “inferno”, “civids”, “mako”, and “rocket” -, and a rainbow
|
|||
|
|
color map - “turbo”.</p>
|
|||
|
|
<p>The color maps <code>viridis</code>, <code>magma</code>,
|
|||
|
|
<code>inferno</code>, and <code>plasma</code> were created by Stéfan van
|
|||
|
|
der Walt (<a href="https://github.com/stefanv"><span class="citation">@stefanv</span></a>) and Nathaniel Smith (<a href="https://github.com/njsmith"><span class="citation">@njsmith</span></a>). If you want to know more about
|
|||
|
|
the science behind the creation of these color maps, you can watch this
|
|||
|
|
<a href="https://youtu.be/xAoljeRJ3lU">presentation of
|
|||
|
|
<code>viridis</code></a> by their authors at SciPy 2015.</p>
|
|||
|
|
<p>The color map <code>cividis</code> is a corrected version of
|
|||
|
|
‘viridis’, developed by Jamie R. Nuñez, Christopher R. Anderton, and
|
|||
|
|
Ryan S. Renslow, and originally ported to <code>R</code> by Marco
|
|||
|
|
Sciaini (<a href="https://github.com/marcosci"><span class="citation">@msciain</span></a>). More info about
|
|||
|
|
<code>cividis</code> can be found in <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0199239">this
|
|||
|
|
paper</a>.</p>
|
|||
|
|
<p>The color maps <code>mako</code> and <code>rocket</code> were
|
|||
|
|
originally created for the <code>Seaborn</code> statistical data
|
|||
|
|
visualization package for Python. More info about <code>mako</code> and
|
|||
|
|
<code>rocket</code> can be found on the <a href="https://seaborn.pydata.org/tutorial/color_palettes.html"><code>Seaborn</code>
|
|||
|
|
website</a>.</p>
|
|||
|
|
<p>The color map <code>turbo</code> was developed by Anton Mikhailov to
|
|||
|
|
address the shortcomings of the Jet rainbow color map such as false
|
|||
|
|
detail, banding and color blindness ambiguity. More infor about
|
|||
|
|
<code>turbo</code> can be found <a href="https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html">here</a>.</p>
|
|||
|
|
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAKuCAYAAABg77ZHAAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAVAoAMABAAAAAEAAAKuAAAAAFtP2dgAAEAASURBVHgB7N0JmFxVmTju05BAEggJsoQQViGAIERAFgUXAsMqS9zRv+KAoGZ+iruijOKC6IgP4IKIKMoijCgqIsKAojjqIIKsirLJEgiCEnZCuqr+fS7esrrT6f46XVs3732epO5y6jvnvOdWdefLvff01PqWZCFAgAABAgQIECBAgAABAgQIECBAgMA4FFhhHPZJlwgQIECAAAECBAgQIECAAAECBAgQIFAISIA6EQgQIECAAAECBAgQIECAAAECBAgQGLcCE8Ztz3SMAAECBAgQIEBgVAILFy5Mjz32WBFj0qRJab311htxvNHEqFQq6Y477qjXOWPGjDR16tRi++mnn0533XVX/di6666bpkyZUt+2QoAAAQIECBAgQKAUcAVoKeGVAAECBAgQIECgn8D8+fPT7Nmziz/z5s3rdyy6MZoY999/f73+3I7zzz+/Xu3NN9/c79jll19eP2aFAAECBAgQIECAQKOABGijhnUCBAgQIECAAAECBAgQIECAAAECBMaVgATouBpOnSFAgAABAgQIECBAgAABAgQIECBAoFGgp9a3NO6wToAAAQIECBAgQCALPProoyk/azMvEyZMSNOmTSvWR/LXaGLce++9adasWfXqvvWtb6VDDjmk2M7PB120aFH92GqrrZYmTpxY37ZCgAABAgQIECBAoBQwCVIp4ZUAAQIECBAgMM4E8gRGeRKicpk5c2ZaZZVVys366+OPP57uu++++nZOOk6ePDnl/eUkSCuvvHK/BGiegKhMjq699topJyCvvfbadMMNN6SXvvSlacMNNyziDRWjXmHfyl//+td0zTXXFDFf9KIX1d/fWKZxPSdAH3roofqu3N6BCdD8//zXX399uvPOO1NOpuYJlNZZZ52044471idTqgewQoAAAQIECBAgMG4FJEDH7dDqGAECBAgQIPBsF8gJws033zxVq9WC4qijjkqf+cxnlmL59Kc/nT772c8W+3MiMScL82uewOgHP/hBsf+FL3xhuuqqq+rv3XfffdNNN91UbJ9yyinpV7/6VTr77LPrxz/xiU+kj33sY0PGyIUfeOCB4qrOn/70p/X35pUDDjggHXfccf32NW7kSZDmzJlT33XhhRem/fbbr7596aWXpg984APpuuuuq+8rV1ZdddV02GGHpeOPP764srXc75UAAQIECBAgQGB8CngG6PgcV70iQIAAAQIECKT1118/5URluZx11ln1ZGi5LydHzzzzzHIzveY1r0nTp0+vb0dWcgK0MfmZ37PHHnsM+9acoH3BC16QBiY/8xsvuOCCUIzBKsmJ2v3333/Q5Gcun69qPemkk9Khhx462NvtI0CAAAECBAgQGGcCEqDjbEB1hwABAgQIECDQKPC2t72tvnn33Xenyy+/vL6dVy677LK0YMGC+r7DDz+8vh5dybe+52XLLbdMm2yySdp4443Ti1/84mHffuyxxxZXm5YFd9ttt3TeeecVf3bfffd+t+WXZSKvOe7ixYuLonPnzk0XXXRRcRt87mvjVaI58Ztv2bcQIECAAAECBAiMbwG3wI/v8dU7AgQIECBA4FkusM8++xRXgubkZ17OOOOMlJOL5fLtb3+7XC0SmLvuumt9eyQrRx99dPrUpz5VvKXxuaPLipEnMPrSl75UP7zzzjunfNv6iiuuWOybN29e2mWXXdKVV15ZLxNdaUxqbrvttmmvvfZKK6ywQtpggw3S9ttvn/Lt+VtttVVxC/3s2bOjYZUjQIAAAQIECBAYowKuAB2jA6fZBAgQIECAAIGIQE4ovvWtb60X/f73v19MbpR3PPLII/VnfObt5bn6M78vP1PzQx/6UF4tljzR0HDLLbfcUp9EKZd9//vfX09+5u3c7iOPPDKvjnjJzz0tly984QvFFan5Sthzzz035cmTTjjhhMJkhx12SJMmTSqLeiVAgAABAgQIEBinAhKg43RgdYsAAQIECBAgUArkCX/KKyvzrOw5CZqX7373u+nJJ58s1vMs729+85uL9ZH+temmmxZJ0JG87/bbb+9XfIsttui3nTcaE5lLHRxixxFHHFFc8VkWyTPWn3rqqenggw9OM2bMSPkq169//etFMrQs45UAAQIECBAgQGD8CkiAjt+x1TMCBAgQIECAQCEwa9asYlKgkqOc9Kjx9vdXvepV6TnPeU5ZZESvM2fOHFH5XDjP/t645KtIBy5rrrnmwF2h7YMOOiidfvrpabB25StAf/3rX6ecJN1uu+2Kq2BDQRUiQIAAAQIECBAYswISoGN26DScAAECBAgQIBAXaJwMKU+EdN111xWJwDJCTggu75KvHh3pkp/H2bg8+OCDjZvFeuRZoku96Z878tWsd955Z/re976X8hWweWKmgcv111+fPvaxjw3cbZsAAQIECBAgQGCcCUiAjrMB1R0CBAgQIECAwGACe+65Z9poo42KQ/kqyPy8z1qtVmxvttlm6WUve9lgbwvtW54EaL5tvnH5zW9+07hZrP/2t79dal90R77V/8Yb
|
|||
|
|
<hr />
|
|||
|
|
</div>
|
|||
|
|
<div id="comparison" class="section level1">
|
|||
|
|
<h1>Comparison</h1>
|
|||
|
|
<p>Let’s compare the viridis and magma scales against these other
|
|||
|
|
commonly used sequential color palettes in R:</p>
|
|||
|
|
<ul>
|
|||
|
|
<li>Base R palettes: <code>rainbow.colors</code>,
|
|||
|
|
<code>heat.colors</code>, <code>cm.colors</code></li>
|
|||
|
|
<li>The default <strong>ggplot2</strong> palette</li>
|
|||
|
|
<li>Sequential <a href="https://colorbrewer2.org/">colorbrewer</a>
|
|||
|
|
palettes, both default blues and the more viridis-like
|
|||
|
|
yellow-green-blue</li>
|
|||
|
|
</ul>
|
|||
|
|
<p><img src="data:image/png;base64,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
|
|||
|
|
<p>It is immediately clear that the “rainbow” palette is not
|
|||
|
|
perceptually uniform; there are several “kinks” where the apparent color
|
|||
|
|
changes quickly over a short range of values. This is also true, though
|
|||
|
|
less so, for the “heat” colors. The other scales are more perceptually
|
|||
|
|
uniform, but “viridis” stands out for its large <em>perceptual
|
|||
|
|
range</em>. It makes as much use of the available color space as
|
|||
|
|
possible while maintaining uniformity.</p>
|
|||
|
|
<p>Now, let’s compare these as they might appear under various forms of
|
|||
|
|
colorblindness, which can be simulated using the <strong><a href="https://cran.r-project.org/package=dichromat">dichromat</a></strong>
|
|||
|
|
package:</p>
|
|||
|
|
<div id="green-blind-deuteranopia" class="section level3">
|
|||
|
|
<h3>Green-Blind (Deuteranopia)</h3>
|
|||
|
|
<p><img src="data:image/png;base64,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
|
|||
|
|
</div>
|
|||
|
|
<div id="red-blind-protanopia" class="section level3">
|
|||
|
|
<h3>Red-Blind (Protanopia)</h3>
|
|||
|
|
<p><img src="data:image/png;base64,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
|
|||
|
|
</div>
|
|||
|
|
<div id="blue-blind-tritanopia" class="section level3">
|
|||
|
|
<h3>Blue-Blind (Tritanopia)</h3>
|
|||
|
|
<p><img src="data:image/png;base64,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
|
|||
|
|
</div>
|
|||
|
|
<div id="desaturated" class="section level3">
|
|||
|
|
<h3>Desaturated</h3>
|
|||
|
|
<p><img src="data:image/png;base64,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
|
|||
|
|
<p>We can see that in these cases, “rainbow” is quite problematic - it
|
|||
|
|
is not perceptually consistent across its range. “Heat” washes out at
|
|||
|
|
bright colors, as do the brewer scales to a lesser extent. The ggplot
|
|||
|
|
scale does not wash out, but it has a low perceptual range - there’s not
|
|||
|
|
much contrast between low and high values. The “viridis” and “magma”
|
|||
|
|
scales do better - they cover a wide perceptual range in brightness in
|
|||
|
|
brightness and blue-yellow, and do not rely as much on red-green
|
|||
|
|
contrast. They do less well under tritanopia (blue-blindness), but this
|
|||
|
|
is an extrememly rare form of colorblindness.</p>
|
|||
|
|
<hr />
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
<div id="usage" class="section level1">
|
|||
|
|
<h1>Usage</h1>
|
|||
|
|
<p>The <code>viridis()</code> function produces the <code>viridis</code>
|
|||
|
|
color scale. You can choose the other color scale options using the
|
|||
|
|
<code>option</code> parameter or the convenience functions
|
|||
|
|
<code>magma()</code>, <code>plasma()</code>, <code>inferno()</code>,
|
|||
|
|
<code>cividis()</code>, <code>mako()</code>,
|
|||
|
|
<code>rocket</code>()<code>, and</code>turbo()`.</p>
|
|||
|
|
<p>Here the <code>inferno()</code> scale is used for a raster of U.S.
|
|||
|
|
max temperature:</p>
|
|||
|
|
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="fu">library</span>(terra)</span>
|
|||
|
|
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="fu">library</span>(httr)</span>
|
|||
|
|
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a><span class="fu">par</span>(<span class="at">mfrow=</span><span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">1</span>), <span class="at">mar=</span><span class="fu">rep</span>(<span class="fl">0.5</span>, <span class="dv">4</span>))</span>
|
|||
|
|
<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a>temp_raster <span class="ot"><-</span> <span class="st">"http://ftp.cpc.ncep.noaa.gov/GIS/GRADS_GIS/GeoTIFF/TEMP/us_tmax/us.tmax_nohads_ll_20150219_float.tif"</span></span>
|
|||
|
|
<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a><span class="fu">try</span>(<span class="fu">GET</span>(temp_raster,</span>
|
|||
|
|
<span id="cb4-6"><a href="#cb4-6" tabindex="-1"></a> <span class="fu">write_disk</span>(<span class="st">"us.tmax_nohads_ll_20150219_float.tif"</span>)), <span class="at">silent=</span><span class="cn">TRUE</span>)</span>
|
|||
|
|
<span id="cb4-7"><a href="#cb4-7" tabindex="-1"></a>us <span class="ot"><-</span> <span class="fu">rast</span>(<span class="st">"us.tmax_nohads_ll_20150219_float.tif"</span>)</span>
|
|||
|
|
<span id="cb4-8"><a href="#cb4-8" tabindex="-1"></a>us <span class="ot"><-</span> <span class="fu">project</span>(us, <span class="at">y=</span><span class="st">"+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs"</span>)</span>
|
|||
|
|
<span id="cb4-9"><a href="#cb4-9" tabindex="-1"></a><span class="fu">image</span>(us, <span class="at">col=</span><span class="fu">inferno</span>(<span class="dv">256</span>), <span class="at">asp=</span><span class="dv">1</span>, <span class="at">axes=</span><span class="cn">FALSE</span>, <span class="at">xaxs=</span><span class="st">"i"</span>, <span class="at">xaxt=</span><span class="st">'n'</span>, <span class="at">yaxt=</span><span class="st">'n'</span>, <span class="at">ann=</span><span class="cn">FALSE</span>)</span></code></pre></div>
|
|||
|
|
<p><img src="data:image/png;base64,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
|
|||
|
|
<p>The package also contains color scale functions for
|
|||
|
|
<strong>ggplot</strong> plots: <code>scale_color_viridis()</code> and
|
|||
|
|
<code>scale_fill_viridis()</code>. As with <code>viridis()</code>, you
|
|||
|
|
can use the other scales with the <code>option</code> argument in the
|
|||
|
|
<code>ggplot</code> scales.<br />
|
|||
|
|
Here the “magma” scale is used for a cloropleth map of U.S.
|
|||
|
|
unemployment:</p>
|
|||
|
|
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">library</span>(maps)</span></code></pre></div>
|
|||
|
|
<pre><code>##
|
|||
|
|
## Attaching package: 'maps'</code></pre>
|
|||
|
|
<pre><code>## The following object is masked from 'package:viridis':
|
|||
|
|
##
|
|||
|
|
## unemp</code></pre>
|
|||
|
|
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="fu">library</span>(mapproj)</span>
|
|||
|
|
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a></span>
|
|||
|
|
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a><span class="fu">data</span>(unemp, <span class="at">package =</span> <span class="st">"viridis"</span>)</span>
|
|||
|
|
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a></span>
|
|||
|
|
<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a>county_df <span class="ot"><-</span> <span class="fu">map_data</span>(<span class="st">"county"</span>, <span class="at">projection =</span> <span class="st">"albers"</span>, <span class="at">parameters =</span> <span class="fu">c</span>(<span class="dv">39</span>, <span class="dv">45</span>))</span>
|
|||
|
|
<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a><span class="fu">names</span>(county_df) <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"long"</span>, <span class="st">"lat"</span>, <span class="st">"group"</span>, <span class="st">"order"</span>, <span class="st">"state_name"</span>, <span class="st">"county"</span>)</span>
|
|||
|
|
<span id="cb8-7"><a href="#cb8-7" tabindex="-1"></a>county_df<span class="sc">$</span>state <span class="ot"><-</span> state.abb[<span class="fu">match</span>(county_df<span class="sc">$</span>state_name, <span class="fu">tolower</span>(state.name))]</span>
|
|||
|
|
<span id="cb8-8"><a href="#cb8-8" tabindex="-1"></a>county_df<span class="sc">$</span>state_name <span class="ot"><-</span> <span class="cn">NULL</span></span>
|
|||
|
|
<span id="cb8-9"><a href="#cb8-9" tabindex="-1"></a></span>
|
|||
|
|
<span id="cb8-10"><a href="#cb8-10" tabindex="-1"></a>state_df <span class="ot"><-</span> <span class="fu">map_data</span>(<span class="st">"state"</span>, <span class="at">projection =</span> <span class="st">"albers"</span>, <span class="at">parameters =</span> <span class="fu">c</span>(<span class="dv">39</span>, <span class="dv">45</span>))</span>
|
|||
|
|
<span id="cb8-11"><a href="#cb8-11" tabindex="-1"></a></span>
|
|||
|
|
<span id="cb8-12"><a href="#cb8-12" tabindex="-1"></a>choropleth <span class="ot"><-</span> <span class="fu">merge</span>(county_df, unemp, <span class="at">by =</span> <span class="fu">c</span>(<span class="st">"state"</span>, <span class="st">"county"</span>))</span>
|
|||
|
|
<span id="cb8-13"><a href="#cb8-13" tabindex="-1"></a>choropleth <span class="ot"><-</span> choropleth[<span class="fu">order</span>(choropleth<span class="sc">$</span>order), ]</span>
|
|||
|
|
<span id="cb8-14"><a href="#cb8-14" tabindex="-1"></a></span>
|
|||
|
|
<span id="cb8-15"><a href="#cb8-15" tabindex="-1"></a><span class="fu">ggplot</span>(choropleth, <span class="fu">aes</span>(long, lat, <span class="at">group =</span> group)) <span class="sc">+</span></span>
|
|||
|
|
<span id="cb8-16"><a href="#cb8-16" tabindex="-1"></a> <span class="fu">geom_polygon</span>(<span class="fu">aes</span>(<span class="at">fill =</span> rate), <span class="at">colour =</span> <span class="fu">alpha</span>(<span class="st">"white"</span>, <span class="dv">1</span> <span class="sc">/</span> <span class="dv">2</span>), <span class="at">linewidth =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
|
|||
|
|
<span id="cb8-17"><a href="#cb8-17" tabindex="-1"></a> <span class="fu">geom_polygon</span>(<span class="at">data =</span> state_df, <span class="at">colour =</span> <span class="st">"white"</span>, <span class="at">fill =</span> <span class="cn">NA</span>) <span class="sc">+</span></span>
|
|||
|
|
<span id="cb8-18"><a href="#cb8-18" tabindex="-1"></a> <span class="fu">coord_fixed</span>() <span class="sc">+</span></span>
|
|||
|
|
<span id="cb8-19"><a href="#cb8-19" tabindex="-1"></a> <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
|
|||
|
|
<span id="cb8-20"><a href="#cb8-20" tabindex="-1"></a> <span class="fu">ggtitle</span>(<span class="st">"US unemployment rate by county"</span>) <span class="sc">+</span></span>
|
|||
|
|
<span id="cb8-21"><a href="#cb8-21" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">axis.line =</span> <span class="fu">element_blank</span>(), <span class="at">axis.text =</span> <span class="fu">element_blank</span>(),</span>
|
|||
|
|
<span id="cb8-22"><a href="#cb8-22" tabindex="-1"></a> <span class="at">axis.ticks =</span> <span class="fu">element_blank</span>(), <span class="at">axis.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span></span>
|
|||
|
|
<span id="cb8-23"><a href="#cb8-23" tabindex="-1"></a> <span class="fu">scale_fill_viridis</span>(<span class="at">option=</span><span class="st">"magma"</span>)</span></code></pre></div>
|
|||
|
|
<p><img src="data:image/png;base64,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
|
|||
|
|
<p>The ggplot functions also can be used for discrete scales with the
|
|||
|
|
argument <code>discrete=TRUE</code>.</p>
|
|||
|
|
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>p <span class="ot"><-</span> <span class="fu">ggplot</span>(mtcars, <span class="fu">aes</span>(wt, mpg))</span>
|
|||
|
|
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a>p <span class="sc">+</span> <span class="fu">geom_point</span>(<span class="at">size=</span><span class="dv">4</span>, <span class="fu">aes</span>(<span class="at">colour =</span> <span class="fu">factor</span>(cyl))) <span class="sc">+</span></span>
|
|||
|
|
<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a> <span class="fu">scale_color_viridis</span>(<span class="at">discrete=</span><span class="cn">TRUE</span>) <span class="sc">+</span></span>
|
|||
|
|
<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a> <span class="fu">theme_bw</span>()</span></code></pre></div>
|
|||
|
|
<p><img src="data:image/png;base64,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
|
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</div>
|
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<div id="gallery" class="section level1">
|
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<h1>Gallery</h1>
|
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<p>Here are some examples of viridis being used in the wild:</p>
|
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<p>James Curley uses <strong>viridis</strong> for matrix plots (<a href="https://gist.github.com/jalapic/9a1c069aa8cee4089c1e">Code</a>):</p>
|
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<p><a href="http://pbs.twimg.com/media/CQWw9EgWsAAoUi0.png"><img src="data:image/png;base64,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
|
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|
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<p>Christopher Moore created these contour plots of potential in a
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dynamic plankton-consumer model:</p>
|
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