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Merge branch 'master' of github.com:dmcdougall/scipy14-colormaps
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best-practices/Makefile

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talk.pdf: talk.tex
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pdflatex talk.tex

best-practices/chesapeake.jpg

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best-practices/florida.jpg

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best-practices/florida_masked.jpg

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best-practices/jetnoise.jpg

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best-practices/magfield.jpg

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best-practices/mri.jpg

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best-practices/sinc.jpg

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best-practices/talk.tex

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\documentclass[10pt,presentation,compress]{beamer}
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\usepackage{graphicx}
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\usepackage{parskip,amssymb,amsmath,amsthm,url}
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\usepackage{subfigure}
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\usepackage{esdiff}
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\usepackage{latexsym}
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\usepackage{color}
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\title{How to choose a good colour map}
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\author{Damon McDougall}
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\institute{Institute for Computational and Engineering Sciences, UT Austin, USA}
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\date{20th April 2012}
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\mode<presentation> {
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\usetheme{Singapore}
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\setbeamertemplate{navigation symbols}{}
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\setbeamertemplate{mini frames}{}
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\setbeamertemplate{section in head/foot}{}
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\setbeamercovered{transparent=0}
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\setbeameroption{show notes}
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\setbeamertemplate{footline}
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{
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\leavevmode
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\hbox{
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\begin{beamercolorbox}[wd=.99\paperwidth,ht=2.5ex,dp=1.125ex,right]{title in head/foot}
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\usebeamerfont{}\insertframenumber/\inserttotalframenumber
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\end{beamercolorbox}
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}
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\vskip 0pt
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}
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}
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\mode<handout> {
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\usetheme{Rochester}
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\usepackage{pgfpages}
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\pgfpagesuselayout{4 on 1}[a4paper,landscape,border shrink=5mm]
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\setbeamertemplate{background canvas}{bg=black!5}
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\setbeamercolor{normal text}{fg=black}
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}
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\newcommand{\ud}{\,\mathrm{d}}
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\newcommand{\grad}{\nabla}
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\newcommand{\Var}{\mbox{Var}}
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\newcommand{\Cov}{\mbox{Cov}}
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\newcommand{\argmin}{\mbox{argmin}}
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\newcommand{\iid}{
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\ensuremath{
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\stackrel{\mbox{\scriptsize{i.i.d}}}{\sim}
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}
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}
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\newcommand{\norm}[1]{
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\ensuremath{\left\| #1 \right\|}
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}
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\begin{document}
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\begin{frame}
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\titlepage
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\end{frame}
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\begin{frame}{Overview}
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\begin{enumerate}
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\item Introduction
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\item Hating on the jet colourmap
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\item ITEM 2
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\item ITEM 3
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\item ITEM 4
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\end{enumerate}
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\end{frame}
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\begin{frame}{Introduction}
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Most of the content is taking from this excellent article:
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\linebreak
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\linebreak
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\textcolor{blue}{\small{\url{http://www.research.ibm.com/people/l/lloydt/color/color.HTM}}}
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\end{frame}
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\begin{frame}{Introduction}
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\begin{itemize}
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\item Data is a huge aspect of science
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\item By and large we (scientists) treat data well\dots
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\item \dots and we visualise it poorly. Why?
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\item Colour maps
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\item Data is of some field
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$f : \Omega \subset \mathbb{R}^2 \to [0, 1]$
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\item A colour is assigned to the output of $f$ (a scalar). Seems
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reasonable.
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\item Colour map is a function
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$g : [0, 1] \to \Omega' \subset \mathbb{R}^3$
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\item Mismatch in dimensions: $\mathbb{R}^3$ versus $\mathbb{R}$
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\item The point? Colour maps can be misleading.
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\end{itemize}
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\end{frame}
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\begin{frame}{Hating on the jet colour map}
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What is this?
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\begin{figure}[htp]
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\includegraphics[scale=7.0]{florida_masked.jpg}
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\end{figure}
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\end{frame}
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\begin{frame}{Hating on the jet colour map}
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Did anybody see Florida?
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\begin{figure}[htp]
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\includegraphics[scale=7.0]{florida.jpg}
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\end{figure}
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\begin{itemize}
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\item Left: Linear interpolation in RGB space between red and blue.
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\item Right: Changes in data are \textit{perceived} as proportional
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changes in colour (subjective)
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\item Right: Domain specific knowledge used to reveal important features
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\end{itemize}
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\end{frame}
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\begin{frame}
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\begin{center}
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Thank you.
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\end{center}
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\end{frame}
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\end{document}

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