johncolby

macOS hiDPI retina scaling options compared

These are some photos comparing different macOS retina scaling options. All use a non-vector test pattern displayed at 100% (AAPM TG18). All photos are taken at roughly the same distance, using the same crop. Forgive the slight hand-held motion blur, and focus on these variables:

• Pixel size
• UI size
• Pixel perfection (has any interpolation taken place?)
• Real estate (how much of the image is displayed in this representative patch?)

Dell U2713H

johncolby

# $Id: textplot.R 1213 2007-11-01 20:20:10Z warnes $

textplot <- function(object, halign="center", valign="center", cex, ... )
  UseMethod('textplot')


textplot.default <- function(object,
                             halign=c("center","left","right"),
                             valign=c("center","top","bottom"),
                             cex, ... )

johncolby

Pf1 Pf2 Pf3 Pf4
0.4 0.3 0.1 0.6
0.1 0.7 0.7 1
0.4 0.9 0.5 0.7
0.3 0.4 0.4 0.7
1 0.8 0.4 0.2
 0.4 0 0.3
 0.4 0.8 0.5
 0.1 0.2 
 0.2 0.2 

johncolby

original = expand.grid(ID1=c('a', 'b', 'c'), ID2 = c('red', 'green', 'blue'))
original$value = 0

replacement = data.frame(ID1='b', ID2='red', value=1)

rownames(original) = paste(original$ID1, original$ID2)
rownames(replacement) = paste(replacement$ID1, replacement$ID2)

original[rownames(replacement), ] = replacement

johncolby

require(plyr)
set.seed(12345)

n = 10
n.sum = 2

# Simulate data
a = sample.int(10, n, replace=T)
df = data.frame(n=1:n, a)

johncolby

# Download example data from: https://github.com/johncolby/SVM-RFE/zipball/master 

setwd('/path/to/SVM-RFE/') # Change this to your setup

load('demo/input.Rdata')
data  = input[, 2:3]
input = input[, -(1:3)]

# Function to residualize a vector x for the covariates in data
residualize <- function(x, fun=x~., data) {

johncolby

-1.22920245520088 1.26297072626596 1.83337525320795
1.20936180458861 0.611215979963239 -1.30133702133403
0.522987838117479 -0.451731971345027 -0.58388185591968
0.101340272969554 -0.435726497850704 0.569398963635685
0.426031516466784 0.151080475310323 0.956491447694583
-0.252059713648723 -0.175471660877733 -0.980883888395024
0.662052189667485 1.33212600996006 -1.47268894052709
1.48817331808508 -0.0473711069814109 -0.492789669961388
-0.503441036994115 -0.294926138113795 0.366179342735957
-2.12920560829524 1.02332212331524 -1.58266122316052

johncolby

library(plyr)

data = arrange(data.frame(ID=1:10, FA=runif(50, 0.2, 0.8)), ID)

ddply(data, 'ID', mean) # using plyr library
aggregate(data, list(ID=data$ID), mean) # using base

johncolby

library(plyr)
library(ggplot2)

# Simulate data
numSubs = 40

x = runif(numSubs, 0, 30)
x = c(x, x + abs(rnorm(numSubs, 2)))
y = 2 + x - 0.02*x^2 + rnorm(2*numSubs, 0, 1)

johncolby

# Function to plot color bar
color.bar <- function(lut, min, max=-min, nticks=11, ticks=seq(min, max, len=nticks), title='') {
    scale = (length(lut)-1)/(max-min)

    dev.new(width=1.75, height=5)
    plot(c(0,10), c(min,max), type='n', bty='n', xaxt='n', xlab='', yaxt='n', ylab='', main=title)
    axis(2, ticks, las=1)
    for (i in 1:(length(lut)-1)) {
    	y = (i-1)/scale + min
    	rect(0,y,10,y+1/scale, col=lut[i], border=NA)