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# All matrices in this file are adapted from https://github.com/njsmith/colorspacious/blob/master/colorspacious/cvd.py
#' Color Vision Deficiency (CVD) Conversion Tables
#'
#' Conversion tables for simulating different types of color vision deficiency (CVD):
#' Protanomaly, deutanomaly, tritanomaly.
#'
#' Machado et al. (2009) have established a novel model, that allows to handle normal color
#' vision, anomalous trichromacy, and dichromacy in a unified way. They also provide conversion
#' formulas along with tables of certain constants that allow to simulate various types of
#' CVD. See \code{\link{simulate_cvd}} for the corresponding simulation functions.
#'
#' @name cvd
#' @rdname cvd
#' @format Lists of 3x3 RGB-color transformation matrices for the various types of CVD. Each list contains 11 transformation matrices
#' representing increasingly severe color vision deficiency.
#' @usage NULL
#' @seealso \code{\link{simulate_cvd}}
#' @references Machado GM, Oliveira MM, Fernandes LAF (2009).
#' A Physiologically-Based Model for Simulation of Color Vision Deficiency.
#' \emph{IEEE Transactions on Visualization and Computer Graphics}. \bold{15}(6), 1291--1298.
#' \doi{10.1109/TVCG.2009.113}
#' Online version with supplements at
#' \url{http://www.inf.ufrgs.br/~oliveira/pubs_files/CVD_Simulation/CVD_Simulation.html}.
#'
#' Zeileis A, Fisher JC, Hornik K, Ihaka R, McWhite CD, Murrell P, Stauffer R, Wilke CO (2020).
#' \dQuote{colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes.}
#' \emph{Journal of Statistical Software}, \bold{96}(1), 1--49. \doi{10.18637/jss.v096.i01}
NULL
#' @rdname cvd
#' @format NULL
#' @usage protanomaly_cvd
#' @export
protanomaly_cvd <- list(
"0" = matrix(c(
1.000000, 0.000000, -0.000000,
0.000000, 1.000000, 0.000000,
-0.000000, -0.000000, 1.000000
), 3,3,byrow = TRUE),
"1" = matrix(c(
0.856167, 0.182038, -0.038205,
0.029342, 0.955115, 0.015544,
-0.002880, -0.001563, 1.004443
),3,3,byrow=TRUE),
"2" = matrix(c(
0.734766, 0.334872, -0.069637,
0.051840, 0.919198, 0.028963,
-0.004928, -0.004209, 1.009137
),3,3,byrow=TRUE),
"3" = matrix(c(
0.630323, 0.465641, -0.095964,
0.069181, 0.890046, 0.040773,
-0.006308, -0.007724, 1.014032
),3,3,byrow=TRUE),
"4" = matrix(c(
0.539009, 0.579343, -0.118352,
0.082546, 0.866121, 0.051332,
-0.007136, -0.011959, 1.019095
),3,3,byrow=TRUE),
"5" = matrix(c(
0.458064, 0.679578, -0.137642,
0.092785, 0.846313, 0.060902,
-0.007494, -0.016807, 1.024301
),3,3,byrow=TRUE),
"6" = matrix(c(
0.385450, 0.769005, -0.154455,
0.100526, 0.829802, 0.069673,
-0.007442, -0.022190, 1.029632
),3,3,byrow=TRUE),
"7" = matrix(c(
0.319627, 0.849633, -0.169261,
0.106241, 0.815969, 0.077790,
-0.007025, -0.028051, 1.035076
),3,3,byrow=TRUE),
"8" = matrix(c(
0.259411, 0.923008, -0.182420,
0.110296, 0.804340, 0.085364,
-0.006276, -0.034346, 1.040622
),3,3,byrow=TRUE),
"9" = matrix(c(
0.203876, 0.990338, -0.194214,
0.112975, 0.794542, 0.092483,
-0.005222, -0.041043, 1.046265
),3,3,byrow=TRUE),
"10" = matrix(c(
0.152286, 1.052583, -0.204868,
0.114503, 0.786281, 0.099216,
-0.003882, -0.048116, 1.051998
),3,3,byrow=TRUE)
)
#' @rdname cvd
#' @format NULL
#' @usage deutanomaly_cvd
#' @export
deutanomaly_cvd <- list(
"0" = matrix(c(
1.000000, 0.000000, -0.000000,
0.000000, 1.000000, 0.000000,
-0.000000, -0.000000, 1.000000
),3,3,byrow=TRUE),
"1" = matrix(c(
0.866435, 0.177704, -0.044139,
0.049567, 0.939063, 0.011370,
-0.003453, 0.007233, 0.996220
),3,3,byrow=TRUE),
"2" = matrix(c(
0.760729, 0.319078, -0.079807,
0.090568, 0.889315, 0.020117,
-0.006027, 0.013325, 0.992702
),3,3,byrow=TRUE),
"3" = matrix(c(
0.675425, 0.433850, -0.109275,
0.125303, 0.847755, 0.026942,
-0.007950, 0.018572, 0.989378
),3,3,byrow=TRUE),
"4" = matrix(c(
0.605511, 0.528560, -0.134071,
0.155318, 0.812366, 0.032316,
-0.009376, 0.023176, 0.986200
),3,3,byrow=TRUE),
"5" = matrix(c(
0.547494, 0.607765, -0.155259,
0.181692, 0.781742, 0.036566,
-0.010410, 0.027275, 0.983136
),3,3,byrow=TRUE),
"6" = matrix(c(
0.498864, 0.674741, -0.173604,
0.205199, 0.754872, 0.039929,
-0.011131, 0.030969, 0.980162
),3,3,byrow=TRUE),
"7" = matrix(c(
0.457771, 0.731899, -0.189670,
0.226409, 0.731012, 0.042579,
-0.011595, 0.034333, 0.977261
),3,3,byrow=TRUE),
"8" = matrix(c(
0.422823, 0.781057, -0.203881,
0.245752, 0.709602, 0.044646,
-0.011843, 0.037423, 0.974421
),3,3,byrow=TRUE),
"9" = matrix(c(
0.392952, 0.823610, -0.216562,
0.263559, 0.690210, 0.046232,
-0.011910, 0.040281, 0.971630
),3,3,byrow=TRUE),
"10" = matrix(c(
0.367322, 0.860646, -0.227968,
0.280085, 0.672501, 0.047413,
-0.011820, 0.042940, 0.968881
),3,3,byrow=TRUE)
)
#' @rdname cvd
#' @format NULL
#' @usage tritanomaly_cvd
#' @export
tritanomaly_cvd <- list(
"0" = matrix(c(
1.000000, 0.000000, -0.000000,
0.000000, 1.000000, 0.000000,
-0.000000, -0.000000, 1.000000
),3,3,byrow=TRUE),
"1" = matrix(c(
0.926670, 0.092514, -0.019184,
0.021191, 0.964503, 0.014306,
0.008437, 0.054813, 0.936750
),3,3,byrow=TRUE),
"2" = matrix(c(
0.895720, 0.133330, -0.029050,
0.029997, 0.945400, 0.024603,
0.013027, 0.104707, 0.882266
),3,3,byrow=TRUE),
"3" = matrix(c(
0.905871, 0.127791, -0.033662,
0.026856, 0.941251, 0.031893,
0.013410, 0.148296, 0.838294
),3,3,byrow=TRUE),
"4" = matrix(c(
0.948035, 0.089490, -0.037526,
0.014364, 0.946792, 0.038844,
0.010853, 0.193991, 0.795156
),3,3,byrow=TRUE),
"5" = matrix(c(
1.017277, 0.027029, -0.044306,
-0.006113, 0.958479, 0.047634,
0.006379, 0.248708, 0.744913
),3,3,byrow=TRUE),
"6" = matrix(c(
1.104996, -0.046633, -0.058363,
-0.032137, 0.971635, 0.060503,
0.001336, 0.317922, 0.680742
),3,3,byrow=TRUE),
"7" = matrix(c(
1.193214, -0.109812, -0.083402,
-0.058496, 0.979410, 0.079086,
-0.002346, 0.403492, 0.598854
),3,3,byrow=TRUE),
"8" = matrix(c(
1.257728, -0.139648, -0.118081,
-0.078003, 0.975409, 0.102594,
-0.003316, 0.501214, 0.502102
),3,3,byrow=TRUE),
"9" = matrix(c(
1.278864, -0.125333, -0.153531,
-0.084748, 0.957674, 0.127074,
-0.000989, 0.601151, 0.399838
),3,3,byrow=TRUE),
"10" = matrix(c(
1.255528, -0.076749, -0.178779,
-0.078411, 0.930809, 0.147602,
0.004733, 0.691367, 0.303900
),3,3,byrow=TRUE)
)
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