data-raw/build-datasets.R
In USGS-R/inlmisc: Miscellaneous Functions for the USGS INL Project Office
#!/usr/bin/env Rscript
.ReadCpt <- function(file, cite=NULL, type="Sequential") {
checkmate::assertString(file)
checkmate::assertString(cite, null.ok=TRUE)
type <- match.arg(type, c("Sequential", "Diverging", "Qualitative"))
line <- readLines(file)
line <- line[-grep("^(#$|#-+)", line)]
nm <- c("COLOR_MODEL", "RANGE", "HINGE", "CYCLIC")
option <- lapply(nm, function(opt) {
idx <- grep(sprintf("^#[ \t]%s", opt), line)
if (length(idx) == 0) return(NULL)
x <- tail(strsplit(line[idx], "[ \t]")[[1]], 1)
line <<- line[-idx]
if (opt == "COLOR_MODEL") x <- toupper(x)
if (opt == "RANGE") x <- as.numeric(strsplit(x, "/")[[1]])
if (opt == "HINGE") x <- as.numeric(x)
if (opt == "CYCLIC") x <- TRUE
x
})
names(option) <- nm
if (option$COLOR_MODEL != "RGB") return(NULL)
color <- lapply(c("N"="N", "B"="B", "F"="F"), function(key) {
idx <- grep(sprintf("^%s[ |\t]", key), line)
if (length(idx) == 0) return(NULL)
x <- strsplit(line[idx], "[ \t]")[[1]]
x <- tail(x, 1)
line <<- line[-idx]
.Cpt2Hex(x)
})
idx <- grep("^#[ \t]", line)
note <- strwrap(substring(line[idx], 3), width=.Machine$integer.max)
line <- line[-idx]
m <- do.call("rbind", lapply(line, function(x) {
x <- strsplit(x, "[ \t]")[[1]]
x <- x[x != ""]
if (length(x) == 4) {
elem <- c(x[1], .Cpt2Hex(x[2]), x[3], .Cpt2Hex(x[4]))
} else if (length(x) == 8) {
elem <- c(x[1], .Cpt2Hex(x[2:4]), x[5], .Cpt2Hex(x[6:8]))
} else {
return(NULL)
}
elem
}))
if (is.null(m)) return(NULL)
for (i in seq_len(nrow(m) - 1))
if (!identical(m[i, 4], m[i + 1, 2]))
return(NULL)
d <- as.data.frame(rbind(m[, 1:2], m[nrow(m), 3:4]), stringsAsFactors=FALSE)
names(d) <- c("value", "color")
d$value <- as.numeric(d$value)
if (is.numeric(option$RANGE)) {
if (is.numeric(option$HINGE)) {
x <- c(-1, 0, 1)
y <- c(option$RANGE[1], option$HINGE, option$RANGE[2])
d$value <- stats::approx(x, y, xout=d$value)
} else {
d$value <- d$value * diff(option$RANGE) + option$RANGE[1]
}
}
l <- list("data" = d,
"type" = type,
"cite" = cite,
"nmax" = Inf,
"nan" = color$N,
"back" = color$B,
"fore" = color$F,
"note" = note)
l[vapply(l, is.null, FALSE)] <- NULL
l
}
.Cpt2Hex <- function(x) {
checkmate::assertVector(x, strict=TRUE, any.missing=FALSE, min.len=1, max.len=3)
if (length(x) == 1) {
if (grepl("/", x))
x <- as.integer(strsplit(x, "/")[[1]])
else
x <- t(grDevices::col2rgb(x))[1, ]
}
grDevices::rgb(x[1], x[2], x[3], maxColorValue=255)
}
.GetCptGmt <- function() {
# Generic Mapping Tools (GMT)
cite <- "Wessel and others (2013)"
# code adapted from stackoverflow answer by lukeA, accessed October 27, 2018
# at https://stackoverflow.com/questions/25485216
host <- "api.github.com"
owner <- "GenericMappingTools"
repo <- "gmt"
fmt <- "https://%s/repos/%s/%s/git/trees/master?recursive=1"
path <- sprintf(fmt, host, owner, repo)
info <- httr::GET(sprintf(fmt, host, owner, repo))
httr::stop_for_status(info)
tree <- unlist(lapply(httr::content(info)$tree, "[", "path"), use.names=FALSE)
path <- grep("share/cpt/", tree, value=TRUE, fixed=TRUE)
host <- "raw.githubusercontent.com"
file <- sprintf("https://%s/%s/%s/master/%s", host, owner, repo, path)
destfile <- file.path(getwd(), "cpt", basename(file))
for (i in seq_along(file)) {
utils::download.file(file[i], destfile[i], quiet=TRUE)
}
nm <- tools::file_path_sans_ext(basename(file))
type <- rep("Sequential", length(nm))
div <- c("polar",
"red2green",
"roma",
"split")
type[nm %in% div] <- "Diverging"
cpt <- lapply(seq_along(destfile), function(i) {
.ReadCpt(destfile[i], cite=cite, type=type[i])
})
names(cpt) <- nm
is <- !vapply(cpt, is.null, FALSE)
cpt <- cpt[is]
unlink(destfile[!is])
cpt
}
.CheckScheme <- function(x) {
checkmate::assertDataFrame(x$data, any.missing=FALSE,
min.rows=2, min.cols=1, null.ok=TRUE)
pattern <- "^#(\\d|[a-f]){6}$"
checkmate::assertCharacter(x$data$color, pattern=pattern,
ignore.case=TRUE, null.ok=TRUE)
stopifnot(all(inlmisc::IsColor(x$data$color)))
checkmate::qassert(x$data$name, c("0", "S", "X(0,)"))
checkmate::qassert(x$gray, c("0", "S", "X(0,)"))
checkmate::assertSubset(x$gray, x$data$name)
checkmate::assertNumeric(x$data$value, finite=TRUE, unique=TRUE,
sorted=TRUE, null.ok=TRUE)
checkmate::assertSubset(x$type, c("Qualitative", "Diverging", "Sequential"))
checkmate::assertString(x$cite)
checkmate::assertNumber(x$nmax)
checkmate::assertCharacter(x$back, pattern=pattern, ignore.case=TRUE, null.ok=TRUE)
checkmate::assertCharacter(x$fore, pattern=pattern, ignore.case=TRUE, null.ok=TRUE)
checkmate::assertCharacter(x$nan, pattern=pattern, ignore.case=TRUE, null.ok=TRUE)
stopifnot(inlmisc::IsColor(x$back, null.ok=TRUE))
stopifnot(inlmisc::IsColor(x$fore, null.ok=TRUE))
stopifnot(inlmisc::IsColor(x$nan, null.ok=TRUE))
checkmate::assertCharacter(x$note, null.ok=TRUE)
invisible()
}
options(stringsAsFactors=FALSE)
dir.create(file.path(getwd(), "cpt"), showWarnings=FALSE)
schemes <- .GetCptGmt()
# Thomas Dewez (SRON) granted permission to add his color schemes on October 17, 2018.
schemes[["DEM screen"]] <- list(
data = read.csv(strip.white=TRUE, text="
value, color
0, #008435
100, #33CC00
200, #F4F071
400, #F4BD45
600, #99642B
800, #FFFFFF
"),
type = "Sequential",
cite = "Thomas Dewez (2004)",
nmax = Inf,
back = "#FFFFFF",
fore = "#008435",
nan = "#008435"
)
schemes[["DEM print"]] <- list(
data = read.csv(strip.white=TRUE, text="
value, color
0, #336600
100, #81C31F
200, #FFFFCC
400, #F4BD45
500, #66330C
600, #663300
800, #FFFFFF
"),
type = "Sequential",
cite = "Thomas Dewez (2004)",
nmax = Inf,
back = "#336600",
fore = "#FFFFFF",
nan = "#336600"
)
schemes[["DEM poster"]] <- list(
data = read.csv(strip.white=TRUE, text="
value, color
0, #006147
50, #107A2F
500, #E8D77D
1200, #A14300
1700, #9E0000
2800, #6E6E6E
4000, #FFFFFF
4900, #FFFFFF
"),
type = "Sequential",
cite = "Thomas Dewez (2004)",
nmax = Inf,
back = "#99CCFF",
fore = "#99CCFF",
nan = "#99CCFF"
)
# Paul Tol (SRON) granted permission to add his color schemes on October 17, 2018.
schemes[["bright"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
blue, #4477AA
red, #EE6677
green, #228833
yellow, #CCBB44
cyan, #66CCEE
purple, #AA3377
grey, #BBBBBB
"),
gray = c("yellow", "red", "green"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 7
)
schemes[["high-contrast"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
blue, #004488
yellow, #DDAA33
red, #BB5566
black, #000000
white, #FFFFFF
"),
gray = c("white", "yellow", "red", "blue", "black"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 5
)
schemes[["vibrant"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
orange, #EE7733
blue, #0077BB
cyan, #33BBEE
magenta, #EE3377
red, #CC3311
teal, #009988
grey, #BBBBBB
"),
gray = c("grey", "orange", "magenta", "blue"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 7
)
schemes[["muted"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
rose, #CC6677
indigo, #332288
sand, #DDCC77
green, #117733
cyan, #88CCEE
wine, #882255
teal, #44AA99
olive, #999933
purple, #AA4499
"),
gray = c("sand", "teal", "purple", "green", "indigo"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 9,
nas = "#DDDDDD"
)
schemes[["pale"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
pale blue, #BBCCEE
pale cyan, #CCEEFF
pale green, #CCDDAA
pale yellow, #EEEEBB
pale red, #FFCCCC
pale grey, #DDDDDD
"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 6
)
schemes[["dark"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
dark blue, #222255
dark cyan, #225555
dark green, #225522
dark yellow, #666633
dark red, #663333
dark grey, #555555
"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 6
)
schemes[["light"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
light blue, #77AADD
orange, #EE8866
light yellow, #EEDD88
pink, #FFAABB
light cyan, #99DDFF
mint, #44BB99
pear, #BBCC33
olive, #AAAA00
pale grey, #DDDDDD
"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 9
)
schemes[["ground cover"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
water, #5566AA
evergreen needleleaf forest, #117733
deciduous needleleaf forest, #44AA66
mixed forest, #55AA22
evergreen broadleaf forest, #668822
deciduous broadleaf forest, #99BB55
woodland, #558877
wooded grassland, #88BBAA
grassland, #AADDCC
cropland, #44AA88
closed shrubland, #DDCC66
open shrubland, #FFDD44
bare ground, #FFEE88
urban and built, #BB0011
"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 14
)
schemes[["sunset"]] <- list(
data = read.csv(strip.white=TRUE, text="
color
#364B9A
#4A7BB7
#6EA6CD
#98CAE1
#C2E4EF
#EAECCC
#FEDA8B
#FDB366
#F67E4B
#DD3D2D
#A50026
"),
type = "Diverging",
cite = "Paul Tol (2018)",
nmax = Inf,
nan = "#FFFFFF"
)
schemes[["BuRd"]] <- list(
data = read.csv(strip.white=TRUE, text="
color
#2166AC
#4393C3
#92C5DE
#D1E5F0
#F7F7F7
#FDDBC7
#F4A582
#D6604D
#B2182B
"),
type = "Diverging",
cite = "Paul Tol (2018)",
nmax = Inf,
nan = "#FFEE99"
)
schemes[["PRGn"]] <- list(
data = read.csv(strip.white=TRUE, text="
color
#762A83
#9970AB
#C2A5CF
#E7D4E8
#F7F7F7
#D9F0D3
#ACD39E
#5AAE61
#1B7837
"),
type = "Diverging",
cite = "Paul Tol (2018)",
nmax = Inf,
nan = "#FFEE99"
)
schemes[["YlOrBr"]] <- list(
data = read.csv(strip.white=TRUE, text="
color
#FFFFE5
#FFF7BC
#FEE391
#FEC44F
#FB9A29
#EC7014
#CC4C02
#993404
#662506
"),
type = "Sequential",
cite = "Paul Tol (2018)",
nmax = Inf,
nan = "#888888"
)
schemes[["iridescent"]] <- list(
data = read.csv(strip.white=TRUE, text="
color
#FEFBE9
#FCF7D5
#F5F3C1
#EAF0B5
#DDECBF
#D0E7CA
#C2E3D2
#B5DDD8
#A8D8DC
#9BD2E1
#8DCBE4
#81C4E7
#7BBCE7
#7EB2E4
#88A5DD
#9398D2
#9B8AC4
#9D7DB2
#9A709E
#906388
#805770
#684957
#46353A
"),
type = "Sequential",
cite = "Paul Tol (2018)",
nmax = Inf,
nan = "#999999"
)
schemes[["smooth rainbow"]] <- list(
data = read.csv(strip.white=TRUE, text="
color
#E8ECFB
#DDD8EF
#D1C1E1
#C3A8D1
#B58FC2
#A778B4
#9B62A7
#8C4E99
#6F4C9B
#6059A9
#5568B8
#4E79C5
#4D8AC6
#4E96BC
#549EB3
#59A5A9
#60AB9E
#69B190
#77B77D
#8CBC68
#A6BE54
#BEBC48
#D1B541
#DDAA3C
#E49C39
#E78C35
#E67932
#E4632D
#DF4828
#DA2222
#B8221E
#95211B
#721E17
#521A13
"),
type = "Sequential",
cite = "Paul Tol (2018)",
nmax = Inf,
nan = "#666666"
)
schemes[["discrete rainbow"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
1, #E8ECFB
2, #D9CCE3
3, #D1BBD7
4, #CAACCB
5, #BA8DB4
6, #AE76A3
7, #AA6F9E
8, #994F88
9, #882E72
10, #1965B0
11, #437DBF
12, #5289C7
13, #6195CF
14, #7BAFDE
15, #4EB265
16, #90C987
17, #CAE0AB
18, #F7F056
19, #F7CB45
20, #F6C141
21, #F4A736
22, #F1932D
23, #EE8026
24, #E8601C
25, #E65518
26, #DC050C
27, #A5170E
28, #72190E
29, #42150A
"),
type = "Sequential",
cite = "Paul Tol (2018)",
nmax = 23,
nan = "#777777"
)
discrete_rainbow_indexes <- list(
c(10),
c(10, 26),
c(10, 18, 26),
c(10, 15, 18, 26),
c(10, 14, 15, 18, 26),
c(10, 14, 15, 17, 18, 26),
c( 9, 10, 14, 15, 17, 18, 26),
c( 9, 10, 14, 15, 17, 18, 23, 26),
c( 9, 10, 14, 15, 17, 18, 23, 26, 28),
c( 9, 10, 14, 15, 17, 18, 21, 24, 26, 28),
c( 9, 10, 12, 14, 15, 17, 18, 21, 24, 26, 28),
c( 3, 6, 9, 10, 12, 14, 15, 17, 18, 21, 24, 26),
c( 3, 6, 9, 10, 12, 14, 15, 16, 17, 18, 21, 24, 26),
c( 3, 6, 9, 10, 12, 14, 15, 16, 17, 18, 20, 22, 24, 26),
c( 3, 6, 9, 10, 12, 14, 15, 16, 17, 18, 20, 22, 24, 26, 28),
c( 3, 5, 7, 9, 10, 12, 14, 15, 16, 17, 18, 20, 22, 24, 26, 28),
c( 3, 5, 7, 8, 9, 10, 12, 14, 15, 16, 17, 18, 20, 22, 24, 26, 28),
c( 3, 5, 7, 8, 9, 10, 12, 14, 15, 16, 17, 18, 20, 22, 24, 26, 27, 28),
c( 2, 4, 5, 7, 8, 9, 10, 12, 14, 15, 16, 17, 18, 20, 22, 24, 26, 27, 28),
c( 2, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 22, 24, 26, 27, 28),
c( 2, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 21, 23, 25, 26, 27, 28),
c( 2, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 21, 23, 25, 26, 27, 28, 29),
c( 1, 2, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 21, 23, 25, 26, 27, 28, 29)
)
# Unknown author on gnuplot-info
schemes[["bpy"]] <- list(
type = "Sequential",
cite = "unknown",
nmax = Inf
)
# Anton Mikhailov (Google)
schemes[["turbo"]] <- list(
type = "Sequential",
cite = "Anton Mikhailov (2019)",
nmax = Inf
)
# data from turbo colormap look-up table,
# copyright 2019 Google LLC, Apache-2.0 license,
# accessed August 21, 2019
# at https://gist.github.com/mikhailov-work/ee72ba4191942acecc03fe6da94fc73f
turbo_colormap_data <- rbind(c(0.18995, 0.07176, 0.23217),
c(0.19483, 0.08339, 0.26149),
c(0.19956, 0.09498, 0.29024),
c(0.20415, 0.10652, 0.31844),
c(0.20860, 0.11802, 0.34607),
c(0.21291, 0.12947, 0.37314),
c(0.21708, 0.14087, 0.39964),
c(0.22111, 0.15223, 0.42558),
c(0.22500, 0.16354, 0.45096),
c(0.22875, 0.17481, 0.47578),
c(0.23236, 0.18603, 0.50004),
c(0.23582, 0.19720, 0.52373),
c(0.23915, 0.20833, 0.54686),
c(0.24234, 0.21941, 0.56942),
c(0.24539, 0.23044, 0.59142),
c(0.24830, 0.24143, 0.61286),
c(0.25107, 0.25237, 0.63374),
c(0.25369, 0.26327, 0.65406),
c(0.25618, 0.27412, 0.67381),
c(0.25853, 0.28492, 0.69300),
c(0.26074, 0.29568, 0.71162),
c(0.26280, 0.30639, 0.72968),
c(0.26473, 0.31706, 0.74718),
c(0.26652, 0.32768, 0.76412),
c(0.26816, 0.33825, 0.78050),
c(0.26967, 0.34878, 0.79631),
c(0.27103, 0.35926, 0.81156),
c(0.27226, 0.36970, 0.82624),
c(0.27334, 0.38008, 0.84037),
c(0.27429, 0.39043, 0.85393),
c(0.27509, 0.40072, 0.86692),
c(0.27576, 0.41097, 0.87936),
c(0.27628, 0.42118, 0.89123),
c(0.27667, 0.43134, 0.90254),
c(0.27691, 0.44145, 0.91328),
c(0.27701, 0.45152, 0.92347),
c(0.27698, 0.46153, 0.93309),
c(0.27680, 0.47151, 0.94214),
c(0.27648, 0.48144, 0.95064),
c(0.27603, 0.49132, 0.95857),
c(0.27543, 0.50115, 0.96594),
c(0.27469, 0.51094, 0.97275),
c(0.27381, 0.52069, 0.97899),
c(0.27273, 0.53040, 0.98461),
c(0.27106, 0.54015, 0.98930),
c(0.26878, 0.54995, 0.99303),
c(0.26592, 0.55979, 0.99583),
c(0.26252, 0.56967, 0.99773),
c(0.25862, 0.57958, 0.99876),
c(0.25425, 0.58950, 0.99896),
c(0.24946, 0.59943, 0.99835),
c(0.24427, 0.60937, 0.99697),
c(0.23874, 0.61931, 0.99485),
c(0.23288, 0.62923, 0.99202),
c(0.22676, 0.63913, 0.98851),
c(0.22039, 0.64901, 0.98436),
c(0.21382, 0.65886, 0.97959),
c(0.20708, 0.66866, 0.97423),
c(0.20021, 0.67842, 0.96833),
c(0.19326, 0.68812, 0.96190),
c(0.18625, 0.69775, 0.95498),
c(0.17923, 0.70732, 0.94761),
c(0.17223, 0.71680, 0.93981),
c(0.16529, 0.72620, 0.93161),
c(0.15844, 0.73551, 0.92305),
c(0.15173, 0.74472, 0.91416),
c(0.14519, 0.75381, 0.90496),
c(0.13886, 0.76279, 0.89550),
c(0.13278, 0.77165, 0.88580),
c(0.12698, 0.78037, 0.87590),
c(0.12151, 0.78896, 0.86581),
c(0.11639, 0.79740, 0.85559),
c(0.11167, 0.80569, 0.84525),
c(0.10738, 0.81381, 0.83484),
c(0.10357, 0.82177, 0.82437),
c(0.10026, 0.82955, 0.81389),
c(0.09750, 0.83714, 0.80342),
c(0.09532, 0.84455, 0.79299),
c(0.09377, 0.85175, 0.78264),
c(0.09287, 0.85875, 0.77240),
c(0.09267, 0.86554, 0.76230),
c(0.09320, 0.87211, 0.75237),
c(0.09451, 0.87844, 0.74265),
c(0.09662, 0.88454, 0.73316),
c(0.09958, 0.89040, 0.72393),
c(0.10342, 0.89600, 0.71500),
c(0.10815, 0.90142, 0.70599),
c(0.11374, 0.90673, 0.69651),
c(0.12014, 0.91193, 0.68660),
c(0.12733, 0.91701, 0.67627),
c(0.13526, 0.92197, 0.66556),
c(0.14391, 0.92680, 0.65448),
c(0.15323, 0.93151, 0.64308),
c(0.16319, 0.93609, 0.63137),
c(0.17377, 0.94053, 0.61938),
c(0.18491, 0.94484, 0.60713),
c(0.19659, 0.94901, 0.59466),
c(0.20877, 0.95304, 0.58199),
c(0.22142, 0.95692, 0.56914),
c(0.23449, 0.96065, 0.55614),
c(0.24797, 0.96423, 0.54303),
c(0.26180, 0.96765, 0.52981),
c(0.27597, 0.97092, 0.51653),
c(0.29042, 0.97403, 0.50321),
c(0.30513, 0.97697, 0.48987),
c(0.32006, 0.97974, 0.47654),
c(0.33517, 0.98234, 0.46325),
c(0.35043, 0.98477, 0.45002),
c(0.36581, 0.98702, 0.43688),
c(0.38127, 0.98909, 0.42386),
c(0.39678, 0.99098, 0.41098),
c(0.41229, 0.99268, 0.39826),
c(0.42778, 0.99419, 0.38575),
c(0.44321, 0.99551, 0.37345),
c(0.45854, 0.99663, 0.36140),
c(0.47375, 0.99755, 0.34963),
c(0.48879, 0.99828, 0.33816),
c(0.50362, 0.99879, 0.32701),
c(0.51822, 0.99910, 0.31622),
c(0.53255, 0.99919, 0.30581),
c(0.54658, 0.99907, 0.29581),
c(0.56026, 0.99873, 0.28623),
c(0.57357, 0.99817, 0.27712),
c(0.58646, 0.99739, 0.26849),
c(0.59891, 0.99638, 0.26038),
c(0.61088, 0.99514, 0.25280),
c(0.62233, 0.99366, 0.24579),
c(0.63323, 0.99195, 0.23937),
c(0.64362, 0.98999, 0.23356),
c(0.65394, 0.98775, 0.22835),
c(0.66428, 0.98524, 0.22370),
c(0.67462, 0.98246, 0.21960),
c(0.68494, 0.97941, 0.21602),
c(0.69525, 0.97610, 0.21294),
c(0.70553, 0.97255, 0.21032),
c(0.71577, 0.96875, 0.20815),
c(0.72596, 0.96470, 0.20640),
c(0.73610, 0.96043, 0.20504),
c(0.74617, 0.95593, 0.20406),
c(0.75617, 0.95121, 0.20343),
c(0.76608, 0.94627, 0.20311),
c(0.77591, 0.94113, 0.20310),
c(0.78563, 0.93579, 0.20336),
c(0.79524, 0.93025, 0.20386),
c(0.80473, 0.92452, 0.20459),
c(0.81410, 0.91861, 0.20552),
c(0.82333, 0.91253, 0.20663),
c(0.83241, 0.90627, 0.20788),
c(0.84133, 0.89986, 0.20926),
c(0.85010, 0.89328, 0.21074),
c(0.85868, 0.88655, 0.21230),
c(0.86709, 0.87968, 0.21391),
c(0.87530, 0.87267, 0.21555),
c(0.88331, 0.86553, 0.21719),
c(0.89112, 0.85826, 0.21880),
c(0.89870, 0.85087, 0.22038),
c(0.90605, 0.84337, 0.22188),
c(0.91317, 0.83576, 0.22328),
c(0.92004, 0.82806, 0.22456),
c(0.92666, 0.82025, 0.22570),
c(0.93301, 0.81236, 0.22667),
c(0.93909, 0.80439, 0.22744),
c(0.94489, 0.79634, 0.22800),
c(0.95039, 0.78823, 0.22831),
c(0.95560, 0.78005, 0.22836),
c(0.96049, 0.77181, 0.22811),
c(0.96507, 0.76352, 0.22754),
c(0.96931, 0.75519, 0.22663),
c(0.97323, 0.74682, 0.22536),
c(0.97679, 0.73842, 0.22369),
c(0.98000, 0.73000, 0.22161),
c(0.98289, 0.72140, 0.21918),
c(0.98549, 0.71250, 0.21650),
c(0.98781, 0.70330, 0.21358),
c(0.98986, 0.69382, 0.21043),
c(0.99163, 0.68408, 0.20706),
c(0.99314, 0.67408, 0.20348),
c(0.99438, 0.66386, 0.19971),
c(0.99535, 0.65341, 0.19577),
c(0.99607, 0.64277, 0.19165),
c(0.99654, 0.63193, 0.18738),
c(0.99675, 0.62093, 0.18297),
c(0.99672, 0.60977, 0.17842),
c(0.99644, 0.59846, 0.17376),
c(0.99593, 0.58703, 0.16899),
c(0.99517, 0.57549, 0.16412),
c(0.99419, 0.56386, 0.15918),
c(0.99297, 0.55214, 0.15417),
c(0.99153, 0.54036, 0.14910),
c(0.98987, 0.52854, 0.14398),
c(0.98799, 0.51667, 0.13883),
c(0.98590, 0.50479, 0.13367),
c(0.98360, 0.49291, 0.12849),
c(0.98108, 0.48104, 0.12332),
c(0.97837, 0.46920, 0.11817),
c(0.97545, 0.45740, 0.11305),
c(0.97234, 0.44565, 0.10797),
c(0.96904, 0.43399, 0.10294),
c(0.96555, 0.42241, 0.09798),
c(0.96187, 0.41093, 0.09310),
c(0.95801, 0.39958, 0.08831),
c(0.95398, 0.38836, 0.08362),
c(0.94977, 0.37729, 0.07905),
c(0.94538, 0.36638, 0.07461),
c(0.94084, 0.35566, 0.07031),
c(0.93612, 0.34513, 0.06616),
c(0.93125, 0.33482, 0.06218),
c(0.92623, 0.32473, 0.05837),
c(0.92105, 0.31489, 0.05475),
c(0.91572, 0.30530, 0.05134),
c(0.91024, 0.29599, 0.04814),
c(0.90463, 0.28696, 0.04516),
c(0.89888, 0.27824, 0.04243),
c(0.89298, 0.26981, 0.03993),
c(0.88691, 0.26152, 0.03753),
c(0.88066, 0.25334, 0.03521),
c(0.87422, 0.24526, 0.03297),
c(0.86760, 0.23730, 0.03082),
c(0.86079, 0.22945, 0.02875),
c(0.85380, 0.22170, 0.02677),
c(0.84662, 0.21407, 0.02487),
c(0.83926, 0.20654, 0.02305),
c(0.83172, 0.19912, 0.02131),
c(0.82399, 0.19182, 0.01966),
c(0.81608, 0.18462, 0.01809),
c(0.80799, 0.17753, 0.01660),
c(0.79971, 0.17055, 0.01520),
c(0.79125, 0.16368, 0.01387),
c(0.78260, 0.15693, 0.01264),
c(0.77377, 0.15028, 0.01148),
c(0.76476, 0.14374, 0.01041),
c(0.75556, 0.13731, 0.00942),
c(0.74617, 0.13098, 0.00851),
c(0.73661, 0.12477, 0.00769),
c(0.72686, 0.11867, 0.00695),
c(0.71692, 0.11268, 0.00629),
c(0.70680, 0.10680, 0.00571),
c(0.69650, 0.10102, 0.00522),
c(0.68602, 0.09536, 0.00481),
c(0.67535, 0.08980, 0.00449),
c(0.66449, 0.08436, 0.00424),
c(0.65345, 0.07902, 0.00408),
c(0.64223, 0.07380, 0.00401),
c(0.63082, 0.06868, 0.00401),
c(0.61923, 0.06367, 0.00410),
c(0.60746, 0.05878, 0.00427),
c(0.59550, 0.05399, 0.00453),
c(0.58336, 0.04931, 0.00486),
c(0.57103, 0.04474, 0.00529),
c(0.55852, 0.04028, 0.00579),
c(0.54583, 0.03593, 0.00638),
c(0.53295, 0.03169, 0.00705),
c(0.51989, 0.02756, 0.00780),
c(0.50664, 0.02354, 0.00863),
c(0.49321, 0.01963, 0.00955),
c(0.47960, 0.01583, 0.01055))
colnames(turbo_colormap_data) <- c("red", "green", "blue")
schemes <- schemes[order(vapply(schemes, function(x) x$type, ""), names(schemes))]
invisible(lapply(schemes, .CheckScheme))
save(schemes, discrete_rainbow_indexes, turbo_colormap_data, file="sysdata.rda")
tools::resaveRdaFiles(getwd(), compress="auto")
USGS-R/inlmisc documentation built on Sept. 17, 2022, 2:38 a.m.
R Package Documentation
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#!/usr/bin/env Rscript
.ReadCpt <- function(file, cite=NULL, type="Sequential") {
checkmate::assertString(file)
checkmate::assertString(cite, null.ok=TRUE)
type <- match.arg(type, c("Sequential", "Diverging", "Qualitative"))
line <- readLines(file)
line <- line[-grep("^(#$|#-+)", line)]
nm <- c("COLOR_MODEL", "RANGE", "HINGE", "CYCLIC")
option <- lapply(nm, function(opt) {
idx <- grep(sprintf("^#[ \t]%s", opt), line)
if (length(idx) == 0) return(NULL)
x <- tail(strsplit(line[idx], "[ \t]")[[1]], 1)
line <<- line[-idx]
if (opt == "COLOR_MODEL") x <- toupper(x)
if (opt == "RANGE") x <- as.numeric(strsplit(x, "/")[[1]])
if (opt == "HINGE") x <- as.numeric(x)
if (opt == "CYCLIC") x <- TRUE
x
})
names(option) <- nm
if (option$COLOR_MODEL != "RGB") return(NULL)
color <- lapply(c("N"="N", "B"="B", "F"="F"), function(key) {
idx <- grep(sprintf("^%s[ |\t]", key), line)
if (length(idx) == 0) return(NULL)
x <- strsplit(line[idx], "[ \t]")[[1]]
x <- tail(x, 1)
line <<- line[-idx]
.Cpt2Hex(x)
})
idx <- grep("^#[ \t]", line)
note <- strwrap(substring(line[idx], 3), width=.Machine$integer.max)
line <- line[-idx]
m <- do.call("rbind", lapply(line, function(x) {
x <- strsplit(x, "[ \t]")[[1]]
x <- x[x != ""]
if (length(x) == 4) {
elem <- c(x[1], .Cpt2Hex(x[2]), x[3], .Cpt2Hex(x[4]))
} else if (length(x) == 8) {
elem <- c(x[1], .Cpt2Hex(x[2:4]), x[5], .Cpt2Hex(x[6:8]))
} else {
return(NULL)
}
elem
}))
if (is.null(m)) return(NULL)
for (i in seq_len(nrow(m) - 1))
if (!identical(m[i, 4], m[i + 1, 2]))
return(NULL)
d <- as.data.frame(rbind(m[, 1:2], m[nrow(m), 3:4]), stringsAsFactors=FALSE)
names(d) <- c("value", "color")
d$value <- as.numeric(d$value)
if (is.numeric(option$RANGE)) {
if (is.numeric(option$HINGE)) {
x <- c(-1, 0, 1)
y <- c(option$RANGE[1], option$HINGE, option$RANGE[2])
d$value <- stats::approx(x, y, xout=d$value)
} else {
d$value <- d$value * diff(option$RANGE) + option$RANGE[1]
}
}
l <- list("data" = d,
"type" = type,
"cite" = cite,
"nmax" = Inf,
"nan" = color$N,
"back" = color$B,
"fore" = color$F,
"note" = note)
l[vapply(l, is.null, FALSE)] <- NULL
l
}
.Cpt2Hex <- function(x) {
checkmate::assertVector(x, strict=TRUE, any.missing=FALSE, min.len=1, max.len=3)
if (length(x) == 1) {
if (grepl("/", x))
x <- as.integer(strsplit(x, "/")[[1]])
else
x <- t(grDevices::col2rgb(x))[1, ]
}
grDevices::rgb(x[1], x[2], x[3], maxColorValue=255)
}
.GetCptGmt <- function() {
# Generic Mapping Tools (GMT)
cite <- "Wessel and others (2013)"
# code adapted from stackoverflow answer by lukeA, accessed October 27, 2018
# at https://stackoverflow.com/questions/25485216
host <- "api.github.com"
owner <- "GenericMappingTools"
repo <- "gmt"
fmt <- "https://%s/repos/%s/%s/git/trees/master?recursive=1"
path <- sprintf(fmt, host, owner, repo)
info <- httr::GET(sprintf(fmt, host, owner, repo))
httr::stop_for_status(info)
tree <- unlist(lapply(httr::content(info)$tree, "[", "path"), use.names=FALSE)
path <- grep("share/cpt/", tree, value=TRUE, fixed=TRUE)
host <- "raw.githubusercontent.com"
file <- sprintf("https://%s/%s/%s/master/%s", host, owner, repo, path)
destfile <- file.path(getwd(), "cpt", basename(file))
for (i in seq_along(file)) {
utils::download.file(file[i], destfile[i], quiet=TRUE)
}
nm <- tools::file_path_sans_ext(basename(file))
type <- rep("Sequential", length(nm))
div <- c("polar",
"red2green",
"roma",
"split")
type[nm %in% div] <- "Diverging"
cpt <- lapply(seq_along(destfile), function(i) {
.ReadCpt(destfile[i], cite=cite, type=type[i])
})
names(cpt) <- nm
is <- !vapply(cpt, is.null, FALSE)
cpt <- cpt[is]
unlink(destfile[!is])
cpt
}
.CheckScheme <- function(x) {
checkmate::assertDataFrame(x$data, any.missing=FALSE,
min.rows=2, min.cols=1, null.ok=TRUE)
pattern <- "^#(\\d|[a-f]){6}$"
checkmate::assertCharacter(x$data$color, pattern=pattern,
ignore.case=TRUE, null.ok=TRUE)
stopifnot(all(inlmisc::IsColor(x$data$color)))
checkmate::qassert(x$data$name, c("0", "S", "X(0,)"))
checkmate::qassert(x$gray, c("0", "S", "X(0,)"))
checkmate::assertSubset(x$gray, x$data$name)
checkmate::assertNumeric(x$data$value, finite=TRUE, unique=TRUE,
sorted=TRUE, null.ok=TRUE)
checkmate::assertSubset(x$type, c("Qualitative", "Diverging", "Sequential"))
checkmate::assertString(x$cite)
checkmate::assertNumber(x$nmax)
checkmate::assertCharacter(x$back, pattern=pattern, ignore.case=TRUE, null.ok=TRUE)
checkmate::assertCharacter(x$fore, pattern=pattern, ignore.case=TRUE, null.ok=TRUE)
checkmate::assertCharacter(x$nan, pattern=pattern, ignore.case=TRUE, null.ok=TRUE)
stopifnot(inlmisc::IsColor(x$back, null.ok=TRUE))
stopifnot(inlmisc::IsColor(x$fore, null.ok=TRUE))
stopifnot(inlmisc::IsColor(x$nan, null.ok=TRUE))
checkmate::assertCharacter(x$note, null.ok=TRUE)
invisible()
}
options(stringsAsFactors=FALSE)
dir.create(file.path(getwd(), "cpt"), showWarnings=FALSE)
schemes <- .GetCptGmt()
# Thomas Dewez (SRON) granted permission to add his color schemes on October 17, 2018.
schemes[["DEM screen"]] <- list(
data = read.csv(strip.white=TRUE, text="
value, color
0, #008435
100, #33CC00
200, #F4F071
400, #F4BD45
600, #99642B
800, #FFFFFF
"),
type = "Sequential",
cite = "Thomas Dewez (2004)",
nmax = Inf,
back = "#FFFFFF",
fore = "#008435",
nan = "#008435"
)
schemes[["DEM print"]] <- list(
data = read.csv(strip.white=TRUE, text="
value, color
0, #336600
100, #81C31F
200, #FFFFCC
400, #F4BD45
500, #66330C
600, #663300
800, #FFFFFF
"),
type = "Sequential",
cite = "Thomas Dewez (2004)",
nmax = Inf,
back = "#336600",
fore = "#FFFFFF",
nan = "#336600"
)
schemes[["DEM poster"]] <- list(
data = read.csv(strip.white=TRUE, text="
value, color
0, #006147
50, #107A2F
500, #E8D77D
1200, #A14300
1700, #9E0000
2800, #6E6E6E
4000, #FFFFFF
4900, #FFFFFF
"),
type = "Sequential",
cite = "Thomas Dewez (2004)",
nmax = Inf,
back = "#99CCFF",
fore = "#99CCFF",
nan = "#99CCFF"
)
# Paul Tol (SRON) granted permission to add his color schemes on October 17, 2018.
schemes[["bright"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
blue, #4477AA
red, #EE6677
green, #228833
yellow, #CCBB44
cyan, #66CCEE
purple, #AA3377
grey, #BBBBBB
"),
gray = c("yellow", "red", "green"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 7
)
schemes[["high-contrast"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
blue, #004488
yellow, #DDAA33
red, #BB5566
black, #000000
white, #FFFFFF
"),
gray = c("white", "yellow", "red", "blue", "black"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 5
)
schemes[["vibrant"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
orange, #EE7733
blue, #0077BB
cyan, #33BBEE
magenta, #EE3377
red, #CC3311
teal, #009988
grey, #BBBBBB
"),
gray = c("grey", "orange", "magenta", "blue"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 7
)
schemes[["muted"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
rose, #CC6677
indigo, #332288
sand, #DDCC77
green, #117733
cyan, #88CCEE
wine, #882255
teal, #44AA99
olive, #999933
purple, #AA4499
"),
gray = c("sand", "teal", "purple", "green", "indigo"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 9,
nas = "#DDDDDD"
)
schemes[["pale"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
pale blue, #BBCCEE
pale cyan, #CCEEFF
pale green, #CCDDAA
pale yellow, #EEEEBB
pale red, #FFCCCC
pale grey, #DDDDDD
"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 6
)
schemes[["dark"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
dark blue, #222255
dark cyan, #225555
dark green, #225522
dark yellow, #666633
dark red, #663333
dark grey, #555555
"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 6
)
schemes[["light"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
light blue, #77AADD
orange, #EE8866
light yellow, #EEDD88
pink, #FFAABB
light cyan, #99DDFF
mint, #44BB99
pear, #BBCC33
olive, #AAAA00
pale grey, #DDDDDD
"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 9
)
schemes[["ground cover"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
water, #5566AA
evergreen needleleaf forest, #117733
deciduous needleleaf forest, #44AA66
mixed forest, #55AA22
evergreen broadleaf forest, #668822
deciduous broadleaf forest, #99BB55
woodland, #558877
wooded grassland, #88BBAA
grassland, #AADDCC
cropland, #44AA88
closed shrubland, #DDCC66
open shrubland, #FFDD44
bare ground, #FFEE88
urban and built, #BB0011
"),
type = "Qualitative",
cite = "Paul Tol (2018)",
nmax = 14
)
schemes[["sunset"]] <- list(
data = read.csv(strip.white=TRUE, text="
color
#364B9A
#4A7BB7
#6EA6CD
#98CAE1
#C2E4EF
#EAECCC
#FEDA8B
#FDB366
#F67E4B
#DD3D2D
#A50026
"),
type = "Diverging",
cite = "Paul Tol (2018)",
nmax = Inf,
nan = "#FFFFFF"
)
schemes[["BuRd"]] <- list(
data = read.csv(strip.white=TRUE, text="
color
#2166AC
#4393C3
#92C5DE
#D1E5F0
#F7F7F7
#FDDBC7
#F4A582
#D6604D
#B2182B
"),
type = "Diverging",
cite = "Paul Tol (2018)",
nmax = Inf,
nan = "#FFEE99"
)
schemes[["PRGn"]] <- list(
data = read.csv(strip.white=TRUE, text="
color
#762A83
#9970AB
#C2A5CF
#E7D4E8
#F7F7F7
#D9F0D3
#ACD39E
#5AAE61
#1B7837
"),
type = "Diverging",
cite = "Paul Tol (2018)",
nmax = Inf,
nan = "#FFEE99"
)
schemes[["YlOrBr"]] <- list(
data = read.csv(strip.white=TRUE, text="
color
#FFFFE5
#FFF7BC
#FEE391
#FEC44F
#FB9A29
#EC7014
#CC4C02
#993404
#662506
"),
type = "Sequential",
cite = "Paul Tol (2018)",
nmax = Inf,
nan = "#888888"
)
schemes[["iridescent"]] <- list(
data = read.csv(strip.white=TRUE, text="
color
#FEFBE9
#FCF7D5
#F5F3C1
#EAF0B5
#DDECBF
#D0E7CA
#C2E3D2
#B5DDD8
#A8D8DC
#9BD2E1
#8DCBE4
#81C4E7
#7BBCE7
#7EB2E4
#88A5DD
#9398D2
#9B8AC4
#9D7DB2
#9A709E
#906388
#805770
#684957
#46353A
"),
type = "Sequential",
cite = "Paul Tol (2018)",
nmax = Inf,
nan = "#999999"
)
schemes[["smooth rainbow"]] <- list(
data = read.csv(strip.white=TRUE, text="
color
#E8ECFB
#DDD8EF
#D1C1E1
#C3A8D1
#B58FC2
#A778B4
#9B62A7
#8C4E99
#6F4C9B
#6059A9
#5568B8
#4E79C5
#4D8AC6
#4E96BC
#549EB3
#59A5A9
#60AB9E
#69B190
#77B77D
#8CBC68
#A6BE54
#BEBC48
#D1B541
#DDAA3C
#E49C39
#E78C35
#E67932
#E4632D
#DF4828
#DA2222
#B8221E
#95211B
#721E17
#521A13
"),
type = "Sequential",
cite = "Paul Tol (2018)",
nmax = Inf,
nan = "#666666"
)
schemes[["discrete rainbow"]] <- list(
data = read.csv(strip.white=TRUE, text="
name, color
1, #E8ECFB
2, #D9CCE3
3, #D1BBD7
4, #CAACCB
5, #BA8DB4
6, #AE76A3
7, #AA6F9E
8, #994F88
9, #882E72
10, #1965B0
11, #437DBF
12, #5289C7
13, #6195CF
14, #7BAFDE
15, #4EB265
16, #90C987
17, #CAE0AB
18, #F7F056
19, #F7CB45
20, #F6C141
21, #F4A736
22, #F1932D
23, #EE8026
24, #E8601C
25, #E65518
26, #DC050C
27, #A5170E
28, #72190E
29, #42150A
"),
type = "Sequential",
cite = "Paul Tol (2018)",
nmax = 23,
nan = "#777777"
)
discrete_rainbow_indexes <- list(
c(10),
c(10, 26),
c(10, 18, 26),
c(10, 15, 18, 26),
c(10, 14, 15, 18, 26),
c(10, 14, 15, 17, 18, 26),
c( 9, 10, 14, 15, 17, 18, 26),
c( 9, 10, 14, 15, 17, 18, 23, 26),
c( 9, 10, 14, 15, 17, 18, 23, 26, 28),
c( 9, 10, 14, 15, 17, 18, 21, 24, 26, 28),
c( 9, 10, 12, 14, 15, 17, 18, 21, 24, 26, 28),
c( 3, 6, 9, 10, 12, 14, 15, 17, 18, 21, 24, 26),
c( 3, 6, 9, 10, 12, 14, 15, 16, 17, 18, 21, 24, 26),
c( 3, 6, 9, 10, 12, 14, 15, 16, 17, 18, 20, 22, 24, 26),
c( 3, 6, 9, 10, 12, 14, 15, 16, 17, 18, 20, 22, 24, 26, 28),
c( 3, 5, 7, 9, 10, 12, 14, 15, 16, 17, 18, 20, 22, 24, 26, 28),
c( 3, 5, 7, 8, 9, 10, 12, 14, 15, 16, 17, 18, 20, 22, 24, 26, 28),
c( 3, 5, 7, 8, 9, 10, 12, 14, 15, 16, 17, 18, 20, 22, 24, 26, 27, 28),
c( 2, 4, 5, 7, 8, 9, 10, 12, 14, 15, 16, 17, 18, 20, 22, 24, 26, 27, 28),
c( 2, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 22, 24, 26, 27, 28),
c( 2, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 21, 23, 25, 26, 27, 28),
c( 2, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 21, 23, 25, 26, 27, 28, 29),
c( 1, 2, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 21, 23, 25, 26, 27, 28, 29)
)
# Unknown author on gnuplot-info
schemes[["bpy"]] <- list(
type = "Sequential",
cite = "unknown",
nmax = Inf
)
# Anton Mikhailov (Google)
schemes[["turbo"]] <- list(
type = "Sequential",
cite = "Anton Mikhailov (2019)",
nmax = Inf
)
# data from turbo colormap look-up table,
# copyright 2019 Google LLC, Apache-2.0 license,
# accessed August 21, 2019
# at https://gist.github.com/mikhailov-work/ee72ba4191942acecc03fe6da94fc73f
turbo_colormap_data <- rbind(c(0.18995, 0.07176, 0.23217),
c(0.19483, 0.08339, 0.26149),
c(0.19956, 0.09498, 0.29024),
c(0.20415, 0.10652, 0.31844),
c(0.20860, 0.11802, 0.34607),
c(0.21291, 0.12947, 0.37314),
c(0.21708, 0.14087, 0.39964),
c(0.22111, 0.15223, 0.42558),
c(0.22500, 0.16354, 0.45096),
c(0.22875, 0.17481, 0.47578),
c(0.23236, 0.18603, 0.50004),
c(0.23582, 0.19720, 0.52373),
c(0.23915, 0.20833, 0.54686),
c(0.24234, 0.21941, 0.56942),
c(0.24539, 0.23044, 0.59142),
c(0.24830, 0.24143, 0.61286),
c(0.25107, 0.25237, 0.63374),
c(0.25369, 0.26327, 0.65406),
c(0.25618, 0.27412, 0.67381),
c(0.25853, 0.28492, 0.69300),
c(0.26074, 0.29568, 0.71162),
c(0.26280, 0.30639, 0.72968),
c(0.26473, 0.31706, 0.74718),
c(0.26652, 0.32768, 0.76412),
c(0.26816, 0.33825, 0.78050),
c(0.26967, 0.34878, 0.79631),
c(0.27103, 0.35926, 0.81156),
c(0.27226, 0.36970, 0.82624),
c(0.27334, 0.38008, 0.84037),
c(0.27429, 0.39043, 0.85393),
c(0.27509, 0.40072, 0.86692),
c(0.27576, 0.41097, 0.87936),
c(0.27628, 0.42118, 0.89123),
c(0.27667, 0.43134, 0.90254),
c(0.27691, 0.44145, 0.91328),
c(0.27701, 0.45152, 0.92347),
c(0.27698, 0.46153, 0.93309),
c(0.27680, 0.47151, 0.94214),
c(0.27648, 0.48144, 0.95064),
c(0.27603, 0.49132, 0.95857),
c(0.27543, 0.50115, 0.96594),
c(0.27469, 0.51094, 0.97275),
c(0.27381, 0.52069, 0.97899),
c(0.27273, 0.53040, 0.98461),
c(0.27106, 0.54015, 0.98930),
c(0.26878, 0.54995, 0.99303),
c(0.26592, 0.55979, 0.99583),
c(0.26252, 0.56967, 0.99773),
c(0.25862, 0.57958, 0.99876),
c(0.25425, 0.58950, 0.99896),
c(0.24946, 0.59943, 0.99835),
c(0.24427, 0.60937, 0.99697),
c(0.23874, 0.61931, 0.99485),
c(0.23288, 0.62923, 0.99202),
c(0.22676, 0.63913, 0.98851),
c(0.22039, 0.64901, 0.98436),
c(0.21382, 0.65886, 0.97959),
c(0.20708, 0.66866, 0.97423),
c(0.20021, 0.67842, 0.96833),
c(0.19326, 0.68812, 0.96190),
c(0.18625, 0.69775, 0.95498),
c(0.17923, 0.70732, 0.94761),
c(0.17223, 0.71680, 0.93981),
c(0.16529, 0.72620, 0.93161),
c(0.15844, 0.73551, 0.92305),
c(0.15173, 0.74472, 0.91416),
c(0.14519, 0.75381, 0.90496),
c(0.13886, 0.76279, 0.89550),
c(0.13278, 0.77165, 0.88580),
c(0.12698, 0.78037, 0.87590),
c(0.12151, 0.78896, 0.86581),
c(0.11639, 0.79740, 0.85559),
c(0.11167, 0.80569, 0.84525),
c(0.10738, 0.81381, 0.83484),
c(0.10357, 0.82177, 0.82437),
c(0.10026, 0.82955, 0.81389),
c(0.09750, 0.83714, 0.80342),
c(0.09532, 0.84455, 0.79299),
c(0.09377, 0.85175, 0.78264),
c(0.09287, 0.85875, 0.77240),
c(0.09267, 0.86554, 0.76230),
c(0.09320, 0.87211, 0.75237),
c(0.09451, 0.87844, 0.74265),
c(0.09662, 0.88454, 0.73316),
c(0.09958, 0.89040, 0.72393),
c(0.10342, 0.89600, 0.71500),
c(0.10815, 0.90142, 0.70599),
c(0.11374, 0.90673, 0.69651),
c(0.12014, 0.91193, 0.68660),
c(0.12733, 0.91701, 0.67627),
c(0.13526, 0.92197, 0.66556),
c(0.14391, 0.92680, 0.65448),
c(0.15323, 0.93151, 0.64308),
c(0.16319, 0.93609, 0.63137),
c(0.17377, 0.94053, 0.61938),
c(0.18491, 0.94484, 0.60713),
c(0.19659, 0.94901, 0.59466),
c(0.20877, 0.95304, 0.58199),
c(0.22142, 0.95692, 0.56914),
c(0.23449, 0.96065, 0.55614),
c(0.24797, 0.96423, 0.54303),
c(0.26180, 0.96765, 0.52981),
c(0.27597, 0.97092, 0.51653),
c(0.29042, 0.97403, 0.50321),
c(0.30513, 0.97697, 0.48987),
c(0.32006, 0.97974, 0.47654),
c(0.33517, 0.98234, 0.46325),
c(0.35043, 0.98477, 0.45002),
c(0.36581, 0.98702, 0.43688),
c(0.38127, 0.98909, 0.42386),
c(0.39678, 0.99098, 0.41098),
c(0.41229, 0.99268, 0.39826),
c(0.42778, 0.99419, 0.38575),
c(0.44321, 0.99551, 0.37345),
c(0.45854, 0.99663, 0.36140),
c(0.47375, 0.99755, 0.34963),
c(0.48879, 0.99828, 0.33816),
c(0.50362, 0.99879, 0.32701),
c(0.51822, 0.99910, 0.31622),
c(0.53255, 0.99919, 0.30581),
c(0.54658, 0.99907, 0.29581),
c(0.56026, 0.99873, 0.28623),
c(0.57357, 0.99817, 0.27712),
c(0.58646, 0.99739, 0.26849),
c(0.59891, 0.99638, 0.26038),
c(0.61088, 0.99514, 0.25280),
c(0.62233, 0.99366, 0.24579),
c(0.63323, 0.99195, 0.23937),
c(0.64362, 0.98999, 0.23356),
c(0.65394, 0.98775, 0.22835),
c(0.66428, 0.98524, 0.22370),
c(0.67462, 0.98246, 0.21960),
c(0.68494, 0.97941, 0.21602),
c(0.69525, 0.97610, 0.21294),
c(0.70553, 0.97255, 0.21032),
c(0.71577, 0.96875, 0.20815),
c(0.72596, 0.96470, 0.20640),
c(0.73610, 0.96043, 0.20504),
c(0.74617, 0.95593, 0.20406),
c(0.75617, 0.95121, 0.20343),
c(0.76608, 0.94627, 0.20311),
c(0.77591, 0.94113, 0.20310),
c(0.78563, 0.93579, 0.20336),
c(0.79524, 0.93025, 0.20386),
c(0.80473, 0.92452, 0.20459),
c(0.81410, 0.91861, 0.20552),
c(0.82333, 0.91253, 0.20663),
c(0.83241, 0.90627, 0.20788),
c(0.84133, 0.89986, 0.20926),
c(0.85010, 0.89328, 0.21074),
c(0.85868, 0.88655, 0.21230),
c(0.86709, 0.87968, 0.21391),
c(0.87530, 0.87267, 0.21555),
c(0.88331, 0.86553, 0.21719),
c(0.89112, 0.85826, 0.21880),
c(0.89870, 0.85087, 0.22038),
c(0.90605, 0.84337, 0.22188),
c(0.91317, 0.83576, 0.22328),
c(0.92004, 0.82806, 0.22456),
c(0.92666, 0.82025, 0.22570),
c(0.93301, 0.81236, 0.22667),
c(0.93909, 0.80439, 0.22744),
c(0.94489, 0.79634, 0.22800),
c(0.95039, 0.78823, 0.22831),
c(0.95560, 0.78005, 0.22836),
c(0.96049, 0.77181, 0.22811),
c(0.96507, 0.76352, 0.22754),
c(0.96931, 0.75519, 0.22663),
c(0.97323, 0.74682, 0.22536),
c(0.97679, 0.73842, 0.22369),
c(0.98000, 0.73000, 0.22161),
c(0.98289, 0.72140, 0.21918),
c(0.98549, 0.71250, 0.21650),
c(0.98781, 0.70330, 0.21358),
c(0.98986, 0.69382, 0.21043),
c(0.99163, 0.68408, 0.20706),
c(0.99314, 0.67408, 0.20348),
c(0.99438, 0.66386, 0.19971),
c(0.99535, 0.65341, 0.19577),
c(0.99607, 0.64277, 0.19165),
c(0.99654, 0.63193, 0.18738),
c(0.99675, 0.62093, 0.18297),
c(0.99672, 0.60977, 0.17842),
c(0.99644, 0.59846, 0.17376),
c(0.99593, 0.58703, 0.16899),
c(0.99517, 0.57549, 0.16412),
c(0.99419, 0.56386, 0.15918),
c(0.99297, 0.55214, 0.15417),
c(0.99153, 0.54036, 0.14910),
c(0.98987, 0.52854, 0.14398),
c(0.98799, 0.51667, 0.13883),
c(0.98590, 0.50479, 0.13367),
c(0.98360, 0.49291, 0.12849),
c(0.98108, 0.48104, 0.12332),
c(0.97837, 0.46920, 0.11817),
c(0.97545, 0.45740, 0.11305),
c(0.97234, 0.44565, 0.10797),
c(0.96904, 0.43399, 0.10294),
c(0.96555, 0.42241, 0.09798),
c(0.96187, 0.41093, 0.09310),
c(0.95801, 0.39958, 0.08831),
c(0.95398, 0.38836, 0.08362),
c(0.94977, 0.37729, 0.07905),
c(0.94538, 0.36638, 0.07461),
c(0.94084, 0.35566, 0.07031),
c(0.93612, 0.34513, 0.06616),
c(0.93125, 0.33482, 0.06218),
c(0.92623, 0.32473, 0.05837),
c(0.92105, 0.31489, 0.05475),
c(0.91572, 0.30530, 0.05134),
c(0.91024, 0.29599, 0.04814),
c(0.90463, 0.28696, 0.04516),
c(0.89888, 0.27824, 0.04243),
c(0.89298, 0.26981, 0.03993),
c(0.88691, 0.26152, 0.03753),
c(0.88066, 0.25334, 0.03521),
c(0.87422, 0.24526, 0.03297),
c(0.86760, 0.23730, 0.03082),
c(0.86079, 0.22945, 0.02875),
c(0.85380, 0.22170, 0.02677),
c(0.84662, 0.21407, 0.02487),
c(0.83926, 0.20654, 0.02305),
c(0.83172, 0.19912, 0.02131),
c(0.82399, 0.19182, 0.01966),
c(0.81608, 0.18462, 0.01809),
c(0.80799, 0.17753, 0.01660),
c(0.79971, 0.17055, 0.01520),
c(0.79125, 0.16368, 0.01387),
c(0.78260, 0.15693, 0.01264),
c(0.77377, 0.15028, 0.01148),
c(0.76476, 0.14374, 0.01041),
c(0.75556, 0.13731, 0.00942),
c(0.74617, 0.13098, 0.00851),
c(0.73661, 0.12477, 0.00769),
c(0.72686, 0.11867, 0.00695),
c(0.71692, 0.11268, 0.00629),
c(0.70680, 0.10680, 0.00571),
c(0.69650, 0.10102, 0.00522),
c(0.68602, 0.09536, 0.00481),
c(0.67535, 0.08980, 0.00449),
c(0.66449, 0.08436, 0.00424),
c(0.65345, 0.07902, 0.00408),
c(0.64223, 0.07380, 0.00401),
c(0.63082, 0.06868, 0.00401),
c(0.61923, 0.06367, 0.00410),
c(0.60746, 0.05878, 0.00427),
c(0.59550, 0.05399, 0.00453),
c(0.58336, 0.04931, 0.00486),
c(0.57103, 0.04474, 0.00529),
c(0.55852, 0.04028, 0.00579),
c(0.54583, 0.03593, 0.00638),
c(0.53295, 0.03169, 0.00705),
c(0.51989, 0.02756, 0.00780),
c(0.50664, 0.02354, 0.00863),
c(0.49321, 0.01963, 0.00955),
c(0.47960, 0.01583, 0.01055))
colnames(turbo_colormap_data) <- c("red", "green", "blue")
schemes <- schemes[order(vapply(schemes, function(x) x$type, ""), names(schemes))]
invisible(lapply(schemes, .CheckScheme))
save(schemes, discrete_rainbow_indexes, turbo_colormap_data, file="sysdata.rda")
tools::resaveRdaFiles(getwd(), compress="auto")
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