rolfunctions.r

normalise <- function(x){
    if (is.vector(x)){
        (x - min(x, na.rm=T)) / (max(x, na.rm=T) - min(x, na.rm=T))
    } else {
        do.call(cbind, lapply(x, normalise))
    }
}
score = function(dat, include_manual = FALSE, minimal = FALSE){
    stopifnot(data.table::is.data.table(dat))

    dat[, mlastdist := stringdist(mlast_from, mlast_to, method='jw', p=0.1)]
    dat[, mfirstdist := stringdist(mfirst_from, mfirst_to, method='jw', p=0.1)]
    dat[, minidist := stringdist(minitials_from, minitials_to, method='jw', p=0.1)]
    dat[, wlastdist := stringdist(wlast_from, wlast_to, method='jw', p=0.1)]
    dat[, wfirstdist := stringdist(wfirst_from, wfirst_to, method='jw', p=0.1)]
    dat[, winidist := stringdist(winitials_from, winitials_to, method='jw', p=0.1)]


    # bad for model performance
    # dat[, mlast_neighbour_lag_dist := stringdist(mlast_neighbour_lag_from, mlast_neighbour_lag_to, method='jw', p=0.1)]
    # dat[, mlast_neighbour_lead_dist := stringdist(mlast_neighbour_lead_from, mlast_neighbour_lead_to, method='jw', p=0.1)]

    # somewhat expensive and hardly used
    dat[, mlastsdx := stringdist(mlast_from, mlast_to, method='soundex')]
    dat[, mfirstsdx := stringdist(mfirst_from, mfirst_to, method='soundex')]
    dat[, wlastsdx := stringdist(wlast_from, wlast_to, method='soundex')]
    dat[, wfirstsdx := stringdist(wfirst_from, wfirst_to, method='soundex')]

    dat[, wifeinboth := wifepresent_from == wifepresent_to]
    dat[, dchildren := settlerchildren_from - settlerchildren_to]
    
    if (minimal){
        dat[, bothyoung := young_from == young_to]
        dat[, bothold := old_from == old_to]
        dat[, nrdist := nr_from - nr_to]
        dat[, wmlastdist_from := stringdist(mlast_from, wlast_from, method='jw', p=0.1)]
        dat[, wmlastdist_to := stringdist(mlast_to, wlast_to, method='jw', p=0.1)]
        dat[, wmlastdist_cross := stringdist(mlast_from, wlast_to, method='jw', p=0.1)]
        dat[, samedistrict := districtall_from == districtall_to]
        dat[, bothwineprod := wineproducer_from == wineproducer_to]
    }
    # stay open

    dat[, mtchs := .N, by=persid_from]

    dat[, exactmtch := mfirst_from==mfirst_to & mlast_from==mlast_to]

    if (include_manual){
        weights <- c(mlastdist=10, mfirstdist=6, minidist=2, 
                     winidist=1, wlastdist=5, wfirstdist=2.5,  
                     mlastsdx=4, mfirstsdx=2, wlastsdx=2, wfirstsdx=1,
                     mtchs=1)
        mwgts <- c(mlastdist=10, mfirstdist=6, minidist=2, 
                     mlastsdx=4, mfirstsdx=2) #, old=2, young=2)
        wwgts <- c(winidist=1, wlastdist=5, wfirstdist=2.5,  
                     wlastsdx=2, wfirstsdx=1)

        dat[, score:= rowSums(.SD * weights) / sum(weights), .SDcols=names(weights)]
        # dat[, score:= rowSums(.SD * mwgts) / sum(mwgts), .SDcols=names(mwgts)]
        # dat[, score:= rowSums(.SD * wwgts) / sum(wwgts), .SDcols=names(wwgts)]
    }

    # return not necessary if all assign by reference
    return(dat)
}


candidates = function(dat_firstyear, dat_rest){
    stopifnot(data.table::is.data.table(dat_firstyear))
    stopifnot(data.table::is.data.table(dat_rest))

    # dat_firstyear = dat[year==baseyear, ]
    # dat_rest      = dat[year < baseyear, ]

    stopifnot(nrow(dat_firstyear) > 0)
    stopifnot(nrow(dat_rest) > 0)

    distmat <- stringdist::stringdistmatrix(dat_firstyear$mlast, dat_rest$mlast, 
        method='jw', p=0.1)

    candidate_list = apply(distmat, 1, function(x) which(x < 0.15))
    # names(candidate_list) = dat_firstyear$persid

    # distmat[725, ]
    # dat_rest[candidate_list[[725]], list(persid, mlast)][persid %in% c(5608, 4103), ]

    dat_tomerge = dat_rest[unlist(candidate_list), ]
    dat_tomerge[, linked_to     := rep(dat_firstyear$persid, times=sapply(candidate_list, length))]
    dat_firstyear[, linked_from := persid]

    # dat_tomerge[linked_to==725, ][persid %in% c(5608, 4103), ]
    # dat_firstyear[linked_from==725, ]

    combined = merge(dat_firstyear, dat_tomerge, all=T, 
        by.x='linked_from', by.y='linked_to', suffixes=c('_from', '_to'))

    # combined[persid_from==725, ][persid_to %in% c(5608, 4103), ]

    return(combined)
}

gregexprr <- function(pattern, string){
    # return all string matches of a regular expression
    # todo: check whether/how it work on multiple strings at once

    rgx <- gregexpr(pattern, string)
    out <- substring(string, rgx[[1]], rgx[[1]] + attr(rgx[[1]], 'match.length') - 1)
    return(out)
}

initials <- function(strings){
    # return first letter of each word in a string

    intls <- gregexprr("^[A-z]|\\s[A-z]|[.][A-z]", strings)
    intls <- gsub('[ .]', '', intls)
    return(paste0(intls, collapse=''))
}

modperf <- function(yhat, y){
    sens <- sum(y[yhat==y])
    spec <- sum(!yhat[yhat==y])
    fane <- sum(!yhat[yhat!=y])
    fapo <- sum(yhat[yhat!=y])
    out <- data.frame(spec, fane, fapo, sens)
    return(out)
}

glmperf <- function(cutoff, mod, y){
    yhat <- mod$fit > cutoff
    w <- which(y==1)
    sens <- mean(yhat[w] == 1)
    spec <- mean(yhat[-w] == 0)
    dstnc <- sqrt((sens - 1)^2 + (spec - 1)^2)
    corclsf <- mean(y==yhat)
    out <- t(as.matrix(c(sens, spec, dstnc, corclsf)))
    return(out)
}

strdistcombine <- function(dat_y1, dat_y2, mtchvrb1='mlast', mtchvrb2='mlast', ...){
    lastmat <- stringdistmatrix(dat_y1[[mtchvrb1]], dat_y2[[mtchvrb2]], method='jw', p=0.1, useNames=TRUE, ...)
    candidates <- apply(lastmat, 2, function(x) which(x < 0.15))
    candidates[lapply(candidates, length)==0] <- NA
    y1candidates <- unlist(candidates)
    y2positions <- rep(1:length(candidates), lapply(candidates, length))
    dat_y2$linkid[y2positions] <- y1candidates

    dat_y12 <- data.frame(dat_y1[y1candidates, ], dat_y2[y2positions, ])

    dat_y12$mtchs <- rep(sapply(candidates, length), lapply(candidates, length))
    dat_y12$mtchs[is.na(y1candidates)] <- 0 # easier way?
    dat_y12$mtchs <- dat_y12$mtchs / max(dat_y12$mtchs, na.rm=T)

    return(dat_y12)
}

closeindex <- function(stringvrbs, cutoff=0.1){
    # return index of similar strings
    # multiple string variables permitted, weighted equally
    # cutoff bit arbitrary

    stopifnot(class(stringvrbs)=='list')

    N <- length(stringvrbs[[1]])
    distmat <- matrix(0, nrow=N, ncol=N)
    for (vrb in stringvrbs){
        distmat <- distmat + stringdistmatrix(vrb, vrb, method='jw', p=0.1)
    }
    
    candidates <- apply(distmat, 2, function(x) which(x < cutoff))
    out <- 1:N
    for (i in seq_along(candidates)){
        out[candidates[[i]][candidates[[i]] > i]] <- i
    }

    return(out)
}

smplseries <- function(dat, index, len=NULL){
    lens <- tapply(index, index, length)[index]
    if (is.null(len)){
        len <- sample(1:max(lens, na.rm=T), 1)
    }
    dat[index==sample(index[dat$len %in% len], 1) & !is.na(index), ]
}

uniformise_string <- function(string, maxdist=0.2, quiet=FALSE){
    str_srtd <- names(sort(-table(string)))
    n_start <- length(str_srtd)
    strmat <- stringdistmatrix(str_srtd, str_srtd, method='jw', p=0.1)
    fill <- NULL
    while(nrow(strmat) > 0){
        ind <- strmat[1, ] < maxdist
        similar_strs <- str_srtd[ind]
        str_srtd <- str_srtd[!ind]
        strmat <- strmat[!ind, !ind, drop=FALSE]
        string[string %in% similar_strs] <- similar_strs[1]
        if (length(similar_strs) > 1 & !quiet){
            cat(similar_strs, sep=', ')
            cat('----->')
            cat(similar_strs[1], '\n')
        }
    }
    cat('From', n_start, ' to ', length(unique(string)), '\n')
    return(string)
}

# virids_map = read.csv("https://raw.githubusercontent.com/sjmgarnier/viridis/master/data-raw/viridis_map.csv")
# clsA = read.csv("https://raw.githubusercontent.com/sjmgarnier/viridis/master/data-raw/optionA.csv")
# clsB = read.csv("https://raw.githubusercontent.com/sjmgarnier/viridis/master/data-raw/optionB.csv")
# clsC = read.csv("https://raw.githubusercontent.com/sjmgarnier/viridis/master/data-raw/optionC.csv")
# clsD = read.csv("https://raw.githubusercontent.com/sjmgarnier/viridis/master/data-raw/optionD.csv")

clsA = structure(list(R = c(0.00146159096, 0.00225764007, 0.00327943222, 
0.00451230222, 0.00594976987, 0.0075879855, 0.0094260439, 0.0114654337, 
0.0137075706, 0.0161557566, 0.018815367, 0.021691934, 0.0247917814, 
0.0281228154, 0.0316955304, 0.0355204468, 0.0396084872, 0.043829535, 
0.0480616391, 0.0523204388, 0.0566148978, 0.060949393, 0.0653301801, 
0.0697637296, 0.0742565152, 0.0788150034, 0.0834456313, 0.088154773, 
0.0929486914, 0.097833477, 0.102814972, 0.107898679, 0.113094451, 
0.118405035, 0.123832651, 0.129380192, 0.135053322, 0.140857952, 
0.146785234, 0.152839217, 0.159017511, 0.165308131, 0.171713033, 
0.17821173, 0.184800877, 0.191459745, 0.198176877, 0.204934882, 
0.211718061, 0.21851159, 0.225302032, 0.232076515, 0.238825991, 
0.245543175, 0.252220252, 0.258857304, 0.265446744, 0.271994089, 
0.2784933, 0.284951097, 0.291365817, 0.297740413, 0.304080941, 
0.310382027, 0.316654235, 0.322899126, 0.329114038, 0.335307503, 
0.341481725, 0.347635742, 0.353773161, 0.359897941, 0.366011928, 
0.372116205, 0.378210547, 0.384299445, 0.390384361, 0.39646667, 
0.402547663, 0.408628505, 0.414708664, 0.420791157, 0.426876965, 
0.432967001, 0.439062114, 0.445163096, 0.451270678, 0.457385535, 
0.463508291, 0.469639514, 0.475779723, 0.481928997, 0.488088169, 
0.494257673, 0.500437834, 0.506628929, 0.512831195, 0.519044825, 
0.525269968, 0.531506735, 0.537755194, 0.544015371, 0.550287252, 
0.556570783, 0.562865867, 0.569172368, 0.575490107, 0.581818864, 
0.588158375, 0.594508337, 0.600868399, 0.607238169, 0.613617209, 
0.620005032, 0.626401108, 0.632804854, 0.639215638, 0.645632778, 
0.652055535, 0.658483116, 0.664914668, 0.671349279, 0.677785975, 
0.684223712, 0.69066138, 0.697097796, 0.7035317, 0.709961888, 
0.716387038, 0.722805451, 0.729215521, 0.735615545, 0.742003713, 
0.748378107, 0.754736692, 0.761077312, 0.767397681, 0.77369538, 
0.779967847, 0.786212372, 0.792426972, 0.79860776, 0.804751511, 
0.810854841, 0.816914186, 0.822925797, 0.82888574, 0.834790818, 
0.84063568, 0.846415804, 0.85212649, 0.85776287, 0.863320397, 
0.868793368, 0.874176342, 0.879463944, 0.884650824, 0.889731418, 
0.894700194, 0.899551884, 0.904281297, 0.908883524, 0.913354091, 
0.917688852, 0.921884187, 0.925937102, 0.92984509, 0.933606454, 
0.937220874, 0.940687443, 0.944006448, 0.947179528, 0.95021015, 
0.953099077, 0.955849237, 0.958464079, 0.960949221, 0.963310281, 
0.965549351, 0.967671128, 0.969680441, 0.971582181, 0.973381238, 
0.975082439, 0.976690494, 0.978209957, 0.979645181, 0.981000291, 
0.982279159, 0.983485387, 0.984622298, 0.985692925, 0.986700017, 
0.987646038, 0.988533173, 0.989363341, 0.990138201, 0.990871208, 
0.991558165, 0.992195728, 0.992784669, 0.993325561, 0.993834412, 
0.994308514, 0.994737698, 0.995121854, 0.995480469, 0.995809924, 
0.996095703, 0.996341406, 0.996579803, 0.996774784, 0.996925427, 
0.997077185, 0.997186253, 0.997253982, 0.99732518, 0.997350983, 
0.997350583, 0.997341259, 0.997284689, 0.997228367, 0.99713848, 
0.997019342, 0.996898254, 0.996726862, 0.996570645, 0.996369065, 
0.996162309, 0.995932448, 0.995680107, 0.995423973, 0.995131288, 
0.994851089, 0.994523666, 0.9942219, 0.993865767, 0.993545285, 
0.993169558, 0.992830963, 0.992439881, 0.992089454, 0.991687744, 
0.991331929, 0.990929685, 0.990569914, 0.990174637, 0.989814839, 
0.989433736, 0.989077438, 0.988717064, 0.988367028, 0.988032885, 
0.987690702, 0.987386827, 0.987052509), G = c(0.000466127766, 
0.00129495431, 0.00230452991, 0.00349037666, 0.00484285, 0.00635613622, 
0.00802185006, 0.00982831486, 0.0117705913, 0.0138404966, 0.0160262753, 
0.0183201254, 0.0207147875, 0.0232009284, 0.0257651161, 0.028397457, 
0.0310895652, 0.0338299885, 0.0366066101, 0.039406602, 0.0421598925, 
0.0447944924, 0.0473177796, 0.0497264666, 0.0520167766, 0.0541844801, 
0.0562249365, 0.0581331465, 0.0599038167, 0.0615314414, 0.0630104053, 
0.0643351102, 0.0654920358, 0.0664791593, 0.0672946449, 0.0679349264, 
0.0683912798, 0.068654071, 0.0687382323, 0.0686368599, 0.0683540225, 
0.0679108689, 0.067305326, 0.0665758073, 0.0657324381, 0.0648183312, 
0.0638624166, 0.0629066192, 0.0619917876, 0.0611584918, 0.0604451843, 
0.0598886855, 0.0595170384, 0.0593524384, 0.0594147119, 0.0597055998, 
0.0602368754, 0.0609935552, 0.0619778136, 0.0631676261, 0.0645534486, 
0.0661170432, 0.0678353452, 0.0697024767, 0.0716895272, 0.0737819504, 
0.0759715081, 0.0782361045, 0.0805635079, 0.0829463512, 0.0853726329, 
0.0878311772, 0.0903143031, 0.0928159917, 0.0953322947, 0.0978549106, 
0.100379466, 0.102902194, 0.105419865, 0.107929771, 0.110431177, 
0.11292021, 0.115395258, 0.117854987, 0.120298314, 0.122724371, 
0.125132484, 0.127522145, 0.129892998, 0.132244819, 0.1345775, 
0.13689139, 0.139186217, 0.141462106, 0.143719323, 0.145958202, 
0.148179144, 0.150382611, 0.152569121, 0.154739247, 0.156893613, 
0.159032895, 0.161157816, 0.163269149, 0.165367714, 0.167454379, 
0.169530062, 0.171595728, 0.173652392, 0.175701122, 0.177743036, 
0.179779309, 0.18181117, 0.183839907, 0.185866869, 0.187893468, 
0.189921182, 0.191951556, 0.19398621, 0.196026835, 0.198075202, 
0.200133166, 0.202202663, 0.204285721, 0.206384461, 0.2085011, 
0.210637956, 0.212797337, 0.214981693, 0.217193831, 0.219436516, 
0.221712634, 0.224025196, 0.226377345, 0.228772352, 0.231213625, 
0.233704708, 0.236249283, 0.23885117, 0.241514325, 0.24424225, 
0.247039798, 0.24991135, 0.252861399, 0.25589455, 0.259015505, 
0.262229049, 0.265539703, 0.268952874, 0.272473491, 0.276106469, 
0.279856666, 0.283729003, 0.287728205, 0.291858679, 0.296124596, 
0.30053009, 0.305078817, 0.309773445, 0.314616425, 0.319609981, 
0.324755126, 0.330051947, 0.335500068, 0.341098112, 0.346843685, 
0.352733817, 0.358764377, 0.364929312, 0.371224168, 0.377642889, 
0.384177874, 0.390819546, 0.397562894, 0.404400213, 0.411323666, 
0.418323245, 0.425389724, 0.432518707, 0.439702976, 0.446935635, 
0.45421017, 0.461520484, 0.468860936, 0.47622635, 0.483612031, 
0.491013764, 0.4984278, 0.505850848, 0.513280054, 0.520712972, 
0.528147545, 0.53558207, 0.543015173, 0.550445778, 0.557873075, 
0.565296495, 0.572706259, 0.580106828, 0.587501706, 0.594891088, 
0.602275297, 0.60964354, 0.616998953, 0.624349657, 0.631696376, 
0.639026596, 0.646343897, 0.653658756, 0.660969379, 0.668255621, 
0.675541484, 0.682827953, 0.690087897, 0.697348991, 0.704610791, 
0.711847714, 0.719089119, 0.726324415, 0.733544671, 0.740771893, 
0.747980563, 0.755189852, 0.762397883, 0.769590975, 0.77679486, 
0.783976508, 0.791167346, 0.798347709, 0.805527126, 0.812705773, 
0.819875302, 0.827051773, 0.834212826, 0.841386618, 0.848540474, 
0.855711038, 0.862858846, 0.870024467, 0.877168404, 0.884329694, 
0.891469549, 0.89862705, 0.905762748, 0.91291501, 0.920048699, 
0.927195612, 0.93432854, 0.941470354, 0.948604077, 0.95574152, 
0.962878026, 0.970012413, 0.977154231, 0.984287561, 0.991437853
), B = c(0.01386552, 0.0183311461, 0.0237083291, 0.0299647059, 
0.0371296695, 0.0449730774, 0.0528443561, 0.060749638, 0.0686665843, 
0.076602666, 0.0845844897, 0.092610105, 0.100675555, 0.108786954, 
0.116964722, 0.125209396, 0.133515085, 0.141886249, 0.150326989, 
0.158841025, 0.167445592, 0.176128834, 0.184891506, 0.193735088, 
0.202660374, 0.211667355, 0.220755099, 0.229921611, 0.239163669, 
0.248476662, 0.2578544, 0.267288933, 0.276783978, 0.286320656, 
0.295879431, 0.305442931, 0.31499989, 0.32453764, 0.334011109, 
0.34340445, 0.352688028, 0.361816426, 0.370770827, 0.379497161, 
0.387972507, 0.396151969, 0.404008953, 0.411514273, 0.418646741, 
0.425391816, 0.431741767, 0.437694665, 0.443255999, 0.448435938, 
0.453247729, 0.457709924, 0.461840297, 0.465660375, 0.469190328, 
0.472450879, 0.475462193, 0.478243482, 0.480811572, 0.48318634, 
0.485380429, 0.487408399, 0.489286796, 0.491024144, 0.492631321, 
0.494120923, 0.495501096, 0.496778331, 0.497959963, 0.499053326, 
0.500066568, 0.501001964, 0.501864236, 0.50265759, 0.503385761, 
0.504052118, 0.504661843, 0.505214935, 0.505713602, 0.506159754, 
0.506555026, 0.506900806, 0.507198258, 0.507448336, 0.507651812, 
0.507809282, 0.507921193, 0.507988509, 0.508010737, 0.507987836, 
0.507919772, 0.50780642, 0.50764757, 0.507442938, 0.507192172, 
0.50689486, 0.506550538, 0.506158696, 0.505718782, 0.50523021, 
0.504692365, 0.504104606, 0.503466273, 0.50277669, 0.502035167, 
0.501241011, 0.500393522, 0.499491999, 0.498535746, 0.497524075, 
0.496456304, 0.495331769, 0.494149821, 0.492909832, 0.491611196, 
0.490253338, 0.488835712, 0.487357807, 0.485819154, 0.484219325, 
0.482557941, 0.480834678, 0.47904927, 0.477201121, 0.47528978, 
0.473315708, 0.471278924, 0.469179541, 0.467017774, 0.464793954, 
0.462508534, 0.460162106, 0.457755411, 0.455289354, 0.452765022, 
0.450183695, 0.447543155, 0.444848441, 0.442101615, 0.439304963, 
0.436461074, 0.433572874, 0.430643647, 0.427671352, 0.42466562, 
0.421631064, 0.418572767, 0.415496319, 0.412402889, 0.409303002, 
0.406205397, 0.403118034, 0.40004706, 0.397001559, 0.393994634, 
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0.742001547, 0.749504188)), .Names = c("R", "G", "B"), class = "data.frame", row.names = c(NA, 
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clsB = structure(list(R = c(0.00146159096, 0.00226726368, 0.00329899092, 
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0.631017009, 0.644924005)), .Names = c("R", "G", "B"), class = "data.frame", row.names = c(NA, 
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clsC = structure(list(R = c(0.0503832136, 0.0635363639, 0.0753531234, 
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0.956808068, 0.954286813, 0.951726083, 0.949150533, 0.94660227, 
0.944151742, 0.94189612, 0.940015097), G = c(0.0298028976, 0.0284259729, 
0.0272063728, 0.0261253206, 0.0251650976, 0.0243092436, 0.02355625, 
0.0228781011, 0.0222583774, 0.0216866674, 0.0211535876, 0.0206507174, 
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0.874622194, 0.881250063, 0.887896125, 0.894563989, 0.901249365, 
0.907950379, 0.914672479, 0.921406537, 0.928152065, 0.93490773, 
0.941670605, 0.9484349, 0.95518986, 0.961916487, 0.968589814, 
0.975158357), B = c(0.527974883, 0.533123681, 0.538007001, 0.542657691, 
0.547103487, 0.551367851, 0.555467728, 0.55942348, 0.563250116, 
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0.652900629, 0.65158601, 0.650165396, 0.648639668, 0.647009884, 
0.645277275, 0.64344325, 0.641509389, 0.63947744, 0.637348841, 
0.635126108, 0.632811608, 0.630407727, 0.627916992, 0.625342058, 
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0.483917732, 0.480306702, 0.476706319, 0.473116798, 0.469538286, 
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0.448305861, 0.444805781, 0.441315901, 0.437835947, 0.434365616, 
0.430905052, 0.427454836, 0.424013059, 0.420579333, 0.417153264, 
0.413734445, 0.410322469, 0.406916975, 0.403518809, 0.400126027, 
0.396738211, 0.393354947, 0.389975832, 0.386600468, 0.383228622, 
0.379860246, 0.376494232, 0.373130228, 0.369767893, 0.366406907, 
0.363046965, 0.359687758, 0.356328796, 0.352969777, 0.349610469, 
0.346250656, 0.342890148, 0.339528771, 0.336165582, 0.332800827, 
0.329434512, 0.32606655, 0.322696876, 0.319325444, 0.315952211, 
0.31257544, 0.309196628, 0.305815824, 0.302433101, 0.299048555, 
0.295662308, 0.292274506, 0.288883445, 0.285490391, 0.282096149, 
0.27870099, 0.275305214, 0.271909159, 0.2685132, 0.265117752, 
0.261721488, 0.258325424, 0.254931256, 0.251539615, 0.2481512, 
0.244766775, 0.241387186, 0.238013359, 0.234646316, 0.231287178, 
0.227937141, 0.224595006, 0.221264889, 0.217948456, 0.214647532, 
0.211364122, 0.208100426, 0.204858855, 0.201642049, 0.1984529, 
0.195294567, 0.1921705, 0.189084459, 0.186040537, 0.18304318, 
0.180097207, 0.177207826, 0.174380656, 0.171621733, 0.168937522, 
0.166334918, 0.163821243, 0.161404226, 0.159091984, 0.156890625, 
0.154807583, 0.152854862, 0.151041581, 0.149376885, 0.14786981, 
0.146529128, 0.145357284, 0.144362644, 0.143556679, 0.142945116, 
0.142528388, 0.142302653, 0.142278607, 0.142453425, 0.142808191, 
0.143350944, 0.144061156, 0.144922913, 0.145918663, 0.147014438, 
0.148179639, 0.149370428, 0.150520343, 0.151566019, 0.152409489, 
0.152921158, 0.152925363, 0.152177604, 0.150327944, 0.146860789, 
0.140955606, 0.131325517)), .Names = c("R", "G", "B"), class = "data.frame", row.names = c(NA, 
-256L))
clsD = structure(list(R = c(0.26700401, 0.26851048, 0.26994384, 0.27130489, 
0.27259384, 0.27380934, 0.27495242, 0.27602238, 0.2770184, 0.27794143, 
0.27879067, 0.2795655, 0.28026658, 0.28089358, 0.28144581, 0.28192358, 
0.28232739, 0.28265633, 0.28291049, 0.28309095, 0.28319704, 0.28322882, 
0.28318684, 0.283072, 0.28288389, 0.28262297, 0.28229037, 0.28188676, 
0.28141228, 0.28086773, 0.28025468, 0.27957399, 0.27882618, 0.27801236, 
0.27713437, 0.27619376, 0.27519116, 0.27412802, 0.27300596, 0.27182812, 
0.27059473, 0.26930756, 0.26796846, 0.26657984, 0.2651445, 0.2636632, 
0.26213801, 0.26057103, 0.25896451, 0.25732244, 0.25564519, 0.25393498, 
0.25219404, 0.25042462, 0.24862899, 0.2468114, 0.24497208, 0.24311324, 
0.24123708, 0.23934575, 0.23744138, 0.23552606, 0.23360277, 0.2316735, 
0.22973926, 0.22780192, 0.2258633, 0.22392515, 0.22198915, 0.22005691, 
0.21812995, 0.21620971, 0.21429757, 0.21239477, 0.2105031, 0.20862342, 
0.20675628, 0.20490257, 0.20306309, 0.20123854, 0.1994295, 0.1976365, 
0.19585993, 0.19410009, 0.19235719, 0.19063135, 0.18892259, 0.18723083, 
0.18555593, 0.18389763, 0.18225561, 0.18062949, 0.17901879, 0.17742298, 
0.17584148, 0.17427363, 0.17271876, 0.17117615, 0.16964573, 0.16812641, 
0.1666171, 0.16511703, 0.16362543, 0.16214155, 0.16066467, 0.15919413, 
0.15772933, 0.15626973, 0.15481488, 0.15336445, 0.1519182, 0.15047605, 
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0.14053599, 0.13914708, 0.13777048, 0.1364085, 0.13506561, 0.13374299, 
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0.15785146, 0.16201598, 0.1663832, 0.1709484, 0.17570671, 0.18065314, 
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0.25985676, 0.26694127, 0.27414922, 0.28147681, 0.28892102, 0.29647899, 
0.30414796, 0.31192534, 0.3198086, 0.3277958, 0.33588539, 0.34407411, 
0.35235985, 0.36074053, 0.3692142, 0.37777892, 0.38643282, 0.39517408, 
0.40400101, 0.4129135, 0.42190813, 0.43098317, 0.44013691, 0.44936763, 
0.45867362, 0.46805314, 0.47750446, 0.4870258, 0.49661536, 0.5062713, 
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0.76237342, 0.77285183, 0.78331535, 0.79375994, 0.80418159, 0.81457634, 
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0.88627146, 0.89632002, 0.90631121, 0.91624212, 0.92610579, 0.93590444, 
0.94563626, 0.95529972, 0.96489353, 0.97441665, 0.98386829, 0.99324789
), G = c(0.00487433, 0.00960483, 0.01462494, 0.01994186, 0.02556309, 
0.03149748, 0.03775181, 0.04416723, 0.05034437, 0.05632444, 0.06214536, 
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0.2469217, 0.25153685, 0.2561304, 0.26070284, 0.26525384, 0.26978306, 
0.27429024, 0.27877509, 0.28323662, 0.28767547, 0.29209154, 0.29648471, 
0.30085494, 0.30520222, 0.30952657, 0.31382773, 0.3181058, 0.32236127, 
0.32659432, 0.33080515, 0.334994, 0.33916114, 0.34330688, 0.34743154, 
0.35153548, 0.35561907, 0.35968273, 0.36372671, 0.36775151, 0.37175775, 
0.37574589, 0.37971644, 0.38366989, 0.38760678, 0.39152762, 0.39543297, 
0.39932336, 0.40319934, 0.40706148, 0.41091033, 0.41474645, 0.4185704, 
0.42238275, 0.42618405, 0.42997486, 0.43375572, 0.4375272, 0.44128981, 
0.4450441, 0.4487906, 0.4525298, 0.45626209, 0.45998802, 0.46370813, 
0.4674229, 0.47113278, 0.47483821, 0.47853961, 0.4822374, 0.48593197, 
0.4896237, 0.49331293, 0.49700003, 0.50068529, 0.50436904, 0.50805136, 
0.51173263, 0.51541316, 0.51909319, 0.52277292, 0.52645254, 0.53013219, 
0.53381201, 0.53749213, 0.54117264, 0.54485335, 0.54853458, 0.55221637, 
0.55589872, 0.55958162, 0.56326503, 0.56694891, 0.57063316, 0.57431754, 
0.57800205, 0.58168661, 0.58537105, 0.58905521, 0.59273889, 0.59642187, 
0.60010387, 0.60378459, 0.60746388, 0.61114146, 0.61481702, 0.61849025, 
0.62216081, 0.62582833, 0.62949242, 0.63315277, 0.63680899, 0.64046069, 
0.64410744, 0.64774881, 0.65138436, 0.65501363, 0.65863619, 0.66225157, 
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0.68731632, 0.69085611, 0.69438405, 0.6978996, 0.70140222, 0.70489133, 
0.70836635, 0.71182668, 0.71527175, 0.71870095, 0.72211371, 0.72550945, 
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0.88602943, 0.88732243, 0.88860134, 0.88986815, 0.89112487, 0.89237353, 
0.89361614, 0.89485467, 0.89609127, 0.89732977, 0.8985704, 0.899815, 
0.90106534, 0.90232311, 0.90358991, 0.90486726, 0.90615657), 
    B = c(0.32941519, 0.33542652, 0.34137895, 0.34726862, 0.35309303, 
    0.35885256, 0.36454323, 0.37016418, 0.37571452, 0.38119074, 
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    0.5571201, 0.55728221, 0.55743035, 0.55756466, 0.55768526, 
    0.55779216, 0.55788532, 0.55796464, 0.55803034, 0.55808199, 
    0.55811913, 0.55814141, 0.55814842, 0.55813967, 0.55811466, 
    0.5580728, 0.55801347, 0.557936, 0.55783967, 0.55772371, 
    0.55758733, 0.55742968, 0.5572505, 0.55704861, 0.55682271, 
    0.55657181, 0.55629491, 0.55599097, 0.55565893, 0.55529773, 
    0.55490625, 0.55448339, 0.55402906, 0.55354108, 0.55301828, 
    0.55245948, 0.55186354, 0.55122927, 0.55055551, 0.5498411, 
    0.54908564, 0.5482874, 0.54744498, 0.54655722, 0.54562298, 
    0.54464114, 0.54361058, 0.54253043, 0.54139999, 0.54021751, 
    0.53898192, 0.53769219, 0.53634733, 0.53494633, 0.53348834, 
    0.53197275, 0.53039808, 0.52876343, 0.52706792, 0.52531069, 
    0.52349092, 0.52160791, 0.51966086, 0.5176488, 0.51557101, 
    0.5134268, 0.51121549, 0.50893644, 0.5065889, 0.50417217, 
    0.50168574, 0.49912906, 0.49650163, 0.49380294, 0.49103252, 
    0.48818938, 0.48527326, 0.48228395, 0.47922108, 0.47608431, 
    0.4728733, 0.46958774, 0.46622638, 0.46278934, 0.45927675, 
    0.45568838, 0.45202405, 0.44828355, 0.44446673, 0.44057284, 
    0.4366009, 0.43255207, 0.42842626, 0.42422341, 0.41994346, 
    0.41558638, 0.41115215, 0.40664011, 0.40204917, 0.39738103, 
    0.39263579, 0.38781353, 0.38291438, 0.3779385, 0.37288606, 
    0.36775726, 0.36255223, 0.35726893, 0.35191009, 0.34647607, 
    0.3409673, 0.33538426, 0.32972749, 0.32399761, 0.31819529, 
    0.31232133, 0.30637661, 0.30036211, 0.29427888, 0.2881265, 
    0.28190832, 0.27562602, 0.26928147, 0.26287683, 0.25641457, 
    0.24989748, 0.24332878, 0.23671214, 0.23005179, 0.22335258, 
    0.21662012, 0.20986086, 0.20308229, 0.19629307, 0.18950326, 
    0.18272455, 0.17597055, 0.16925712, 0.16260273, 0.15602894, 
    0.14956101, 0.14322828, 0.13706449, 0.13110864, 0.12540538, 
    0.12000532, 0.11496505, 0.11034678, 0.10621724, 0.1026459, 
    0.09970219, 0.09745186, 0.09595277, 0.09525046, 0.09537439, 
    0.09633538, 0.09812496, 0.1007168, 0.10407067, 0.10813094, 
    0.11283773, 0.11812832, 0.12394051, 0.13021494, 0.13689671, 
    0.1439362)), .Names = c("R", "G", "B"), class = "data.frame", row.names = c(NA, 
-256L))

colschemes = list(A=clsA, B=clsB, C=clsC, D=clsD)


viridis <- function(n, alpha = 1, begin = 0, end = 1, option = "D") {
    # https://github.com/sjmgarnier/viridis but w/o ggplot2 dependencies
  if (begin < 0 | end < 0 | begin > 1 | end > 1) {
    stop("begin and end must be in [0,1]")
  }

  option <- switch(option,
                   A = "A", magma = "A",
                   B = "B", inferno = "B",
                   C = "C", plasma = "C",
                   D = "D", viridis = "D",
                   {warning(paste0("Option '", option, "' does not exist. Defaulting to 'viridis'.")); "D"})

  # map <- viridis::viridis.map[viridis::viridis.map$opt == option, ]
  map = colschemes[[option]]
  map_cols <- grDevices::rgb(map$R, map$G, map$B)
  fn_cols <- grDevices::colorRamp(map_cols, space = "Lab", interpolate = "spline")
  cols <- fn_cols(seq(begin, end, length.out = n)) / 255
  grDevices::rgb(cols[, 1], cols[, 2], cols[, 3], alpha = alpha)
}

magma = function(n, alpha = 1, begin = 0, end = 1) {
  viridis(n, alpha, begin, end, option = "magma")
}

inferno = function(n, alpha = 1, begin = 0, end = 1) {
  viridis(n, alpha, begin, end, option = "inferno")
}

plasma = function(n, alpha = 1, begin = 0, end = 1) {
  viridis(n, alpha, begin, end, option = "plasma")
}