diff --git a/.Rbuildignore b/.Rbuildignore index 9e0bbd3e4..4998b2b02 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,3 +1,7 @@ ^.*\.Rproj$ ^\.Rproj\.user$ +.travis.yml$ +vignettes/.texpadtmp$ +^packrat/ +^\.Rprofile$ ^\.tmp$ diff --git a/.directory b/.directory new file mode 100644 index 000000000..1f9d540ce --- /dev/null +++ b/.directory @@ -0,0 +1,4 @@ +[Dolphin] +Timestamp=2017,6,13,19,25,37 +Version=3 +ViewMode=1 diff --git a/.gitignore b/.gitignore index d5e630233..8a96d6c43 100644 --- a/.gitignore +++ b/.gitignore @@ -2,4 +2,11 @@ .Rhistory .RData .Ruserdata +.DS_Store +*.xml +packrat/lib*/ .tmp +*.json +src/*.o +src/*.dll +src/*.so diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 000000000..dcf573440 --- /dev/null +++ b/.travis.yml @@ -0,0 +1,18 @@ +language: c++ +cache: packages +os: + - linux + +dist: trusty +sudo: required + +before_install: + - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9; fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then echo 'deb http://cran.rstudio.com/bin/linux/ubuntu trusty/' | sudo tee --append /etc/apt/sources.list; fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo apt-get update; fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo apt-get install -y libgdal-dev libproj-dev r-base r-base-dev; fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo Rscript -e "install.packages('devtools',repos='http://cran.us.r-project.org')"; fi + +script: + - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo Rscript -e "devtools::install_github('gilbertocamara/sits')"; fi + diff --git a/DESCRIPTION b/DESCRIPTION index 3c6b3b93a..ee3b38738 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -7,6 +7,7 @@ Authors@R: c(person('Gilberto', 'Camara', role = c('aut', 'cre'), email = 'gilbe person('Victor', 'Maus', role = c('aut'), email = 'vwmaus1@gmail.com'), person('Rolf', 'Simoes', role = c('aut')), person('Luiz', 'Assis', role = c('aut')), + person('Alexandre', 'Carvalho', role = c('aut')), person('Adeline', 'Marinho', role = c('ctb')), person('Gilberto', 'Queiroz', role = c('ctb')) ) @@ -17,54 +18,55 @@ Description: A set of tools for working with satellite image time series. Enables different clustering methods. Provides smoothing methods for noisy time series. Classifies time series using TWDTW method contained in the dtwSat package. -URL: https://github.com/gilbertocamara/sits/ + Support machine learning methods for satellite image time series. +URL: https://github.com/e-sensing/sits/ BugReports: https://github.com/gilbertocamara/sits/issues License: GPL-2 | file LICENSE LazyData: true Depends: - dtwclust, dtwSat, - magrittr, - Rcpp Imports: + raster, + caret, dplyr, + dtwclust, + e1071, ensurer, entropy, + gbm, ggplot2, + glmnet, graphics, kohonen, jsonlite, lubridate, + magrittr, + MASS, methods, mgcv, + neuralnet, + nnet, parallel, ptw, purrr, purrrlyr, readr, reshape2, - rfUtilities, RCurl, + Rcpp, signal, sp, + snow, stats, stringr, + randomForest, roxygen2, tibble, tidyr, tools, utils, wtss, - zoo, - raster, - snow, - nnet, - randomForest, - gbm, - glmnet, - MASS, - e1071, - caret + zoo LinkingTo: Rcpp Remotes: @@ -89,3 +91,4 @@ Collate: 'sits_smooth.R' 'sits_stack_proc.R' 'sits_table.R' + diff --git a/NAMESPACE b/NAMESPACE index e249dc137..d07ebde17 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -37,8 +37,6 @@ export(sits_missing_values) export(sits_mutate) export(sits_patterns) export(sits_plot) -export(sits_predict) -export(sits_predict_stack) export(sits_prune) export(sits_reassess) export(sits_relabel) @@ -55,132 +53,9 @@ export(sits_table) export(sits_table_result) export(sits_test_patterns) export(sits_toJSON) -export(sits_train) +export(sits_train_svm) export(sits_transmute) export(sits_values) export(sits_whittaker) export(statusProcessing) -import(Rcpp) -import(dtwSat) -import(dtwclust) -import(magrittr) -importFrom(MASS,lda) -importFrom(caret,createDataPartition) -importFrom(dplyr,bind_rows) -importFrom(dplyr,contains) -importFrom(dplyr,distinct) -importFrom(dplyr,do) -importFrom(dplyr,filter) -importFrom(dplyr,if_else) -importFrom(dplyr,inner_join) -importFrom(dplyr,left_join) -importFrom(dplyr,matches) -importFrom(dplyr,mutate) -importFrom(dplyr,num_range) -importFrom(dplyr,one_of) -importFrom(dplyr,rename) -importFrom(dplyr,rename_) -importFrom(dplyr,rowwise) -importFrom(dplyr,select) -importFrom(dplyr,starts_with) -importFrom(dplyr,transmute) -importFrom(e1071,svm) -importFrom(ensurer,check) -importFrom(ensurer,check_that) -importFrom(ensurer,ensure) -importFrom(ensurer,ensure_that) -importFrom(entropy,entropy) -importFrom(gbm,gbm) -importFrom(ggplot2,aes) -importFrom(ggplot2,geom_line) -importFrom(ggplot2,ggplot) -importFrom(ggplot2,labs) -importFrom(ggplot2,scale_color_brewer) -importFrom(ggplot2,scale_colour_hue) -importFrom(glmnet,cv.glmnet) -importFrom(jsonlite,fromJSON) -importFrom(jsonlite,toJSON) -importFrom(kohonen,somgrid) -importFrom(kohonen,supersom) -importFrom(lubridate,as_date) -importFrom(lubridate,days) -importFrom(lubridate,dyears) -importFrom(lubridate,period) -importFrom(lubridate,yday) -importFrom(lubridate,year) -importFrom(lubridate,ymd) -importFrom(methods,as) -importFrom(methods,new) -importFrom(mgcv,gam) -importFrom(mgcv,predict.gam) -importFrom(mgcv,s) -importFrom(nnet,class.ind) -importFrom(nnet,multinom) -importFrom(parallel,mcMap) -importFrom(ptw,whit2) -importFrom(purrr,is_null) -importFrom(purrr,map) -importFrom(purrr,map2) -importFrom(purrr,map_df) -importFrom(purrrlyr,by_row) -importFrom(randomForest,randomForest) -importFrom(raster,beginCluster) -importFrom(raster,blockSize) -importFrom(raster,brick) -importFrom(raster,canProcessInMemory) -importFrom(raster,endCluster) -importFrom(raster,getCluster) -importFrom(raster,getValues) -importFrom(raster,pbClose) -importFrom(raster,pbCreate) -importFrom(raster,pbStep) -importFrom(raster,rasterTmpFile) -importFrom(raster,returnCluster) -importFrom(raster,setValues) -importFrom(raster,shapefile) -importFrom(raster,trim) -importFrom(raster,writeStart) -importFrom(raster,writeStop) -importFrom(raster,writeValues) -importFrom(readr,col_character) -importFrom(readr,col_date) -importFrom(readr,col_double) -importFrom(readr,col_integer) -importFrom(readr,cols) -importFrom(readr,read_csv) -importFrom(readr,write_lines) -importFrom(reshape2,melt) -importFrom(rfUtilities,accuracy) -importFrom(signal,sgolayfilt) -importFrom(snow,clusterExport) -importFrom(snow,recvOneData) -importFrom(snow,sendCall) -importFrom(sp,CRS) -importFrom(sp,SpatialPoints) -importFrom(sp,bbox) -importFrom(sp,proj4string) -importFrom(stats,as.formula) -importFrom(stats,predict) -importFrom(stats,sd) -importFrom(stats,setNames) -importFrom(stringr,str_detect) -importFrom(stringr,str_extract) -importFrom(tibble,add_column) -importFrom(tibble,add_row) -importFrom(tibble,as_tibble) -importFrom(tibble,lst) -importFrom(tibble,tibble) -importFrom(tidyr,drop_na) -importFrom(tidyr,nest) -importFrom(tidyr,unnest) -importFrom(tools,file_ext) -importFrom(tools,file_path_sans_ext) -importFrom(utils,head) -importFrom(utils,tail) -importFrom(wtss,WTSS) -importFrom(wtss,describeCoverage) -importFrom(wtss,listCoverages) -importFrom(wtss,timeSeries) -importFrom(zoo,fortify.zoo) -importFrom(zoo,zoo) useDynLib(sits, .registration = TRUE) diff --git a/R/.Rapp.history b/R/.Rapp.history new file mode 100644 index 000000000..e69de29bb diff --git a/R/sits_TWDTW.R b/R/sits_TWDTW.R index 33a713b32..1c453747f 100644 --- a/R/sits_TWDTW.R +++ b/R/sits_TWDTW.R @@ -37,6 +37,7 @@ sits_TWDTW_matches <- function (series.tb, patterns.tb, bands, dist.method = "eu # add a progress bar progress_bar <- NULL if (nrow (series.tb) > 10) { + message("Classifying...") progress_bar <- utils::txtProgressBar(min = 0, max = nrow(series.tb), style = 3) i <- 0 } diff --git a/R/sits_assessment.R b/R/sits_assessment.R index 18dac1731..b8843cc6e 100644 --- a/R/sits_assessment.R +++ b/R/sits_assessment.R @@ -85,17 +85,20 @@ sits_accuracy_area <- function (results.tb, area, conf.int = 0.95, rm.nosample = #' first check of accuracy. When the area of each class for the region of interest is available, #' please use sits_accuracy_area instead #' -#'@param results.tb a sits table with a set of lat/long/time locations with known and trusted labels and +#' @param results.tb a sits table with a set of lat/long/time locations with known and trusted labels and #' with the result of a classification method +#' @return assessment a list containing overall accuracy, producers and users accuracy, and confusion matrix. #'@export sits_accuracy <- function (results.tb){ - # Get reference classes - ref.vec <- as.vector(results.tb$label) + # get reference classes + ref.vec <- results.tb$label + # create a vector to store the result of the predictions - pred.vec <- as.vector(results.tb$class) - # Classification accuracy measures - assessment <- rfUtilities::accuracy(pred.vec, ref.vec) + pred.vec <- results.tb$class + + # classification accuracy measures + assessment <- .sits_accuracy(pred.vec, ref.vec) return (assessment) } @@ -140,6 +143,8 @@ sits_accuracy <- function (results.tb){ #' @param overlap minimum overlapping between one match and the interval of classification #' @param n_clusters the maximum number of clusters to be identified (for clustering methods) #' @param grouping_method the agglomeration method to be used. Any `hclust` method (see `hclust`) (ignored in `kohonen` method). Default is 'ward.D2'. +#' @param unsupervised if TRUE, proceeds an unsupervised cluster followed by a relabel taking original label majority ( +#' this option has not any effect if method == "gam") #' @param min_clu_perc the minimum percentagem of valid cluster members, with reference to the total number of samples (for clustering methods) #' @param apply_gam apply gam method after a clustering algorithm (ignored if method is `gam`). #' @param koh_xgrid x dimension of the SOM grid (used only in `kohonen` or `kohonen-dendogram` methods). Defaul is 5. @@ -150,6 +155,7 @@ sits_accuracy <- function (results.tb){ #' Default is to decline linearly from 0.05 to 0.01 over rlen updates. #' @param file file to save the results #' @param .multicores number of threads to process the validation (Linux only). Each process will run a whole partition validation (see `times` parameter). +#' @param ... any additional parameters to be passed to `sits_pattern` function. #' @return cm a validation assessment #' @export @@ -161,9 +167,9 @@ sits_cross_validate <- function (data.tb, method = "gam", bands = NULL, times = from = NULL, to = NULL, freq = 8, formula = y ~ s(x), tw_alpha = -0.1, tw_beta = 100, tw_theta = 0.5, tw_span = 0, interval = "12 month", overlap = 0.5, - n_clusters = 2, grouping_method = "ward.D2", min_clu_perc = 0.10, + n_clusters = 2, grouping_method = "ward.D2", unsupervised = FALSE, min_clu_perc = 0.10, apply_gam = FALSE, koh_xgrid = 5, koh_ygrid = 5, koh_rlen = 100, koh_alpha = c(0.05, 0.01), - file = "./conf_matrix.json", .multicores = 1){ + file = "./conf_matrix.json", .multicores = 1, ...){ ensurer::ensure_that (data.tb, !("NoClass" %in% sits_labels(.)$label), err_desc = "sits_cross_validate: please provide a labelled set of time series") @@ -178,7 +184,7 @@ sits_cross_validate <- function (data.tb, method = "gam", bands = NULL, times = formula = formula, n_clusters = n_clusters, grouping_method = grouping_method, min_clu_perc = min_clu_perc, apply_gam = apply_gam, koh_xgrid = koh_xgrid, koh_ygrid = koh_ygrid, koh_rlen = koh_rlen, koh_alpha = koh_alpha, - show = FALSE) + unsupervised = unsupervised, show = FALSE, ...) # use the rest of the data for classification non_p.tb <- dplyr::anti_join(data.tb, p, by = c("longitude", "latitude", "start_date", "end_date", "label", "coverage")) @@ -239,7 +245,7 @@ sits_cross_validate <- function (data.tb, method = "gam", bands = NULL, times = sits_toJSON (confusion.vec, file) # Classification accuracy measures - assessment <- rfUtilities::accuracy(pred.vec, ref.vec) + assessment <- .sits_accuracy(pred.vec, ref.vec, pred_sans_ext = TRUE) return (assessment) } @@ -290,12 +296,9 @@ sits_reassess <- function (file = NULL, conv = NULL){ pred.vec <- confusion.vec[1:mid] ref.vec <- confusion.vec[(mid+1):length(confusion.vec)] - if (!purrr::is_null(conv)) { - pred.vec <- as.character(conv[pred.vec]) - ref.vec <- as.character(conv[ref.vec]) - } # calculate the accuracy assessment - assess <- rfUtilities::accuracy(pred.vec, ref.vec) + assess <- .sits_accuracy(pred.vec, ref.vec, pred_sans_ext = TRUE, conv.lst = conv) + return (assess) } @@ -313,7 +316,7 @@ sits_reassess <- function (file = NULL, conv = NULL){ #' #' @param data.tb A sits tibble containing a set of samples with known and trusted labels #' @param patterns.tb A sits tibble containing a set of patterns -#' @param bands The bands used for classification +#' @param bands the bands used for classification #' @param alpha (double) - the steepness of the logistic function used for temporal weighting #' @param beta (integer) - the midpoint (in days) of the logistic function #' @param theta (double) - the relative weight of the time distance compared to the dtw distance @@ -345,23 +348,72 @@ sits_test_patterns <- function (data.tb, patterns.tb, bands, # retrieve the reference labels ref.vec <- as.character(class.tb$label) # retrieve the predicted labels - pred.vec <- as.character( - purrr::map(class.tb$best_matches, function (e) as.character(e$label))) + pred.vec <- as.character(purrr::map(class.tb$best_matches, function (e) as.character(e$label))) # calculate the accuracy assessment - assess <- rfUtilities::accuracy(pred.vec, ref.vec) + assess <- .sits_accuracy(pred.vec, ref.vec, pred_sans_ext = TRUE) + return (assess) } -#' @title Post-classification accuracy assessment of classified maps -#' @name sits_compare -#' @author Victor Maus, \email{vwmaus1@@gmail.com} -#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br} -#' @param map_ref a Raster R object with the classified map to be used as a reference -#' @param labels_ref a vector with the labels of the classified map to be used as a refence -#' @param map_comp a Raster R object with the classified map to be compared with reference -#' @param labels_comp a vector with the labels of the classified map to be used as a refence -#' @return confusion.matrix - a global confusion matrix (area-weighted) +#' @title Evaluates the accuracy of classification +#' @name .sits_accuracy +#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br} +# +#' @description Evaluates the accuracy of classification stored in two vectors. +#' Returns the overall accuracy, producers and users accuracy, and confusion matrix. +#' This algorith was inspired by `rfUtilities::accuracy` function, and fix the user and +#' producer accuracy computation inconsistency. +#' +#' @param pred.vec A vector of all predicted labels. +#' @param ref.vec A vector of all reference labels. +#' @param pred_sans_ext (Boolean) remove all label extension (i.e. every string after last '.' character) from predictors before compute assesment. +#' @param conv.lst A list conversion list of labels. If NULL no conversion is done. +#' @return result.lst a list with accuracy measures and confusion matrix +.sits_accuracy <- function(pred.vec, ref.vec, pred_sans_ext = FALSE, conv.lst = NULL){ + + # remove predicted labels' extensions + if (pred_sans_ext) + pred.vec <- tools::file_path_sans_ext(pred.vec) + + # count all pairs of labels + # rows: predicted labels; cols: reference labels + if (is.null(conv.lst)) + conf.mtx <- table(pred.vec, ref.vec) + else{ + ensurer::ensure_that(c(pred.vec, ref.vec), + all(names(.) %in% names(conv.lst)), + err_desc = ".sits_accuracy: conversion list does not contain all labels provided in `pred.vec` and/or `ref.vec` arguments.") + conf.mtx <- table(as.character(conv.lst[[pred.vec]]), as.character(conv.lst[[ref.vec]])) + } + + # ensures that the confusion matrix is square + ensurer::ensure_that(conf.mtx, NCOL(.) == NROW(.), + err_desc = ".sits_accuracy: predicted and reference vectors does not produce a squared matrix. Try to convert `pred.vec` entries before compute accuracy.") + + # sort rows (predicted labels) according to collumn names (reference labels) + conf.mtx <- conf.mtx[colnames(conf.mtx),] + # get labels' agreement (matrix diagonal) + agreement <- diag(conf.mtx) + # get total of predicted labels (to compute users accuracy) + users <- apply(conf.mtx, 1, sum) + # get total of reference labels (to compute producers accuracy) + producers <- apply(conf.mtx, 2, sum) + + # get grand totals + agreement_total <- sum(agreement) + grand_total <- sum(conf.mtx) + + # compose result list + result.lst <- tibble::lst( + overall.accuracy = round(agreement_total / grand_total * 100, 4), + producer.accuracy = round(agreement / producers * 100, 4), + user.accuracy = round(agreement / users * 100, 4), + confusion = conf.mtx + ) + + return(result.lst) +} diff --git a/R/sits_cluster.R b/R/sits_cluster.R index 3bc77df85..aa2cc1a1e 100644 --- a/R/sits_cluster.R +++ b/R/sits_cluster.R @@ -25,28 +25,32 @@ #' @param unsupervised (boolean) should labels be ignored in clustering algorithms? #' If `return_members` parameter is TRUE, resulting sits table will gain an extra column called `original_label` with all original labels. #' This column may be useful to measure confusion between clusters' members. Default is FALSE. -#' @param show (boolean) should the results be shown? Default is TRUE. -#' @param ... Other arguments to pass to the distance method \code{dist_method}, see \code{\link[dtwclust]{dtwclust}} for details. +#' @param show (boolean) should the results be shown? Default is TRUE. +#' @param ... Other arguments to pass to the distance method \code{dist_method}, and cluster method. See \code{\link[dtwclust]{dtwclust}} for details. #' @return clusters.tb a SITS tibble with the clusters time series or cluster' members time series according to return_member parameter. #' If return_members are FALSE, the returning SITS table will contain a new collumn called `n_members` informing how many members has each cluster. #' @export -sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_clusters = 2, dist_method = "dtw_basic", +sits_cluster <- function (data.tb, bands = NULL, method = "dendogram", n_clusters = 2, dist_method = "dtw_basic", grouping_method = "ward.D2",koh_xgrid = 5, koh_ygrid = 5, koh_rlen = 100, - koh_alpha = c(0.05, 0.01), return_members = FALSE, unsupervised = FALSE, show = TRUE, ...) { + koh_alpha = c(0.05, 0.01), return_members = FALSE, unsupervised = FALSE, show = FALSE, ...) { ensurer::ensure_that(method, (. == "dendogram" || . == "centroids" || . == "kohonen" || . == "kohonen-dendogram"), err_desc = "sits_cluster: valid cluster methods are 'dendogram', 'centroids', 'kohonen', or 'kohonen-dendogram'.") + # if no bands informed, get all bands available in SITS table + if (purrr::is_null(bands)) + bands <- sits_bands(data.tb) + # creates the resulting table cluster.tb <- sits_table() + # if no original_label exists, creates one + if (!("original_label" %in% colnames(data.tb))) + data.tb$original_label <- data.tb$label + # if unsupervised cluster is enabled, change all input labels. - if (unsupervised) { - # if return_members is True, create an new column called `old_label` - if (return_members) - data.tb$original_label <- data.tb$label - data.tb$label <- "Class" - } + if (unsupervised) + data.tb$label <- "Unsupervised" # how many different labels are there? labels <- dplyr::distinct (data.tb, label)$label @@ -57,38 +61,63 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ # add a progress bar progress_bar <- utils::txtProgressBar(min = 0, max = length(labels), style = 3) + # traverse labels purrr::map2(labels, seq_along(labels), function (lb, i){ - # filter only those rows with the same labels - # cut time series to fit in one year - label.tb <- dplyr::filter (data.tb, label == lb) #%>% - #sits_prune() ## FIX-ME! sits_prune() returns a singular time series dates for specific cases! - - # apply the clustering method - if (method == "dendogram") - clu.tb <- .sits_cluster_dendogram (label.tb, bands=bands, n_clusters=n_clusters, dist_method=dist_method, grouping_method=grouping_method, - return_members=return_members, show=show, ...) - else if (method == "centroids") - clu.tb <- .sits_cluster_partitional (label.tb, bands=bands, n_clusters=n_clusters, dist_method=dist_method, grouping_method=grouping_method, - return_members=return_members, show=show, ...) - else if (method == "kohonen") - clu.tb <- .sits_cluster_kohonen (label.tb, bands=bands, grid_xdim=koh_xgrid, grid_ydim=koh_ygrid, - rlen=koh_rlen, alpha=koh_alpha, return_members=return_members, show=show) - else if (method == "kohonen-dendogram") - clu.tb <- .sits_cluster_kohodogram (label.tb, bands=bands, n_clusters=n_clusters, dist_method=dist_method, grouping_method=grouping_method, - grid_xdim=koh_xgrid, grid_ydim=koh_ygrid, - rlen=koh_rlen, alpha=koh_alpha, return_members=return_members, show=show) - - # append the result - cluster.tb <<- dplyr::bind_rows(cluster.tb, clu.tb) - - # update progress bar - utils::setTxtProgressBar(progress_bar, i) + # filter only those rows with the same labels + # cut time series to fit in one year + label.tb <- dplyr::filter (data.tb, label == lb) #%>% + #sits_prune() ## FIX-ME! sits_prune() returns a singular time series dates for specific cases! + + # apply the clustering method + if (method == "dendogram") + clu.tb <- .sits_cluster_dendogram (label.tb, bands=bands, n_clusters=n_clusters, dist_method=dist_method, + grouping_method=grouping_method, + return_members=return_members, show=show, ... = ...) + else if (method == "centroids") + clu.tb <- .sits_cluster_partitional (label.tb, bands=bands, n_clusters=n_clusters, dist_method=dist_method, + grouping_method=grouping_method, + return_members=return_members, show=show, ... = ...) + else if (method == "kohonen") + clu.tb <- .sits_cluster_kohonen (label.tb, bands=bands, grid_xdim=koh_xgrid, grid_ydim=koh_ygrid, + rlen=koh_rlen, alpha=koh_alpha, return_members=return_members, show=show, ... = ...) + else if (method == "kohonen-dendogram") + clu.tb <- .sits_cluster_kohodogram (label.tb, bands=bands, n_clusters=n_clusters, dist_method=dist_method, + grouping_method=grouping_method, + grid_xdim=koh_xgrid, grid_ydim=koh_ygrid, + rlen=koh_rlen, alpha=koh_alpha, return_members=return_members, show=show, ... = ...) + + # append the result + cluster.tb <<- dplyr::bind_rows(cluster.tb, clu.tb) + + # update progress bar + utils::setTxtProgressBar(progress_bar, i) + }) close(progress_bar) + return (cluster.tb) } + +# global variable to store the last cluster object generated by `sits_cluster()` function +.sits_last_cluster <- new.env() + +#' @title Last cluster object +#' @name sits_last_cluster +#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br} +#' +#' @description Returns the content of global variable `.sits_last_cluster` that stores the last +#' cluster object generated by sits_cluster function. +#' +#' @return .sits_last_cluster an object created by dtwclust or SOM +#' @export +sits_last_cluster <- function(){ + if (exists("last_cluster", envir = .sits_last_cluster)) + return(get("last_cluster", envir = .sits_last_cluster)) + return(NULL) +} + #' @title Cluster a set of time series using hierarchical clustering #' @name .sits_cluster_dendogram #' @author Gilberto Camara, \email{gilberto.camara@@inpe.br} @@ -111,6 +140,7 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ #' @param grouping_method the agglomeration method to be used. Any `hclust` method (see `hclust`). #' @param return_members (boolean) should the results be the clusters' members instead of clusters' centroids? #' @param show (boolean) should the results be shown? +#' @param ... any additional parameters to be passed to dtwclust::tsclust() function #' @return clusters.tb a SITS tibble with the clusters .sits_cluster_dendogram <- function (data.tb, bands, n_clusters, dist_method, grouping_method, return_members, show, ...){ @@ -127,9 +157,10 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ } clusters <- dtwclust::tsclust (values.tb, - type = "hierarchical", - k = n_clusters, - distance = dist_method, ...) + type = "hierarchical", + k = n_clusters, + distance = dist_method, + control = hierarchical_control(method = grouping_method), ...) # dtwclust does not handle zoo, therefore we convert zoo to matrix to allow for clusters visualization if( tolower(dist_method) %in% "twdtw" ){ @@ -140,6 +171,9 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ # Plot the series and the obtained prototypes if (show) .sits_dtwclust_show (clusters) + # stores in .sits_last_cluster environment + assign("last_cluster", clusters, envir = .sits_last_cluster) + # if return_members parameter is TRUE, returns a sits samples table with updated labels # else, returns cluster's centroids return (.sits_from_dtwclust (data.tb, clusters = clusters, return_members = return_members)) @@ -167,6 +201,7 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ #' @param grouping_method the agglomeration method to be used. Any `hclust` method (see `hclust`). #' @param return_members (boolean) should the results be the clusters' members instead of clusters' centroids? #' @param show (boolean) should the results be shown? +#' @param ... any additional parameters to be passed to dtwclust::tsclust() function #' @return clusters.tb a SITS tibble with the clusters .sits_cluster_partitional <- function (data.tb, bands, n_clusters, dist_method, grouping_method, return_members, show, ...) { @@ -183,11 +218,9 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ } clusters <- dtwclust::tsclust (values.tb, - type = "partitional", - k = n_clusters, - distance = dist_method, - centroid = "pam", - seed = 899, ...) + type = "partitional", + k = n_clusters, + distance = dist_method, ...) # dtwclust does not handle zoo, therefore we convert zoo to matrix to allow for clusters visualization if( tolower(dist_method) %in% "twdtw" ){ @@ -198,6 +231,9 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ # Plot the series and the obtained prototypes if (show) .sits_dtwclust_show (clusters) + # stores in .sits_last_cluster environment + assign("last_cluster", clusters, envir = .sits_last_cluster) + # if return_members parameter is TRUE, returns a sits samples table with updated labels # else, returns cluster's centroids return (.sits_from_dtwclust (data.tb, clusters = clusters, return_members = return_members)) @@ -226,8 +262,9 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ #' Default is to decline linearly from 0.05 to 0.01 over rlen updates. #' @param return_members (boolean) should the results be the clusters' members instead of clusters' centroids? #' @param show (boolean) should the results be shown? +#' @param ... any additional parameters to be passed to kohonen::supersom() function #' @return clusters.tb a SITS tibble with the clusters -.sits_cluster_kohonen <- function (data.tb, bands, grid_xdim, grid_ydim, rlen, alpha, return_members, show){ +.sits_cluster_kohonen <- function (data.tb, bands, grid_xdim, grid_ydim, rlen, alpha, return_members, show, ...){ # recalculate grid dimension if the number of neurons is greater than the number of data input cases #### TO-DO: Document this recalculation! @@ -243,12 +280,14 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ grid <- kohonen::somgrid(xdim = grid_xdim, ydim = grid_ydim, topo = "rectangular") kohonen_obj <- kohonen::supersom (values.tb, grid=grid, rlen = rlen, alpha = alpha, - keep.data = TRUE, - dist.fcts = "euclidean") + keep.data = TRUE, ...) # Plot the series and the obtained prototypes if (show) .sits_kohonen_show (kohonen_obj) + # stores in .sits_last_cluster environment + assign("last_cluster", kohonen_obj, envir = .sits_last_cluster) + # if return_members parameter is TRUE, returns a sits samples table with updated labels else, returns cluster's centroids return (.sits_from_kohonen (data.tb, kohonen_obj = kohonen_obj, return_members = return_members)) } @@ -272,8 +311,10 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ #' @param grouping_method the agglomeration method to be used. Any `hclust` method (see `hclust`). #' @param return_members (boolean) should the results be the clusters' members instead of clusters' centroids? #' @param show (boolean) should the results be shown? +#' @param ... any additional parameters #' @return result.tb a SITS tibble with the clusters -.sits_cluster_kohodogram <- function (data.tb, bands, grid_xdim, grid_ydim, rlen, alpha, n_clusters, dist_method, grouping_method, return_members, show){ +.sits_cluster_kohodogram <- function (data.tb, bands, grid_xdim, grid_ydim, rlen, alpha, n_clusters, + dist_method, grouping_method, return_members, show, ...){ # recalculate grid dimension if the number of neurons is greater than the number of data input cases #### TO-DO: Document this recalculation! @@ -291,12 +332,14 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ kohonen_obj <- kohonen::supersom (values.tb, grid=grid, rlen = rlen, alpha = alpha, - keep.data = TRUE, - dist.fcts = "euclidean") + keep.data = TRUE, ...) # Plot the series and the obtained prototypes if (show) .sits_kohonen_show (kohonen_obj) + # stores in .sits_last_cluster environment + assign("last_cluster", kohonen_obj, envir = .sits_last_cluster) + # returns kohonen's neurons neurons.tb <- .sits_from_kohonen (data.tb, kohonen_obj, return_members = FALSE) @@ -305,14 +348,20 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ # pass neurons to dendogram clustering clusters.tb <- .sits_cluster_dendogram (neurons.tb, bands = bands, n_clusters = n_clusters, dist_method = dist_method, - grouping_method = grouping_method, return_members = return_members, show = FALSE) + grouping_method = grouping_method, return_members = return_members, show = FALSE, ... = ...) # return a sits table with all input data with new labels if (return_members) { # set clusters' labels to result data result.tb <- data.tb + + # if no original_label exists, creates one + if (!("original_label" %in% colnames(result.tb))) + result.tb$original_label <- tools::file_path_sans_ext(result.tb$label) + + # update labels according to clusters result.tb$label <- clusters.tb$label[kohonen_obj$unit.classif] - result.tb$original_label <- clusters.tb$original_label[kohonen_obj$unit.classif] + # return a sits table with clusters' centroids } else result.tb <- dplyr::select(clusters.tb, longitude, latitude, start_date, end_date, label, coverage, time_series, original_label, n_members) @@ -334,35 +383,55 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ #' @return result.tb a SITS table with the clusters or clusters' members .sits_from_dtwclust <- function (data.tb, clusters, return_members) { - # get prefix label to be used in clusters' labels - label_prefix <- data.tb[1,]$label[[1]] - # return a sits table with all input data with new labels - if (return_members){ + if (return_members) { + + # get prefix label to be used in clusters' labels + label_prefix <- data.tb[1,]$label[[1]] + # create a table to store the results result.tb <- data.tb - # create 'original_cluster' collumn - result.tb$original_label <- result.tb$label - # apply new labels according to clusters' id result.tb$label <- paste0(label_prefix, ".", clusters@cluster) + # verify if there is n_members column. If not exists initialize it with ones. + if (!any("n_members" %in% names(result.tb))) + result.tb$n_members <- result.tb$original_label %>% + purrr::map(function(label) return(tibble::tibble(original_label = label, n = 1))) + + clusters_labels <- base::unique(result.tb$label) %>% + purrr::map(function(lbl) + result.tb %>% + dplyr::filter(label == lbl) %>% + dplyr::count(original_label, sort = TRUE) %>% + .[1,] %>% .$original_label) %>% + as.character() + + result.tb$label <- paste0(clusters_labels[clusters@cluster], ".", clusters@cluster) + # return a sits table with clusters' centroids } else { - # computes num of members for each case. If no previous n_members, initialize with ones. - if (!any("n_members" %in% names(data.tb))) - data.tb$n_members <- 1 + # # computes num of members for each case. If no previous n_members, initialize with ones. + # if (!any("n_members" %in% names(data.tb))) + # data.tb$n_members <- 1 # create a table to store the results result.tb <- sits_table() # return the number of each cluster members - result.tb <- tibble::add_column(result.tb, original_label = character(), n_members = integer()) + result.tb <- tibble::add_column(result.tb, original_label = character(), #n_members = integer(), + n_members = list()) # populates the result table with centroids purrr::map2(clusters@centroids, seq(clusters@centroids), function (ts, i) { + # get prefix label to be used in clusters' labels as the most frequent original label + n_members.lst <- tibble::lst() + n_members.lst[[1]] <- dplyr::count(data.tb[which(clusters@cluster == i), ], + original_label, sort = TRUE) + label_prefix <- n_members.lst[[1]][1,]$original_label + new_ts <- dplyr::select(data.tb[1,]$time_series[[1]], Index) new_ts <- dplyr::bind_cols(new_ts, tibble::as_tibble(ts)) result.tb <<- tibble::add_row (result.tb, @@ -374,7 +443,7 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ coverage = data.tb[1,]$coverage[[1]], time_series = list(new_ts), original_label = label_prefix, - n_members = sum(data.tb$n_members[which(clusters@cluster == i)], na.rm = TRUE)) + n_members = n_members.lst) }) } @@ -396,20 +465,27 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ #' @return result.tb a SITS table with the clusters or clusters' members .sits_from_kohonen <- function (data.tb, kohonen_obj, return_members) { - # get label prefix to form sub-class label result - label_prefix <- data.tb[1,]$label[[1]] + # if no original_label exists, creates one + if (!("original_label" %in% colnames(data.tb))) + data.tb$original_label <- data.tb$label if (return_members){ + # get label prefix to form sub-class label result + label_prefix <- data.tb[1,]$label[[1]] + # create a table to store the results result.tb <- data.tb - # create 'original_cluster' column - result.tb$original_label <- result.tb$label - # assign new labels result.tb$label <- paste0(label_prefix, ".", kohonen_obj$unit.classif) + # organizes the resulting SITS table + result.tb <- dplyr::select(result.tb, longitude, latitude, start_date, end_date, label, coverage, time_series, original_label) + } else { + # get label prefix to form sub-class label result + label_prefix <- data.tb[1,]$label[[1]] + # get num of neurons' members neurons_size <- rle(sort(kohonen_obj$unit.classif))$lengths @@ -452,6 +528,9 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ result_band.lst %>% purrr::map(function (result_band.tb){ result.tb <<- sits_merge(result.tb, result_band.tb) }) + + # organizes the resulting SITS table + result.tb <- dplyr::select(result.tb, longitude, latitude, start_date, end_date, label, coverage, time_series, original_label, n_members) } return (result.tb) @@ -537,72 +616,3 @@ sits_cluster <- function (data.tb = NULL, bands = NULL, method = "dendogram", n_ # for (i in 1:nrow(df)) # cat (paste ("Cluster ", i,": ", df[i,"size"], "\n", sep = "")) } - -#' @title compare clusters against original labels and computes a segregation matrix. -#' @name sits_cluster_segregation -#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br} -#' -#' @description Given a sits table with `original_label` column, computes a segregation matrix -#' between the original labels (`original_label` column) and new labels. This is useful -#' to analyse the separability of samples for a given clustering algorithm. -#' -#' @param data.tb a SITS table with the samples to be validated -#' @return result.tb a tibble with segregation matrix. -#' @export -sits_cluster_segregation <- function (data.tb){ - ensurer::ensure_that(data.tb, !purrr::is_null(.), - err_desc = "sits_cluster_segregation: SITS table not provided") - # do the input data have the `original_label` column? - ensurer::ensure_that(data.tb, "original_label" %in% colnames(data.tb), - err_desc = "sits_cluster_segregation: informed SITS table has not an `original_label` column.") - - result.tb <- data.tb %>% - dplyr::group_by(original_label, label) %>% - dplyr::summarise(count = n()) %>% - tidyr::spread(key = label, value = count) %>% - dplyr::ungroup() - - return (result.tb) -} - -#' @title computes a segregation measure from a clusterized SITS table data. -#' @name sits_segregation_measure -#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br} -#' -#' @description Computes an measure of seggregation from SITS table based on mean relative Shannon Entropy, -#' where maximum entropy is 1.0 and minimum entropy is 0.0, from each cluster label against -#' original_label occorrences in it. To obtain this measure, first we compute Shannon -#' information entropy from counts of each cluster label. To get the relative entropy, we -#' divide this value by the maximum entropy. After that, we compute the weighted mean relative -#' to the frequency of each cluster label. The final values represents an average of -#' maximum entropy fraction. This is useful to assess the separability of samples for a given clustering algorithm. -#' -#' @param data.tb a SITS table with the samples to be validated -#' @param per_cluster (boolean) should return a total average segregation measure, or a per cluster measure? -#' @return result a segregation measure. -#' @export -sits_segregation_measure <- function (data.tb, per_cluster = FALSE){ - ensurer::ensure_that(data.tb, !purrr::is_null(.), - err_desc = "sits_segregation_measure: SITS table not provided") - # do the input data have the `original_label` column? - ensurer::ensure_that(data.tb, "original_label" %in% colnames(data.tb), - err_desc = "sits_segregation_measure: informed SITS table has not an `original_label` column.") - - # do we have at least two original labels? - labels_count <- length(table(data.tb$original_label)) - if (labels_count == 1) - return (0.0) - - result.tb <- data.tb %>% - dplyr::group_by(original_label, label) %>% - dplyr::summarise(count = n()) %>% - dplyr::ungroup() %>% - dplyr::group_by(label) %>% - dplyr::summarise(segr = entropy::entropy(count) / log(labels_count) * sum(count, na.rm = TRUE) / nrow(data.tb)) - - if (per_cluster) - return (result.tb) - - return (dplyr::summarise(result.tb, mean_segr = sum(segr, na.rm = TRUE)) %>% .$mean_segr) - -} diff --git a/R/sits_colors.R b/R/sits_colors.R new file mode 100644 index 000000000..5cae31c41 --- /dev/null +++ b/R/sits_colors.R @@ -0,0 +1,5011 @@ +#' @title Brewer color schemes +#' @name sits_plot_title +#' +#' @description brewer color schemes names to be used with `sits_plot_dendrogram` +#' +#' @param name name of the brewer color set +#' @return name string - a common color set name +#' @export +sits_color_name <- function(name = NULL){ + # approximate equivalent names in ISCC-NBS system + # see http://en.wikipedia.or"g/wiki/ISCC-NBS_system + names <- tibble::lst("BuGn" = "BuGn", + "BlueishGreens" = "BuGn", + "BlueGreens" = "BuGn", + "BlueGreen" = "BuGn", + "BuPu" = "BuPu", + "BlueishPurples" = "BuPu", + "BluePurples" = "BuPu", + "BluePurple" = "BuPu", + "GnBu" = "GnBu", + "GreenishBlues" = "GnBu", + "GreenBlues" = "GnBu", + "GreenBlue" = "GnBu", + "OrRd" = "OrRd", + "OrangishReds" = "OrRd", + "OrangeReds" = "OrRd", + "OrangeRed" = "OrRd", + "PuBu" = "PuBu", + "PurplishBlues" = "PuBu", + "PurpleBlues" = "PuBu", + "PurpleBlue" = "PuBu", + "PuBuGn" = "PuBuGn", + "PurplishBlueGreens" = "PuBuGn", + "PurpleBlueGreens" = "PuBuGn", + "PurpleBlueGreen" = "PuBuGn", + "PuRd" = "PuRd", + "PurplishReds" = "PuRd", + "PurpleReds" = "PuRd", + "PurpleRed" = "PuRd", + "RdPu" = "RdPu", + "ReddishPurples" = "RdPu", + "RedPurples" = "RdPu", + "RedPurple" = "RdPu", + "YlGn" = "YlGn", + "YellowishGreens" = "YlGn", + "YellowGreens" = "YlGn", + "YellowGreen" = "YlGn", + "YlGnBu" = "YlGnBu", + "YellowishGreenBlues" = "YlGnBu", + "YellowGreenBlues" = "YlGnBu", + "YellowGreenBlue" = "YlGnBu", + "YlOrBr" = "YlOrBr", + "YellowishOrangeBrowns" = "YlOrBr", + "YellowOrangeBrowns" = "YlOrBr", + "YellowOrangeBrown" = "YlOrBr", + "YlOrRd" = "YlOrRd", + "YellowishOrangereds" = "YlOrRd", + "YellowOrangeReds" = "YlOrRd", + "YellowOrangeRed" = "YlOrRd", + "Blues" = "Blues", + "Blue" = "Blues", + "Greens" = "Greens", + "Green" = "Greens", + "Greys" = "Greys", + "Grey" = "Greys", + "Grays" = "Greys", + "Gray" = "Greys", + "Oranges" = "Oranges", + "Orange" = "Oranges", + "Purples" = "Purples", + "Purple" = "Purples", + "Reds" = "Reds", + "Red" = "Reds", + "BrBG" = "BrBG", + "BrownsBlueGrens" = "BrBG", + "BrownsWhiteBlueGreens" = "BrBG", + "BrownsWhiteBlueGreen" = "BrBG", + "BrownsBlueGreens" = "BrBG", + "BrownBlueGreen" = "BrBG", + "PiYG" = "PiYG", + "PurplesWhiteYellowGreens" = "PiYG", + "PurplesYellowGreens" = "PiYG", + "PurplesYellowGreen" = "PiYG", + "PurplesYellowsGreens" = "PiYG", + "PRGn" = "PRGn", + "PurpleRedsWhiteGreens" = "PRGn", + "PurplesRedsWhiteGreens" = "PRGn", + "PurplesRedsGreens" = "PRGn", + "PurpleRedWhiteGreen" = "PRGn", + "PurpleRedGreen" = "PRGn", + "PuOr" = "PuOr", + "PurplesWhiteOranges" = "PuOr", + "PurplesOranges" = "PuOr", + "PurpleOrange" = "PuOr", + "RdBu" = "RdBu", + "RedsWhiteBlues" = "RdBu", + "RedWhiteBlue" = "RdBu", + "RedBlue" = "RdBu", + "RedsBlues" = "RdBu", + "RdGy" = "RdGy", + "RedsWhiteGreys" = "RdGy", + "RedsGreys" = "RdGy", + "RedGrey" = "RdGy", + "RdYlBu" = "RdYlBu", + "ReddishYellowWhiteBlues" = "RdYlBu", + "RedsYellowsBlues" = "RdYlBu", + "RedYellowWhiteBlue" = "RdYlBu", + "RedYellowBlue" = "RdYlBu", + "RdYlGn" = "RdYlGn", + "ReddishYellowWhiteGreens" = "RdYlGn", + "RedsYellowsWhitesGreens" = "RdYlGn", + "RedYellowWhiteGreen" = "RdYlGn", + "RedsYellowsGreens" = "RdYlGn", + "RedYellowGreen" = "RdYlGn", + "Spectral" = "Spectral", + "Paired" = "Paired", + "Pastel1" = "Pastel1", + "Pastel2" = "Pastel2", + "Set1" = "Set1", + "Set2" = "Set2", + "Set3" = "Set3", + "Accent" = "Accent", + "Dark" = "Dark" + ) + if (is.null(name)) + return(names) + return(names[[name]]) +} + +#' @title Brewer color schemes +#' @name sits_max_colors +#' +#' @description Number of colors available in brewer color schemes +#' +#' @param name name of the brewer color set +#' @return numeric number of available colors +#' @export +sits_max_colors <- function(name = NULL){ + if (is.null(name)) + return(.sits_brewerRGB %>% purrr::map(function(sch){ + sum((sch %>% + purrr::map(function(n) length(n)) %>% + as.numeric()) > 1) + })) + return(sum((.sits_brewerRGB[[sits_color_name(name)]] %>% + purrr::map(function(n) length(n)) %>% + as.numeric()) > 1)) +} + +# Brewer color set constant +# based on http://colorbrewer2.org colors' schemes +.sits_brewerRGB = tibble::lst( + "Spectral" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(), + "3" = tibble::lst(rgb(252 / 255, 141 / 255, 89 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(153 / 255, 213 / 255, 148 / 255)), + "4" = tibble::lst(rgb(215 / 255, 25 / 255, 28 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(171 / 255, 221 / 255, 164 / 255), + rgb(43 / 255, 131 / 255, 186 / 255)), + "5" = tibble::lst(rgb(215 / 255, 25 / 255, 28 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(171 / 255, 221 / 255, 164 / 255), + rgb(43 / 255, 131 / 255, 186 / 255)), + "6" = tibble::lst(rgb(213 / 255, 62 / 255, 79 / 255), + rgb(252 / 255, 141 / 255, 89 / 255), + rgb(254 / 255, 224 / 255, 139 / 255), + rgb(230 / 255, 245 / 255, 152 / 255), + rgb(153 / 255, 213 / 255, 148 / 255), + rgb(50 / 255, 136 / 255, 189 / 255)), + "7" = tibble::lst(rgb(213 / 255, 62 / 255, 79 / 255), + rgb(252 / 255, 141 / 255, 89 / 255), + rgb(254 / 255, 224 / 255, 139 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(230 / 255, 245 / 255, 152 / 255), + rgb(153 / 255, 213 / 255, 148 / 255), + rgb(50 / 255, 136 / 255, 189 / 255)), + "8" = tibble::lst(rgb(213 / 255, 62 / 255, 79 / 255), + rgb(244 / 255, 109 / 255, 67 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(254 / 255, 224 / 255, 139 / 255), + rgb(230 / 255, 245 / 255, 152 / 255), + rgb(171 / 255, 221 / 255, 164 / 255), + rgb(102 / 255, 194 / 255, 165 / 255), + rgb(50 / 255, 136 / 255, 189 / 255)), + "9" = tibble::lst(rgb(213 / 255, 62 / 255, 79 / 255), + rgb(244 / 255, 109 / 255, 67 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(254 / 255, 224 / 255, 139 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(230 / 255, 245 / 255, 152 / 255), + rgb(171 / 255, 221 / 255, 164 / 255), + rgb(102 / 255, 194 / 255, 165 / 255), + rgb(50 / 255, 136 / 255, 189 / 255)), + "10" = tibble::lst(rgb(158 / 255, 1 / 255, 66 / 255), + rgb(213 / 255, 62 / 255, 79 / 255), + rgb(244 / 255, 109 / 255, 67 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(254 / 255, 224 / 255, 139 / 255), + rgb(230 / 255, 245 / 255, 152 / 255), + rgb(171 / 255, 221 / 255, 164 / 255), + rgb(102 / 255, 194 / 255, 165 / 255), + rgb(50 / 255, 136 / 255, 189 / 255), + rgb(94 / 255, 79 / 255, 162 / 255)), + "11" = tibble::lst(rgb(158 / 255, 1 / 255, 66 / 255), + rgb(213 / 255, 62 / 255, 79 / 255), + rgb(244 / 255, 109 / 255, 67 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(254 / 255, 224 / 255, 139 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(230 / 255, 245 / 255, 152 / 255), + rgb(171 / 255, 221 / 255, 164 / 255), + rgb(102 / 255, 194 / 255, 165 / 255), + rgb(50 / 255, 136 / 255, 189 / 255), + rgb(94 / 255, 79 / 255, 162 / 255)), + "type" = "divergent" + ), + "RdYlGn" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(), + "3" = tibble::lst(rgb(252 / 255, 141 / 255, 89 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(145 / 255, 207 / 255, 96 / 255)), + "4" = tibble::lst(rgb(215 / 255, 25 / 255, 28 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(166 / 255, 217 / 255, 106 / 255), + rgb(26 / 255, 150 / 255, 65 / 255)), + "5" = tibble::lst(rgb(215 / 255, 25 / 255, 28 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(166 / 255, 217 / 255, 106 / 255), + rgb(26 / 255, 150 / 255, 65 / 255)), + "6" = tibble::lst(rgb(215 / 255, 48 / 255, 39 / 255), + rgb(252 / 255, 141 / 255, 89 / 255), + rgb(254 / 255, 224 / 255, 139 / 255), + rgb(217 / 255, 239 / 255, 139 / 255), + rgb(145 / 255, 207 / 255, 96 / 255), + rgb(26 / 255, 152 / 255, 80 / 255)), + "7" = tibble::lst(rgb(215 / 255, 48 / 255, 39 / 255), + rgb(252 / 255, 141 / 255, 89 / 255), + rgb(254 / 255, 224 / 255, 139 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(217 / 255, 239 / 255, 139 / 255), + rgb(145 / 255, 207 / 255, 96 / 255), + rgb(26 / 255, 152 / 255, 80 / 255)), + "8" = tibble::lst(rgb(215 / 255, 48 / 255, 39 / 255), + rgb(244 / 255, 109 / 255, 67 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(254 / 255, 224 / 255, 139 / 255), + rgb(217 / 255, 239 / 255, 139 / 255), + rgb(166 / 255, 217 / 255, 106 / 255), + rgb(102 / 255, 189 / 255, 99 / 255), + rgb(26 / 255, 152 / 255, 80 / 255)), + "9" = tibble::lst(rgb(215 / 255, 48 / 255, 39 / 255), + rgb(244 / 255, 109 / 255, 67 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(254 / 255, 224 / 255, 139 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(217 / 255, 239 / 255, 139 / 255), + rgb(166 / 255, 217 / 255, 106 / 255), + rgb(102 / 255, 189 / 255, 99 / 255), + rgb(26 / 255, 152 / 255, 80 / 255)), + "10" = tibble::lst(rgb(165 / 255, 0 / 255, 38 / 255), + rgb(215 / 255, 48 / 255, 39 / 255), + rgb(244 / 255, 109 / 255, 67 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(254 / 255, 224 / 255, 139 / 255), + rgb(217 / 255, 239 / 255, 139 / 255), + rgb(166 / 255, 217 / 255, 106 / 255), + rgb(102 / 255, 189 / 255, 99 / 255), + rgb(26 / 255, 152 / 255, 80 / 255), + rgb(0 / 255, 104 / 255, 55 / 255)), + "11" = tibble::lst(rgb(165 / 255, 0 / 255, 38 / 255), + rgb(215 / 255, 48 / 255, 39 / 255), + rgb(244 / 255, 109 / 255, 67 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(254 / 255, 224 / 255, 139 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(217 / 255, 239 / 255, 139 / 255), + rgb(166 / 255, 217 / 255, 106 / 255), + rgb(102 / 255, 189 / 255, 99 / 255), + rgb(26 / 255, 152 / 255, 80 / 255), + rgb(0 / 255, 104 / 255, 55 / 255)), + "type" = "divergent" + ), + "RdBu" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(239 / 255, 138 / 255, 98 / 255), + rgb(103 / 255, 169 / 255, 207 / 255)), + "3" = tibble::lst(rgb(239 / 255, 138 / 255, 98 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(103 / 255, 169 / 255, 207 / 255)), + "4" = tibble::lst(rgb(202 / 255, 0 / 255, 32 / 255), + rgb(244 / 255, 165 / 255, 130 / 255), + rgb(146 / 255, 197 / 255, 222 / 255), + rgb(5 / 255, 113 / 255, 176 / 255)), + "5" = tibble::lst(rgb(202 / 255, 0 / 255, 32 / 255), + rgb(244 / 255, 165 / 255, 130 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(146 / 255, 197 / 255, 222 / 255), + rgb(5 / 255, 113 / 255, 176 / 255)), + "6" = tibble::lst(rgb(178 / 255, 24 / 255, 43 / 255), + rgb(239 / 255, 138 / 255, 98 / 255), + rgb(253 / 255, 219 / 255, 199 / 255), + rgb(209 / 255, 229 / 255, 240 / 255), + rgb(103 / 255, 169 / 255, 207 / 255), + rgb(33 / 255, 102 / 255, 172 / 255)), + "7" = tibble::lst(rgb(178 / 255, 24 / 255, 43 / 255), + rgb(239 / 255, 138 / 255, 98 / 255), + rgb(253 / 255, 219 / 255, 199 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(209 / 255, 229 / 255, 240 / 255), + rgb(103 / 255, 169 / 255, 207 / 255), + rgb(33 / 255, 102 / 255, 172 / 255)), + "8" = tibble::lst(rgb(178 / 255, 24 / 255, 43 / 255), + rgb(214 / 255, 96 / 255, 77 / 255), + rgb(244 / 255, 165 / 255, 130 / 255), + rgb(253 / 255, 219 / 255, 199 / 255), + rgb(209 / 255, 229 / 255, 240 / 255), + rgb(146 / 255, 197 / 255, 222 / 255), + rgb(67 / 255, 147 / 255, 195 / 255), + rgb(33 / 255, 102 / 255, 172 / 255)), + "9" = tibble::lst(rgb(178 / 255, 24 / 255, 43 / 255), + rgb(214 / 255, 96 / 255, 77 / 255), + rgb(244 / 255, 165 / 255, 130 / 255), + rgb(253 / 255, 219 / 255, 199 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(209 / 255, 229 / 255, 240 / 255), + rgb(146 / 255, 197 / 255, 222 / 255), + rgb(67 / 255, 147 / 255, 195 / 255), + rgb(33 / 255, 102 / 255, 172 / 255)), + "10" = tibble::lst(rgb(103 / 255, 0 / 255, 31 / 255), + rgb(178 / 255, 24 / 255, 43 / 255), + rgb(214 / 255, 96 / 255, 77 / 255), + rgb(244 / 255, 165 / 255, 130 / 255), + rgb(253 / 255, 219 / 255, 199 / 255), + rgb(209 / 255, 229 / 255, 240 / 255), + rgb(146 / 255, 197 / 255, 222 / 255), + rgb(67 / 255, 147 / 255, 195 / 255), + rgb(33 / 255, 102 / 255, 172 / 255), + rgb(5 / 255, 48 / 255, 97 / 255)), + "11" = tibble::lst(rgb(103 / 255, 0 / 255, 31 / 255), + rgb(178 / 255, 24 / 255, 43 / 255), + rgb(214 / 255, 96 / 255, 77 / 255), + rgb(244 / 255, 165 / 255, 130 / 255), + rgb(253 / 255, 219 / 255, 199 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(209 / 255, 229 / 255, 240 / 255), + rgb(146 / 255, 197 / 255, 222 / 255), + rgb(67 / 255, 147 / 255, 195 / 255), + rgb(33 / 255, 102 / 255, 172 / 255), + rgb(5 / 255, 48 / 255, 97 / 255)), + "type" = "divergent" + ), + "PiYG" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(233 / 255, 163 / 255, 201 / 255), + rgb(161 / 255, 215 / 255, 106 / 255)), + "3" = tibble::lst(rgb(233 / 255, 163 / 255, 201 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(161 / 255, 215 / 255, 106 / 255)), + "4" = tibble::lst(rgb(208 / 255, 28 / 255, 139 / 255), + rgb(241 / 255, 182 / 255, 218 / 255), + rgb(184 / 255, 225 / 255, 134 / 255), + rgb(77 / 255, 172 / 255, 38 / 255)), + "5" = tibble::lst(rgb(208 / 255, 28 / 255, 139 / 255), + rgb(241 / 255, 182 / 255, 218 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(184 / 255, 225 / 255, 134 / 255), + rgb(77 / 255, 172 / 255, 38 / 255)), + "6" = tibble::lst(rgb(197 / 255, 27 / 255, 125 / 255), + rgb(233 / 255, 163 / 255, 201 / 255), + rgb(253 / 255, 224 / 255, 239 / 255), + rgb(230 / 255, 245 / 255, 208 / 255), + rgb(161 / 255, 215 / 255, 106 / 255), + rgb(77 / 255, 146 / 255, 33 / 255)), + "7" = tibble::lst(rgb(197 / 255, 27 / 255, 125 / 255), + rgb(233 / 255, 163 / 255, 201 / 255), + rgb(253 / 255, 224 / 255, 239 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(230 / 255, 245 / 255, 208 / 255), + rgb(161 / 255, 215 / 255, 106 / 255), + rgb(77 / 255, 146 / 255, 33 / 255)), + "8" = tibble::lst(rgb(197 / 255, 27 / 255, 125 / 255), + rgb(222 / 255, 119 / 255, 174 / 255), + rgb(241 / 255, 182 / 255, 218 / 255), + rgb(253 / 255, 224 / 255, 239 / 255), + rgb(230 / 255, 245 / 255, 208 / 255), + rgb(184 / 255, 225 / 255, 134 / 255), + rgb(127 / 255, 188 / 255, 65 / 255), + rgb(77 / 255, 146 / 255, 33 / 255)), + "9" = tibble::lst(rgb(197 / 255, 27 / 255, 125 / 255), + rgb(222 / 255, 119 / 255, 174 / 255), + rgb(241 / 255, 182 / 255, 218 / 255), + rgb(253 / 255, 224 / 255, 239 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(230 / 255, 245 / 255, 208 / 255), + rgb(184 / 255, 225 / 255, 134 / 255), + rgb(127 / 255, 188 / 255, 65 / 255), + rgb(77 / 255, 146 / 255, 33 / 255)), + "10" = tibble::lst(rgb(142 / 255, 1 / 255, 82 / 255), + rgb(197 / 255, 27 / 255, 125 / 255), + rgb(222 / 255, 119 / 255, 174 / 255), + rgb(241 / 255, 182 / 255, 218 / 255), + rgb(253 / 255, 224 / 255, 239 / 255), + rgb(230 / 255, 245 / 255, 208 / 255), + rgb(184 / 255, 225 / 255, 134 / 255), + rgb(127 / 255, 188 / 255, 65 / 255), + rgb(77 / 255, 146 / 255, 33 / 255), + rgb(39 / 255, 100 / 255, 25 / 255)), + "11" = tibble::lst(rgb(142 / 255, 1 / 255, 82 / 255), + rgb(197 / 255, 27 / 255, 125 / 255), + rgb(222 / 255, 119 / 255, 174 / 255), + rgb(241 / 255, 182 / 255, 218 / 255), + rgb(253 / 255, 224 / 255, 239 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(230 / 255, 245 / 255, 208 / 255), + rgb(184 / 255, 225 / 255, 134 / 255), + rgb(127 / 255, 188 / 255, 65 / 255), + rgb(77 / 255, 146 / 255, 33 / 255), + rgb(39 / 255, 100 / 255, 25 / 255)), + "type" = "divergent" + ), + "PRGn" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(), + "3" = tibble::lst(rgb(175 / 255, 141 / 255, 195 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(127 / 255, 191 / 255, 123 / 255)), + "4" = tibble::lst(rgb(123 / 255, 50 / 255, 148 / 255), + rgb(194 / 255, 165 / 255, 207 / 255), + rgb(166 / 255, 219 / 255, 160 / 255), + rgb(0 / 255, 136 / 255, 55 / 255)), + "5" = tibble::lst(rgb(123 / 255, 50 / 255, 148 / 255), + rgb(194 / 255, 165 / 255, 207 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(166 / 255, 219 / 255, 160 / 255), + rgb(0 / 255, 136 / 255, 55 / 255)), + "6" = tibble::lst(rgb(118 / 255, 42 / 255, 131 / 255), + rgb(175 / 255, 141 / 255, 195 / 255), + rgb(231 / 255, 212 / 255, 232 / 255), + rgb(217 / 255, 240 / 255, 211 / 255), + rgb(127 / 255, 191 / 255, 123 / 255), + rgb(27 / 255, 120 / 255, 55 / 255)), + "7" = tibble::lst(rgb(118 / 255, 42 / 255, 131 / 255), + rgb(175 / 255, 141 / 255, 195 / 255), + rgb(231 / 255, 212 / 255, 232 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(217 / 255, 240 / 255, 211 / 255), + rgb(127 / 255, 191 / 255, 123 / 255), + rgb(27 / 255, 120 / 255, 55 / 255)), + "8" = tibble::lst(rgb(118 / 255, 42 / 255, 131 / 255), + rgb(153 / 255, 112 / 255, 171 / 255), + rgb(194 / 255, 165 / 255, 207 / 255), + rgb(231 / 255, 212 / 255, 232 / 255), + rgb(217 / 255, 240 / 255, 211 / 255), + rgb(166 / 255, 219 / 255, 160 / 255), + rgb(90 / 255, 174 / 255, 97 / 255), + rgb(27 / 255, 120 / 255, 55 / 255)), + "9" = tibble::lst(rgb(118 / 255, 42 / 255, 131 / 255), + rgb(153 / 255, 112 / 255, 171 / 255), + rgb(194 / 255, 165 / 255, 207 / 255), + rgb(231 / 255, 212 / 255, 232 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(217 / 255, 240 / 255, 211 / 255), + rgb(166 / 255, 219 / 255, 160 / 255), + rgb(90 / 255, 174 / 255, 97 / 255), + rgb(27 / 255, 120 / 255, 55 / 255)), + "10" = tibble::lst(rgb(64 / 255, 0 / 255, 75 / 255), + rgb(118 / 255, 42 / 255, 131 / 255), + rgb(153 / 255, 112 / 255, 171 / 255), + rgb(194 / 255, 165 / 255, 207 / 255), + rgb(231 / 255, 212 / 255, 232 / 255), + rgb(217 / 255, 240 / 255, 211 / 255), + rgb(166 / 255, 219 / 255, 160 / 255), + rgb(90 / 255, 174 / 255, 97 / 255), + rgb(27 / 255, 120 / 255, 55 / 255), + rgb(0 / 255, 68 / 255, 27 / 255)), + "11" = tibble::lst(rgb(64 / 255, 0 / 255, 75 / 255), + rgb(118 / 255, 42 / 255, 131 / 255), + rgb(153 / 255, 112 / 255, 171 / 255), + rgb(194 / 255, 165 / 255, 207 / 255), + rgb(231 / 255, 212 / 255, 232 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(217 / 255, 240 / 255, 211 / 255), + rgb(166 / 255, 219 / 255, 160 / 255), + rgb(90 / 255, 174 / 255, 97 / 255), + rgb(27 / 255, 120 / 255, 55 / 255), + rgb(0 / 255, 68 / 255, 27 / 255)), + "type" = "divergent" + ), + "RdYlBu" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(252 / 255, 141 / 255, 89 / 255), + rgb(145 / 255, 191 / 255, 219 / 255)), + "3" = tibble::lst(rgb(252 / 255, 141 / 255, 89 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(145 / 255, 191 / 255, 219 / 255)), + "4" = tibble::lst(rgb(215 / 255, 25 / 255, 28 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(171 / 255, 217 / 255, 233 / 255), + rgb(44 / 255, 123 / 255, 182 / 255)), + "5" = tibble::lst(rgb(215 / 255, 25 / 255, 28 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(171 / 255, 217 / 255, 233 / 255), + rgb(44 / 255, 123 / 255, 182 / 255)), + "6" = tibble::lst(rgb(215 / 255, 48 / 255, 39 / 255), + rgb(252 / 255, 141 / 255, 89 / 255), + rgb(254 / 255, 224 / 255, 144 / 255), + rgb(224 / 255, 243 / 255, 248 / 255), + rgb(145 / 255, 191 / 255, 219 / 255), + rgb(69 / 255, 117 / 255, 180 / 255)), + "7" = tibble::lst(rgb(215 / 255, 48 / 255, 39 / 255), + rgb(252 / 255, 141 / 255, 89 / 255), + rgb(254 / 255, 224 / 255, 144 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(224 / 255, 243 / 255, 248 / 255), + rgb(145 / 255, 191 / 255, 219 / 255), + rgb(69 / 255, 117 / 255, 180 / 255)), + "8" = tibble::lst(rgb(215 / 255, 48 / 255, 39 / 255), + rgb(244 / 255, 109 / 255, 67 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(254 / 255, 224 / 255, 144 / 255), + rgb(224 / 255, 243 / 255, 248 / 255), + rgb(171 / 255, 217 / 255, 233 / 255), + rgb(116 / 255, 173 / 255, 209 / 255), + rgb(69 / 255, 117 / 255, 180 / 255)), + "9" = tibble::lst(rgb(215 / 255, 48 / 255, 39 / 255), + rgb(244 / 255, 109 / 255, 67 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(254 / 255, 224 / 255, 144 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(224 / 255, 243 / 255, 248 / 255), + rgb(171 / 255, 217 / 255, 233 / 255), + rgb(116 / 255, 173 / 255, 209 / 255), + rgb(69 / 255, 117 / 255, 180 / 255)), + "10" = tibble::lst(rgb(165 / 255, 0 / 255, 38 / 255), + rgb(215 / 255, 48 / 255, 39 / 255), + rgb(244 / 255, 109 / 255, 67 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(254 / 255, 224 / 255, 144 / 255), + rgb(224 / 255, 243 / 255, 248 / 255), + rgb(171 / 255, 217 / 255, 233 / 255), + rgb(116 / 255, 173 / 255, 209 / 255), + rgb(69 / 255, 117 / 255, 180 / 255), + rgb(49 / 255, 54 / 255, 149 / 255)), + "11" = tibble::lst(rgb(165 / 255, 0 / 255, 38 / 255), + rgb(215 / 255, 48 / 255, 39 / 255), + rgb(244 / 255, 109 / 255, 67 / 255), + rgb(253 / 255, 174 / 255, 97 / 255), + rgb(254 / 255, 224 / 255, 144 / 255), + rgb(255 / 255, 255 / 255, 191 / 255), + rgb(224 / 255, 243 / 255, 248 / 255), + rgb(171 / 255, 217 / 255, 233 / 255), + rgb(116 / 255, 173 / 255, 209 / 255), + rgb(69 / 255, 117 / 255, 180 / 255), + rgb(49 / 255, 54 / 255, 149 / 255)), + "type" = "divergent" + ), + "BrBG" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(216 / 255, 179 / 255, 101 / 255), + rgb(90 / 255, 180 / 255, 172 / 255)), + "3" = tibble::lst(rgb(216 / 255, 179 / 255, 101 / 255), + rgb(245 / 255, 245 / 255, 245 / 255), + rgb(90 / 255, 180 / 255, 172 / 255)), + "4" = tibble::lst(rgb(166 / 255, 97 / 255, 26 / 255), + rgb(223 / 255, 194 / 255, 125 / 255), + rgb(128 / 255, 205 / 255, 193 / 255), + rgb(1 / 255, 133 / 255, 113 / 255)), + "5" = tibble::lst(rgb(166 / 255, 97 / 255, 26 / 255), + rgb(223 / 255, 194 / 255, 125 / 255), + rgb(245 / 255, 245 / 255, 245 / 255), + rgb(128 / 255, 205 / 255, 193 / 255), + rgb(1 / 255, 133 / 255, 113 / 255)), + "6" = tibble::lst(rgb(140 / 255, 81 / 255, 10 / 255), + rgb(216 / 255, 179 / 255, 101 / 255), + rgb(246 / 255, 232 / 255, 195 / 255), + rgb(199 / 255, 234 / 255, 229 / 255), + rgb(90 / 255, 180 / 255, 172 / 255), + rgb(1 / 255, 102 / 255, 94 / 255)), + "7" = tibble::lst(rgb(140 / 255, 81 / 255, 10 / 255), + rgb(216 / 255, 179 / 255, 101 / 255), + rgb(246 / 255, 232 / 255, 195 / 255), + rgb(245 / 255, 245 / 255, 245 / 255), + rgb(199 / 255, 234 / 255, 229 / 255), + rgb(90 / 255, 180 / 255, 172 / 255), + rgb(1 / 255, 102 / 255, 94 / 255)), + "8" = tibble::lst(rgb(140 / 255, 81 / 255, 10 / 255), + rgb(191 / 255, 129 / 255, 45 / 255), + rgb(223 / 255, 194 / 255, 125 / 255), + rgb(246 / 255, 232 / 255, 195 / 255), + rgb(199 / 255, 234 / 255, 229 / 255), + rgb(128 / 255, 205 / 255, 193 / 255), + rgb(53 / 255, 151 / 255, 143 / 255), + rgb(1 / 255, 102 / 255, 94 / 255)), + "9" = tibble::lst(rgb(140 / 255, 81 / 255, 10 / 255), + rgb(191 / 255, 129 / 255, 45 / 255), + rgb(223 / 255, 194 / 255, 125 / 255), + rgb(246 / 255, 232 / 255, 195 / 255), + rgb(245 / 255, 245 / 255, 245 / 255), + rgb(199 / 255, 234 / 255, 229 / 255), + rgb(128 / 255, 205 / 255, 193 / 255), + rgb(53 / 255, 151 / 255, 143 / 255), + rgb(1 / 255, 102 / 255, 94 / 255)), + "10" = tibble::lst(rgb(84 / 255, 48 / 255, 5 / 255), + rgb(140 / 255, 81 / 255, 10 / 255), + rgb(191 / 255, 129 / 255, 45 / 255), + rgb(223 / 255, 194 / 255, 125 / 255), + rgb(246 / 255, 232 / 255, 195 / 255), + rgb(199 / 255, 234 / 255, 229 / 255), + rgb(128 / 255, 205 / 255, 193 / 255), + rgb(53 / 255, 151 / 255, 143 / 255), + rgb(1 / 255, 102 / 255, 94 / 255), + rgb(0 / 255, 60 / 255, 48 / 255)), + "11" = tibble::lst(rgb(84 / 255, 48 / 255, 5 / 255), + rgb(140 / 255, 81 / 255, 10 / 255), + rgb(191 / 255, 129 / 255, 45 / 255), + rgb(223 / 255, 194 / 255, 125 / 255), + rgb(246 / 255, 232 / 255, 195 / 255), + rgb(245 / 255, 245 / 255, 245 / 255), + rgb(199 / 255, 234 / 255, 229 / 255), + rgb(128 / 255, 205 / 255, 193 / 255), + rgb(53 / 255, 151 / 255, 143 / 255), + rgb(1 / 255, 102 / 255, 94 / 255), + rgb(0 / 255, 60 / 255, 48 / 255)), + "type" = "divergent" + ), + "RdGy" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(239 / 255, 138 / 255, 98 / 255), + rgb(153 / 255, 153 / 255, 153 / 255)), + "3" = tibble::lst(rgb(239 / 255, 138 / 255, 98 / 255), + rgb(255 / 255, 255 / 255, 255 / 255), + rgb(153 / 255, 153 / 255, 153 / 255)), + "4" = tibble::lst(rgb(202 / 255, 0 / 255, 32 / 255), + rgb(244 / 255, 165 / 255, 130 / 255), + rgb(186 / 255, 186 / 255, 186 / 255), + rgb(64 / 255, 64 / 255, 64 / 255)), + "5" = tibble::lst(rgb(202 / 255, 0 / 255, 32 / 255), + rgb(244 / 255, 165 / 255, 130 / 255), + rgb(255 / 255, 255 / 255, 255 / 255), + rgb(186 / 255, 186 / 255, 186 / 255), + rgb(64 / 255, 64 / 255, 64 / 255)), + "6" = tibble::lst(rgb(178 / 255, 24 / 255, 43 / 255), + rgb(239 / 255, 138 / 255, 98 / 255), + rgb(253 / 255, 219 / 255, 199 / 255), + rgb(224 / 255, 224 / 255, 224 / 255), + rgb(153 / 255, 153 / 255, 153 / 255), + rgb(77 / 255, 77 / 255,77 / 255)), + "7" = tibble::lst(rgb(178 / 255, 24 / 255, 43 / 255), + rgb(239 / 255, 138 / 255, 98 / 255), + rgb(253 / 255, 219 / 255, 199 / 255), + rgb(255 / 255, 255 / 255, 255 / 255), + rgb(224 / 255, 224 / 255, 224 / 255), + rgb(153 / 255, 153 / 255, 153 / 255), + rgb(77 / 255, 77 / 255,77 / 255)), + "8" = tibble::lst(rgb(178 / 255, 24 / 255, 43 / 255), + rgb(214 / 255, 96 / 255, 77 / 255), + rgb(244 / 255, 165 / 255, 130 / 255), + rgb(253 / 255, 219 / 255, 199 / 255), + rgb(224 / 255, 224 / 255, 224 / 255), + rgb(186 / 255, 186 / 255, 186 / 255), + rgb(135 / 255, 135 / 255, 135 / 255), + rgb(77 / 255, 77 / 255,77 / 255)), + "9" = tibble::lst(rgb(178 / 255, 24 / 255, 43 / 255), + rgb(214 / 255, 96 / 255, 77 / 255), + rgb(244 / 255, 165 / 255, 130 / 255), + rgb(253 / 255, 219 / 255, 199 / 255), + rgb(255 / 255, 255 / 255, 255 / 255), + rgb(224 / 255, 224 / 255, 224 / 255), + rgb(186 / 255, 186 / 255, 186 / 255), + rgb(135 / 255, 135 / 255, 135 / 255), + rgb(77 / 255, 77 / 255,77 / 255)), + "10" = tibble::lst(rgb(103 / 255, 0 / 255, 31 / 255), + rgb(178 / 255, 24 / 255, 43 / 255), + rgb(214 / 255, 96 / 255, 77 / 255), + rgb(244 / 255, 165 / 255, 130 / 255), + rgb(253 / 255, 219 / 255, 199 / 255), + rgb(224 / 255, 224 / 255, 224 / 255), + rgb(186 / 255, 186 / 255, 186 / 255), + rgb(135 / 255, 135 / 255, 135 / 255), + rgb(77 / 255, 77 / 255,77 / 255), + rgb(26 / 255, 26 / 255, 26 / 255)), + "11" = tibble::lst(rgb(103 / 255, 0 / 255, 31 / 255), + rgb(178 / 255, 24 / 255, 43 / 255), + rgb(214 / 255, 96 / 255, 77 / 255), + rgb(244 / 255, 165 / 255, 130 / 255), + rgb(253 / 255, 219 / 255, 199 / 255), + rgb(255 / 255, 255 / 255, 255 / 255), + rgb(224 / 255, 224 / 255, 224 / 255), + rgb(186 / 255, 186 / 255, 186 / 255), + rgb(135 / 255, 135 / 255, 135 / 255), + rgb(77 / 255, 77 / 255,77 / 255), + rgb(26 / 255, 26 / 255, 26 / 255)), + "type" = "divergent" + ), + "PuOr" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(241 / 255, 163 / 255, 64 / 255), + rgb(153 / 255, 142 / 255, 195 / 255)), + "3" = tibble::lst(rgb(241 / 255, 163 / 255, 64 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(153 / 255, 142 / 255, 195 / 255)), + "4" = tibble::lst(rgb(230 / 255, 97 / 255, 1 / 255), + rgb(253 / 255, 184 / 255, 99 / 255), + rgb(178 / 255, 171 / 255, 210 / 255), + rgb(94 / 255, 60 / 255, 153 / 255)), + "5" = tibble::lst(rgb(230 / 255, 97 / 255, 1 / 255), + rgb(253 / 255, 184 / 255, 99 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(178 / 255, 171 / 255, 210 / 255), + rgb(94 / 255, 60 / 255, 153 / 255)), + "6" = tibble::lst(rgb(179 / 255, 88 / 255, 6 / 255), + rgb(241 / 255, 163 / 255, 64 / 255), + rgb(254 / 255, 224 / 255, 182 / 255), + rgb(216 / 255, 218 / 255, 235 / 255), + rgb(153 / 255, 142 / 255, 195 / 255), + rgb(84 / 255, 39 / 255, 136 / 255)), + "7" = tibble::lst(rgb(179 / 255, 88 / 255, 6 / 255), + rgb(241 / 255, 163 / 255, 64 / 255), + rgb(254 / 255, 224 / 255, 182 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(216 / 255, 218 / 255, 235 / 255), + rgb(153 / 255, 142 / 255, 195 / 255), + rgb(84 / 255, 39 / 255, 136 / 255)), + "8" = tibble::lst(rgb(179 / 255, 88 / 255, 6 / 255), + rgb(224 / 255, 130 / 255, 20 / 255), + rgb(253 / 255, 184 / 255, 99 / 255), + rgb(254 / 255, 224 / 255, 182 / 255), + rgb(216 / 255, 218 / 255, 235 / 255), + rgb(178 / 255, 171 / 255, 210 / 255), + rgb(128 / 255, 115 / 255, 172 / 255), + rgb(84 / 255, 39 / 255, 136 / 255)), + "9" = tibble::lst(rgb(179 / 255, 88 / 255, 6 / 255), + rgb(224 / 255, 130 / 255, 20 / 255), + rgb(253 / 255, 184 / 255, 99 / 255), + rgb(254 / 255, 224 / 255, 182 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(216 / 255, 218 / 255, 235 / 255), + rgb(178 / 255, 171 / 255, 210 / 255), + rgb(128 / 255, 115 / 255, 172 / 255), + rgb(84 / 255, 39 / 255, 136 / 255)), + "10" = tibble::lst(rgb(127 / 255, 59 / 255, 8 / 255), + rgb(179 / 255, 88 / 255, 6 / 255), + rgb(224 / 255, 130 / 255, 20 / 255), + rgb(253 / 255, 184 / 255, 99 / 255), + rgb(254 / 255, 224 / 255, 182 / 255), + rgb(216 / 255, 218 / 255, 235 / 255), + rgb(178 / 255, 171 / 255, 210 / 255), + rgb(128 / 255, 115 / 255, 172 / 255), + rgb(84 / 255, 39 / 255, 136 / 255), + rgb(45 / 255, 0 / 255, 75 / 255)), + "11" = tibble::lst(rgb(127 / 255, 59 / 255, 8 / 255), + rgb(179 / 255, 88 / 255, 6 / 255), + rgb(224 / 255, 130 / 255, 20 / 255), + rgb(253 / 255, 184 / 255, 99 / 255), + rgb(254 / 255, 224 / 255, 182 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(216 / 255, 218 / 255, 235 / 255), + rgb(178 / 255, 171 / 255, 210 / 255), + rgb(128 / 255, 115 / 255, 172 / 255), + rgb(84 / 255, 39 / 255, 136 / 255), + rgb(45 / 255, 0 / 255, 75 / 255)), + "type" = "divergent" + ), + "Set2" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(), + "3" = tibble::lst(rgb(102 / 255, 194 / 255, 165 / 255), + rgb(252 / 255, 141 / 255, 98 / 255), + rgb(141 / 255, 160 / 255, 203 / 255)), + "4" = tibble::lst(rgb(102 / 255, 194 / 255, 165 / 255), + rgb(252 / 255, 141 / 255, 98 / 255), + rgb(141 / 255, 160 / 255, 203 / 255), + rgb(231 / 255, 138 / 255, 195 / 255)), + "5" = tibble::lst(rgb(102 / 255, 194 / 255, 165 / 255), + rgb(252 / 255, 141 / 255, 98 / 255), + rgb(141 / 255, 160 / 255, 203 / 255), + rgb(231 / 255, 138 / 255, 195 / 255), + rgb(166 / 255, 216 / 255, 84 / 255)), + "6" = tibble::lst(rgb(102 / 255, 194 / 255, 165 / 255), + rgb(252 / 255, 141 / 255, 98 / 255), + rgb(141 / 255, 160 / 255, 203 / 255), + rgb(231 / 255, 138 / 255, 195 / 255), + rgb(166 / 255, 216 / 255, 84 / 255), + rgb(255 / 255, 217 / 255, 47 / 255)), + "7" = tibble::lst(rgb(102 / 255, 194 / 255, 165 / 255), + rgb(252 / 255, 141 / 255, 98 / 255), + rgb(141 / 255, 160 / 255, 203 / 255), + rgb(231 / 255, 138 / 255, 195 / 255), + rgb(166 / 255, 216 / 255, 84 / 255), + rgb(255 / 255, 217 / 255, 47 / 255), + rgb(229 / 255, 196 / 255, 148 / 255)), + "8" = tibble::lst(rgb(102 / 255, 194 / 255, 165 / 255), + rgb(252 / 255, 141 / 255, 98 / 255), + rgb(141 / 255, 160 / 255, 203 / 255), + rgb(231 / 255, 138 / 255, 195 / 255), + rgb(166 / 255, 216 / 255, 84 / 255), + rgb(255 / 255, 217 / 255, 47 / 255), + rgb(229 / 255, 196 / 255, 148 / 255), + rgb(179 / 255, 179 / 255, 179 / 255)), + "type" = "qualitative" + ), + "Accent" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(), + "3" = tibble::lst(rgb(127 / 255, 201 / 255, 127 / 255), + rgb(190 / 255, 174 / 255, 212 / 255), + rgb(253 / 255, 192 / 255, 134 / 255)), + "4" = tibble::lst(rgb(127 / 255, 201 / 255, 127 / 255), + rgb(190 / 255, 174 / 255, 212 / 255), + rgb(253 / 255, 192 / 255, 134 / 255), + rgb(255 / 255, 255 / 255, 153 / 255)), + "5" = tibble::lst(rgb(127 / 255, 201 / 255, 127 / 255), + rgb(190 / 255, 174 / 255, 212 / 255), + rgb(253 / 255, 192 / 255, 134 / 255), + rgb(255 / 255, 255 / 255, 153 / 255), + rgb(56 / 255, 108 / 255, 176 / 255)), + "6" = tibble::lst(rgb(127 / 255, 201 / 255, 127 / 255), + rgb(190 / 255, 174 / 255, 212 / 255), + rgb(253 / 255, 192 / 255, 134 / 255), + rgb(255 / 255, 255 / 255, 153 / 255), + rgb(56 / 255, 108 / 255, 176 / 255), + rgb(240 / 255, 2 / 255, 127 / 255)), + "7" = tibble::lst(rgb(127 / 255, 201 / 255, 127 / 255), + rgb(190 / 255, 174 / 255, 212 / 255), + rgb(253 / 255, 192 / 255, 134 / 255), + rgb(255 / 255, 255 / 255, 153 / 255), + rgb(56 / 255, 108 / 255, 176 / 255), + rgb(240 / 255, 2 / 255, 127 / 255), + rgb(191 / 255, 91 / 255, 23 / 255)), + "8" = tibble::lst(rgb(127 / 255, 201 / 255, 127 / 255), + rgb(190 / 255, 174 / 255, 212 / 255), + rgb(253 / 255, 192 / 255, 134 / 255), + rgb(255 / 255, 255 / 255, 153 / 255), + rgb(56 / 255, 108 / 255, 176 / 255), + rgb(240 / 255, 2 / 255, 127 / 255), + rgb(191 / 255, 91 / 255, 23 / 255), + rgb(102 / 255, 102 / 255, 102 / 255)), + "type" = "qualitative" + ), + "Set1" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(), + "3" = tibble::lst(rgb(228 / 255, 26 / 255, 28 / 255), + rgb(55 / 255, 126 / 255, 184 / 255), + rgb(77 / 255, 175 / 255, 74 / 255)), + "4" = tibble::lst(rgb(228 / 255, 26 / 255, 28 / 255), + rgb(55 / 255, 126 / 255, 184 / 255), + rgb(77 / 255, 175 / 255, 74 / 255), + rgb(152 / 255,78 / 255, 163 / 255)), + "5" = tibble::lst(rgb(228 / 255, 26 / 255, 28 / 255), + rgb(55 / 255, 126 / 255, 184 / 255), + rgb(77 / 255, 175 / 255, 74 / 255), + rgb(152 / 255,78 / 255, 163 / 255), + rgb(255 / 255, 127 / 255, 0 / 255)), + "6" = tibble::lst(rgb(228 / 255, 26 / 255, 28 / 255), + rgb(55 / 255, 126 / 255, 184 / 255), + rgb(77 / 255, 175 / 255, 74 / 255), + rgb(152 / 255,78 / 255, 163 / 255), + rgb(255 / 255, 127 / 255, 0 / 255), + rgb(255 / 255, 255 / 255, 51 / 255)), + "7" = tibble::lst(rgb(228 / 255, 26 / 255, 28 / 255), + rgb(55 / 255, 126 / 255, 184 / 255), + rgb(77 / 255, 175 / 255, 74 / 255), + rgb(152 / 255,78 / 255, 163 / 255), + rgb(255 / 255, 127 / 255, 0 / 255), + rgb(255 / 255, 255 / 255, 51 / 255), + rgb(166 / 255, 86 / 255, 40 / 255)), + "8" = tibble::lst(rgb(228 / 255, 26 / 255, 28 / 255), + rgb(55 / 255, 126 / 255, 184 / 255), + rgb(77 / 255, 175 / 255, 74 / 255), + rgb(152 / 255,78 / 255, 163 / 255), + rgb(255 / 255, 127 / 255, 0 / 255), + rgb(255 / 255, 255 / 255, 51 / 255), + rgb(166 / 255, 86 / 255, 40 / 255), + rgb(247 / 255, 129 / 255, 191 / 255)), + "9" = tibble::lst(rgb(228 / 255, 26 / 255, 28 / 255), + rgb(55 / 255, 126 / 255, 184 / 255), + rgb(77 / 255, 175 / 255, 74 / 255), + rgb(152 / 255,78 / 255, 163 / 255), + rgb(255 / 255, 127 / 255, 0 / 255), + rgb(255 / 255, 255 / 255, 51 / 255), + rgb(166 / 255, 86 / 255, 40 / 255), + rgb(247 / 255, 129 / 255, 191 / 255), + rgb(153 / 255, 153 / 255, 153 / 255)), + "type" = "qualitative" + ), + "Set3" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(), + "3" = tibble::lst(rgb(141 / 255, 211 / 255, 199 / 255), + rgb(255 / 255, 255 / 255, 179 / 255), + rgb(190 / 255, 186 / 255, 218 / 255)), + "4" = tibble::lst(rgb(141 / 255, 211 / 255, 199 / 255), + rgb(255 / 255, 255 / 255, 179 / 255), + rgb(190 / 255, 186 / 255, 218 / 255), + rgb(251 / 255, 128 / 255, 114 / 255)), + "5" = tibble::lst(rgb(141 / 255, 211 / 255, 199 / 255), + rgb(255 / 255, 255 / 255, 179 / 255), + rgb(190 / 255, 186 / 255, 218 / 255), + rgb(251 / 255, 128 / 255, 114 / 255), + rgb(128 / 255, 177 / 255, 211 / 255)), + "6" = tibble::lst(rgb(141 / 255, 211 / 255, 199 / 255), + rgb(255 / 255, 255 / 255, 179 / 255), + rgb(190 / 255, 186 / 255, 218 / 255), + rgb(251 / 255, 128 / 255, 114 / 255), + rgb(128 / 255, 177 / 255, 211 / 255), + rgb(253 / 255, 180 / 255, 98 / 255)), + "7" = tibble::lst(rgb(141 / 255, 211 / 255, 199 / 255), + rgb(255 / 255, 255 / 255, 179 / 255), + rgb(190 / 255, 186 / 255, 218 / 255), + rgb(251 / 255, 128 / 255, 114 / 255), + rgb(128 / 255, 177 / 255, 211 / 255), + rgb(253 / 255, 180 / 255, 98 / 255), + rgb(179 / 255, 222 / 255, 105 / 255)), + "8" = tibble::lst(rgb(141 / 255, 211 / 255, 199 / 255), + rgb(255 / 255, 255 / 255, 179 / 255), + rgb(190 / 255, 186 / 255, 218 / 255), + rgb(251 / 255, 128 / 255, 114 / 255), + rgb(128 / 255, 177 / 255, 211 / 255), + rgb(253 / 255, 180 / 255, 98 / 255), + rgb(179 / 255, 222 / 255, 105 / 255), + rgb(252 / 255, 205 / 255, 229 / 255)), + "9" = tibble::lst(rgb(141 / 255, 211 / 255, 199 / 255), + rgb(255 / 255, 255 / 255, 179 / 255), + rgb(190 / 255, 186 / 255, 218 / 255), + rgb(251 / 255, 128 / 255, 114 / 255), + rgb(128 / 255, 177 / 255, 211 / 255), + rgb(253 / 255, 180 / 255, 98 / 255), + rgb(179 / 255, 222 / 255, 105 / 255), + rgb(252 / 255, 205 / 255, 229 / 255), + rgb(217 / 255, 217 / 255, 217 / 255)), + "10" = tibble::lst(rgb(141 / 255, 211 / 255, 199 / 255), + rgb(255 / 255, 255 / 255, 179 / 255), + rgb(190 / 255, 186 / 255, 218 / 255), + rgb(251 / 255, 128 / 255, 114 / 255), + rgb(128 / 255, 177 / 255, 211 / 255), + rgb(253 / 255, 180 / 255, 98 / 255), + rgb(179 / 255, 222 / 255, 105 / 255), + rgb(252 / 255, 205 / 255, 229 / 255), + rgb(217 / 255, 217 / 255, 217 / 255), + rgb(188 / 255, 128 / 255, 189 / 255)), + "11" = tibble::lst(rgb(141 / 255, 211 / 255, 199 / 255), + rgb(255 / 255, 255 / 255, 179 / 255), + rgb(190 / 255, 186 / 255, 218 / 255), + rgb(251 / 255, 128 / 255, 114 / 255), + rgb(128 / 255, 177 / 255, 211 / 255), + rgb(253 / 255, 180 / 255, 98 / 255), + rgb(179 / 255, 222 / 255, 105 / 255), + rgb(252 / 255, 205 / 255, 229 / 255), + rgb(217 / 255, 217 / 255, 217 / 255), + rgb(188 / 255, 128 / 255, 189 / 255), + rgb(204 / 255, 235 / 255, 197 / 255)), + "12" = tibble::lst(rgb(141 / 255, 211 / 255, 199 / 255), + rgb(255 / 255, 255 / 255, 179 / 255), + rgb(190 / 255, 186 / 255, 218 / 255), + rgb(251 / 255, 128 / 255, 114 / 255), + rgb(128 / 255, 177 / 255, 211 / 255), + rgb(253 / 255, 180 / 255, 98 / 255), + rgb(179 / 255, 222 / 255, 105 / 255), + rgb(252 / 255, 205 / 255, 229 / 255), + rgb(217 / 255, 217 / 255, 217 / 255), + rgb(188 / 255, 128 / 255, 189 / 255), + rgb(204 / 255, 235 / 255, 197 / 255), + rgb(255 / 255, 237 / 255, 111 / 255)), + "type" = "qualitative" + ), + "Dark" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(), + "3" = tibble::lst(rgb(27 / 255, 158 / 255, 119 / 255), + rgb(217 / 255, 95 / 255, 2 / 255), + rgb(117 / 255, 112 / 255, 179 / 255)), + "4" = tibble::lst(rgb(27 / 255, 158 / 255, 119 / 255), + rgb(217 / 255, 95 / 255, 2 / 255), + rgb(117 / 255, 112 / 255, 179 / 255), + rgb(231 / 255, 41 / 255, 138 / 255)), + "5" = tibble::lst(rgb(27 / 255, 158 / 255, 119 / 255), + rgb(217 / 255, 95 / 255, 2 / 255), + rgb(117 / 255, 112 / 255, 179 / 255), + rgb(231 / 255, 41 / 255, 138 / 255), + rgb(102 / 255, 166 / 255, 30 / 255)), + "6" = tibble::lst(rgb(27 / 255, 158 / 255, 119 / 255), + rgb(217 / 255, 95 / 255, 2 / 255), + rgb(117 / 255, 112 / 255, 179 / 255), + rgb(231 / 255, 41 / 255, 138 / 255), + rgb(102 / 255, 166 / 255, 30 / 255), + rgb(230 / 255, 171 / 255, 2 / 255)), + "7" = tibble::lst(rgb(27 / 255, 158 / 255, 119 / 255), + rgb(217 / 255, 95 / 255, 2 / 255), + rgb(117 / 255, 112 / 255, 179 / 255), + rgb(231 / 255, 41 / 255, 138 / 255), + rgb(102 / 255, 166 / 255, 30 / 255), + rgb(230 / 255, 171 / 255, 2 / 255), + rgb(166 / 255, 118 / 255, 29 / 255)), + "8" = tibble::lst(rgb(27 / 255, 158 / 255, 119 / 255), + rgb(217 / 255, 95 / 255, 2 / 255), + rgb(117 / 255, 112 / 255, 179 / 255), + rgb(231 / 255, 41 / 255, 138 / 255), + rgb(102 / 255, 166 / 255, 30 / 255), + rgb(230 / 255, 171 / 255, 2 / 255), + rgb(166 / 255, 118 / 255, 29 / 255), + rgb(102 / 255, 102 / 255, 102 / 255)), + "type" = "qualitative" + ), + "Paired" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(), + "3" = tibble::lst(rgb(166 / 255, 206 / 255, 227 / 255), + rgb(31 / 255, 120 / 255, 180 / 255), + rgb(178 / 255, 223 / 255, 138 / 255)), + "4" = tibble::lst(rgb(166 / 255, 206 / 255, 227 / 255), + rgb(31 / 255, 120 / 255, 180 / 255), + rgb(178 / 255, 223 / 255, 138 / 255), + rgb(51 / 255, 160 / 255, 44 / 255)), + "5" = tibble::lst(rgb(166 / 255, 206 / 255, 227 / 255), + rgb(31 / 255, 120 / 255, 180 / 255), + rgb(178 / 255, 223 / 255, 138 / 255), + rgb(51 / 255, 160 / 255, 44 / 255), + rgb(251 / 255, 154 / 255, 153 / 255)), + "6" = tibble::lst(rgb(166 / 255, 206 / 255, 227 / 255), + rgb(31 / 255, 120 / 255, 180 / 255), + rgb(178 / 255, 223 / 255, 138 / 255), + rgb(51 / 255, 160 / 255, 44 / 255), + rgb(251 / 255, 154 / 255, 153 / 255), + rgb(227 / 255, 26 / 255, 28 / 255)), + "7" = tibble::lst(rgb(166 / 255, 206 / 255, 227 / 255), + rgb(31 / 255, 120 / 255, 180 / 255), + rgb(178 / 255, 223 / 255, 138 / 255), + rgb(51 / 255, 160 / 255, 44 / 255), + rgb(251 / 255, 154 / 255, 153 / 255), + rgb(227 / 255, 26 / 255, 28 / 255), + rgb(253 / 255, 191 / 255, 111 / 255)), + "8" = tibble::lst(rgb(166 / 255, 206 / 255, 227 / 255), + rgb(31 / 255, 120 / 255, 180 / 255), + rgb(178 / 255, 223 / 255, 138 / 255), + rgb(51 / 255, 160 / 255, 44 / 255), + rgb(251 / 255, 154 / 255, 153 / 255), + rgb(227 / 255, 26 / 255, 28 / 255), + rgb(253 / 255, 191 / 255, 111 / 255), + rgb(255 / 255, 127 / 255, 0 / 255)), + "9" = tibble::lst(rgb(166 / 255, 206 / 255, 227 / 255), + rgb(31 / 255, 120 / 255, 180 / 255), + rgb(178 / 255, 223 / 255, 138 / 255), + rgb(51 / 255, 160 / 255, 44 / 255), + rgb(251 / 255, 154 / 255, 153 / 255), + rgb(227 / 255, 26 / 255, 28 / 255), + rgb(253 / 255, 191 / 255, 111 / 255), + rgb(255 / 255, 127 / 255, 0 / 255), + rgb(202 / 255, 178 / 255, 214 / 255)), + "10" = tibble::lst(rgb(166 / 255, 206 / 255, 227 / 255), + rgb(31 / 255, 120 / 255, 180 / 255), + rgb(178 / 255, 223 / 255, 138 / 255), + rgb(51 / 255, 160 / 255, 44 / 255), + rgb(251 / 255, 154 / 255, 153 / 255), + rgb(227 / 255, 26 / 255, 28 / 255), + rgb(253 / 255, 191 / 255, 111 / 255), + rgb(255 / 255, 127 / 255, 0 / 255), + rgb(202 / 255, 178 / 255, 214 / 255), + rgb(106 / 255, 61 / 255, 154 / 255)), + "11" = tibble::lst(rgb(166 / 255, 206 / 255, 227 / 255), + rgb(31 / 255, 120 / 255, 180 / 255), + rgb(178 / 255, 223 / 255, 138 / 255), + rgb(51 / 255, 160 / 255, 44 / 255), + rgb(251 / 255, 154 / 255, 153 / 255), + rgb(227 / 255, 26 / 255, 28 / 255), + rgb(253 / 255, 191 / 255, 111 / 255), + rgb(255 / 255, 127 / 255, 0 / 255), + rgb(202 / 255, 178 / 255, 214 / 255), + rgb(106 / 255, 61 / 255, 154 / 255), + rgb(255 / 255, 255 / 255, 153 / 255)), + "12" = tibble::lst(rgb(166 / 255, 206 / 255, 227 / 255), + rgb(31 / 255, 120 / 255, 180 / 255), + rgb(178 / 255, 223 / 255, 138 / 255), + rgb(51 / 255, 160 / 255, 44 / 255), + rgb(251 / 255, 154 / 255, 153 / 255), + rgb(227 / 255, 26 / 255, 28 / 255), + rgb(253 / 255, 191 / 255, 111 / 255), + rgb(255 / 255, 127 / 255, 0 / 255), + rgb(202 / 255, 178 / 255, 214 / 255), + rgb(106 / 255, 61 / 255, 154 / 255), + rgb(255 / 255, 255 / 255, 153 / 255), + rgb(177 / 255, 89 / 255, 40 / 255)), + "type" = "qualitative" + ), + "Pastel2" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(179 / 255, 226 / 255, 205 / 255), + rgb(203 / 255, 213 / 255, 232 / 255)), + "3" = tibble::lst(rgb(179 / 255, 226 / 255, 205 / 255), + rgb(253 / 255, 205 / 255, 172 / 255), + rgb(203 / 255, 213 / 255, 232 / 255)), + "4" = tibble::lst(rgb(179 / 255, 226 / 255, 205 / 255), + rgb(253 / 255, 205 / 255, 172 / 255), + rgb(203 / 255, 213 / 255, 232 / 255), + rgb(244 / 255, 202 / 255, 228 / 255)), + "5" = tibble::lst(rgb(179 / 255, 226 / 255, 205 / 255), + rgb(253 / 255, 205 / 255, 172 / 255), + rgb(203 / 255, 213 / 255, 232 / 255), + rgb(244 / 255, 202 / 255, 228 / 255), + rgb(230 / 255, 245 / 255, 201 / 255)), + "6" = tibble::lst(rgb(179 / 255, 226 / 255, 205 / 255), + rgb(253 / 255, 205 / 255, 172 / 255), + rgb(203 / 255, 213 / 255, 232 / 255), + rgb(244 / 255, 202 / 255, 228 / 255), + rgb(230 / 255, 245 / 255, 201 / 255), + rgb(255 / 255, 242 / 255, 174 / 255)), + "7" = tibble::lst(rgb(179 / 255, 226 / 255, 205 / 255), + rgb(253 / 255, 205 / 255, 172 / 255), + rgb(203 / 255, 213 / 255, 232 / 255), + rgb(244 / 255, 202 / 255, 228 / 255), + rgb(230 / 255, 245 / 255, 201 / 255), + rgb(255 / 255, 242 / 255, 174 / 255), + rgb(241 / 255, 226 / 255, 204 / 255)), + "8" = tibble::lst(rgb(179 / 255, 226 / 255, 205 / 255), + rgb(253 / 255, 205 / 255, 172 / 255), + rgb(203 / 255, 213 / 255, 232 / 255), + rgb(244 / 255, 202 / 255, 228 / 255), + rgb(230 / 255, 245 / 255, 201 / 255), + rgb(255 / 255, 242 / 255, 174 / 255), + rgb(241 / 255, 226 / 255, 204 / 255), + rgb(204 / 255, 204 / 255, 204 / 255)), + "type" = "qualitative" + ), + "Pastel1" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(251 / 255, 180 / 255, 174 / 255), + rgb(204 / 255, 235 / 255, 197 / 255)), + "3" = tibble::lst(rgb(251 / 255, 180 / 255, 174 / 255), + rgb(179 / 255, 205 / 255, 227 / 255), + rgb(204 / 255, 235 / 255, 197 / 255)), + "4" = tibble::lst(rgb(251 / 255, 180 / 255, 174 / 255), + rgb(179 / 255, 205 / 255, 227 / 255), + rgb(204 / 255, 235 / 255, 197 / 255), + rgb(222 / 255, 203 / 255, 228 / 255)), + "5" = tibble::lst(rgb(251 / 255, 180 / 255, 174 / 255), + rgb(179 / 255, 205 / 255, 227 / 255), + rgb(204 / 255, 235 / 255, 197 / 255), + rgb(222 / 255, 203 / 255, 228 / 255), + rgb(254 / 255, 217 / 255, 166 / 255)), + "6" = tibble::lst(rgb(251 / 255, 180 / 255, 174 / 255), + rgb(179 / 255, 205 / 255, 227 / 255), + rgb(204 / 255, 235 / 255, 197 / 255), + rgb(222 / 255, 203 / 255, 228 / 255), + rgb(254 / 255, 217 / 255, 166 / 255), + rgb(255 / 255, 255 / 255, 204 / 255)), + "7" = tibble::lst(rgb(251 / 255, 180 / 255, 174 / 255), + rgb(179 / 255, 205 / 255, 227 / 255), + rgb(204 / 255, 235 / 255, 197 / 255), + rgb(222 / 255, 203 / 255, 228 / 255), + rgb(254 / 255, 217 / 255, 166 / 255), + rgb(255 / 255, 255 / 255, 204 / 255), + rgb(229 / 255, 216 / 255, 189 / 255)), + "8" = tibble::lst(rgb(251 / 255, 180 / 255, 174 / 255), + rgb(179 / 255, 205 / 255, 227 / 255), + rgb(204 / 255, 235 / 255, 197 / 255), + rgb(222 / 255, 203 / 255, 228 / 255), + rgb(254 / 255, 217 / 255, 166 / 255), + rgb(255 / 255, 255 / 255, 204 / 255), + rgb(229 / 255, 216 / 255, 189 / 255), + rgb(253 / 255, 218 / 255, 236 / 255)), + "9" = tibble::lst(rgb(251 / 255, 180 / 255, 174 / 255), + rgb(179 / 255, 205 / 255, 227 / 255), + rgb(204 / 255, 235 / 255, 197 / 255), + rgb(222 / 255, 203 / 255, 228 / 255), + rgb(254 / 255, 217 / 255, 166 / 255), + rgb(255 / 255, 255 / 255, 204 / 255), + rgb(229 / 255, 216 / 255, 189 / 255), + rgb(253 / 255, 218 / 255, 236 / 255), + rgb(242 / 255, 242 / 255, 242 / 255)), + "type" = "qualitative" + ), + "OrRd" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(), + "3" = tibble::lst(rgb(254 / 255, 232 / 255, 200 / 255), + rgb(253 / 255, 187 / 255, 132 / 255), + rgb(227 / 255, 74 / 255, 51 / 255)), + "4" = tibble::lst(rgb(254 / 255, 240 / 255, 217 / 255), + rgb(253 / 255, 204 / 255, 138 / 255), + rgb(252 / 255, 141 / 255, 89 / 255), + rgb(215 / 255, 48 / 255, 31 / 255)), + "5" = tibble::lst(rgb(254 / 255, 240 / 255, 217 / 255), + rgb(253 / 255, 204 / 255, 138 / 255), + rgb(252 / 255, 141 / 255, 89 / 255), + rgb(227 / 255, 74 / 255, 51 / 255), + rgb(179 / 255, 0 / 255, 0 / 255)), + "6" = tibble::lst(rgb(254 / 255, 240 / 255, 217 / 255), + rgb(253 / 255, 212 / 255, 158 / 255), + rgb(253 / 255, 187 / 255, 132 / 255), + rgb(252 / 255, 141 / 255, 89 / 255), + rgb(227 / 255, 74 / 255, 51 / 255), + rgb(179 / 255, 0 / 255, 0 / 255)), + "7" = tibble::lst(rgb(254 / 255, 240 / 255, 217 / 255), + rgb(253 / 255, 212 / 255, 158 / 255), + rgb(253 / 255, 187 / 255, 132 / 255), + rgb(252 / 255, 141 / 255, 89 / 255), + rgb(239 / 255, 101 / 255, 72 / 255), + rgb(215 / 255, 48 / 255, 31 / 255), + rgb(153 / 255, 0 / 255, 0 / 255)), + "8" = tibble::lst(rgb(255 / 255, 247 / 255, 236 / 255), + rgb(254 / 255, 232 / 255, 200 / 255), + rgb(253 / 255, 212 / 255, 158 / 255), + rgb(253 / 255, 187 / 255, 132 / 255), + rgb(252 / 255, 141 / 255, 89 / 255), + rgb(239 / 255, 101 / 255, 72 / 255), + rgb(215 / 255, 48 / 255, 31 / 255), + rgb(153 / 255, 0 / 255, 0 / 255)), + "9" = tibble::lst(rgb(255 / 255, 247 / 255, 236 / 255), + rgb(254 / 255, 232 / 255, 200 / 255), + rgb(253 / 255, 212 / 255, 158 / 255), + rgb(253 / 255, 187 / 255, 132 / 255), + rgb(252 / 255, 141 / 255, 89 / 255), + rgb(239 / 255, 101 / 255, 72 / 255), + rgb(215 / 255, 48 / 255, 31 / 255), + rgb(179 / 255, 0 / 255, 0 / 255), + rgb(127 / 255, 0 / 255,0 / 255)), + "10" = tibble::lst(rgb(255 / 255, 247 / 255, 235 / 255), + rgb(254 / 255, 235 / 255, 207 / 255), + rgb(253 / 255, 220 / 255, 176 / 255), + rgb(253 / 255, 203 / 255, 149 / 255), + rgb(253 / 255, 182 / 255, 127 / 255), + rgb(252 / 255, 146 / 255, 93 / 255), + rgb(243 / 255, 114 / 255, 77 / 255), + rgb(228 / 255,79 / 255, 53 / 255), + rgb(200 / 255, 39 / 255, 24 / 255), + rgb(153 / 255, 0 / 255, 0 / 255)), + "11" = tibble::lst(rgb(255 / 255, 247 / 255, 235 / 255), + rgb(254 / 255, 236 / 255, 210 / 255), + rgb(253 / 255, 223 / 255, 183 / 255), + rgb(253 / 255, 209 / 255, 155 / 255), + rgb(253 / 255, 192 / 255, 137 / 255), + rgb(253 / 255, 164 / 255, 110 / 255), + rgb(249 / 255, 133 / 255, 85 / 255), + rgb(240 / 255, 105 / 255, 73 / 255), + rgb(224 / 255,71 / 255, 47 / 255), + rgb(196 / 255, 36 / 255, 21 / 255), + rgb(153 / 255, 0 / 255, 0 / 255)), + "12" = tibble::lst(rgb(255 / 255, 247 / 255, 235 / 255), + rgb(254 / 255, 237 / 255, 213 / 255), + rgb(254 / 255, 226 / 255, 188 / 255), + rgb(253 / 255, 213 / 255, 161 / 255), + rgb(253 / 255, 198 / 255, 143 / 255), + rgb(253 / 255, 178 / 255, 124 / 255), + rgb(252 / 255, 149 / 255, 96 / 255), + rgb(246 / 255, 123 / 255, 81 / 255), + rgb(236 / 255, 96 / 255, 68 / 255), + rgb(221 / 255, 64 / 255, 42 / 255), + rgb(192 / 255, 33 / 255, 19 / 255), + rgb(153 / 255, 0 / 255, 0 / 255)), + "13" = tibble::lst(rgb(255 / 255, 247 / 255, 235 / 255), + rgb(254 / 255, 238 / 255, 214 / 255), + rgb(254 / 255, 228 / 255, 192 / 255), + rgb(253 / 255, 216 / 255, 168 / 255), + rgb(253 / 255, 203 / 255, 149 / 255), + rgb(253 / 255, 189 / 255, 134 / 255), + rgb(253 / 255, 164 / 255, 110 / 255), + rgb(250 / 255, 137 / 255, 87 / 255), + rgb(243 / 255, 114 / 255, 77 / 255), + rgb(233 / 255, 89 / 255, 61 / 255), + rgb(219 / 255, 58 / 255, 37 / 255), + rgb(188 / 255, 31 / 255, 18 / 255), + rgb(153 / 255, 0 / 255, 0 / 255)), + "14" = tibble::lst(rgb(255 / 255, 247 / 255, 235 / 255), + rgb(254 / 255, 238 / 255, 216 / 255), + rgb(254 / 255, 230 / 255, 196 / 255), + rgb(253 / 255, 219 / 255, 174 / 255), + rgb(253 / 255, 208 / 255, 153 / 255), + rgb(253 / 255, 194 / 255, 139 / 255), + rgb(253 / 255, 176 / 255, 121 / 255), + rgb(252 / 255, 151 / 255, 98 / 255), + rgb(248 / 255, 129 / 255, 83 / 255), + rgb(241 / 255, 107 / 255, 74 / 255), + rgb(230 / 255, 82 / 255, 56 / 255), + rgb(216 / 255, 52 / 255, 34 / 255), + rgb(185 / 255, 29 / 255, 16 / 255), + rgb(153 / 255, 0 / 255, 0 / 255)), + "15" = tibble::lst(rgb(255 / 255, 247 / 255, 235 / 255), + rgb(254 / 255, 239 / 255, 217 / 255), + rgb(254 / 255, 231 / 255, 199 / 255), + rgb(253 / 255, 221 / 255, 178 / 255), + rgb(253 / 255, 211 / 255, 158 / 255), + rgb(253 / 255, 199 / 255, 144 / 255), + rgb(253 / 255, 186 / 255, 132 / 255), + rgb(253 / 255, 164 / 255, 110 / 255), + rgb(252 / 255, 140 / 255, 89 / 255), + rgb(245 / 255, 121 / 255, 80 / 255), + rgb(238 / 255, 101 / 255, 71 / 255), + rgb(227 / 255,76 / 255, 51 / 255), + rgb(215 / 255, 48 / 255, 31 / 255), + rgb(183 / 255, 27 / 255, 15 / 255), + rgb(153 / 255, 0 / 255, 0 / 255)), + "16" = tibble::lst(rgb(255 / 255, 247 / 255, 235 / 255), + rgb(254 / 255, 239 / 255, 219 / 255), + rgb(254 / 255, 232 / 255, 202 / 255), + rgb(253 / 255, 223 / 255, 183 / 255), + rgb(253 / 255, 214 / 255, 163 / 255), + rgb(253 / 255, 203 / 255, 149 / 255), + rgb(253 / 255, 192 / 255, 137 / 255), + rgb(253 / 255, 174 / 255, 120 / 255), + rgb(252 / 255, 153 / 255, 100 / 255), + rgb(249 / 255, 133 / 255, 85 / 255), + rgb(243 / 255, 114 / 255, 77 / 255), + rgb(235 / 255, 94 / 255, 66 / 255), + rgb(224 / 255,71 / 255, 47 / 255), + rgb(210 / 255, 45 / 255, 28 / 255), + rgb(181 / 255, 26 / 255, 14 / 255), + rgb(153 / 255, 0 / 255, 0 / 255)), + "17" = tibble::lst(rgb(255 / 255, 247 / 255, 235 / 255), + rgb(254 / 255, 240 / 255, 220 / 255), + rgb(254 / 255, 233 / 255, 204 / 255), + rgb(254 / 255, 225 / 255, 186 / 255), + rgb(253 / 255, 216 / 255, 168 / 255), + rgb(253 / 255, 207 / 255, 153 / 255), + rgb(253 / 255, 196 / 255, 141 / 255), + rgb(253 / 255, 184 / 255, 129 / 255), + rgb(253 / 255, 164 / 255, 110 / 255), + rgb(252 / 255, 143 / 255, 91 / 255), + rgb(247 / 255, 126 / 255, 82 / 255), + rgb(241 / 255, 108 / 255, 75 / 255), + rgb(233 / 255, 89 / 255, 61 / 255), + rgb(222 / 255, 66 / 255, 43 / 255), + rgb(207 / 255, 43 / 255, 27 / 255), + rgb(179 / 255, 24 / 255, 13 / 255), + rgb(153 / 255, 0 / 255, 0 / 255)), + "18" = tibble::lst(rgb(255 / 255, 247 / 255, 235 / 255), + rgb(254 / 255, 240 / 255, 221 / 255), + rgb(254 / 255, 234 / 255, 206 / 255), + rgb(254 / 255, 227 / 255, 190 / 255), + rgb(253 / 255, 219 / 255, 172 / 255), + rgb(253 / 255, 210 / 255, 156 / 255), + rgb(253 / 255, 200 / 255, 145 / 255), + rgb(253 / 255, 189 / 255, 135 / 255), + rgb(253 / 255, 173 / 255, 119 / 255), + rgb(252 / 255, 154 / 255, 101 / 255), + rgb(250 / 255, 136 / 255, 86 / 255), + rgb(245 / 255, 120 / 255, 79 / 255), + rgb(239 / 255, 103 / 255, 72 / 255), + rgb(230 / 255, 84 / 255, 57 / 255), + rgb(220 / 255, 62 / 255, 40 / 255), + rgb(203 / 255, 41 / 255, 25 / 255), + rgb(178 / 255, 23 / 255, 12 / 255), + rgb(153 / 255, 0 / 255, 0 / 255)), + "19" = tibble::lst(rgb(255 / 255, 247 / 255, 235 / 255), + rgb(254 / 255, 241 / 255, 221 / 255), + rgb(254 / 255, 235 / 255, 207 / 255), + rgb(254 / 255, 228 / 255, 192 / 255), + rgb(253 / 255, 220 / 255, 176 / 255), + rgb(253 / 255, 213 / 255, 160 / 255), + rgb(253 / 255, 203 / 255, 149 / 255), + rgb(253 / 255, 193 / 255, 139 / 255), + rgb(253 / 255, 182 / 255, 127 / 255), + rgb(253 / 255, 164 / 255, 110 / 255), + rgb(252 / 255, 146 / 255, 93 / 255), + rgb(248 / 255, 130 / 255, 84 / 255), + rgb(243 / 255, 114 / 255, 77 / 255), + rgb(237 / 255, 98 / 255, 69 / 255), + rgb(228 / 255,79 / 255, 53 / 255), + rgb(219 / 255, 58 / 255, 37 / 255), + rgb(200 / 255, 39 / 255, 24 / 255), + rgb(176 / 255, 22 / 255, 11 / 255), + rgb(153 / 255, 0 / 255, 0 / 255)), + "20" = tibble::lst(rgb(255 / 255, 247 / 255, 235 / 255), + rgb(254 / 255, 241 / 255, 222 / 255), + rgb(254 / 255, 235 / 255, 209 / 255), + rgb(254 / 255, 229 / 255, 195 / 255), + rgb(253 / 255, 222 / 255, 180 / 255), + rgb(253 / 255, 215 / 255, 164 / 255), + rgb(253 / 255, 206 / 255, 152 / 255), + rgb(253 / 255, 197 / 255, 142 / 255), + rgb(253 / 255, 188 / 255, 133 / 255), + rgb(253 / 255, 172 / 255, 118 / 255), + rgb(252 / 255, 155 / 255, 102 / 255), + rgb(251 / 255, 138 / 255, 88 / 255), + rgb(246 / 255, 124 / 255, 81 / 255), + rgb(241 / 255, 109 / 255, 75 / 255), + rgb(235 / 255, 93 / 255, 65 / 255), + rgb(226 / 255,75 / 255, 50 / 255), + rgb(217 / 255, 54 / 255, 35 / 255), + rgb(198 / 255, 37 / 255, 22 / 255), + rgb(175 / 255, 21 / 255, 11 / 255), + rgb(153 / 255, 0 / 255, 0 / 255)), + "type" = "sequential" + ), + "PuBu" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(), + "3" = tibble::lst(rgb(236 / 255, 231 / 255, 242 / 255), + rgb(166 / 255, 189 / 255, 219 / 255), + rgb(43 / 255, 140 / 255, 190 / 255)), + "4" = tibble::lst(rgb(241 / 255, 238 / 255, 246 / 255), + rgb(189 / 255, 201 / 255, 225 / 255), + rgb(116 / 255, 169 / 255, 207 / 255), + rgb(5 / 255, 112 / 255, 176 / 255)), + "5" = tibble::lst(rgb(241 / 255, 238 / 255, 246 / 255), + rgb(189 / 255, 201 / 255, 225 / 255), + rgb(116 / 255, 169 / 255, 207 / 255), + rgb(43 / 255, 140 / 255, 190 / 255), + rgb(4 / 255, 90 / 255, 141 / 255)), + "6" = tibble::lst(rgb(241 / 255, 238 / 255, 246 / 255), + rgb(208 / 255, 209 / 255, 230 / 255), + rgb(166 / 255, 189 / 255, 219 / 255), + rgb(116 / 255, 169 / 255, 207 / 255), + rgb(43 / 255, 140 / 255, 190 / 255), + rgb(4 / 255, 90 / 255, 141 / 255)), + "7" = tibble::lst(rgb(241 / 255, 238 / 255, 246 / 255), + rgb(208 / 255, 209 / 255, 230 / 255), + rgb(166 / 255, 189 / 255, 219 / 255), + rgb(116 / 255, 169 / 255, 207 / 255), + rgb(54 / 255, 144 / 255, 192 / 255), + rgb(5 / 255, 112 / 255, 176 / 255), + rgb(3 / 255, 78 / 255, 123 / 255)), + "8" = tibble::lst(rgb(255 / 255, 247 / 255, 251 / 255), + rgb(236 / 255, 231 / 255, 242 / 255), + rgb(208 / 255, 209 / 255, 230 / 255), + rgb(166 / 255, 189 / 255, 219 / 255), + rgb(116 / 255, 169 / 255, 207 / 255), + rgb(54 / 255, 144 / 255, 192 / 255), + rgb(5 / 255, 112 / 255, 176 / 255), + rgb(3 / 255, 78 / 255, 123 / 255)), + "9" = tibble::lst(rgb(255 / 255, 247 / 255, 251 / 255), + rgb(236 / 255, 231 / 255, 242 / 255), + rgb(208 / 255, 209 / 255, 230 / 255), + rgb(166 / 255, 189 / 255, 219 / 255), + rgb(116 / 255, 169 / 255, 207 / 255), + rgb(54 / 255, 144 / 255, 192 / 255), + rgb(5 / 255, 112 / 255, 176 / 255), + rgb(4 / 255, 90 / 255, 141 / 255), + rgb(2 / 255, 56 / 255, 88 / 255)), + "10" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(240 / 255, 234 / 255, 243 / 255), + rgb(220 / 255, 218 / 255, 235 / 255), + rgb(194 / 255, 202 / 255, 226 / 255), + rgb(160 / 255, 186 / 255, 217 / 255), + rgb(121 / 255, 171 / 255, 208 / 255), + rgb(78 / 255, 152 / 255, 197 / 255), + rgb(38 / 255, 129 / 255, 184 / 255), + rgb(4 / 255, 104 / 255, 163 / 255), + rgb(2 / 255, 77 / 255, 122 / 255)), + "11" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(241 / 255, 235 / 255, 244 / 255), + rgb(224 / 255, 222 / 255, 237 / 255), + rgb(203 / 255, 206 / 255, 228 / 255), + rgb(174 / 255, 192 / 255, 221 / 255), + rgb(141 / 255, 178 / 255, 213 / 255), + rgb(105 / 255, 163 / 255, 204 / 255), + rgb(62 / 255, 146 / 255, 193 / 255), + rgb(32 / 255, 124 / 255, 182 / 255), + rgb(4 / 255, 101 / 255, 159 / 255), + rgb(2 / 255, 77 / 255, 122 / 255)), + "12" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(242 / 255, 236 / 255, 245 / 255), + rgb(228 / 255, 224 / 255, 238 / 255), + rgb(210 / 255, 210 / 255, 231 / 255), + rgb(185 / 255, 198 / 255, 224 / 255), + rgb(157 / 255, 185 / 255, 216 / 255), + rgb(125 / 255, 172 / 255, 209 / 255), + rgb(91 / 255, 157 / 255, 200 / 255), + rgb(51 / 255, 141 / 255, 190 / 255), + rgb(25 / 255, 120 / 255, 180 / 255), + rgb(4 / 255, 99 / 255, 156 / 255), + rgb(2 / 255, 77 / 255, 122 / 255)), + "13" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(243 / 255, 237 / 255, 245 / 255), + rgb(231 / 255, 227 / 255, 239 / 255), + rgb(214 / 255, 214 / 255, 232 / 255), + rgb(194 / 255, 202 / 255, 226 / 255), + rgb(169 / 255, 190 / 255, 219 / 255), + rgb(141 / 255, 178 / 255, 213 / 255), + rgb(111 / 255, 166 / 255, 205 / 255), + rgb(78 / 255, 152 / 255, 197 / 255), + rgb(45 / 255, 135 / 255, 188 / 255), + rgb(19 / 255, 117 / 255, 178 / 255), + rgb(4 / 255, 97 / 255, 153 / 255), + rgb(2 / 255, 77 / 255, 122 / 255)), + "14" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(244 / 255, 238 / 255, 246 / 255), + rgb(233 / 255, 229 / 255, 241 / 255), + rgb(218 / 255, 217 / 255, 234 / 255), + rgb(201 / 255, 205 / 255, 228 / 255), + rgb(179 / 255, 195 / 255, 222 / 255), + rgb(155 / 255, 184 / 255, 216 / 255), + rgb(128 / 255, 173 / 255, 209 / 255), + rgb(99 / 255, 161 / 255, 202 / 255), + rgb(66 / 255, 147 / 255, 194 / 255), + rgb(40 / 255, 131 / 255, 185 / 255), + rgb(12 / 255, 114 / 255, 177 / 255), + rgb(4 / 255, 96 / 255, 151 / 255), + rgb(2 / 255, 77 / 255, 122 / 255)), + "15" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(245 / 255, 238 / 255, 246 / 255), + rgb(235 / 255, 230 / 255, 242 / 255), + rgb(221 / 255, 219 / 255, 235 / 255), + rgb(208 / 255, 209 / 255, 229 / 255), + rgb(187 / 255, 198 / 255, 224 / 255), + rgb(165 / 255, 189 / 255, 218 / 255), + rgb(141 / 255, 178 / 255, 213 / 255), + rgb(115 / 255, 169 / 255, 206 / 255), + rgb(88 / 255, 156 / 255, 199 / 255), + rgb(53 / 255, 144 / 255, 191 / 255), + rgb(36 / 255, 127 / 255, 184 / 255), + rgb(5 / 255, 112 / 255, 176 / 255), + rgb(3 / 255, 94 / 255, 149 / 255), + rgb(2 / 255, 77 / 255, 122 / 255)), + "16" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(246 / 255, 239 / 255, 246 / 255), + rgb(237 / 255, 232 / 255, 242 / 255), + rgb(224 / 255, 222 / 255, 237 / 255), + rgb(211 / 255, 211 / 255, 231 / 255), + rgb(194 / 255, 202 / 255, 226 / 255), + rgb(174 / 255, 192 / 255, 221 / 255), + rgb(153 / 255, 183 / 255, 215 / 255), + rgb(130 / 255, 174 / 255, 210 / 255), + rgb(105 / 255, 163 / 255, 204 / 255), + rgb(78 / 255, 152 / 255, 197 / 255), + rgb(49 / 255, 139 / 255, 189 / 255), + rgb(32 / 255, 124 / 255, 182 / 255), + rgb(4 / 255, 109 / 255, 172 / 255), + rgb(3 / 255, 93 / 255, 147 / 255), + rgb(2 / 255, 77 / 255, 122 / 255)), + "17" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(246 / 255, 239 / 255, 247 / 255), + rgb(238 / 255, 232 / 255, 243 / 255), + rgb(227 / 255, 224 / 255, 238 / 255), + rgb(214 / 255, 214 / 255, 232 / 255), + rgb(200 / 255, 205 / 255, 227 / 255), + rgb(181 / 255, 196 / 255, 223 / 255), + rgb(163 / 255, 187 / 255, 218 / 255), + rgb(141 / 255, 178 / 255, 213 / 255), + rgb(119 / 255, 170 / 255, 207 / 255), + rgb(95 / 255, 159 / 255, 201 / 255), + rgb(68 / 255, 148 / 255, 194 / 255), + rgb(45 / 255, 135 / 255, 188 / 255), + rgb(27 / 255, 121 / 255, 181 / 255), + rgb(4 / 255, 107 / 255, 169 / 255), + rgb(3 / 255, 92 / 255, 145 / 255), + rgb(2 / 255, 77 / 255, 122 / 255)), + "18" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(247 / 255, 240 / 255, 247 / 255), + rgb(239 / 255, 233 / 255, 243 / 255), + rgb(229 / 255, 225 / 255, 239 / 255), + rgb(217 / 255, 216 / 255, 234 / 255), + rgb(205 / 255, 207 / 255, 229 / 255), + rgb(188 / 255, 199 / 255, 224 / 255), + rgb(171 / 255, 191 / 255, 220 / 255), + rgb(151 / 255, 183 / 255, 215 / 255), + rgb(131 / 255, 174 / 255, 210 / 255), + rgb(109 / 255, 166 / 255, 205 / 255), + rgb(86 / 255, 155 / 255, 199 / 255), + rgb(58 / 255, 145 / 255, 192 / 255), + rgb(42 / 255, 132 / 255, 186 / 255), + rgb(23 / 255, 119 / 255, 179 / 255), + rgb(4 / 255, 105 / 255, 166 / 255), + rgb(3 / 255, 91 / 255, 144 / 255), + rgb(2 / 255, 77 / 255, 122 / 255)), + "19" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(247 / 255, 240 / 255, 247 / 255), + rgb(240 / 255, 234 / 255, 243 / 255), + rgb(231 / 255, 227 / 255, 239 / 255), + rgb(220 / 255, 218 / 255, 235 / 255), + rgb(209 / 255, 210 / 255, 230 / 255), + rgb(194 / 255, 202 / 255, 226 / 255), + rgb(177 / 255, 194 / 255, 222 / 255), + rgb(160 / 255, 186 / 255, 217 / 255), + rgb(141 / 255, 178 / 255, 213 / 255), + rgb(121 / 255, 171 / 255, 208 / 255), + rgb(101 / 255, 161 / 255, 202 / 255), + rgb(78 / 255, 152 / 255, 197 / 255), + rgb(52 / 255, 142 / 255, 191 / 255), + rgb(38 / 255, 129 / 255, 184 / 255), + rgb(19 / 255, 117 / 255, 178 / 255), + rgb(4 / 255, 104 / 255, 163 / 255), + rgb(3 / 255, 90 / 255, 143 / 255), + rgb(2 / 255, 77 / 255, 122 / 255)), + "20" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(247 / 255, 241 / 255, 247 / 255), + rgb(240 / 255, 235 / 255, 244 / 255), + rgb(233 / 255, 228 / 255, 240 / 255), + rgb(222 / 255, 220 / 255, 236 / 255), + rgb(212 / 255, 212 / 255, 231 / 255), + rgb(199 / 255, 204 / 255, 227 / 255), + rgb(183 / 255, 197 / 255, 223 / 255), + rgb(168 / 255, 190 / 255, 219 / 255), + rgb(150 / 255, 182 / 255, 215 / 255), + rgb(132 / 255, 175 / 255, 210 / 255), + rgb(113 / 255, 167 / 255, 206 / 255), + rgb(93 / 255, 158 / 255, 200 / 255), + rgb(70 / 255, 149 / 255, 195 / 255), + rgb(48 / 255, 138 / 255, 189 / 255), + rgb(35 / 255, 126 / 255, 183 / 255), + rgb(15 / 255, 115 / 255, 177 / 255), + rgb(4 / 255, 102 / 255, 161 / 255), + rgb(3 / 255, 90 / 255, 142 / 255), + rgb(2 / 255, 77 / 255, 122 / 255)), + "type" = "sequential" + ), + "BuPu" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(), + "3" = tibble::lst(rgb(224 / 255, 236 / 255, 244 / 255), + rgb(158 / 255, 188 / 255, 218 / 255), + rgb(136 / 255, 86 / 255, 167 / 255)), + "4" = tibble::lst(rgb(237 / 255, 248 / 255, 251 / 255), + rgb(179 / 255, 205 / 255, 227 / 255), + rgb(140 / 255, 150 / 255, 198 / 255), + rgb(136 / 255, 65 / 255, 157 / 255)), + "5" = tibble::lst(rgb(237 / 255, 248 / 255, 251 / 255), + rgb(179 / 255, 205 / 255, 227 / 255), + rgb(140 / 255, 150 / 255, 198 / 255), + rgb(136 / 255, 86 / 255, 167 / 255), + rgb(129 / 255, 15 / 255, 124 / 255)), + "6" = tibble::lst(rgb(237 / 255, 248 / 255, 251 / 255), + rgb(191 / 255, 211 / 255, 230 / 255), + rgb(158 / 255, 188 / 255, 218 / 255), + rgb(140 / 255, 150 / 255, 198 / 255), + rgb(136 / 255, 86 / 255, 167 / 255), + rgb(129 / 255, 15 / 255, 124 / 255)), + "7" = tibble::lst(rgb(237 / 255, 248 / 255, 251 / 255), + rgb(191 / 255, 211 / 255, 230 / 255), + rgb(158 / 255, 188 / 255, 218 / 255), + rgb(140 / 255, 150 / 255, 198 / 255), + rgb(140 / 255, 107 / 255, 177 / 255), + rgb(136 / 255, 65 / 255, 157 / 255), + rgb(110 / 255, 1 / 255, 107 / 255)), + "8" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(224 / 255, 236 / 255, 244 / 255), + rgb(191 / 255, 211 / 255, 230 / 255), + rgb(158 / 255, 188 / 255, 218 / 255), + rgb(140 / 255, 150 / 255, 198 / 255), + rgb(140 / 255, 107 / 255, 177 / 255), + rgb(136 / 255, 65 / 255, 157 / 255), + rgb(110 / 255, 1 / 255, 107 / 255)), + "9" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(224 / 255, 236 / 255, 244 / 255), + rgb(191 / 255, 211 / 255, 230 / 255), + rgb(158 / 255, 188 / 255, 218 / 255), + rgb(140 / 255, 150 / 255, 198 / 255), + rgb(140 / 255, 107 / 255, 177 / 255), + rgb(136 / 255, 65 / 255, 157 / 255), + rgb(129 / 255, 15 / 255, 124 / 255), + rgb(77 / 255, 0 / 255, 75 / 255)), + "10" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(229 / 255, 239 / 255, 245 / 255), + rgb(205 / 255, 222 / 255, 236 / 255), + rgb(180 / 255, 203 / 255, 226 / 255), + rgb(156 / 255, 183 / 255, 215 / 255), + rgb(142 / 255, 154 / 255, 200 / 255), + rgb(140 / 255, 121 / 255, 183 / 255), + rgb(138 / 255, 89 / 255, 168 / 255), + rgb(130 / 255, 54 / 255, 145 / 255), + rgb(109 / 255, 0 / 255, 107 / 255)), + "11" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(230 / 255, 240 / 255, 246 / 255), + rgb(210 / 255, 225 / 255, 238 / 255), + rgb(187 / 255, 208 / 255, 228 / 255), + rgb(164 / 255, 192 / 255, 220 / 255), + rgb(149 / 255, 168 / 255, 207 / 255), + rgb(140 / 255, 141 / 255, 193 / 255), + rgb(140 / 255, 111 / 255, 179 / 255), + rgb(138 / 255, 82 / 255, 164 / 255), + rgb(128 / 255, 50 / 255, 141 / 255), + rgb(109 / 255, 0 / 255, 107 / 255)), + "12" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(232 / 255, 241 / 255, 247 / 255), + rgb(215 / 255, 229 / 255, 240 / 255), + rgb(194 / 255, 213 / 255, 231 / 255), + rgb(173 / 255, 198 / 255, 223 / 255), + rgb(154 / 255, 181 / 255, 214 / 255), + rgb(143 / 255, 156 / 255, 201 / 255), + rgb(140 / 255, 130 / 255, 188 / 255), + rgb(139 / 255, 103 / 255, 175 / 255), + rgb(137 / 255, 77 / 255, 162 / 255), + rgb(126 / 255, 46 / 255, 138 / 255), + rgb(109 / 255, 0 / 255, 107 / 255)), + "13" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(233 / 255, 242 / 255, 247 / 255), + rgb(218 / 255, 231 / 255, 241 / 255), + rgb(199 / 255, 217 / 255, 233 / 255), + rgb(180 / 255, 203 / 255, 226 / 255), + rgb(160 / 255, 189 / 255, 219 / 255), + rgb(149 / 255, 168 / 255, 207 / 255), + rgb(140 / 255, 146 / 255, 196 / 255), + rgb(140 / 255, 121 / 255, 183 / 255), + rgb(139 / 255, 97 / 255, 171 / 255), + rgb(136 / 255, 72 / 255, 160 / 255), + rgb(125 / 255, 43 / 255, 135 / 255), + rgb(109 / 255, 0 / 255, 107 / 255)), + "14" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(234 / 255, 243 / 255, 248 / 255), + rgb(221 / 255, 234 / 255, 242 / 255), + rgb(203 / 255, 220 / 255, 235 / 255), + rgb(185 / 255, 207 / 255, 228 / 255), + rgb(168 / 255, 195 / 255, 221 / 255), + rgb(153 / 255, 179 / 255, 213 / 255), + rgb(144 / 255, 158 / 255, 202 / 255), + rgb(140 / 255, 136 / 255, 191 / 255), + rgb(140 / 255, 113 / 255, 180 / 255), + rgb(138 / 255, 91 / 255, 169 / 255), + rgb(136 / 255, 68 / 255, 158 / 255), + rgb(124 / 255, 41 / 255, 133 / 255), + rgb(109 / 255, 0 / 255, 107 / 255)), + "15" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(235 / 255, 243 / 255, 248 / 255), + rgb(223 / 255, 235 / 255, 243 / 255), + rgb(207 / 255, 223 / 255, 236 / 255), + rgb(191 / 255, 210 / 255, 229 / 255), + rgb(174 / 255, 199 / 255, 224 / 255), + rgb(158 / 255, 187 / 255, 217 / 255), + rgb(149 / 255, 168 / 255, 207 / 255), + rgb(140 / 255, 150 / 255, 197 / 255), + rgb(140 / 255, 128 / 255, 187 / 255), + rgb(140 / 255, 107 / 255, 177 / 255), + rgb(138 / 255, 86 / 255, 166 / 255), + rgb(135 / 255, 64 / 255, 157 / 255), + rgb(123 / 255, 39 / 255, 131 / 255), + rgb(109 / 255, 0 / 255, 107 / 255)), + "16" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(236 / 255, 244 / 255, 248 / 255), + rgb(225 / 255, 237 / 255, 244 / 255), + rgb(210 / 255, 225 / 255, 238 / 255), + rgb(195 / 255, 214 / 255, 231 / 255), + rgb(180 / 255, 203 / 255, 226 / 255), + rgb(164 / 255, 192 / 255, 220 / 255), + rgb(153 / 255, 177 / 255, 212 / 255), + rgb(144 / 255, 160 / 255, 203 / 255), + rgb(140 / 255, 141 / 255, 193 / 255), + rgb(140 / 255, 121 / 255, 183 / 255), + rgb(139 / 255, 101 / 255, 174 / 255), + rgb(138 / 255, 82 / 255, 164 / 255), + rgb(134 / 255, 61 / 255, 153 / 255), + rgb(122 / 255, 37 / 255, 129 / 255), + rgb(109 / 255, 0 / 255, 107 / 255)), + "17" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(236 / 255, 244 / 255, 249 / 255), + rgb(226 / 255, 237 / 255, 245 / 255), + rgb(213 / 255, 228 / 255, 239 / 255), + rgb(199 / 255, 217 / 255, 233 / 255), + rgb(184 / 255, 206 / 255, 227 / 255), + rgb(170 / 255, 196 / 255, 222 / 255), + rgb(156 / 255, 185 / 255, 216 / 255), + rgb(149 / 255, 168 / 255, 207 / 255), + rgb(141 / 255, 152 / 255, 199 / 255), + rgb(140 / 255, 134 / 255, 190 / 255), + rgb(140 / 255, 115 / 255, 180 / 255), + rgb(139 / 255, 97 / 255, 171 / 255), + rgb(137 / 255, 78 / 255, 163 / 255), + rgb(132 / 255, 58 / 255, 150 / 255), + rgb(121 / 255, 35 / 255, 128 / 255), + rgb(109 / 255, 0 / 255, 107 / 255)), + "18" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(237 / 255, 245 / 255, 249 / 255), + rgb(228 / 255, 238 / 255, 245 / 255), + rgb(216 / 255, 230 / 255, 240 / 255), + rgb(202 / 255, 219 / 255, 234 / 255), + rgb(189 / 255, 209 / 255, 229 / 255), + rgb(175 / 255, 200 / 255, 224 / 255), + rgb(161 / 255, 190 / 255, 219 / 255), + rgb(152 / 255, 176 / 255, 212 / 255), + rgb(145 / 255, 161 / 255, 203 / 255), + rgb(140 / 255, 145 / 255, 195 / 255), + rgb(140 / 255, 127 / 255, 186 / 255), + rgb(140 / 255, 109 / 255, 178 / 255), + rgb(139 / 255, 92 / 255, 169 / 255), + rgb(137 / 255, 75 / 255, 161 / 255), + rgb(131 / 255, 56 / 255, 147 / 255), + rgb(121 / 255, 34 / 255, 127 / 255), + rgb(109 / 255, 0 / 255, 107 / 255)), + "19" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(238 / 255, 245 / 255, 249 / 255), + rgb(229 / 255, 239 / 255, 245 / 255), + rgb(218 / 255, 231 / 255, 241 / 255), + rgb(205 / 255, 222 / 255, 236 / 255), + rgb(192 / 255, 212 / 255, 230 / 255), + rgb(180 / 255, 203 / 255, 226 / 255), + rgb(167 / 255, 194 / 255, 221 / 255), + rgb(156 / 255, 183 / 255, 215 / 255), + rgb(149 / 255, 168 / 255, 207 / 255), + rgb(142 / 255, 154 / 255, 200 / 255), + rgb(140 / 255, 138 / 255, 192 / 255), + rgb(140 / 255, 121 / 255, 183 / 255), + rgb(139 / 255, 104 / 255, 175 / 255), + rgb(138 / 255, 89 / 255, 168 / 255), + rgb(136 / 255, 72 / 255, 160 / 255), + rgb(130 / 255, 54 / 255, 145 / 255), + rgb(120 / 255, 32 / 255, 126 / 255), + rgb(109 / 255, 0 / 255, 107 / 255)), + "20" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(238 / 255, 246 / 255, 249 / 255), + rgb(230 / 255, 240 / 255, 246 / 255), + rgb(220 / 255, 233 / 255, 242 / 255), + rgb(208 / 255, 224 / 255, 237 / 255), + rgb(196 / 255, 214 / 255, 232 / 255), + rgb(184 / 255, 206 / 255, 227 / 255), + rgb(172 / 255, 197 / 255, 223 / 255), + rgb(159 / 255, 189 / 255, 218 / 255), + rgb(152 / 255, 175 / 255, 211 / 255), + rgb(145 / 255, 161 / 255, 204 / 255), + rgb(140 / 255, 147 / 255, 196 / 255), + rgb(140 / 255, 132 / 255, 189 / 255), + rgb(140 / 255, 116 / 255, 181 / 255), + rgb(139 / 255, 100 / 255, 173 / 255), + rgb(138 / 255, 85 / 255, 166 / 255), + rgb(136 / 255, 69 / 255, 159 / 255), + rgb(129 / 255, 51 / 255, 143 / 255), + rgb(119 / 255, 31 / 255, 125 / 255), + rgb(109 / 255, 0 / 255, 107 / 255)), + "type" = "sequential" + ), + "Oranges" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(254 / 255, 230 / 255, 206 / 255), + rgb(230 / 255, 85 / 255, 13 / 255)), + "3" = tibble::lst(rgb(254 / 255, 230 / 255, 206 / 255), + rgb(253 / 255, 174 / 255, 107 / 255), + rgb(230 / 255, 85 / 255, 13 / 255)), + "4" = tibble::lst(rgb(254 / 255, 237 / 255, 222 / 255), + rgb(253 / 255, 190 / 255, 133 / 255), + rgb(253 / 255, 141 / 255, 60 / 255), + rgb(217 / 255, 71 / 255, 1 / 255)), + "5" = tibble::lst(rgb(254 / 255, 237 / 255, 222 / 255), + rgb(253 / 255, 190 / 255, 133 / 255), + rgb(253 / 255, 141 / 255, 60 / 255), + rgb(230 / 255, 85 / 255, 13 / 255), + rgb(166 / 255, 54 / 255, 3 / 255)), + "6" = tibble::lst(rgb(254 / 255, 237 / 255, 222 / 255), + rgb(253 / 255, 208 / 255, 162 / 255), + rgb(253 / 255, 174 / 255, 107 / 255), + rgb(253 / 255, 141 / 255, 60 / 255), + rgb(230 / 255, 85 / 255, 13 / 255), + rgb(166 / 255, 54 / 255, 3 / 255)), + "7" = tibble::lst(rgb(254 / 255, 237 / 255, 222 / 255), + rgb(253 / 255, 208 / 255, 162 / 255), + rgb(253 / 255, 174 / 255, 107 / 255), + rgb(253 / 255, 141 / 255, 60 / 255), + rgb(241 / 255, 105 / 255, 19 / 255), + rgb(217 / 255, 72 / 255, 1 / 255), + rgb(140 / 255, 45 / 255, 4 / 255)), + "8" = tibble::lst(rgb(255 / 255, 245 / 255, 235 / 255), + rgb(254 / 255, 230 / 255, 206 / 255), + rgb(253 / 255, 208 / 255, 162 / 255), + rgb(253 / 255, 174 / 255, 107 / 255), + rgb(253 / 255, 141 / 255, 60 / 255), + rgb(241 / 255, 105 / 255, 19 / 255), + rgb(217 / 255, 72 / 255, 1 / 255), + rgb(140 / 255, 45 / 255, 4 / 255)), + "9" = tibble::lst(rgb(255 / 255, 245 / 255, 235 / 255), + rgb(254 / 255, 230 / 255, 206 / 255), + rgb(253 / 255, 208 / 255, 162 / 255), + rgb(253 / 255, 174 / 255, 107 / 255), + rgb(253 / 255, 141 / 255, 60 / 255), + rgb(241 / 255, 105 / 255, 19 / 255), + rgb(217 / 255, 72 / 255, 1 / 255), + rgb(166 / 255, 54 / 255, 3 / 255), + rgb(127 / 255, 39 / 255, 4 / 255)), + "10" = tibble::lst(rgb(255 / 255, 244 / 255, 235 / 255), + rgb(254 / 255, 233 / 255, 212 / 255), + rgb(253 / 255, 217 / 255, 181 / 255), + rgb(253 / 255, 196 / 255, 143 / 255), + rgb(253 / 255, 170 / 255, 101 / 255), + rgb(253 / 255, 144 / 255, 65 / 255), + rgb(245 / 255, 117 / 255, 34 / 255), + rgb(230 / 255, 90 / 255, 10 / 255), + rgb(199 / 255, 65 / 255, 2 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "11" = tibble::lst(rgb(255 / 255, 244 / 255, 235 / 255), + rgb(254 / 255, 234 / 255, 214 / 255), + rgb(254 / 255, 221 / 255, 188 / 255), + rgb(253 / 255, 204 / 255, 156 / 255), + rgb(253 / 255, 180 / 255, 117 / 255), + rgb(253 / 255, 157 / 255, 83 / 255), + rgb(250 / 255, 134 / 255, 52 / 255), + rgb(242 / 255, 108 / 255, 24 / 255), + rgb(226 / 255, 85 / 255, 7 / 255), + rgb(193 / 255, 63 / 255, 2 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "12" = tibble::lst(rgb(255 / 255, 244 / 255, 235 / 255), + rgb(254 / 255, 235 / 255, 216 / 255), + rgb(254 / 255, 223 / 255, 193 / 255), + rgb(253 / 255, 209 / 255, 165 / 255), + rgb(253 / 255, 189 / 255, 131 / 255), + rgb(253 / 255, 168 / 255, 98 / 255), + rgb(253 / 255, 147 / 255, 68 / 255), + rgb(247 / 255, 124 / 255, 43 / 255), + rgb(238 / 255, 102 / 255, 17 / 255), + rgb(223 / 255, 81 / 255, 5 / 255), + rgb(188 / 255, 61 / 255, 2 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "13" = tibble::lst(rgb(255 / 255, 244 / 255, 235 / 255), + rgb(254 / 255, 236 / 255, 218 / 255), + rgb(254 / 255, 226 / 255, 198 / 255), + rgb(253 / 255, 213 / 255, 172 / 255), + rgb(253 / 255, 196 / 255, 143 / 255), + rgb(253 / 255, 176 / 255, 111 / 255), + rgb(253 / 255, 157 / 255, 83 / 255), + rgb(252 / 255, 138 / 255, 56 / 255), + rgb(245 / 255, 117 / 255, 34 / 255), + rgb(235 / 255, 97 / 255, 14 / 255), + rgb(221 / 255, 77 / 255, 3 / 255), + rgb(184 / 255, 60 / 255, 2 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "14" = tibble::lst(rgb(255 / 255, 244 / 255, 235 / 255), + rgb(254 / 255, 236 / 255, 219 / 255), + rgb(254 / 255, 228 / 255, 202 / 255), + rgb(253 / 255, 216 / 255, 178 / 255), + rgb(253 / 255, 202 / 255, 153 / 255), + rgb(253 / 255, 184 / 255, 123 / 255), + rgb(253 / 255, 166 / 255, 96 / 255), + rgb(253 / 255, 148 / 255, 71 / 255), + rgb(249 / 255, 130 / 255, 48 / 255), + rgb(242 / 255, 110 / 255, 26 / 255), + rgb(231 / 255, 92 / 255, 11 / 255), + rgb(218 / 255, 74 / 255, 2 / 255), + rgb(180 / 255, 59 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "15" = tibble::lst(rgb(255 / 255, 244 / 255, 235 / 255), + rgb(254 / 255, 237 / 255, 220 / 255), + rgb(254 / 255, 229 / 255, 205 / 255), + rgb(253 / 255, 218 / 255, 183 / 255), + rgb(253 / 255, 208 / 255, 161 / 255), + rgb(253 / 255, 191 / 255, 134 / 255), + rgb(253 / 255, 173 / 255, 107 / 255), + rgb(253 / 255, 157 / 255, 83 / 255), + rgb(253 / 255, 140 / 255, 59 / 255), + rgb(247 / 255, 123 / 255, 41 / 255), + rgb(241 / 255, 104 / 255, 19 / 255), + rgb(229 / 255, 88 / 255, 9 / 255), + rgb(216 / 255, 71 / 255, 0 / 255), + rgb(177 / 255, 58 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "16" = tibble::lst(rgb(255 / 255, 244 / 255, 235 / 255), + rgb(254 / 255, 237 / 255, 221 / 255), + rgb(254 / 255, 230 / 255, 207 / 255), + rgb(254 / 255, 221 / 255, 188 / 255), + rgb(253 / 255, 210 / 255, 167 / 255), + rgb(253 / 255, 196 / 255, 143 / 255), + rgb(253 / 255, 180 / 255, 117 / 255), + rgb(253 / 255, 165 / 255, 94 / 255), + rgb(253 / 255, 149 / 255, 72 / 255), + rgb(250 / 255, 134 / 255, 52 / 255), + rgb(245 / 255, 117 / 255, 34 / 255), + rgb(237 / 255, 100 / 255, 16 / 255), + rgb(226 / 255, 85 / 255, 7 / 255), + rgb(211 / 255,70 / 255, 1 / 255), + rgb(175 / 255, 57 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "17" = tibble::lst(rgb(255 / 255, 244 / 255, 235 / 255), + rgb(254 / 255, 238 / 255, 222 / 255), + rgb(254 / 255, 231 / 255, 209 / 255), + rgb(254 / 255, 223 / 255, 192 / 255), + rgb(253 / 255, 213 / 255, 172 / 255), + rgb(253 / 255, 201 / 255, 151 / 255), + rgb(253 / 255, 186 / 255, 127 / 255), + rgb(253 / 255, 171 / 255, 104 / 255), + rgb(253 / 255, 157 / 255, 83 / 255), + rgb(253 / 255, 143 / 255, 63 / 255), + rgb(248 / 255, 127 / 255, 46 / 255), + rgb(243 / 255, 111 / 255, 28 / 255), + rgb(235 / 255, 97 / 255, 14 / 255), + rgb(224 / 255, 82 / 255, 5 / 255), + rgb(207 / 255, 68 / 255, 1 / 255), + rgb(172 / 255, 56 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "18" = tibble::lst(rgb(255 / 255, 244 / 255, 235 / 255), + rgb(254 / 255, 238 / 255, 223 / 255), + rgb(254 / 255, 232 / 255, 211 / 255), + rgb(254 / 255, 224 / 255, 195 / 255), + rgb(253 / 255, 215 / 255, 177 / 255), + rgb(253 / 255, 206 / 255, 158 / 255), + rgb(253 / 255, 192 / 255, 136 / 255), + rgb(253 / 255, 178 / 255, 113 / 255), + rgb(253 / 255, 164 / 255, 93 / 255), + rgb(253 / 255, 150 / 255, 74 / 255), + rgb(251 / 255, 136 / 255, 55 / 255), + rgb(246 / 255, 122 / 255, 40 / 255), + rgb(241 / 255, 107 / 255, 22 / 255), + rgb(232 / 255, 93 / 255, 12 / 255), + rgb(222 / 255, 80 / 255, 4 / 255), + rgb(202 / 255, 67 / 255, 1 / 255), + rgb(170 / 255, 55 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "19" = tibble::lst(rgb(255 / 255, 244 / 255, 235 / 255), + rgb(254 / 255, 239 / 255, 223 / 255), + rgb(254 / 255, 233 / 255, 212 / 255), + rgb(254 / 255, 226 / 255, 198 / 255), + rgb(253 / 255, 217 / 255, 181 / 255), + rgb(253 / 255, 209 / 255, 164 / 255), + rgb(253 / 255, 196 / 255, 143 / 255), + rgb(253 / 255, 183 / 255, 122 / 255), + rgb(253 / 255, 170 / 255, 101 / 255), + rgb(253 / 255, 157 / 255, 83 / 255), + rgb(253 / 255, 144 / 255, 65 / 255), + rgb(249 / 255, 131 / 255, 49 / 255), + rgb(245 / 255, 117 / 255, 34 / 255), + rgb(239 / 255, 103 / 255, 18 / 255), + rgb(230 / 255, 90 / 255, 10 / 255), + rgb(221 / 255, 77 / 255, 3 / 255), + rgb(199 / 255, 65 / 255, 2 / 255), + rgb(169 / 255, 55 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "20" = tibble::lst(rgb(255 / 255, 244 / 255, 235 / 255), + rgb(254 / 255, 239 / 255, 224 / 255), + rgb(254 / 255, 233 / 255, 213 / 255), + rgb(254 / 255, 227 / 255, 201 / 255), + rgb(253 / 255, 219 / 255, 185 / 255), + rgb(253 / 255, 211 / 255, 168 / 255), + rgb(253 / 255, 200 / 255, 150 / 255), + rgb(253 / 255, 188 / 255, 130 / 255), + rgb(253 / 255, 175 / 255, 109 / 255), + rgb(253 / 255, 163 / 255, 92 / 255), + rgb(253 / 255, 151 / 255, 75 / 255), + rgb(252 / 255, 139 / 255, 58 / 255), + rgb(248 / 255, 126 / 255, 44 / 255), + rgb(243 / 255, 112 / 255, 29 / 255), + rgb(237 / 255, 99 / 255, 16 / 255), + rgb(228 / 255, 88 / 255, 8 / 255), + rgb(219 / 255, 75 / 255, 2 / 255), + rgb(196 / 255, 64 / 255, 2 / 255), + rgb(167 / 255, 54 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "type" = "sequential" + ), + "BuGn" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(), + "3" = tibble::lst(rgb(229 / 255, 245 / 255, 249 / 255), + rgb(153 / 255, 216 / 255, 201 / 255), + rgb(44 / 255, 162 / 255, 95 / 255)), + "4" = tibble::lst(rgb(237 / 255, 248 / 255, 251 / 255), + rgb(178 / 255, 226 / 255, 226 / 255), + rgb(102 / 255, 194 / 255, 164 / 255), + rgb(35 / 255, 139 / 255, 69 / 255)), + "5" = tibble::lst(rgb(237 / 255, 248 / 255, 251 / 255), + rgb(178 / 255, 226 / 255, 226 / 255), + rgb(102 / 255, 194 / 255, 164 / 255), + rgb(44 / 255, 162 / 255, 95 / 255), + rgb(0 / 255, 109 / 255, 44 / 255)), + "6" = tibble::lst(rgb(237 / 255, 248 / 255, 251 / 255), + rgb(204 / 255, 236 / 255, 230 / 255), + rgb(153 / 255, 216 / 255, 201 / 255), + rgb(102 / 255, 194 / 255, 164 / 255), + rgb(44 / 255, 162 / 255, 95 / 255), + rgb(0 / 255, 109 / 255, 44 / 255)), + "7" = tibble::lst(rgb(237 / 255, 248 / 255, 251 / 255), + rgb(204 / 255, 236 / 255, 230 / 255), + rgb(153 / 255, 216 / 255, 201 / 255), + rgb(102 / 255, 194 / 255, 164 / 255), + rgb(65 / 255, 174 / 255, 118 / 255), + rgb(35 / 255, 139 / 255, 69 / 255), + rgb(0 / 255, 88 / 255, 36 / 255)), + "8" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(229 / 255, 245 / 255, 249 / 255), + rgb(204 / 255, 236 / 255, 230 / 255), + rgb(153 / 255, 216 / 255, 201 / 255), + rgb(102 / 255, 194 / 255, 164 / 255), + rgb(65 / 255, 174 / 255, 118 / 255), + rgb(35 / 255, 139 / 255, 69 / 255), + rgb(0 / 255, 88 / 255, 36 / 255)), + "9" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(229 / 255, 245 / 255, 249 / 255), + rgb(204 / 255, 236 / 255, 230 / 255), + rgb(153 / 255, 216 / 255, 201 / 255), + rgb(102 / 255, 194 / 255, 164 / 255), + rgb(65 / 255, 174 / 255, 118 / 255), + rgb(35 / 255, 139 / 255, 69 / 255), + rgb(0 / 255, 109 / 255, 44 / 255), + rgb(0 / 255, 68 / 255, 27 / 255)), + "10" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(233 / 255, 246 / 255, 249 / 255), + rgb(215 / 255, 240 / 255, 238 / 255), + rgb(187 / 255, 229 / 255, 220 / 255), + rgb(147 / 255, 213 / 255, 196 / 255), + rgb(108 / 255, 196 / 255, 168 / 255), + rgb(78 / 255, 180 / 255, 133 / 255), + rgb(52 / 255, 158 / 255, 96 / 255), + rgb(27 / 255, 127 / 255, 61 / 255), + rgb(0 / 255, 88 / 255, 36 / 255)), + "11" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(234 / 255, 247 / 255, 250 / 255), + rgb(219 / 255, 241 / 255, 241 / 255), + rgb(199 / 255, 234 / 255, 227 / 255), + rgb(163 / 255, 220 / 255, 206 / 255), + rgb(128 / 255, 205 / 255, 182 / 255), + rgb(95 / 255, 190 / 255, 154 / 255), + rgb(69 / 255, 176 / 255, 122 / 255), + rgb(47 / 255, 152 / 255, 88 / 255), + rgb(25 / 255, 123 / 255, 58 / 255), + rgb(0 / 255, 88 / 255, 36 / 255)), + "12" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(235 / 255, 247 / 255, 250 / 255), + rgb(222 / 255, 242 / 255, 243 / 255), + rgb(206 / 255, 236 / 255, 231 / 255), + rgb(176 / 255, 225 / 255, 214 / 255), + rgb(144 / 255, 212 / 255, 194 / 255), + rgb(111 / 255, 198 / 255, 170 / 255), + rgb(86 / 255, 184 / 255, 142 / 255), + rgb(62 / 255, 170 / 255, 113 / 255), + rgb(44 / 255, 148 / 255, 82 / 255), + rgb(23 / 255, 119 / 255, 56 / 255), + rgb(0 / 255, 88 / 255, 36 / 255)), + "13" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(236 / 255, 247 / 255, 250 / 255), + rgb(224 / 255, 243 / 255, 245 / 255), + rgb(210 / 255, 238 / 255, 234 / 255), + rgb(187 / 255, 229 / 255, 220 / 255), + rgb(157 / 255, 217 / 255, 203 / 255), + rgb(128 / 255, 205 / 255, 182 / 255), + rgb(99 / 255, 192 / 255, 160 / 255), + rgb(78 / 255, 180 / 255, 133 / 255), + rgb(58 / 255, 165 / 255, 105 / 255), + rgb(40 / 255, 144 / 255, 77 / 255), + rgb(21 / 255, 117 / 255, 54 / 255), + rgb(0 / 255, 88 / 255, 36 / 255)), + "14" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(237 / 255, 248 / 255, 250 / 255), + rgb(227 / 255, 244 / 255, 247 / 255), + rgb(213 / 255, 239 / 255, 237 / 255), + rgb(196 / 255, 232 / 255, 225 / 255), + rgb(168 / 255, 222 / 255, 209 / 255), + rgb(141 / 255, 210 / 255, 192 / 255), + rgb(114 / 255, 199 / 255, 172 / 255), + rgb(91 / 255, 187 / 255, 149 / 255), + rgb(71 / 255, 177 / 255, 125 / 255), + rgb(54 / 255, 160 / 255, 98 / 255), + rgb(37 / 255, 141 / 255, 72 / 255), + rgb(19 / 255, 114 / 255, 53 / 255), + rgb(0 / 255, 88 / 255, 36 / 255)), + "15" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(238 / 255, 248 / 255, 250 / 255), + rgb(229 / 255, 244 / 255, 248 / 255), + rgb(216 / 255, 240 / 255, 239 / 255), + rgb(204 / 255, 235 / 255, 229 / 255), + rgb(178 / 255, 226 / 255, 215 / 255), + rgb(153 / 255, 216 / 255, 201 / 255), + rgb(128 / 255, 205 / 255, 182 / 255), + rgb(102 / 255, 193 / 255, 164 / 255), + rgb(84 / 255, 184 / 255, 140 / 255), + rgb(64 / 255, 173 / 255, 118 / 255), + rgb(50 / 255, 156 / 255, 93 / 255), + rgb(34 / 255, 139 / 255, 69 / 255), + rgb(18 / 255, 112 / 255, 52 / 255), + rgb(0 / 255, 88 / 255, 36 / 255)), + "16" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(238 / 255, 248 / 255, 251 / 255), + rgb(230 / 255, 245 / 255, 249 / 255), + rgb(219 / 255, 241 / 255, 241 / 255), + rgb(207 / 255, 237 / 255, 232 / 255), + rgb(187 / 255, 229 / 255, 220 / 255), + rgb(163 / 255, 220 / 255, 206 / 255), + rgb(139 / 255, 210 / 255, 191 / 255), + rgb(116 / 255, 199 / 255, 173 / 255), + rgb(95 / 255, 190 / 255, 154 / 255), + rgb(78 / 255, 180 / 255, 133 / 255), + rgb(61 / 255, 169 / 255, 111 / 255), + rgb(47 / 255, 152 / 255, 88 / 255), + rgb(32 / 255, 135 / 255, 66 / 255), + rgb(17 / 255, 111 / 255, 51 / 255), + rgb(0 / 255, 88 / 255, 36 / 255)), + "17" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(239 / 255, 248 / 255, 251 / 255), + rgb(231 / 255, 245 / 255, 249 / 255), + rgb(221 / 255, 242 / 255, 243 / 255), + rgb(210 / 255, 238 / 255, 234 / 255), + rgb(194 / 255, 232 / 255, 224 / 255), + rgb(172 / 255, 223 / 255, 211 / 255), + rgb(149 / 255, 214 / 255, 198 / 255), + rgb(128 / 255, 205 / 255, 182 / 255), + rgb(105 / 255, 195 / 255, 166 / 255), + rgb(89 / 255, 186 / 255, 146 / 255), + rgb(72 / 255, 177 / 255, 126 / 255), + rgb(58 / 255, 165 / 255, 105 / 255), + rgb(45 / 255, 149 / 255, 84 / 255), + rgb(30 / 255, 132 / 255, 64 / 255), + rgb(15 / 255, 109 / 255, 50 / 255), + rgb(0 / 255, 88 / 255, 36 / 255)), + "18" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(239 / 255, 249 / 255, 251 / 255), + rgb(232 / 255, 246 / 255, 249 / 255), + rgb(223 / 255, 242 / 255, 244 / 255), + rgb(212 / 255, 239 / 255, 236 / 255), + rgb(201 / 255, 234 / 255, 228 / 255), + rgb(180 / 255, 226 / 255, 216 / 255), + rgb(159 / 255, 218 / 255, 204 / 255), + rgb(138 / 255, 209 / 255, 189 / 255), + rgb(117 / 255, 200 / 255, 174 / 255), + rgb(98 / 255, 191 / 255, 158 / 255), + rgb(83 / 255, 183 / 255, 139 / 255), + rgb(67 / 255, 175 / 255, 120 / 255), + rgb(55 / 255, 161 / 255, 100 / 255), + rgb(42 / 255, 147 / 255, 80 / 255), + rgb(29 / 255, 129 / 255, 62 / 255), + rgb(14 / 255, 108 / 255, 49 / 255), + rgb(0 / 255, 88 / 255, 36 / 255)), + "19" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(240 / 255, 249 / 255, 251 / 255), + rgb(233 / 255, 246 / 255, 249 / 255), + rgb(224 / 255, 243 / 255, 245 / 255), + rgb(215 / 255, 240 / 255, 238 / 255), + rgb(205 / 255, 236 / 255, 231 / 255), + rgb(187 / 255, 229 / 255, 220 / 255), + rgb(167 / 255, 221 / 255, 208 / 255), + rgb(147 / 255, 213 / 255, 196 / 255), + rgb(128 / 255, 205 / 255, 182 / 255), + rgb(108 / 255, 196 / 255, 168 / 255), + rgb(92 / 255, 188 / 255, 151 / 255), + rgb(78 / 255, 180 / 255, 133 / 255), + rgb(63 / 255, 172 / 255, 115 / 255), + rgb(52 / 255, 158 / 255, 96 / 255), + rgb(40 / 255, 144 / 255, 77 / 255), + rgb(27 / 255, 127 / 255, 61 / 255), + rgb(14 / 255, 107 / 255, 48 / 255), + rgb(0 / 255, 88 / 255, 36 / 255)), + "20" = tibble::lst(rgb(247 / 255, 252 / 255, 253 / 255), + rgb(240 / 255, 249 / 255, 251 / 255), + rgb(233 / 255, 246 / 255, 250 / 255), + rgb(226 / 255, 244 / 255, 246 / 255), + rgb(217 / 255, 240 / 255, 239 / 255), + rgb(207 / 255, 237 / 255, 232 / 255), + rgb(193 / 255, 231 / 255, 223 / 255), + rgb(174 / 255, 224 / 255, 213 / 255), + rgb(155 / 255, 217 / 255, 202 / 255), + rgb(137 / 255, 209 / 255, 189 / 255), + rgb(118 / 255, 200 / 255, 175 / 255), + rgb(100 / 255, 192 / 255, 161 / 255), + rgb(87 / 255, 185 / 255, 144 / 255), + rgb(73 / 255, 178 / 255, 127 / 255), + rgb(60 / 255, 168 / 255, 110 / 255), + rgb(50 / 255, 155 / 255, 92 / 255), + rgb(38 / 255, 142 / 255, 74 / 255), + rgb(26 / 255, 125 / 255, 60 / 255), + rgb(13 / 255, 106 / 255, 47 / 255), + rgb(0 / 255, 88 / 255, 36 / 255)), + "type" = "sequential" + ), + "YlOrBr" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(255 / 255, 247 / 255, 188 / 255), + rgb(217 / 255, 95 / 255, 14 / 255)), + "3" = tibble::lst(rgb(255 / 255, 247 / 255, 188 / 255), + rgb(254 / 255, 196 / 255, 79 / 255), + rgb(217 / 255, 95 / 255, 14 / 255)), + "4" = tibble::lst(rgb(255 / 255, 255 / 255, 212 / 255), + rgb(254 / 255, 217 / 255, 142 / 255), + rgb(254 / 255, 153 / 255, 41 / 255), + rgb(204 / 255, 76 / 255, 2 / 255)), + "5" = tibble::lst(rgb(255 / 255, 255 / 255, 212 / 255), + rgb(254 / 255, 217 / 255, 142 / 255), + rgb(254 / 255, 153 / 255, 41 / 255), + rgb(217 / 255, 95 / 255, 14 / 255), + rgb(153 / 255, 52 / 255, 4 / 255)), + "6" = tibble::lst(rgb(255 / 255, 255 / 255, 212 / 255), + rgb(254 / 255, 227 / 255, 145 / 255), + rgb(254 / 255, 196 / 255, 79 / 255), + rgb(254 / 255, 153 / 255, 41 / 255), + rgb(217 / 255, 95 / 255, 14 / 255), + rgb(153 / 255, 52 / 255, 4 / 255)), + "7" = tibble::lst(rgb(255 / 255, 255 / 255, 212 / 255), + rgb(254 / 255, 227 / 255, 145 / 255), + rgb(254 / 255, 196 / 255, 79 / 255), + rgb(254 / 255, 153 / 255, 41 / 255), + rgb(236 / 255, 112 / 255, 20 / 255), + rgb(204 / 255, 76 / 255, 2 / 255), + rgb(140 / 255, 45 / 255, 4 / 255)), + "8" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(255 / 255, 247 / 255, 188 / 255), + rgb(254 / 255, 227 / 255, 145 / 255), + rgb(254 / 255, 196 / 255, 79 / 255), + rgb(254 / 255, 153 / 255, 41 / 255), + rgb(236 / 255, 112 / 255, 20 / 255), + rgb(204 / 255, 76 / 255, 2 / 255), + rgb(140 / 255, 45 / 255, 4 / 255)), + "9" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(255 / 255, 247 / 255, 188 / 255), + rgb(254 / 255, 227 / 255, 145 / 255), + rgb(254 / 255, 196 / 255, 79 / 255), + rgb(254 / 255, 153 / 255, 41 / 255), + rgb(236 / 255, 112 / 255, 20 / 255), + rgb(204 / 255, 76 / 255, 2 / 255), + rgb(153 / 255, 52 / 255, 4 / 255), + rgb(102 / 255, 37 / 255, 6 / 255)), + "10" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(255 / 255, 248 / 255, 197 / 255), + rgb(254 / 255, 235 / 255, 164 / 255), + rgb(254 / 255, 216 / 255, 123 / 255), + rgb(254 / 255, 191 / 255, 74 / 255), + rgb(254 / 255, 157 / 255, 45 / 255), + rgb(242 / 255, 125 / 255, 27 / 255), + rgb(221 / 255, 96 / 255, 11 / 255), + rgb(189 / 255, 68 / 255, 2 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "11" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(255 / 255, 249 / 255, 200 / 255), + rgb(254 / 255, 238 / 255, 170 / 255), + rgb(254 / 255, 223 / 255, 138 / 255), + rgb(254 / 255, 202 / 255, 92 / 255), + rgb(254 / 255, 174 / 255, 60 / 255), + rgb(250 / 255, 144 / 255, 36 / 255), + rgb(237 / 255, 116 / 255, 22 / 255), + rgb(216 / 255, 90 / 255, 8 / 255), + rgb(184 / 255, 66 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "12" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(255 / 255, 249 / 255, 202 / 255), + rgb(255 / 255, 241 / 255, 176 / 255), + rgb(254 / 255, 228 / 255, 148 / 255), + rgb(254 / 255, 210 / 255, 109 / 255), + rgb(254 / 255, 188 / 255, 72 / 255), + rgb(254 / 255, 161 / 255, 48 / 255), + rgb(246 / 255, 134 / 255, 31 / 255), + rgb(233 / 255, 108 / 255, 18 / 255), + rgb(212 / 255, 85 / 255, 6 / 255), + rgb(180 / 255, 64 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "13" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(255 / 255, 250 / 255, 205 / 255), + rgb(255 / 255, 243 / 255, 180 / 255), + rgb(254 / 255, 231 / 255, 155 / 255), + rgb(254 / 255, 216 / 255, 123 / 255), + rgb(254 / 255, 198 / 255, 84 / 255), + rgb(254 / 255, 174 / 255, 60 / 255), + rgb(252 / 255, 149 / 255, 39 / 255), + rgb(242 / 255, 125 / 255, 27 / 255), + rgb(227 / 255, 103 / 255, 15 / 255), + rgb(209 / 255, 82 / 255, 4 / 255), + rgb(176 / 255, 62 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "14" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(255 / 255, 250 / 255, 206 / 255), + rgb(255 / 255, 245 / 255, 184 / 255), + rgb(254 / 255, 234 / 255, 161 / 255), + rgb(254 / 255, 222 / 255, 135 / 255), + rgb(254 / 255, 205 / 255, 100 / 255), + rgb(254 / 255, 186 / 255, 70 / 255), + rgb(254 / 255, 163 / 255, 50 / 255), + rgb(248 / 255, 140 / 255, 34 / 255), + rgb(238 / 255, 118 / 255, 23 / 255), + rgb(223 / 255, 98 / 255, 12 / 255), + rgb(206 / 255, 78 / 255, 3 / 255), + rgb(173 / 255, 61 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "15" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(255 / 255, 250 / 255, 208 / 255), + rgb(255 / 255, 247 / 255, 187 / 255), + rgb(254 / 255, 236 / 255, 166 / 255), + rgb(254 / 255, 227 / 255, 145 / 255), + rgb(254 / 255, 211 / 255, 112 / 255), + rgb(254 / 255, 196 / 255, 78 / 255), + rgb(254 / 255, 174 / 255, 60 / 255), + rgb(254 / 255, 153 / 255, 40 / 255), + rgb(245 / 255, 132 / 255, 30 / 255), + rgb(235 / 255, 112 / 255, 19 / 255), + rgb(219 / 255, 94 / 255, 10 / 255), + rgb(204 / 255, 76 / 255, 1 / 255), + rgb(171 / 255, 60 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "16" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(255 / 255, 251 / 255, 209 / 255), + rgb(255 / 255, 247 / 255, 190 / 255), + rgb(254 / 255, 238 / 255, 170 / 255), + rgb(254 / 255, 229 / 255, 150 / 255), + rgb(254 / 255, 216 / 255, 123 / 255), + rgb(254 / 255, 202 / 255, 92 / 255), + rgb(254 / 255, 184 / 255, 69 / 255), + rgb(254 / 255, 164 / 255, 51 / 255), + rgb(250 / 255, 144 / 255, 36 / 255), + rgb(242 / 255, 125 / 255, 27 / 255), + rgb(231 / 255, 107 / 255, 17 / 255), + rgb(216 / 255, 90 / 255, 8 / 255), + rgb(199 / 255, 73 / 255, 2 / 255), + rgb(169 / 255, 59 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "17" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(255 / 255, 251 / 255, 211 / 255), + rgb(255 / 255, 247 / 255, 193 / 255), + rgb(255 / 255, 240 / 255, 174 / 255), + rgb(254 / 255, 231 / 255, 155 / 255), + rgb(254 / 255, 221 / 255, 133 / 255), + rgb(254 / 255, 207 / 255, 104 / 255), + rgb(254 / 255, 193 / 255, 76 / 255), + rgb(254 / 255, 174 / 255, 60 / 255), + rgb(254 / 255, 155 / 255, 43 / 255), + rgb(247 / 255, 137 / 255, 33 / 255), + rgb(239 / 255, 119 / 255, 24 / 255), + rgb(227 / 255, 103 / 255, 15 / 255), + rgb(213 / 255, 87 / 255, 6 / 255), + rgb(195 / 255, 72 / 255, 2 / 255), + rgb(167 / 255, 58 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "18" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(255 / 255, 251 / 255, 212 / 255), + rgb(255 / 255, 248 / 255, 195 / 255), + rgb(255 / 255, 242 / 255, 177 / 255), + rgb(254 / 255, 234 / 255, 160 / 255), + rgb(254 / 255, 225 / 255, 141 / 255), + rgb(254 / 255, 212 / 255, 114 / 255), + rgb(254 / 255, 199 / 255, 87 / 255), + rgb(254 / 255, 183 / 255, 68 / 255), + rgb(254 / 255, 165 / 255, 52 / 255), + rgb(251 / 255, 148 / 255, 38 / 255), + rgb(244 / 255, 131 / 255, 30 / 255), + rgb(237 / 255, 114 / 255, 21 / 255), + rgb(224 / 255, 99 / 255, 13 / 255), + rgb(211 / 255, 84 / 255, 5 / 255), + rgb(192 / 255, 70 / 255, 2 / 255), + rgb(165 / 255, 57 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "19" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(255 / 255, 251 / 255, 213 / 255), + rgb(255 / 255, 248 / 255, 197 / 255), + rgb(255 / 255, 243 / 255, 180 / 255), + rgb(254 / 255, 235 / 255, 164 / 255), + rgb(254 / 255, 228 / 255, 147 / 255), + rgb(254 / 255, 216 / 255, 123 / 255), + rgb(254 / 255, 204 / 255, 98 / 255), + rgb(254 / 255, 191 / 255, 74 / 255), + rgb(254 / 255, 174 / 255, 60 / 255), + rgb(254 / 255, 157 / 255, 45 / 255), + rgb(249 / 255, 141 / 255, 35 / 255), + rgb(242 / 255, 125 / 255, 27 / 255), + rgb(234 / 255, 110 / 255, 19 / 255), + rgb(221 / 255, 96 / 255, 11 / 255), + rgb(209 / 255, 82 / 255, 4 / 255), + rgb(189 / 255, 68 / 255, 2 / 255), + rgb(164 / 255, 56 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "20" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(255 / 255, 252 / 255, 213 / 255), + rgb(255 / 255, 249 / 255, 198 / 255), + rgb(255 / 255, 244 / 255, 183 / 255), + rgb(254 / 255, 237 / 255, 167 / 255), + rgb(254 / 255, 230 / 255, 151 / 255), + rgb(254 / 255, 220 / 255, 131 / 255), + rgb(254 / 255, 209 / 255, 107 / 255), + rgb(254 / 255, 197 / 255, 82 / 255), + rgb(254 / 255, 182 / 255, 67 / 255), + rgb(254 / 255, 166 / 255, 53 / 255), + rgb(253 / 255, 150 / 255, 39 / 255), + rgb(246 / 255, 136 / 255, 32 / 255), + rgb(239 / 255, 120 / 255, 24 / 255), + rgb(230 / 255, 106 / 255, 17 / 255), + rgb(219 / 255, 93 / 255, 10 / 255), + rgb(207 / 255, 79 / 255, 3 / 255), + rgb(186 / 255, 67 / 255, 2 / 255), + rgb(163 / 255, 56 / 255, 3 / 255), + rgb(140 / 255, 44 / 255, 4 / 255)), + "type" = "sequential" + ), + "YlGn" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(247 / 255, 252 / 255, 185 / 255), + rgb(49 / 255, 163 / 255, 84 / 255)), + "3" = tibble::lst(rgb(247 / 255, 252 / 255, 185 / 255), + rgb(173 / 255, 221 / 255, 142 / 255), + rgb(49 / 255, 163 / 255, 84 / 255)), + "4" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(194 / 255, 230 / 255, 153 / 255), + rgb(120 / 255, 198 / 255, 121 / 255), + rgb(35 / 255, 132 / 255, 67 / 255)), + "5" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(194 / 255, 230 / 255, 153 / 255), + rgb(120 / 255, 198 / 255, 121 / 255), + rgb(49 / 255, 163 / 255, 84 / 255), + rgb(0 / 255, 104 / 255, 55 / 255)), + "6" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(217 / 255, 240 / 255, 163 / 255), + rgb(173 / 255, 221 / 255, 142 / 255), + rgb(120 / 255, 198 / 255, 121 / 255), + rgb(49 / 255, 163 / 255, 84 / 255), + rgb(0 / 255, 104 / 255, 55 / 255)), + "7" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(217 / 255, 240 / 255, 163 / 255), + rgb(173 / 255, 221 / 255, 142 / 255), + rgb(120 / 255, 198 / 255, 121 / 255), + rgb(65 / 255, 171 / 255, 93 / 255), + rgb(35 / 255, 132 / 255, 67 / 255), + rgb(0 / 255, 90 / 255, 50 / 255)), + "8" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(247 / 255, 252 / 255, 185 / 255), + rgb(217 / 255, 240 / 255, 163 / 255), + rgb(173 / 255, 221 / 255, 142 / 255), + rgb(120 / 255, 198 / 255, 121 / 255), + rgb(65 / 255, 171 / 255, 93 / 255), + rgb(35 / 255, 132 / 255, 67 / 255), + rgb(0 / 255, 90 / 255, 50 / 255)), + "9" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(247 / 255, 252 / 255, 185 / 255), + rgb(217 / 255, 240 / 255, 163 / 255), + rgb(173 / 255, 221 / 255, 142 / 255), + rgb(120 / 255, 198 / 255, 121 / 255), + rgb(65 / 255, 171 / 255, 93 / 255), + rgb(35 / 255, 132 / 255, 67 / 255), + rgb(0 / 255, 104 / 255, 55 / 255), + rgb(0 / 255, 69 / 255, 41 / 255)), + "10" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(248 / 255, 252 / 255, 194 / 255), + rgb(230 / 255, 245 / 255, 172 / 255), + rgb(202 / 255, 233 / 255, 155 / 255), + rgb(167 / 255, 218 / 255, 139 / 255), + rgb(126 / 255, 200 / 255, 123 / 255), + rgb(84 / 255, 179 / 255, 102 / 255), + rgb(51 / 255, 153 / 255, 81 / 255), + rgb(28 / 255, 122 / 255, 63 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "11" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(249 / 255, 252 / 255, 198 / 255), + rgb(235 / 255, 247 / 255, 176 / 255), + rgb(212 / 255, 238 / 255, 160 / 255), + rgb(181 / 255, 224 / 255, 146 / 255), + rgb(147 / 255, 209 / 255, 131 / 255), + rgb(109 / 255, 192 / 255, 115 / 255), + rgb(71 / 255, 173 / 255, 95 / 255), + rgb(47 / 255, 147 / 255, 77 / 255), + rgb(25 / 255, 119 / 255, 61 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "12" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(250 / 255, 253 / 255, 201 / 255), + rgb(238 / 255, 248 / 255, 178 / 255), + rgb(219 / 255, 241 / 255, 164 / 255), + rgb(193 / 255, 229 / 255, 151 / 255), + rgb(163 / 255, 216 / 255, 138 / 255), + rgb(130 / 255, 202 / 255, 124 / 255), + rgb(96 / 255, 185 / 255, 108 / 255), + rgb(62 / 255, 167 / 255, 90 / 255), + rgb(43 / 255, 142 / 255, 73 / 255), + rgb(23 / 255, 116 / 255, 60 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "13" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(250 / 255, 253 / 255, 203 / 255), + rgb(242 / 255, 250 / 255, 181 / 255), + rgb(224 / 255, 243 / 255, 168 / 255), + rgb(202 / 255, 233 / 255, 155 / 255), + rgb(176 / 255, 222 / 255, 143 / 255), + rgb(147 / 255, 209 / 255, 131 / 255), + rgb(115 / 255, 195 / 255, 118 / 255), + rgb(84 / 255, 179 / 255, 102 / 255), + rgb(57 / 255, 161 / 255, 86 / 255), + rgb(40 / 255, 138 / 255, 71 / 255), + rgb(21 / 255, 114 / 255, 59 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "14" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(251 / 255, 253 / 255, 205 / 255), + rgb(244 / 255, 251 / 255, 183 / 255), + rgb(228 / 255, 244 / 255, 171 / 255), + rgb(210 / 255, 237 / 255, 159 / 255), + rgb(186 / 255, 226 / 255, 148 / 255), + rgb(161 / 255, 215 / 255, 137 / 255), + rgb(132 / 255, 203 / 255, 125 / 255), + rgb(104 / 255, 189 / 255, 112 / 255), + rgb(74 / 255, 175 / 255, 97 / 255), + rgb(53 / 255, 155 / 255, 82 / 255), + rgb(37 / 255, 134 / 255, 68 / 255), + rgb(20 / 255, 112 / 255, 59 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "15" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(251 / 255, 253 / 255, 207 / 255), + rgb(247 / 255, 252 / 255, 184 / 255), + rgb(232 / 255, 246 / 255, 173 / 255), + rgb(216 / 255, 240 / 255, 163 / 255), + rgb(195 / 255, 230 / 255, 152 / 255), + rgb(172 / 255, 221 / 255, 141 / 255), + rgb(147 / 255, 209 / 255, 131 / 255), + rgb(120 / 255, 197 / 255, 121 / 255), + rgb(94 / 255, 184 / 255, 106 / 255), + rgb(64 / 255, 171 / 255, 93 / 255), + rgb(50 / 255, 151 / 255, 79 / 255), + rgb(34 / 255, 132 / 255, 67 / 255), + rgb(19 / 255, 110 / 255, 58 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "16" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(251 / 255, 253 / 255, 208 / 255), + rgb(247 / 255, 252 / 255, 187 / 255), + rgb(235 / 255, 247 / 255, 176 / 255), + rgb(221 / 255, 241 / 255, 165 / 255), + rgb(202 / 255, 233 / 255, 155 / 255), + rgb(181 / 255, 224 / 255, 146 / 255), + rgb(159 / 255, 214 / 255, 136 / 255), + rgb(134 / 255, 204 / 255, 126 / 255), + rgb(109 / 255, 192 / 255, 115 / 255), + rgb(84 / 255, 179 / 255, 102 / 255), + rgb(61 / 255, 165 / 255, 89 / 255), + rgb(47 / 255, 147 / 255, 77 / 255), + rgb(32 / 255, 129 / 255, 65 / 255), + rgb(17 / 255, 109 / 255, 57 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "17" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(251 / 255, 253 / 255, 209 / 255), + rgb(248 / 255, 252 / 255, 190 / 255), + rgb(237 / 255, 248 / 255, 178 / 255), + rgb(224 / 255, 243 / 255, 168 / 255), + rgb(208 / 255, 236 / 255, 159 / 255), + rgb(189 / 255, 228 / 255, 149 / 255), + rgb(169 / 255, 219 / 255, 140 / 255), + rgb(147 / 255, 209 / 255, 131 / 255), + rgb(123 / 255, 199 / 255, 122 / 255), + rgb(100 / 255, 187 / 255, 110 / 255), + rgb(76 / 255, 176 / 255, 98 / 255), + rgb(57 / 255, 161 / 255, 86 / 255), + rgb(44 / 255, 143 / 255, 74 / 255), + rgb(31 / 255, 126 / 255, 64 / 255), + rgb(16 / 255, 108 / 255, 57 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "18" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(252 / 255, 253 / 255, 210 / 255), + rgb(248 / 255, 252 / 255, 192 / 255), + rgb(239 / 255, 249 / 255, 179 / 255), + rgb(227 / 255, 244 / 255, 170 / 255), + rgb(214 / 255, 238 / 255, 161 / 255), + rgb(196 / 255, 231 / 255, 153 / 255), + rgb(178 / 255, 223 / 255, 144 / 255), + rgb(157 / 255, 214 / 255, 135 / 255), + rgb(136 / 255, 204 / 255, 127 / 255), + rgb(114 / 255, 194 / 255, 117 / 255), + rgb(92 / 255, 183 / 255, 106 / 255), + rgb(68 / 255, 172 / 255, 94 / 255), + rgb(54 / 255, 157 / 255, 83 / 255), + rgb(42 / 255, 141 / 255, 72 / 255), + rgb(29 / 255, 124 / 255, 63 / 255), + rgb(15 / 255, 106 / 255, 56 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "19" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(252 / 255, 253 / 255, 211 / 255), + rgb(248 / 255, 252 / 255, 194 / 255), + rgb(242 / 255, 250 / 255, 181 / 255), + rgb(230 / 255, 245 / 255, 172 / 255), + rgb(218 / 255, 240 / 255, 164 / 255), + rgb(202 / 255, 233 / 255, 155 / 255), + rgb(185 / 255, 226 / 255, 147 / 255), + rgb(167 / 255, 218 / 255, 139 / 255), + rgb(147 / 255, 209 / 255, 131 / 255), + rgb(126 / 255, 200 / 255, 123 / 255), + rgb(105 / 255, 190 / 255, 113 / 255), + rgb(84 / 255, 179 / 255, 102 / 255), + rgb(63 / 255, 168 / 255, 91 / 255), + rgb(51 / 255, 153 / 255, 81 / 255), + rgb(40 / 255, 138 / 255, 71 / 255), + rgb(28 / 255, 122 / 255, 63 / 255), + rgb(15 / 255, 105 / 255, 56 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "20" = tibble::lst(rgb(255 / 255, 255 / 255, 229 / 255), + rgb(252 / 255, 253 / 255, 212 / 255), + rgb(249 / 255, 252 / 255, 196 / 255), + rgb(243 / 255, 250 / 255, 182 / 255), + rgb(232 / 255, 246 / 255, 174 / 255), + rgb(221 / 255, 241 / 255, 166 / 255), + rgb(207 / 255, 236 / 255, 158 / 255), + rgb(191 / 255, 229 / 255, 150 / 255), + rgb(175 / 255, 222 / 255, 143 / 255), + rgb(156 / 255, 213 / 255, 135 / 255), + rgb(137 / 255, 205 / 255, 127 / 255), + rgb(117 / 255, 196 / 255, 119 / 255), + rgb(98 / 255, 186 / 255, 109 / 255), + rgb(77 / 255, 176 / 255, 98 / 255), + rgb(60 / 255, 164 / 255, 88 / 255), + rgb(49 / 255, 150 / 255, 79 / 255), + rgb(38 / 255, 136 / 255, 69 / 255), + rgb(26 / 255, 120 / 255, 62 / 255), + rgb(14 / 255, 105 / 255, 56 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "type" = "sequential" + ), + "Reds" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(254 / 255, 224 / 255, 210 / 255), + rgb(222 / 255, 45 / 255, 38 / 255)), + "3" = tibble::lst(rgb(254 / 255, 224 / 255, 210 / 255), + rgb(252 / 255, 146 / 255, 114 / 255), + rgb(222 / 255, 45 / 255, 38 / 255)), + "4" = tibble::lst(rgb(254 / 255, 229 / 255, 217 / 255), + rgb(252 / 255, 174 / 255, 145 / 255), + rgb(251 / 255, 106 / 255, 74 / 255), + rgb(203 / 255, 24 / 255, 29 / 255)), + "5" = tibble::lst(rgb(254 / 255, 229 / 255, 217 / 255), + rgb(252 / 255, 174 / 255, 145 / 255), + rgb(251 / 255, 106 / 255, 74 / 255), + rgb(222 / 255, 45 / 255, 38 / 255), + rgb(165 / 255, 15 / 255, 21 / 255)), + "6" = tibble::lst(rgb(254 / 255, 229 / 255, 217 / 255), + rgb(252 / 255, 187 / 255, 161 / 255), + rgb(252 / 255, 146 / 255, 114 / 255), + rgb(251 / 255, 106 / 255, 74 / 255), + rgb(222 / 255, 45 / 255, 38 / 255), + rgb(165 / 255, 15 / 255, 21 / 255)), + "7" = tibble::lst(rgb(254 / 255, 229 / 255, 217 / 255), + rgb(252 / 255, 187 / 255, 161 / 255), + rgb(252 / 255, 146 / 255, 114 / 255), + rgb(251 / 255, 106 / 255, 74 / 255), + rgb(239 / 255, 59 / 255, 44 / 255), + rgb(203 / 255, 24 / 255, 29 / 255), + rgb(153 / 255, 0 / 255, 13 / 255)), + "8" = tibble::lst(rgb(255 / 255, 245 / 255, 240 / 255), + rgb(254 / 255, 224 / 255, 210 / 255), + rgb(252 / 255, 187 / 255, 161 / 255), + rgb(252 / 255, 146 / 255, 114 / 255), + rgb(251 / 255, 106 / 255, 74 / 255), + rgb(239 / 255, 59 / 255, 44 / 255), + rgb(203 / 255, 24 / 255, 29 / 255), + rgb(153 / 255, 0 / 255, 13 / 255)), + "9" = tibble::lst(rgb(255 / 255, 245 / 255, 240 / 255), + rgb(254 / 255, 224 / 255, 210 / 255), + rgb(252 / 255, 187 / 255, 161 / 255), + rgb(252 / 255, 146 / 255, 114 / 255), + rgb(251 / 255, 106 / 255, 74 / 255), + rgb(239 / 255, 59 / 255, 44 / 255), + rgb(203 / 255, 24 / 255, 29 / 255), + rgb(165 / 255, 15 / 255, 21 / 255), + rgb(103 / 255, 0 / 255, 13 / 255)), + "10" = tibble::lst(rgb(255 / 255, 244 / 255, 240 / 255), + rgb(254 / 255, 228 / 255, 216 / 255), + rgb(253 / 255, 203 / 255, 182 / 255), + rgb(252 / 255, 173 / 255, 145 / 255), + rgb(252 / 255, 141 / 255, 109 / 255), + rgb(251 / 255, 110 / 255, 78 / 255), + rgb(243 / 255,76 / 255, 53 / 255), + rgb(222 / 255, 45 / 255, 37 / 255), + rgb(191 / 255, 18 / 255, 25 / 255), + rgb(153 / 255, 0 / 255, 12 / 255)), + "11" = tibble::lst(rgb(255 / 255, 244 / 255, 240 / 255), + rgb(254 / 255, 230 / 255, 218 / 255), + rgb(253 / 255, 209 / 255, 190 / 255), + rgb(252 / 255, 182 / 255, 156 / 255), + rgb(252 / 255, 154 / 255, 123 / 255), + rgb(252 / 255, 126 / 255, 93 / 255), + rgb(248 / 255, 97 / 255, 67 / 255), + rgb(240 / 255, 64 / 255, 46 / 255), + rgb(217 / 255, 39 / 255, 34 / 255), + rgb(187 / 255, 17 / 255, 24 / 255), + rgb(153 / 255, 0 / 255, 12 / 255)), + "12" = tibble::lst(rgb(255 / 255, 244 / 255, 240 / 255), + rgb(254 / 255, 231 / 255, 220 / 255), + rgb(254 / 255, 213 / 255, 196 / 255), + rgb(252 / 255, 190 / 255, 165 / 255), + rgb(252 / 255, 164 / 255, 135 / 255), + rgb(252 / 255, 139 / 255, 106 / 255), + rgb(251 / 255, 113 / 255, 81 / 255), + rgb(245 / 255, 86 / 255, 60 / 255), + rgb(235 / 255, 56 / 255, 42 / 255), + rgb(212 / 255, 35 / 255, 33 / 255), + rgb(184 / 255, 15 / 255, 23 / 255), + rgb(153 / 255, 0 / 255, 12 / 255)), + "13" = tibble::lst(rgb(255 / 255, 244 / 255, 240 / 255), + rgb(254 / 255, 232 / 255, 222 / 255), + rgb(254 / 255, 217 / 255, 201 / 255), + rgb(253 / 255, 196 / 255, 173 / 255), + rgb(252 / 255, 173 / 255, 145 / 255), + rgb(252 / 255, 149 / 255, 117 / 255), + rgb(252 / 255, 126 / 255, 93 / 255), + rgb(250 / 255, 102 / 255, 71 / 255), + rgb(243 / 255, 76 / 255, 53 / 255), + rgb(229 / 255, 51 / 255, 40 / 255), + rgb(208 / 255, 31 / 255, 31 / 255), + rgb(181 / 255, 14 / 255, 22 / 255), + rgb(153 / 255, 0 / 255, 12 / 255)), + "14" = tibble::lst(rgb(255 / 255, 244 / 255, 240 / 255), + rgb(254 / 255, 233 / 255, 223 / 255), + rgb(254 / 255, 221 / 255, 206 / 255), + rgb(253 / 255, 201 / 255, 179 / 255), + rgb(252 / 255, 180 / 255, 153 / 255), + rgb(252 / 255, 158 / 255, 128 / 255), + rgb(252 / 255, 137 / 255, 104 / 255), + rgb(251 / 255, 115 / 255, 83 / 255), + rgb(247 / 255, 93 / 255, 64 / 255), + rgb(240 / 255, 67 / 255, 48 / 255), + rgb(225 / 255, 46 / 255, 38 / 255), + rgb(205 / 255, 27 / 255, 30 / 255), + rgb(179 / 255, 13 / 255, 21 / 255), + rgb(153 / 255, 0 / 255, 12 / 255)), + "15" = tibble::lst(rgb(255 / 255, 244 / 255, 240 / 255), + rgb(254 / 255, 234 / 255, 224 / 255), + rgb(254 / 255, 223 / 255, 210 / 255), + rgb(253 / 255, 205 / 255, 185 / 255), + rgb(252 / 255, 186 / 255, 160 / 255), + rgb(252 / 255, 166 / 255, 137 / 255), + rgb(252 / 255, 146 / 255, 114 / 255), + rgb(252 / 255, 126 / 255, 93 / 255), + rgb(250 / 255, 106 / 255, 74 / 255), + rgb(245 / 255, 84 / 255, 58 / 255), + rgb(238 / 255, 58 / 255, 44 / 255), + rgb(220 / 255, 43 / 255, 36 / 255), + rgb(203 / 255, 24 / 255, 29 / 255), + rgb(177 / 255, 12 / 255, 20 / 255), + rgb(153 / 255, 0 / 255, 12 / 255)), + "16" = tibble::lst(rgb(255 / 255, 244 / 255, 240 / 255), + rgb(254 / 255, 235 / 255, 225 / 255), + rgb(254 / 255, 225 / 255, 211 / 255), + rgb(253 / 255, 209 / 255, 190 / 255), + rgb(252 / 255, 191 / 255, 167 / 255), + rgb(252 / 255, 173 / 255, 145 / 255), + rgb(252 / 255, 154 / 255, 123 / 255), + rgb(252 / 255, 135 / 255, 103 / 255), + rgb(251 / 255, 117 / 255, 84 / 255), + rgb(248 / 255, 97 / 255, 67 / 255), + rgb(243 / 255, 76 / 255, 53 / 255), + rgb(234 / 255, 54 / 255, 41 / 255), + rgb(217 / 255, 39 / 255, 34 / 255), + rgb(199 / 255, 22 / 255, 27 / 255), + rgb(176 / 255, 11 / 255, 20 / 255), + rgb(153 / 255, 0 / 255, 12 / 255)), + "17" = tibble::lst(rgb(255 / 255, 244 / 255, 240 / 255), + rgb(254 / 255, 235 / 255, 226 / 255), + rgb(254 / 255, 226 / 255, 213 / 255), + rgb(253 / 255, 212 / 255, 194 / 255), + rgb(253 / 255, 196 / 255, 173 / 255), + rgb(252 / 255, 179 / 255, 152 / 255), + rgb(252 / 255, 161 / 255, 131 / 255), + rgb(252 / 255, 143 / 255, 111 / 255), + rgb(252 / 255, 126 / 255, 93 / 255), + rgb(251 / 255, 108 / 255, 76 / 255), + rgb(246 / 255, 90 / 255, 62 / 255), + rgb(241 / 255, 69 / 255, 49 / 255), + rgb(229 / 255, 51 / 255, 40 / 255), + rgb(214 / 255, 36 / 255, 33 / 255), + rgb(196 / 255, 21 / 255, 26 / 255), + rgb(174 / 255, 10 / 255, 19 / 255), + rgb(153 / 255, 0 / 255, 12 / 255)), + "18" = tibble::lst(rgb(255 / 255, 244 / 255, 240 / 255), + rgb(254 / 255, 236 / 255, 227 / 255), + rgb(254 / 255, 227 / 255, 215 / 255), + rgb(254 / 255, 215 / 255, 198 / 255), + rgb(253 / 255, 200 / 255, 178 / 255), + rgb(252 / 255, 184 / 255, 158 / 255), + rgb(252 / 255, 167 / 255, 138 / 255), + rgb(252 / 255, 150 / 255, 119 / 255), + rgb(252 / 255, 134 / 255, 102 / 255), + rgb(251 / 255, 118 / 255, 85 / 255), + rgb(249 / 255, 101 / 255, 70 / 255), + rgb(244 / 255, 83 / 255, 58 / 255), + rgb(239 / 255, 62 / 255, 45 / 255), + rgb(226 / 255, 47 / 255, 38 / 255), + rgb(211 / 255, 33 / 255, 32 / 255), + rgb(193 / 255, 20 / 255, 26 / 255), + rgb(173 / 255, 9 / 255, 19 / 255), + rgb(153 / 255, 0 / 255, 12 / 255)), + "19" = tibble::lst(rgb(255 / 255, 244 / 255, 240 / 255), + rgb(254 / 255, 236 / 255, 228 / 255), + rgb(254 / 255, 228 / 255, 216 / 255), + rgb(254 / 255, 217 / 255, 201 / 255), + rgb(253 / 255, 203 / 255, 182 / 255), + rgb(252 / 255, 189 / 255, 163 / 255), + rgb(252 / 255, 173 / 255, 145 / 255), + rgb(252 / 255, 157 / 255, 126 / 255), + rgb(252 / 255, 141 / 255, 109 / 255), + rgb(252 / 255, 126 / 255, 93 / 255), + rgb(251 / 255, 110 / 255, 78 / 255), + rgb(247 / 255, 94 / 255, 65 / 255), + rgb(243 / 255, 76 / 255, 53 / 255), + rgb(236 / 255, 57 / 255, 43 / 255), + rgb(222 / 255, 45 / 255, 37 / 255), + rgb(208 / 255, 31 / 255, 31 / 255), + rgb(191 / 255, 18 / 255, 25 / 255), + rgb(172 / 255, 9 / 255, 19 / 255), + rgb(153 / 255, 0 / 255, 12 / 255)), + "20" = tibble::lst(rgb(255 / 255, 244 / 255, 240 / 255), + rgb(254 / 255, 237 / 255, 228 / 255), + rgb(254 / 255, 229 / 255, 217 / 255), + rgb(254 / 255, 220 / 255, 204 / 255), + rgb(253 / 255, 206 / 255, 186 / 255), + rgb(252 / 255, 192 / 255, 168 / 255), + rgb(252 / 255, 178 / 255, 150 / 255), + rgb(252 / 255, 163 / 255, 133 / 255), + rgb(252 / 255, 148 / 255, 116 / 255), + rgb(252 / 255, 133 / 255, 101 / 255), + rgb(251 / 255, 119 / 255, 86 / 255), + rgb(250 / 255, 103 / 255, 72 / 255), + rgb(246 / 255, 88 / 255, 61 / 255), + rgb(241 / 255, 70 / 255, 50 / 255), + rgb(233 / 255, 54 / 255, 41 / 255), + rgb(219 / 255, 42 / 255, 35 / 255), + rgb(206 / 255, 28 / 255, 30 / 255), + rgb(189 / 255, 17 / 255, 24 / 255), + rgb(171 / 255, 8 / 255, 18 / 255), + rgb(153 / 255, 0 / 255, 12 / 255)), + "type" = "sequential" + ), + "RdPu" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(253 / 255, 224 / 255, 221 / 255), + rgb(197 / 255, 27 / 255, 138 / 255)), + "3" = tibble::lst(rgb(253 / 255, 224 / 255, 221 / 255), + rgb(250 / 255, 159 / 255, 181 / 255), + rgb(197 / 255, 27 / 255, 138 / 255)), + "4" = tibble::lst(rgb(254 / 255, 235 / 255, 226 / 255), + rgb(251 / 255, 180 / 255, 185 / 255), + rgb(247 / 255, 104 / 255, 161 / 255), + rgb(174 / 255, 1 / 255, 126 / 255)), + "5" = tibble::lst(rgb(254 / 255, 235 / 255, 226 / 255), + rgb(251 / 255, 180 / 255, 185 / 255), + rgb(247 / 255, 104 / 255, 161 / 255), + rgb(197 / 255, 27 / 255, 138 / 255), + rgb(122 / 255, 1 / 255, 119 / 255)), + "6" = tibble::lst(rgb(254 / 255, 235 / 255, 226 / 255), + rgb(252 / 255, 197 / 255, 192 / 255), + rgb(250 / 255, 159 / 255, 181 / 255), + rgb(247 / 255, 104 / 255, 161 / 255), + rgb(197 / 255, 27 / 255, 138 / 255), + rgb(122 / 255, 1 / 255, 119 / 255)), + "7" = tibble::lst(rgb(254 / 255, 235 / 255, 226 / 255), + rgb(252 / 255, 197 / 255, 192 / 255), + rgb(250 / 255, 159 / 255, 181 / 255), + rgb(247 / 255, 104 / 255, 161 / 255), + rgb(221 / 255, 52 / 255, 151 / 255), + rgb(174 / 255, 1 / 255, 126 / 255), + rgb(122 / 255, 1 / 255, 119 / 255)), + "8" = tibble::lst(rgb(255 / 255, 247 / 255, 243 / 255), + rgb(253 / 255, 224 / 255, 221 / 255), + rgb(252 / 255, 197 / 255, 192 / 255), + rgb(250 / 255, 159 / 255, 181 / 255), + rgb(247 / 255, 104 / 255, 161 / 255), + rgb(221 / 255, 52 / 255, 151 / 255), + rgb(174 / 255, 1 / 255, 126 / 255), + rgb(122 / 255, 1 / 255, 119 / 255)), + "9" = tibble::lst(rgb(255 / 255, 247 / 255, 243 / 255), + rgb(253 / 255, 224 / 255, 221 / 255), + rgb(252 / 255, 197 / 255, 192 / 255), + rgb(250 / 255, 159 / 255, 181 / 255), + rgb(247 / 255, 104 / 255, 161 / 255), + rgb(221 / 255, 52 / 255, 151 / 255), + rgb(174 / 255, 1 / 255, 126 / 255), + rgb(122 / 255, 1 / 255, 119 / 255), + rgb(73 / 255, 0 / 255, 106 / 255)), + "10" = tibble::lst(rgb(255 / 255, 247 / 255, 242 / 255), + rgb(253 / 255, 229 / 255, 225 / 255), + rgb(252 / 255, 209 / 255, 204 / 255), + rgb(251 / 255, 184 / 255, 188 / 255), + rgb(249 / 255, 153 / 255, 178 / 255), + rgb(247 / 255, 110 / 255, 163 / 255), + rgb(229 / 255, 71 / 255, 154 / 255), + rgb(199 / 255, 34 / 255, 139 / 255), + rgb(162 / 255, 0 / 255, 124 / 255), + rgb(121 / 255, 0 / 255, 119 / 255)), + "11" = tibble::lst(rgb(255 / 255, 247 / 255, 242 / 255), + rgb(253 / 255, 230 / 255, 227 / 255), + rgb(252 / 255, 213 / 255, 209 / 255), + rgb(251 / 255, 193 / 255, 190 / 255), + rgb(250 / 255, 166 / 255, 183 / 255), + rgb(249 / 255, 132 / 255, 170 / 255), + rgb(241 / 255, 94 / 255, 159 / 255), + rgb(223 / 255, 58 / 255, 152 / 255), + rgb(192 / 255, 27 / 255, 135 / 255), + rgb(158 / 255, 0 / 255, 123 / 255), + rgb(121 / 255, 0 / 255, 119 / 255)), + "12" = tibble::lst(rgb(255 / 255, 247 / 255, 242 / 255), + rgb(253 / 255, 232 / 255, 228 / 255), + rgb(252 / 255, 216 / 255, 213 / 255), + rgb(252 / 255, 199 / 255, 194 / 255), + rgb(251 / 255, 176 / 255, 186 / 255), + rgb(249 / 255, 149 / 255, 177 / 255), + rgb(247 / 255, 114 / 255, 164 / 255), + rgb(235 / 255, 82 / 255, 156 / 255), + rgb(216 / 255, 48 / 255, 148 / 255), + rgb(186 / 255, 20 / 255, 132 / 255), + rgb(155 / 255, 0 / 255, 123 / 255), + rgb(121 / 255, 0 / 255, 119 / 255)), + "13" = tibble::lst(rgb(255 / 255, 247 / 255, 242 / 255), + rgb(253 / 255, 233 / 255, 230 / 255), + rgb(252 / 255, 219 / 255, 216 / 255), + rgb(252 / 255, 203 / 255, 199 / 255), + rgb(251 / 255, 184 / 255, 188 / 255), + rgb(250 / 255, 162 / 255, 181 / 255), + rgb(249 / 255, 132 / 255, 170 / 255), + rgb(244 / 255, 100 / 255, 160 / 255), + rgb(229 / 255, 71 / 255, 154 / 255), + rgb(209 / 255, 42 / 255, 144 / 255), + rgb(181 / 255, 14 / 255, 130 / 255), + rgb(152 / 255, 0 / 255, 123 / 255), + rgb(121 / 255, 0 / 255, 119 / 255)), + "14" = tibble::lst(rgb(255 / 255, 247 / 255, 242 / 255), + rgb(254 / 255, 234 / 255, 231 / 255), + rgb(253 / 255, 221 / 255, 218 / 255), + rgb(252 / 255, 207 / 255, 203 / 255), + rgb(251 / 255, 191 / 255, 190 / 255), + rgb(250 / 255, 170 / 255, 184 / 255), + rgb(249 / 255, 147 / 255, 176 / 255), + rgb(248 / 255, 117 / 255, 165 / 255), + rgb(239 / 255, 89 / 255, 157 / 255), + rgb(225 / 255, 61 / 255, 152 / 255), + rgb(202 / 255, 36 / 255, 141 / 255), + rgb(177 / 255, 6 / 255, 127 / 255), + rgb(150 / 255, 0 / 255, 122 / 255), + rgb(121 / 255, 0 / 255, 119 / 255)), + "15" = tibble::lst(rgb(255 / 255, 247 / 255, 242 / 255), + rgb(254 / 255, 235 / 255, 231 / 255), + rgb(253 / 255, 223 / 255, 221 / 255), + rgb(252 / 255, 210 / 255, 206 / 255), + rgb(252 / 255, 197 / 255, 191 / 255), + rgb(251 / 255, 178 / 255, 186 / 255), + rgb(249 / 255, 159 / 255, 180 / 255), + rgb(249 / 255, 132 / 255, 170 / 255), + rgb(247 / 255, 103 / 255, 160 / 255), + rgb(234 / 255, 80 / 255, 156 / 255), + rgb(221 / 255, 51 / 255, 151 / 255), + rgb(197 / 255, 31 / 255, 138 / 255), + rgb(173 / 255, 0 / 255, 126 / 255), + rgb(148 / 255, 0 / 255, 122 / 255), + rgb(121 / 255, 0 / 255, 119 / 255)), + "16" = tibble::lst(rgb(255 / 255, 247 / 255, 242 / 255), + rgb(254 / 255, 236 / 255, 232 / 255), + rgb(253 / 255, 225 / 255, 222 / 255), + rgb(252 / 255, 213 / 255, 209 / 255), + rgb(252 / 255, 200 / 255, 195 / 255), + rgb(251 / 255, 184 / 255, 188 / 255), + rgb(250 / 255, 166 / 255, 183 / 255), + rgb(249 / 255, 145 / 255, 175 / 255), + rgb(248 / 255, 119 / 255, 166 / 255), + rgb(241 / 255, 94 / 255, 159 / 255), + rgb(229 / 255, 71 / 255, 154 / 255), + rgb(214 / 255, 46 / 255, 147 / 255), + rgb(192 / 255, 27 / 255, 135 / 255), + rgb(170 / 255, 0 / 255, 125 / 255), + rgb(146 / 255, 0 / 255, 122 / 255), + rgb(121 / 255, 0 / 255, 119 / 255)), + "17" = tibble::lst(rgb(255 / 255, 247 / 255, 242 / 255), + rgb(254 / 255, 236 / 255, 233 / 255), + rgb(253 / 255, 226 / 255, 223 / 255), + rgb(252 / 255, 215 / 255, 211 / 255), + rgb(252 / 255, 203 / 255, 199 / 255), + rgb(251 / 255, 190 / 255, 189 / 255), + rgb(251 / 255, 173 / 255, 185 / 255), + rgb(249 / 255, 155 / 255, 179 / 255), + rgb(249 / 255, 132 / 255, 170 / 255), + rgb(247 / 255, 107 / 255, 162 / 255), + rgb(237 / 255, 86 / 255, 157 / 255), + rgb(225 / 255, 63 / 255, 152 / 255), + rgb(209 / 255, 42 / 255, 144 / 255), + rgb(188 / 255, 22 / 255, 133 / 255), + rgb(167 / 255, 0 / 255, 125 / 255), + rgb(144 / 255, 0 / 255, 122 / 255), + rgb(121 / 255, 0 / 255, 119 / 255)), + "18" = tibble::lst(rgb(255 / 255, 247 / 255, 242 / 255), + rgb(254 / 255, 237 / 255, 233 / 255), + rgb(253 / 255, 228 / 255, 224 / 255), + rgb(252 / 255, 217 / 255, 214 / 255), + rgb(252 / 255, 206 / 255, 202 / 255), + rgb(251 / 255, 194 / 255, 191 / 255), + rgb(251 / 255, 179 / 255, 186 / 255), + rgb(250 / 255, 163 / 255, 182 / 255), + rgb(249 / 255, 143 / 255, 175 / 255), + rgb(248 / 255, 121 / 255, 166 / 255), + rgb(243 / 255, 98 / 255, 159 / 255), + rgb(233 / 255, 78 / 255, 155 / 255), + rgb(222 / 255, 55 / 255, 151 / 255), + rgb(204 / 255, 38 / 255, 142 / 255), + rgb(184 / 255, 18 / 255, 131 / 255), + rgb(164 / 255, 0 / 255, 124 / 255), + rgb(143 / 255, 0 / 255, 121 / 255), + rgb(121 / 255, 0 / 255, 119 / 255)), + "19" = tibble::lst(rgb(255 / 255, 247 / 255, 242 / 255), + rgb(254 / 255, 238 / 255, 234 / 255), + rgb(253 / 255, 229 / 255, 225 / 255), + rgb(252 / 255, 219 / 255, 216 / 255), + rgb(252 / 255, 209 / 255, 204 / 255), + rgb(252 / 255, 198 / 255, 193 / 255), + rgb(251 / 255, 184 / 255, 188 / 255), + rgb(250 / 255, 169 / 255, 184 / 255), + rgb(249 / 255, 153 / 255, 178 / 255), + rgb(249 / 255, 132 / 255, 170 / 255), + rgb(247 / 255, 110 / 255, 163 / 255), + rgb(239 / 255, 91 / 255, 158 / 255), + rgb(229 / 255, 71 / 255, 154 / 255), + rgb(218 / 255, 49 / 255, 149 / 255), + rgb(199 / 255, 34 / 255, 139 / 255), + rgb(181 / 255, 14 / 255, 130 / 255), + rgb(162 / 255, 0 / 255, 124 / 255), + rgb(142 / 255, 0 / 255, 121 / 255), + rgb(121 / 255, 0 / 255, 119 / 255)), + "20" = tibble::lst(rgb(255 / 255, 247 / 255, 242 / 255), + rgb(254 / 255, 238 / 255, 234 / 255), + rgb(253 / 255, 230 / 255, 226 / 255), + rgb(253 / 255, 221 / 255, 217 / 255), + rgb(252 / 255, 211 / 255, 207 / 255), + rgb(252 / 255, 201 / 255, 196 / 255), + rgb(251 / 255, 189 / 255, 189 / 255), + rgb(251 / 255, 175 / 255, 185 / 255), + rgb(250 / 255, 161 / 255, 181 / 255), + rgb(249 / 255, 142 / 255, 174 / 255), + rgb(248 / 255, 122 / 255, 167 / 255), + rgb(245 / 255, 101 / 255, 160 / 255), + rgb(236 / 255, 84 / 255, 156 / 255), + rgb(226 / 255, 64 / 255, 153 / 255), + rgb(213 / 255, 46 / 255, 147 / 255), + rgb(196 / 255, 30 / 255, 137 / 255), + rgb(178 / 255, 9 / 255, 128 / 255), + rgb(160 / 255, 0 / 255, 124 / 255), + rgb(141 / 255, 0 / 255, 121 / 255), + rgb(121 / 255, 0 / 255, 119 / 255)), + "type" = "sequential" + ), + "Greens" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(229 / 255, 245 / 255, 224 / 255), + rgb(49 / 255, 163 / 255, 84 / 255)), + "3" = tibble::lst(rgb(229 / 255, 245 / 255, 224 / 255), + rgb(161 / 255, 217 / 255, 155 / 255), + rgb(49 / 255, 163 / 255, 84 / 255)), + "4" = tibble::lst(rgb(237 / 255, 248 / 255, 233 / 255), + rgb(186 / 255, 228 / 255, 179 / 255), + rgb(116 / 255, 196 / 255, 118 / 255), + rgb(35 / 255, 139 / 255, 69 / 255)), + "5" = tibble::lst(rgb(237 / 255, 248 / 255, 233 / 255), + rgb(186 / 255, 228 / 255, 179 / 255), + rgb(116 / 255, 196 / 255, 118 / 255), + rgb(49 / 255, 163 / 255, 84 / 255), + rgb(0 / 255, 109 / 255, 44 / 255)), + "6" = tibble::lst(rgb(237 / 255, 248 / 255, 233 / 255), + rgb(199 / 255, 233 / 255, 192 / 255), + rgb(161 / 255, 217 / 255, 155 / 255), + rgb(116 / 255, 196 / 255, 118 / 255), + rgb(49 / 255, 163 / 255, 84 / 255), + rgb(0 / 255, 109 / 255, 44 / 255)), + "7" = tibble::lst(rgb(237 / 255, 248 / 255, 233 / 255), + rgb(199 / 255, 233 / 255, 192 / 255), + rgb(161 / 255, 217 / 255, 155 / 255), + rgb(116 / 255, 196 / 255, 118 / 255), + rgb(65 / 255, 171 / 255, 93 / 255), + rgb(35 / 255, 139 / 255, 69 / 255), + rgb(0 / 255, 90 / 255, 50 / 255)), + "8" = tibble::lst(rgb(247 / 255, 252 / 255, 245 / 255), + rgb(229 / 255, 245 / 255, 224 / 255), + rgb(199 / 255, 233 / 255, 192 / 255), + rgb(161 / 255, 217 / 255, 155 / 255), + rgb(116 / 255, 196 / 255, 118 / 255), + rgb(65 / 255, 171 / 255, 93 / 255), + rgb(35 / 255, 139 / 255, 69 / 255), + rgb(0 / 255, 90 / 255, 50 / 255)), + "9" = tibble::lst(rgb(247 / 255, 252 / 255, 245 / 255), + rgb(229 / 255, 245 / 255, 224 / 255), + rgb(199 / 255, 233 / 255, 192 / 255), + rgb(161 / 255, 217 / 255, 155 / 255), + rgb(116 / 255, 196 / 255, 118 / 255), + rgb(65 / 255, 171 / 255, 93 / 255), + rgb(35 / 255, 139 / 255, 69 / 255), + rgb(0 / 255, 109 / 255, 44 / 255), + rgb(0 / 255, 68 / 255, 27 / 255)), + "10" = tibble::lst(rgb(247 / 255, 252 / 255, 244 / 255), + rgb(233 / 255, 246 / 255, 228 / 255), + rgb(212 / 255, 238 / 255, 206 / 255), + rgb(186 / 255, 227 / 255, 179 / 255), + rgb(156 / 255, 214 / 255, 150 / 255), + rgb(121 / 255, 198 / 255, 122 / 255), + rgb(83 / 255, 179 / 255, 101 / 255), + rgb(52 / 255, 156 / 255, 82 / 255), + rgb(28 / 255, 127 / 255, 64 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "11" = tibble::lst(rgb(247 / 255, 252 / 255, 244 / 255), + rgb(234 / 255, 247 / 255, 230 / 255), + rgb(217 / 255, 240 / 255, 211 / 255), + rgb(195 / 255, 231 / 255, 188 / 255), + rgb(168 / 255, 220 / 255, 162 / 255), + rgb(138 / 255, 206 / 255, 136 / 255), + rgb(106 / 255, 190 / 255, 112 / 255), + rgb(70 / 255, 173 / 255, 95 / 255), + rgb(47 / 255, 151 / 255, 78 / 255), + rgb(25 / 255, 123 / 255, 63 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "12" = tibble::lst(rgb(247 / 255, 252 / 255, 244 / 255), + rgb(235 / 255, 247 / 255, 231 / 255), + rgb(220 / 255, 241 / 255, 215 / 255), + rgb(201 / 255, 234 / 255, 194 / 255), + rgb(178 / 255, 224 / 255, 171 / 255), + rgb(153 / 255, 213 / 255, 148 / 255), + rgb(124 / 255, 199 / 255, 124 / 255), + rgb(94 / 255, 184 / 255, 106 / 255), + rgb(62 / 255, 168 / 255, 90 / 255), + rgb(43 / 255, 147 / 255, 75 / 255), + rgb(23 / 255, 120 / 255, 62 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "13" = tibble::lst(rgb(247 / 255, 252 / 255, 244 / 255), + rgb(236 / 255, 247 / 255, 232 / 255), + rgb(224 / 255, 243 / 255, 218 / 255), + rgb(206 / 255, 236 / 255, 199 / 255), + rgb(186 / 255, 227 / 255, 179 / 255), + rgb(164 / 255, 218 / 255, 158 / 255), + rgb(138 / 255, 206 / 255, 136 / 255), + rgb(112 / 255, 193 / 255, 115 / 255), + rgb(83 / 255, 179 / 255, 101 / 255), + rgb(57 / 255, 162 / 255, 86 / 255), + rgb(40 / 255, 144 / 255, 72 / 255), + rgb(21 / 255, 118 / 255, 61 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "14" = tibble::lst(rgb(247 / 255, 252 / 255, 244 / 255), + rgb(237 / 255, 248 / 255, 233 / 255), + rgb(226 / 255, 244 / 255, 221 / 255), + rgb(210 / 255, 237 / 255, 204 / 255), + rgb(193 / 255, 230 / 255, 186 / 255), + rgb(172 / 255, 221 / 255, 166 / 255), + rgb(150 / 255, 212 / 255, 146 / 255), + rgb(126 / 255, 200 / 255, 126 / 255), + rgb(101 / 255, 188 / 255, 110 / 255), + rgb(73 / 255, 174 / 255, 96 / 255), + rgb(53 / 255, 158 / 255, 83 / 255), + rgb(37 / 255, 141 / 255, 70 / 255), + rgb(20 / 255, 115 / 255, 60 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "15" = tibble::lst(rgb(247 / 255, 252 / 255, 244 / 255), + rgb(238 / 255, 248 / 255, 234 / 255), + rgb(229 / 255, 244 / 255, 223 / 255), + rgb(214 / 255, 239 / 255, 207 / 255), + rgb(198 / 255, 233 / 255, 191 / 255), + rgb(180 / 255, 225 / 255, 173 / 255), + rgb(160 / 255, 216 / 255, 154 / 255), + rgb(138 / 255, 206 / 255, 136 / 255), + rgb(115 / 255, 196 / 255, 118 / 255), + rgb(91 / 255, 183 / 255, 105 / 255), + rgb(64 / 255, 171 / 255, 93 / 255), + rgb(50 / 255, 154 / 255, 80 / 255), + rgb(34 / 255, 139 / 255, 69 / 255), + rgb(18 / 255, 114 / 255, 59 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "16" = tibble::lst(rgb(247 / 255, 252 / 255, 244 / 255), + rgb(238 / 255, 248 / 255, 235 / 255), + rgb(230 / 255, 245 / 255, 225 / 255), + rgb(217 / 255, 240 / 255, 211 / 255), + rgb(203 / 255, 234 / 255, 196 / 255), + rgb(186 / 255, 227 / 255, 179 / 255), + rgb(168 / 255, 220 / 255, 162 / 255), + rgb(149 / 255, 211 / 255, 145 / 255), + rgb(128 / 255, 201 / 255, 127 / 255), + rgb(106 / 255, 190 / 255, 112 / 255), + rgb(83 / 255, 179 / 255, 101 / 255), + rgb(61 / 255, 166 / 255, 89 / 255), + rgb(47 / 255, 151 / 255, 78 / 255), + rgb(32 / 255, 135 / 255, 67 / 255), + rgb(17 / 255, 112 / 255, 58 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "17" = tibble::lst(rgb(247 / 255, 252 / 255, 244 / 255), + rgb(239 / 255, 248 / 255, 235 / 255), + rgb(231 / 255, 245 / 255, 226 / 255), + rgb(219 / 255, 241 / 255, 213 / 255), + rgb(206 / 255, 236 / 255, 199 / 255), + rgb(191 / 255, 230 / 255, 185 / 255), + rgb(175 / 255, 223 / 255, 168 / 255), + rgb(158 / 255, 215 / 255, 152 / 255), + rgb(138 / 255, 206 / 255, 136 / 255), + rgb(118 / 255, 197 / 255, 120 / 255), + rgb(98 / 255, 186 / 255, 108 / 255), + rgb(75 / 255, 175 / 255, 97 / 255), + rgb(57 / 255, 162 / 255, 86 / 255), + rgb(44 / 255, 148 / 255, 76 / 255), + rgb(31 / 255, 132 / 255, 66 / 255), + rgb(16 / 255, 110 / 255, 58 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "18" = tibble::lst(rgb(247 / 255, 252 / 255, 244 / 255), + rgb(239 / 255, 249 / 255, 236 / 255), + rgb(232 / 255, 246 / 255, 227 / 255), + rgb(221 / 255, 242 / 255, 216 / 255), + rgb(209 / 255, 237 / 255, 203 / 255), + rgb(196 / 255, 232 / 255, 189 / 255), + rgb(181 / 255, 225 / 255, 174 / 255), + rgb(165 / 255, 218 / 255, 159 / 255), + rgb(148 / 255, 210 / 255, 144 / 255), + rgb(129 / 255, 202 / 255, 128 / 255), + rgb(110 / 255, 193 / 255, 115 / 255), + rgb(90 / 255, 182 / 255, 104 / 255), + rgb(68 / 255, 172 / 255, 94 / 255), + rgb(54 / 255, 159 / 255, 84 / 255), + rgb(42 / 255, 146 / 255, 74 / 255), + rgb(29 / 255, 130 / 255, 65 / 255), + rgb(15 / 255, 109 / 255, 57 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "19" = tibble::lst(rgb(247 / 255, 252 / 255, 244 / 255), + rgb(240 / 255, 249 / 255, 236 / 255), + rgb(233 / 255, 246 / 255, 228 / 255), + rgb(224 / 255, 243 / 255, 218 / 255), + rgb(212 / 255, 238 / 255, 206 / 255), + rgb(200 / 255, 233 / 255, 193 / 255), + rgb(186 / 255, 227 / 255, 179 / 255), + rgb(171 / 255, 221 / 255, 165 / 255), + rgb(156 / 255, 214 / 255, 150 / 255), + rgb(138 / 255, 206 / 255, 136 / 255), + rgb(121 / 255, 198 / 255, 122 / 255), + rgb(102 / 255, 189 / 255, 110 / 255), + rgb(83 / 255, 179 / 255, 101 / 255), + rgb(63 / 255, 169 / 255, 91 / 255), + rgb(52 / 255, 156 / 255, 82 / 255), + rgb(40 / 255, 144 / 255, 72 / 255), + rgb(28 / 255, 127 / 255, 64 / 255), + rgb(14 / 255, 108 / 255, 57 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "20" = tibble::lst(rgb(247 / 255, 252 / 255, 244 / 255), + rgb(240 / 255, 249 / 255, 237 / 255), + rgb(233 / 255, 246 / 255, 229 / 255), + rgb(225 / 255, 243 / 255, 220 / 255), + rgb(214 / 255, 239 / 255, 208 / 255), + rgb(203 / 255, 234 / 255, 197 / 255), + rgb(191 / 255, 229 / 255, 184 / 255), + rgb(177 / 255, 223 / 255, 170 / 255), + rgb(163 / 255, 217 / 255, 156 / 255), + rgb(147 / 255, 210 / 255, 143 / 255), + rgb(130 / 255, 202 / 255, 129 / 255), + rgb(113 / 255, 194 / 255, 116 / 255), + rgb(95 / 255, 185 / 255, 107 / 255), + rgb(76 / 255, 176 / 255, 98 / 255), + rgb(60 / 255, 165 / 255, 89 / 255), + rgb(49 / 255, 154 / 255, 80 / 255), + rgb(38 / 255, 142 / 255, 71 / 255), + rgb(26 / 255, 125 / 255, 63 / 255), + rgb(13 / 255, 107 / 255, 56 / 255), + rgb(0 / 255, 89 / 255, 50 / 255)), + "type" = "sequential" + ), + "YlGnBu" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(237 / 255, 248 / 255, 177 / 255), + rgb(44 / 255, 127 / 255, 184 / 255)), + "3" = tibble::lst(rgb(237 / 255, 248 / 255, 177 / 255), + rgb(127 / 255, 205 / 255, 187 / 255), + rgb(44 / 255, 127 / 255, 184 / 255)), + "4" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(161 / 255, 218 / 255, 180 / 255), + rgb(65 / 255, 182 / 255, 196 / 255), + rgb(34 / 255, 94 / 255, 168 / 255)), + "5" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(161 / 255, 218 / 255, 180 / 255), + rgb(65 / 255, 182 / 255, 196 / 255), + rgb(44 / 255, 127 / 255, 184 / 255), + rgb(37 / 255, 52 / 255, 148 / 255)), + "6" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(199 / 255, 233 / 255, 180 / 255), + rgb(127 / 255, 205 / 255, 187 / 255), + rgb(65 / 255, 182 / 255, 196 / 255), + rgb(44 / 255, 127 / 255, 184 / 255), + rgb(37 / 255, 52 / 255, 148 / 255)), + "7" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(199 / 255, 233 / 255, 180 / 255), + rgb(127 / 255, 205 / 255, 187 / 255), + rgb(65 / 255, 182 / 255, 196 / 255), + rgb(29 / 255, 145 / 255, 192 / 255), + rgb(34 / 255, 94 / 255, 168 / 255), + rgb(12 / 255, 44 / 255, 132 / 255)), + "8" = tibble::lst(rgb(255 / 255, 255 / 255, 217 / 255), + rgb(237 / 255, 248 / 255, 177 / 255), + rgb(199 / 255, 233 / 255, 180 / 255), + rgb(127 / 255, 205 / 255, 187 / 255), + rgb(65 / 255, 182 / 255, 196 / 255), + rgb(29 / 255, 145 / 255, 192 / 255), + rgb(34 / 255, 94 / 255, 168 / 255), + rgb(12 / 255, 44 / 255, 132 / 255)), + "9" = tibble::lst(rgb(255 / 255, 255 / 255, 217 / 255), + rgb(237 / 255, 248 / 255, 177 / 255), + rgb(199 / 255, 233 / 255, 180 / 255), + rgb(127 / 255, 205 / 255, 187 / 255), + rgb(65 / 255, 182 / 255, 196 / 255), + rgb(29 / 255, 145 / 255, 192 / 255), + rgb(34 / 255, 94 / 255, 168 / 255), + rgb(37 / 255, 52 / 255, 148 / 255), + rgb(8 / 255, 29 / 255, 88 / 255)), + "10" = tibble::lst(rgb(255 / 255, 255 / 255, 216 / 255), + rgb(241 / 255, 249 / 255, 185 / 255), + rgb(216 / 255, 239 / 255, 178 / 255), + rgb(176 / 255, 223 / 255, 182 / 255), + rgb(121 / 255, 202 / 255, 188 / 255), + rgb(74 / 255, 184 / 255, 195 / 255), + rgb(44 / 255, 157 / 255, 193 / 255), + rgb(34 / 255, 122 / 255, 181 / 255), + rgb(31 / 255, 82 / 255, 159 / 255), + rgb(12 / 255, 44 / 255, 132 / 255)), + "11" = tibble::lst(rgb(255 / 255, 255 / 255, 216 / 255), + rgb(242 / 255, 250 / 255, 189 / 255), + rgb(222 / 255, 242 / 255, 178 / 255), + rgb(192 / 255, 230 / 255, 180 / 255), + rgb(142 / 255, 210 / 255, 185 / 255), + rgb(100 / 255, 193 / 255, 191 / 255), + rgb(59 / 255, 174 / 255, 195 / 255), + rgb(34 / 255, 148 / 255, 192 / 255), + rgb(35 / 255, 114 / 255, 177 / 255), + rgb(29 / 255, 78 / 255, 157 / 255), + rgb(12 / 255, 44 / 255, 132 / 255)), + "12" = tibble::lst(rgb(255 / 255, 255 / 255, 216 / 255), + rgb(243 / 255, 250 / 255, 191 / 255), + rgb(226 / 255, 243 / 255, 177 / 255), + rgb(202 / 255, 234 / 255, 179 / 255), + rgb(161 / 255, 217 / 255, 184 / 255), + rgb(117 / 255, 200 / 255, 188 / 255), + rgb(79 / 255, 186 / 255, 194 / 255), + rgb(52 / 255, 164 / 255, 194 / 255), + rgb(30 / 255, 140 / 255, 189 / 255), + rgb(35 / 255, 107 / 255, 174 / 255), + rgb(28 / 255, 75 / 255, 154 / 255), + rgb(12 / 255, 44 / 255, 132 / 255)), + "13" = tibble::lst(rgb(255 / 255, 255 / 255, 216 / 255), + rgb(244 / 255, 250 / 255, 193 / 255), + rgb(230 / 255, 245 / 255, 177 / 255), + rgb(208 / 255, 236 / 255, 179 / 255), + rgb(176 / 255, 223 / 255, 182 / 255), + rgb(133 / 255, 207 / 255, 186 / 255), + rgb(100 / 255, 193 / 255, 191 / 255), + rgb(62 / 255, 178 / 255, 195 / 255), + rgb(44 / 255, 157 / 255, 193 / 255), + rgb(33 / 255, 132 / 255, 186 / 255), + rgb(34 / 255, 102 / 255, 172 / 255), + rgb(27 / 255, 72 / 255, 152 / 255), + rgb(12 / 255, 44 / 255, 132 / 255)), + "14" = tibble::lst(rgb(255 / 255, 255 / 255, 216 / 255), + rgb(245 / 255, 251 / 255, 195 / 255), + rgb(234 / 255, 246 / 255, 177 / 255), + rgb(213 / 255, 238 / 255, 178 / 255), + rgb(188 / 255, 228 / 255, 181 / 255), + rgb(151 / 255, 213 / 255, 185 / 255), + rgb(115 / 255, 199 / 255, 189 / 255), + rgb(83 / 255, 187 / 255, 194 / 255), + rgb(56 / 255, 170 / 255, 194 / 255), + rgb(37 / 255, 150 / 255, 192 / 255), + rgb(34 / 255, 125 / 255, 182 / 255), + rgb(34 / 255, 97 / 255, 169 / 255), + rgb(26 / 255, 70 / 255, 151 / 255), + rgb(12 / 255, 44 / 255, 132 / 255)), + "15" = tibble::lst(rgb(255 / 255, 255 / 255, 216 / 255), + rgb(246 / 255, 251 / 255, 197 / 255), + rgb(236 / 255, 248 / 255, 177 / 255), + rgb(218 / 255, 240 / 255, 178 / 255), + rgb(198 / 255, 233 / 255, 179 / 255), + rgb(165 / 255, 218 / 255, 183 / 255), + rgb(127 / 255, 204 / 255, 186 / 255), + rgb(100 / 255, 193 / 255, 191 / 255), + rgb(64 / 255, 182 / 255, 196 / 255), + rgb(50 / 255, 163 / 255, 194 / 255), + rgb(29 / 255, 145 / 255, 191 / 255), + rgb(35 / 255, 119 / 255, 180 / 255), + rgb(33 / 255, 94 / 255, 167 / 255), + rgb(26 / 255, 68 / 255, 149 / 255), + rgb(12 / 255, 44 / 255, 132 / 255)), + "16" = tibble::lst(rgb(255 / 255, 255 / 255, 216 / 255), + rgb(246 / 255, 251 / 255, 198 / 255), + rgb(238 / 255, 248 / 255, 179 / 255), + rgb(222 / 255, 242 / 255, 178 / 255), + rgb(204 / 255, 235 / 255, 179 / 255), + rgb(176 / 255, 223 / 255, 182 / 255), + rgb(142 / 255, 210 / 255, 185 / 255), + rgb(113 / 255, 198 / 255, 189 / 255), + rgb(85 / 255, 188 / 255, 193 / 255), + rgb(59 / 255, 174 / 255, 195 / 255), + rgb(44 / 255, 157 / 255, 193 / 255), + rgb(31 / 255, 138 / 255, 188 / 255), + rgb(35 / 255, 114 / 255, 177 / 255), + rgb(33 / 255, 90 / 255, 165 / 255), + rgb(25 / 255, 67 / 255, 148 / 255), + rgb(12 / 255, 44 / 255, 132 / 255)), + "17" = tibble::lst(rgb(255 / 255, 255 / 255, 216 / 255), + rgb(247 / 255, 251 / 255, 199 / 255), + rgb(239 / 255, 248 / 255, 182 / 255), + rgb(225 / 255, 243 / 255, 177 / 255), + rgb(208 / 255, 236 / 255, 179 / 255), + rgb(186 / 255, 227 / 255, 181 / 255), + rgb(156 / 255, 215 / 255, 184 / 255), + rgb(123 / 255, 203 / 255, 187 / 255), + rgb(100 / 255, 193 / 255, 191 / 255), + rgb(70 / 255, 183 / 255, 195 / 255), + rgb(54 / 255, 167 / 255, 194 / 255), + rgb(38 / 255, 151 / 255, 192 / 255), + rgb(33 / 255, 132 / 255, 186 / 255), + rgb(35 / 255, 109 / 255, 175 / 255), + rgb(32 / 255, 87 / 255, 163 / 255), + rgb(24 / 255, 65 / 255, 147 / 255), + rgb(12 / 255, 44 / 255, 132 / 255)), + "18" = tibble::lst(rgb(255 / 255, 255 / 255, 216 / 255), + rgb(247 / 255, 252 / 255, 200 / 255), + rgb(240 / 255, 249 / 255, 184 / 255), + rgb(228 / 255, 244 / 255, 177 / 255), + rgb(212 / 255, 238 / 255, 178 / 255), + rgb(195 / 255, 231 / 255, 180 / 255), + rgb(167 / 255, 219 / 255, 183 / 255), + rgb(136 / 255, 208 / 255, 186 / 255), + rgb(111 / 255, 198 / 255, 189 / 255), + rgb(87 / 255, 188 / 255, 193 / 255), + rgb(61 / 255, 177 / 255, 195 / 255), + rgb(49 / 255, 162 / 255, 193 / 255), + rgb(32 / 255, 147 / 255, 192 / 255), + rgb(34 / 255, 126 / 255, 183 / 255), + rgb(35 / 255, 105 / 255, 173 / 255), + rgb(31 / 255, 85 / 255, 161 / 255), + rgb(24 / 255, 64 / 255, 146 / 255), + rgb(12 / 255, 44 / 255, 132 / 255)), + "19" = tibble::lst(rgb(255 / 255, 255 / 255, 216 / 255), + rgb(248 / 255, 252 / 255, 201 / 255), + rgb(241 / 255, 249 / 255, 185 / 255), + rgb(230 / 255, 245 / 255, 177 / 255), + rgb(216 / 255, 239 / 255, 178 / 255), + rgb(201 / 255, 233 / 255, 179 / 255), + rgb(176 / 255, 223 / 255, 182 / 255), + rgb(148 / 255, 212 / 255, 185 / 255), + rgb(121 / 255, 202 / 255, 188 / 255), + rgb(100 / 255, 193 / 255, 191 / 255), + rgb(74 / 255, 184 / 255, 195 / 255), + rgb(57 / 255, 171 / 255, 194 / 255), + rgb(44 / 255, 157 / 255, 193 / 255), + rgb(30 / 255, 142 / 255, 190 / 255), + rgb(34 / 255, 122 / 255, 181 / 255), + rgb(34 / 255, 102 / 255, 172 / 255), + rgb(31 / 255, 82 / 255, 159 / 255), + rgb(23 / 255, 63 / 255, 145 / 255), + rgb(12 / 255, 44 / 255, 132 / 255)), + "20" = tibble::lst(rgb(255 / 255, 255 / 255, 216 / 255), + rgb(248 / 255, 252 / 255, 202 / 255), + rgb(241 / 255, 249 / 255, 187 / 255), + rgb(233 / 255, 246 / 255, 177 / 255), + rgb(219 / 255, 240 / 255, 178 / 255), + rgb(205 / 255, 235 / 255, 179 / 255), + rgb(185 / 255, 227 / 255, 181 / 255), + rgb(159 / 255, 216 / 255, 184 / 255), + rgb(131 / 255, 206 / 255, 186 / 255), + rgb(110 / 255, 197 / 255, 189 / 255), + rgb(88 / 255, 189 / 255, 193 / 255), + rgb(63 / 255, 180 / 255, 195 / 255), + rgb(53 / 255, 166 / 255, 194 / 255), + rgb(39 / 255, 152 / 255, 192 / 255), + rgb(31 / 255, 136 / 255, 188 / 255), + rgb(35 / 255, 117 / 255, 179 / 255), + rgb(34 / 255, 99 / 255, 170 / 255), + rgb(30 / 255, 80 / 255, 158 / 255), + rgb(23 / 255, 62 / 255, 145 / 255), + rgb(12 / 255, 44 / 255, 132 / 255)), + "type" = "sequential" + ), + "Purples" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(239 / 255, 237 / 255, 245 / 255), + rgb(117 / 255, 107 / 255, 177 / 255)), + "3" = tibble::lst(rgb(239 / 255, 237 / 255, 245 / 255), + rgb(188 / 255, 189 / 255, 220 / 255), + rgb(117 / 255, 107 / 255, 177 / 255)), + "4" = tibble::lst(rgb(242 / 255, 240 / 255, 247 / 255), + rgb(203 / 255, 201 / 255, 226 / 255), + rgb(158 / 255, 154 / 255, 200 / 255), + rgb(106 / 255, 81 / 255, 163 / 255)), + "5" = tibble::lst(rgb(242 / 255, 240 / 255, 247 / 255), + rgb(203 / 255, 201 / 255, 226 / 255), + rgb(158 / 255, 154 / 255, 200 / 255), + rgb(117 / 255, 107 / 255, 177 / 255), + rgb(84 / 255, 39 / 255, 143 / 255)), + "6" = tibble::lst(rgb(242 / 255, 240 / 255, 247 / 255), + rgb(218 / 255, 218 / 255, 235 / 255), + rgb(188 / 255, 189 / 255, 220 / 255), + rgb(158 / 255, 154 / 255, 200 / 255), + rgb(117 / 255, 107 / 255, 177 / 255), + rgb(84 / 255, 39 / 255, 143 / 255)), + "7" = tibble::lst(rgb(242 / 255, 240 / 255, 247 / 255), + rgb(218 / 255, 218 / 255, 235 / 255), + rgb(188 / 255, 189 / 255, 220 / 255), + rgb(158 / 255, 154 / 255, 200 / 255), + rgb(128 / 255, 125 / 255, 186 / 255), + rgb(106 / 255, 81 / 255, 163 / 255), + rgb(74 / 255, 20 / 255, 134 / 255)), + "8" = tibble::lst(rgb(252 / 255, 251 / 255, 253 / 255), + rgb(239 / 255, 237 / 255, 245 / 255), + rgb(218 / 255, 218 / 255, 235 / 255), + rgb(188 / 255, 189 / 255, 220 / 255), + rgb(158 / 255, 154 / 255, 200 / 255), + rgb(128 / 255, 125 / 255, 186 / 255), + rgb(106 / 255, 81 / 255, 163 / 255), + rgb(74 / 255, 20 / 255, 134 / 255)), + "9" = tibble::lst(rgb(252 / 255, 251 / 255, 253 / 255), + rgb(239 / 255, 237 / 255, 245 / 255), + rgb(218 / 255, 218 / 255, 235 / 255), + rgb(188 / 255, 189 / 255, 220 / 255), + rgb(158 / 255, 154 / 255, 200 / 255), + rgb(128 / 255, 125 / 255, 186 / 255), + rgb(106 / 255, 81 / 255, 163 / 255), + rgb(84 / 255, 39 / 255, 143 / 255), + rgb(63 / 255, 0 / 255, 125 / 255)), + "10" = tibble::lst(rgb(252 / 255, 250 / 255, 253 / 255), + rgb(241 / 255, 240 / 255, 246 / 255), + rgb(227 / 255, 226 / 255, 239 / 255), + rgb(207 / 255, 208 / 255, 229 / 255), + rgb(184 / 255, 185 / 255, 217 / 255), + rgb(161 / 255, 157 / 255, 202 / 255), + rgb(138 / 255, 134 / 255, 190 / 255), + rgb(118 / 255, 105 / 255, 175 / 255), + rgb(99 / 255, 68 / 255, 156 / 255), + rgb(74 / 255, 19 / 255, 133 / 255)), + "11" = tibble::lst(rgb(252 / 255, 250 / 255, 253 / 255), + rgb(242 / 255, 241 / 255, 247 / 255), + rgb(230 / 255, 229 / 255, 241 / 255), + rgb(214 / 255, 215 / 255, 233 / 255), + rgb(193 / 255, 194 / 255, 223 / 255), + rgb(172 / 255, 171 / 255, 209 / 255), + rgb(152 / 255, 148 / 255, 197 / 255), + rgb(131 / 255, 127 / 255, 187 / 255), + rgb(115 / 255, 98 / 255, 172 / 255), + rgb(97 / 255, 64 / 255, 154 / 255), + rgb(74 / 255, 19 / 255, 133 / 255)), + "12" = tibble::lst(rgb(252 / 255, 250 / 255, 253 / 255), + rgb(243 / 255, 242 / 255, 247 / 255), + rgb(233 / 255, 231 / 255, 242 / 255), + rgb(219 / 255, 219 / 255, 235 / 255), + rgb(201 / 255, 202 / 255, 226 / 255), + rgb(182 / 255, 182 / 255, 216 / 255), + rgb(163 / 255, 160 / 255, 203 / 255), + rgb(144 / 255, 140 / 255, 193 / 255), + rgb(126 / 255, 120 / 255, 183 / 255), + rgb(112 / 255, 92 / 255, 169 / 255), + rgb(95 / 255, 60 / 255, 152 / 255), + rgb(74 / 255, 19 / 255, 133 / 255)), + "13" = tibble::lst(rgb(252 / 255, 250 / 255, 253 / 255), + rgb(244 / 255, 242 / 255, 248 / 255), + rgb(235 / 255, 233 / 255, 243 / 255), + rgb(223 / 255, 222 / 255, 237 / 255), + rgb(207 / 255, 208 / 255, 229 / 255), + rgb(190 / 255, 191 / 255, 221 / 255), + rgb(172 / 255, 171 / 255, 209 / 255), + rgb(155 / 255, 151 / 255, 198 / 255), + rgb(138 / 255, 134 / 255, 190 / 255), + rgb(122 / 255, 113 / 255, 180 / 255), + rgb(109 / 255, 88 / 255, 166 / 255), + rgb(93 / 255, 57 / 255, 150 / 255), + rgb(74 / 255, 19 / 255, 133 / 255)), + "14" = tibble::lst(rgb(252 / 255, 250 / 255, 253 / 255), + rgb(244 / 255, 243 / 255, 248 / 255), + rgb(237 / 255, 235 / 255, 244 / 255), + rgb(226 / 255, 225 / 255, 238 / 255), + rgb(213 / 255, 213 / 255, 232 / 255), + rgb(197 / 255, 197 / 255, 224 / 255), + rgb(181 / 255, 180 / 255, 215 / 255), + rgb(164 / 255, 161 / 255, 204 / 255), + rgb(148 / 255, 144 / 255, 195 / 255), + rgb(132 / 255, 129 / 255, 188 / 255), + rgb(119 / 255, 108 / 255, 177 / 255), + rgb(107 / 255, 84 / 255, 164 / 255), + rgb(91 / 255, 55 / 255, 149 / 255), + rgb(74 / 255, 19 / 255, 133 / 255)), + "15" = tibble::lst(rgb(252 / 255, 250 / 255, 253 / 255), + rgb(245 / 255, 243 / 255, 248 / 255), + rgb(238 / 255, 236 / 255, 244 / 255), + rgb(228 / 255, 227 / 255, 239 / 255), + rgb(217 / 255, 217 / 255, 235 / 255), + rgb(202 / 255, 203 / 255, 227 / 255), + rgb(187 / 255, 189 / 255, 220 / 255), + rgb(172 / 255, 171 / 255, 209 / 255), + rgb(158 / 255, 153 / 255, 199 / 255), + rgb(143 / 255, 139 / 255, 193 / 255), + rgb(127 / 255, 125 / 255, 185 / 255), + rgb(117 / 255, 102 / 255, 174 / 255), + rgb(106 / 255, 81 / 255, 163 / 255), + rgb(90 / 255, 52 / 255, 148 / 255), + rgb(74 / 255, 19 / 255, 133 / 255)), + "16" = tibble::lst(rgb(252 / 255, 250 / 255, 253 / 255), + rgb(245 / 255, 244 / 255, 249 / 255), + rgb(239 / 255, 237 / 255, 245 / 255), + rgb(230 / 255, 229 / 255, 241 / 255), + rgb(220 / 255, 220 / 255, 236 / 255), + rgb(207 / 255, 208 / 255, 229 / 255), + rgb(193 / 255, 194 / 255, 223 / 255), + rgb(179 / 255, 179 / 255, 214 / 255), + rgb(165 / 255, 163 / 255, 205 / 255), + rgb(152 / 255, 148 / 255, 197 / 255), + rgb(138 / 255, 134 / 255, 190 / 255), + rgb(125 / 255, 119 / 255, 182 / 255), + rgb(115 / 255, 98 / 255, 172 / 255), + rgb(104 / 255, 77 / 255, 161 / 255), + rgb(89 / 255, 50 / 255, 147 / 255), + rgb(74 / 255, 19 / 255, 133 / 255)), + "17" = tibble::lst(rgb(252 / 255, 250 / 255, 253 / 255), + rgb(246 / 255, 244 / 255, 249 / 255), + rgb(240 / 255, 238 / 255, 245 / 255), + rgb(232 / 255, 231 / 255, 241 / 255), + rgb(223 / 255, 222 / 255, 237 / 255), + rgb(212 / 255, 212 / 255, 232 / 255), + rgb(199 / 255, 199 / 255, 225 / 255), + rgb(186 / 255, 186 / 255, 218 / 255), + rgb(172 / 255, 171 / 255, 209 / 255), + rgb(159 / 255, 156 / 255, 201 / 255), + rgb(146 / 255, 142 / 255, 194 / 255), + rgb(133 / 255, 130 / 255, 188 / 255), + rgb(122 / 255, 113 / 255, 180 / 255), + rgb(113 / 255, 94 / 255, 170 / 255), + rgb(102 / 255, 74 / 255, 159 / 255), + rgb(88 / 255, 49 / 255, 146 / 255), + rgb(74 / 255, 19 / 255, 133 / 255)), + "18" = tibble::lst(rgb(252 / 255, 250 / 255, 253 / 255), + rgb(246 / 255, 245 / 255, 249 / 255), + rgb(241 / 255, 239 / 255, 246 / 255), + rgb(234 / 255, 232 / 255, 242 / 255), + rgb(225 / 255, 224 / 255, 238 / 255), + rgb(216 / 255, 216 / 255, 234 / 255), + rgb(203 / 255, 204 / 255, 227 / 255), + rgb(191 / 255, 192 / 255, 221 / 255), + rgb(179 / 255, 178 / 255, 214 / 255), + rgb(166 / 255, 164 / 255, 205 / 255), + rgb(154 / 255, 150 / 255, 198 / 255), + rgb(142 / 255, 138 / 255, 192 / 255), + rgb(129 / 255, 126 / 255, 186 / 255), + rgb(120 / 255, 109 / 255, 177 / 255), + rgb(111 / 255, 91 / 255, 168 / 255), + rgb(100 / 255, 71 / 255, 157 / 255), + rgb(87 / 255, 47 / 255, 145 / 255), + rgb(74 / 255, 19 / 255, 133 / 255)), + "19" = tibble::lst(rgb(252 / 255, 250 / 255, 253 / 255), + rgb(246 / 255, 245 / 255, 249 / 255), + rgb(241 / 255, 240 / 255, 246 / 255), + rgb(235 / 255, 233 / 255, 243 / 255), + rgb(227 / 255, 226 / 255, 239 / 255), + rgb(219 / 255, 219 / 255, 235 / 255), + rgb(207 / 255, 208 / 255, 229 / 255), + rgb(196 / 255, 196 / 255, 224 / 255), + rgb(184 / 255, 185 / 255, 217 / 255), + rgb(172 / 255, 171 / 255, 209 / 255), + rgb(161 / 255, 157 / 255, 202 / 255), + rgb(149 / 255, 145 / 255, 196 / 255), + rgb(138 / 255, 134 / 255, 190 / 255), + rgb(126 / 255, 122 / 255, 184 / 255), + rgb(118 / 255, 105 / 255, 175 / 255), + rgb(109 / 255, 88 / 255, 166 / 255), + rgb(99 / 255, 68 / 255, 156 / 255), + rgb(87 / 255, 46 / 255, 145 / 255), + rgb(74 / 255, 19 / 255, 133 / 255)), + "20" = tibble::lst(rgb(252 / 255, 250 / 255, 253 / 255), + rgb(247 / 255, 245 / 255, 250 / 255), + rgb(242 / 255, 240 / 255, 247 / 255), + rgb(236 / 255, 234 / 255, 243 / 255), + rgb(229 / 255, 227 / 255, 240 / 255), + rgb(221 / 255, 220 / 255, 236 / 255), + rgb(211 / 255, 211 / 255, 231 / 255), + rgb(200 / 255, 201 / 255, 226 / 255), + rgb(189 / 255, 190 / 255, 220 / 255), + rgb(178 / 255, 177 / 255, 213 / 255), + rgb(167 / 255, 164 / 255, 206 / 255), + rgb(156 / 255, 152 / 255, 199 / 255), + rgb(145 / 255, 141 / 255, 194 / 255), + rgb(134 / 255, 131 / 255, 188 / 255), + rgb(124 / 255, 118 / 255, 182 / 255), + rgb(116 / 255, 101 / 255, 173 / 255), + rgb(108 / 255, 85 / 255, 165 / 255), + rgb(98 / 255, 66 / 255, 155 / 255), + rgb(86 / 255, 45 / 255, 144 / 255), + rgb(74 / 255, 19 / 255, 133 / 255)), + "type" = "sequential" + ), + "GnBu" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(224 / 255, 243 / 255, 219 / 255), + rgb(67 / 255, 162 / 255, 202 / 255)), + "3" = tibble::lst(rgb(224 / 255, 243 / 255, 219 / 255), + rgb(168 / 255, 221 / 255, 181 / 255), + rgb(67 / 255, 162 / 255, 202 / 255)), + "4" = tibble::lst(rgb(240 / 255, 249 / 255, 232 / 255), + rgb(186 / 255, 228 / 255, 188 / 255), + rgb(123 / 255, 204 / 255, 196 / 255), + rgb(43 / 255, 140 / 255, 190 / 255)), + "5" = tibble::lst(rgb(240 / 255, 249 / 255, 232 / 255), + rgb(186 / 255, 228 / 255, 188 / 255), + rgb(123 / 255, 204 / 255, 196 / 255), + rgb(67 / 255, 162 / 255, 202 / 255), + rgb(8 / 255, 104 / 255, 172 / 255)), + "6" = tibble::lst(rgb(240 / 255, 249 / 255, 232 / 255), + rgb(204 / 255, 235 / 255, 197 / 255), + rgb(168 / 255, 221 / 255, 181 / 255), + rgb(123 / 255, 204 / 255, 196 / 255), + rgb(67 / 255, 162 / 255, 202 / 255), + rgb(8 / 255, 104 / 255, 172 / 255)), + "7" = tibble::lst(rgb(240 / 255, 249 / 255, 232 / 255), + rgb(204 / 255, 235 / 255, 197 / 255), + rgb(168 / 255, 221 / 255, 181 / 255), + rgb(123 / 255, 204 / 255, 196 / 255), + rgb(78 / 255, 179 / 255, 211 / 255), + rgb(43 / 255, 140 / 255, 190 / 255), + rgb(8 / 255, 88 / 255, 158 / 255)), + "8" = tibble::lst(rgb(247 / 255, 252 / 255, 240 / 255), + rgb(224 / 255, 243 / 255, 219 / 255), + rgb(204 / 255, 235 / 255, 197 / 255), + rgb(168 / 255, 221 / 255, 181 / 255), + rgb(123 / 255, 204 / 255, 196 / 255), + rgb(78 / 255, 179 / 255, 211 / 255), + rgb(43 / 255, 140 / 255, 190 / 255), + rgb(8 / 255, 88 / 255, 158 / 255)), + "9" = tibble::lst(rgb(247 / 255, 252 / 255, 240 / 255), + rgb(224 / 255, 243 / 255, 219 / 255), + rgb(204 / 255, 235 / 255, 197 / 255), + rgb(168 / 255, 221 / 255, 181 / 255), + rgb(123 / 255, 204 / 255, 196 / 255), + rgb(78 / 255, 179 / 255, 211 / 255), + rgb(43 / 255, 140 / 255, 190 / 255), + rgb(8 / 255, 104 / 255, 172 / 255), + rgb(8 / 255, 64 / 255, 129 / 255)), + "10" = tibble::lst(rgb(247 / 255, 252 / 255, 240 / 255), + rgb(229 / 255, 245 / 255, 223 / 255), + rgb(212 / 255, 238 / 255, 206 / 255), + rgb(192 / 255, 230 / 255, 191 / 255), + rgb(163 / 255, 219 / 255, 182 / 255), + rgb(128 / 255, 205 / 255, 194 / 255), + rgb(95 / 255, 187 / 255, 206 / 255), + rgb(63 / 255, 161 / 255, 201 / 255), + rgb(37 / 255, 128 / 255, 182 / 255), + rgb(7 / 255, 88 / 255, 158 / 255)), + "11" = tibble::lst(rgb(247 / 255, 252 / 255, 240 / 255), + rgb(230 / 255, 245 / 255, 225 / 255), + rgb(216 / 255, 239 / 255, 210 / 255), + rgb(200 / 255, 233 / 255, 195 / 255), + rgb(175 / 255, 223 / 255, 184 / 255), + rgb(146 / 255, 212 / 255, 188 / 255), + rgb(115 / 255, 198 / 255, 199 / 255), + rgb(83 / 255, 181 / 255, 209 / 255), + rgb(58 / 255, 155 / 255, 198 / 255), + rgb(35 / 255, 124 / 255, 180 / 255), + rgb(7 / 255, 88 / 255, 158 / 255)), + "12" = tibble::lst(rgb(247 / 255, 252 / 255, 240 / 255), + rgb(232 / 255, 246 / 255, 226 / 255), + rgb(218 / 255, 240 / 255, 212 / 255), + rgb(205 / 255, 235 / 255, 198 / 255), + rgb(184 / 255, 227 / 255, 188 / 255), + rgb(160 / 255, 217 / 255, 183 / 255), + rgb(132 / 255, 207 / 255, 193 / 255), + rgb(105 / 255, 192 / 255, 202 / 255), + rgb(75 / 255, 175 / 255, 209 / 255), + rgb(53 / 255, 150 / 255, 195 / 255), + rgb(34 / 255, 120 / 255, 178 / 255), + rgb(7 / 255, 88 / 255, 158 / 255)), + "13" = tibble::lst(rgb(247 / 255, 252 / 255, 240 / 255), + rgb(233 / 255, 246 / 255, 227 / 255), + rgb(220 / 255, 241 / 255, 215 / 255), + rgb(209 / 255, 237 / 255, 202 / 255), + rgb(192 / 255, 230 / 255, 191 / 255), + rgb(171 / 255, 222 / 255, 182 / 255), + rgb(146 / 255, 212 / 255, 188 / 255), + rgb(119 / 255, 201 / 255, 197 / 255), + rgb(95 / 255, 187 / 255, 206 / 255), + rgb(70 / 255, 169 / 255, 205 / 255), + rgb(49 / 255, 146 / 255, 193 / 255), + rgb(32 / 255, 117 / 255, 176 / 255), + rgb(7 / 255, 88 / 255, 158 / 255)), + "14" = tibble::lst(rgb(247 / 255, 252 / 255, 240 / 255), + rgb(234 / 255, 247 / 255, 228 / 255), + rgb(222 / 255, 242 / 255, 217 / 255), + rgb(211 / 255, 238 / 255, 205 / 255), + rgb(198 / 255, 232 / 255, 194 / 255), + rgb(179 / 255, 225 / 255, 185 / 255), + rgb(158 / 255, 217 / 255, 184 / 255), + rgb(134 / 255, 207 / 255, 192 / 255), + rgb(111 / 255, 196 / 255, 200 / 255), + rgb(86 / 255, 182 / 255, 208 / 255), + rgb(65 / 255, 163 / 255, 202 / 255), + rgb(46 / 255, 142 / 255, 191 / 255), + rgb(31 / 255, 115 / 255, 175 / 255), + rgb(7 / 255, 88 / 255, 158 / 255)), + "15" = tibble::lst(rgb(247 / 255, 252 / 255, 240 / 255), + rgb(235 / 255, 247 / 255, 229 / 255), + rgb(223 / 255, 242 / 255, 218 / 255), + rgb(214 / 255, 239 / 255, 207 / 255), + rgb(204 / 255, 235 / 255, 197 / 255), + rgb(186 / 255, 228 / 255, 188 / 255), + rgb(167 / 255, 221 / 255, 180 / 255), + rgb(146 / 255, 212 / 255, 188 / 255), + rgb(122 / 255, 204 / 255, 196 / 255), + rgb(103 / 255, 191 / 255, 203 / 255), + rgb(77 / 255, 178 / 255, 210 / 255), + rgb(61 / 255, 159 / 255, 200 / 255), + rgb(43 / 255, 140 / 255, 190 / 255), + rgb(30 / 255, 113 / 255, 173 / 255), + rgb(7 / 255, 88 / 255, 158 / 255)), + "16" = tibble::lst(rgb(247 / 255, 252 / 255, 240 / 255), + rgb(236 / 255, 247 / 255, 230 / 255), + rgb(225 / 255, 243 / 255, 220 / 255), + rgb(216 / 255, 239 / 255, 210 / 255), + rgb(206 / 255, 236 / 255, 199 / 255), + rgb(192 / 255, 230 / 255, 191 / 255), + rgb(175 / 255, 223 / 255, 184 / 255), + rgb(157 / 255, 216 / 255, 185 / 255), + rgb(136 / 255, 208 / 255, 192 / 255), + rgb(115 / 255, 198 / 255, 199 / 255), + rgb(95 / 255, 187 / 255, 206 / 255), + rgb(73 / 255, 173 / 255, 208 / 255), + rgb(58 / 255, 155 / 255, 198 / 255), + rgb(41 / 255, 136 / 255, 187 / 255), + rgb(29 / 255, 111 / 255, 172 / 255), + rgb(7 / 255, 88 / 255, 158 / 255)), + "17" = tibble::lst(rgb(247 / 255, 252 / 255, 240 / 255), + rgb(236 / 255, 248 / 255, 230 / 255), + rgb(226 / 255, 244 / 255, 221 / 255), + rgb(217 / 255, 240 / 255, 212 / 255), + rgb(209 / 255, 237 / 255, 202 / 255), + rgb(197 / 255, 232 / 255, 193 / 255), + rgb(181 / 255, 226 / 255, 186 / 255), + rgb(165 / 255, 219 / 255, 181 / 255), + rgb(146 / 255, 212 / 255, 188 / 255), + rgb(126 / 255, 205 / 255, 195 / 255), + rgb(108 / 255, 194 / 255, 201 / 255), + rgb(88 / 255, 183 / 255, 208 / 255), + rgb(70 / 255, 169 / 255, 205 / 255), + rgb(55 / 255, 151 / 255, 196 / 255), + rgb(40 / 255, 133 / 255, 185 / 255), + rgb(28 / 255, 110 / 255, 171 / 255), + rgb(7 / 255, 88 / 255, 158 / 255)), + "18" = tibble::lst(rgb(247 / 255, 252 / 255, 240 / 255), + rgb(237 / 255, 248 / 255, 231 / 255), + rgb(228 / 255, 244 / 255, 222 / 255), + rgb(219 / 255, 241 / 255, 213 / 255), + rgb(211 / 255, 237 / 255, 204 / 255), + rgb(201 / 255, 234 / 255, 196 / 255), + rgb(187 / 255, 228 / 255, 189 / 255), + rgb(172 / 255, 222 / 255, 182 / 255), + rgb(155 / 255, 215 / 255, 185 / 255), + rgb(137 / 255, 208 / 255, 191 / 255), + rgb(118 / 255, 201 / 255, 197 / 255), + rgb(101 / 255, 190 / 255, 204 / 255), + rgb(81 / 255, 180 / 255, 210 / 255), + rgb(66 / 255, 165 / 255, 203 / 255), + rgb(52 / 255, 149 / 255, 194 / 255), + rgb(38 / 255, 130 / 255, 184 / 255), + rgb(27 / 255, 108 / 255, 171 / 255), + rgb(7 / 255, 88 / 255, 158 / 255)), + "19" = tibble::lst(rgb(247 / 255, 252 / 255, 240 / 255), + rgb(238 / 255, 248 / 255, 231 / 255), + rgb(229 / 255, 245 / 255, 223 / 255), + rgb(220 / 255, 241 / 255, 215 / 255), + rgb(212 / 255, 238 / 255, 206 / 255), + rgb(205 / 255, 235 / 255, 198 / 255), + rgb(192 / 255, 230 / 255, 191 / 255), + rgb(178 / 255, 224 / 255, 185 / 255), + rgb(163 / 255, 219 / 255, 182 / 255), + rgb(146 / 255, 212 / 255, 188 / 255), + rgb(128 / 255, 205 / 255, 194 / 255), + rgb(112 / 255, 196 / 255, 200 / 255), + rgb(95 / 255, 187 / 255, 206 / 255), + rgb(76 / 255, 176 / 255, 209 / 255), + rgb(63 / 255, 161 / 255, 201 / 255), + rgb(49 / 255, 146 / 255, 193 / 255), + rgb(37 / 255, 128 / 255, 182 / 255), + rgb(26 / 255, 107 / 255, 170 / 255), + rgb(7 / 255, 88 / 255, 158 / 255)), + "20" = tibble::lst(rgb(247 / 255, 252 / 255, 240 / 255), + rgb(238 / 255, 248 / 255, 232 / 255), + rgb(230 / 255, 245 / 255, 224 / 255), + rgb(221 / 255, 242 / 255, 216 / 255), + rgb(214 / 255, 239 / 255, 208 / 255), + rgb(207 / 255, 236 / 255, 200 / 255), + rgb(196 / 255, 232 / 255, 193 / 255), + rgb(183 / 255, 226 / 255, 187 / 255), + rgb(169 / 255, 221 / 255, 181 / 255), + rgb(154 / 255, 215 / 255, 185 / 255), + rgb(138 / 255, 209 / 255, 191 / 255), + rgb(121 / 255, 202 / 255, 196 / 255), + rgb(106 / 255, 193 / 255, 202 / 255), + rgb(89 / 255, 184 / 255, 207 / 255), + rgb(73 / 255, 172 / 255, 207 / 255), + rgb(61 / 255, 158 / 255, 199 / 255), + rgb(47 / 255, 144 / 255, 192 / 255), + rgb(36 / 255, 125 / 255, 181 / 255), + rgb(25 / 255, 106 / 255, 169 / 255), + rgb(7 / 255, 88 / 255, 158 / 255)), + "type" = "sequential" + ), + "Greys" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(240 / 255, 240 / 255, 240 / 255), + rgb(99 / 255, 99 / 255, 99 / 255)), + "3" = tibble::lst(rgb(240 / 255, 240 / 255, 240 / 255), + rgb(189 / 255, 189 / 255, 189 / 255), + rgb(99 / 255, 99 / 255, 99 / 255)), + "4" = tibble::lst(rgb(247 / 255, 247 / 255, 247 / 255), + rgb(204 / 255, 204 / 255, 204 / 255), + rgb(150 / 255, 150 / 255, 150 / 255), + rgb(82 / 255, 82 / 255, 82 / 255)), + "5" = tibble::lst(rgb(247 / 255, 247 / 255, 247 / 255), + rgb(204 / 255, 204 / 255, 204 / 255), + rgb(150 / 255, 150 / 255, 150 / 255), + rgb(99 / 255, 99 / 255, 99 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "6" = tibble::lst(rgb(247 / 255, 247 / 255, 247 / 255), + rgb(217 / 255, 217 / 255, 217 / 255), + rgb(189 / 255, 189 / 255, 189 / 255), + rgb(150 / 255, 150 / 255, 150 / 255), + rgb(99 / 255, 99 / 255, 99 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "7" = tibble::lst(rgb(247 / 255, 247 / 255, 247 / 255), + rgb(217 / 255, 217 / 255, 217 / 255), + rgb(189 / 255, 189 / 255, 189 / 255), + rgb(150 / 255, 150 / 255, 150 / 255), + rgb(115 / 255, 115 / 255, 115 / 255), + rgb(82 / 255, 82 / 255, 82 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "8" = tibble::lst(rgb(255 / 255, 255 / 255, 255 / 255), + rgb(240 / 255, 240 / 255, 240 / 255), + rgb(217 / 255, 217 / 255, 217 / 255), + rgb(189 / 255, 189 / 255, 189 / 255), + rgb(150 / 255, 150 / 255, 150 / 255), + rgb(115 / 255, 115 / 255, 115 / 255), + rgb(82 / 255, 82 / 255, 82 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "9" = tibble::lst(rgb(255 / 255, 255 / 255, 255 / 255), + rgb(240 / 255, 240 / 255, 240 / 255), + rgb(217 / 255, 217 / 255, 217 / 255), + rgb(189 / 255, 189 / 255, 189 / 255), + rgb(150 / 255, 150 / 255, 150 / 255), + rgb(115 / 255, 115 / 255, 115 / 255), + rgb(82 / 255, 82 / 255, 82 / 255), + rgb(37 / 255, 37 / 255, 37 / 255), + rgb(0 / 255, 0 / 255, 0 / 255)), + "10" = tibble::lst(rgb(255 / 255, 255 / 255, 255 / 255), + rgb(243 / 255, 243 / 255, 243 / 255), + rgb(227 / 255, 227 / 255, 227 / 255), + rgb(207 / 255, 207 / 255, 207 / 255), + rgb(184 / 255, 184 / 255, 184 / 255), + rgb(154 / 255, 154 / 255, 154 / 255), + rgb(126 / 255, 126 / 255, 126 / 255), + rgb(100 / 255, 100 / 255, 100 / 255), + rgb(71 / 255, 71 / 255,71 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "11" = tibble::lst(rgb(255 / 255, 255 / 255, 255 / 255), + rgb(244 / 255, 244 / 255, 244 / 255), + rgb(230 / 255, 230 / 255, 230 / 255), + rgb(214 / 255, 214 / 255, 214 / 255), + rgb(194 / 255, 194 / 255, 194 / 255), + rgb(169 / 255, 169 / 255, 169 / 255), + rgb(142 / 255, 142 / 255, 142 / 255), + rgb(118 / 255, 118 / 255, 118 / 255), + rgb(94 / 255, 94 / 255, 94 / 255), + rgb(67 / 255, 67 / 255, 67 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "12" = tibble::lst(rgb(255 / 255, 255 / 255, 255 / 255), + rgb(245 / 255, 245 / 255, 245 / 255), + rgb(233 / 255, 233 / 255, 233 / 255), + rgb(219 / 255, 219 / 255, 219 / 255), + rgb(201 / 255, 201 / 255, 201 / 255), + rgb(181 / 255, 181 / 255, 181 / 255), + rgb(156 / 255, 156 / 255, 156 / 255), + rgb(133 / 255, 133 / 255, 133 / 255), + rgb(111 / 255, 111 / 255, 111 / 255), + rgb(90 / 255, 90 / 255, 90 / 255), + rgb(64 / 255, 64 / 255, 64 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "13" = tibble::lst(rgb(255 / 255, 255 / 255, 255 / 255), + rgb(246 / 255, 246 / 255, 246 / 255), + rgb(236 / 255, 236 / 255, 236 / 255), + rgb(222 / 255, 222 / 255, 222 / 255), + rgb(207 / 255, 207 / 255, 207 / 255), + rgb(191 / 255, 191 / 255, 191 / 255), + rgb(169 / 255, 169 / 255, 169 / 255), + rgb(147 / 255, 147 / 255, 147 / 255), + rgb(126 / 255, 126 / 255, 126 / 255), + rgb(106 / 255, 106 / 255, 106 / 255), + rgb(87 / 255, 87 / 255, 87 / 255), + rgb(62 / 255, 62 / 255, 62 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "14" = tibble::lst(rgb(255 / 255, 255 / 255, 255 / 255), + rgb(246 / 255, 246 / 255, 246 / 255), + rgb(238 / 255, 238 / 255, 238 / 255), + rgb(225 / 255, 225 / 255, 225 / 255), + rgb(212 / 255, 212 / 255, 212 / 255), + rgb(197 / 255, 197 / 255, 197 / 255), + rgb(179 / 255, 179 / 255, 179 / 255), + rgb(158 / 255, 158 / 255, 158 / 255), + rgb(139 / 255, 139 / 255, 139 / 255), + rgb(120 / 255, 120 / 255, 120 / 255), + rgb(102 / 255, 102 / 255, 102 / 255), + rgb(84 / 255, 84 / 255, 84 / 255), + rgb(60 / 255, 60 / 255, 60 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "15" = tibble::lst(rgb(255 / 255, 255 / 255, 255 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(240 / 255, 240 / 255, 240 / 255), + rgb(228 / 255, 228 / 255, 228 / 255), + rgb(216 / 255, 216 / 255, 216 / 255), + rgb(202 / 255, 202 / 255, 202 / 255), + rgb(189 / 255, 189 / 255, 189 / 255), + rgb(169 / 255, 169 / 255, 169 / 255), + rgb(150 / 255, 150 / 255, 150 / 255), + rgb(132 / 255, 132 / 255, 132 / 255), + rgb(114 / 255, 114 / 255, 114 / 255), + rgb(98 / 255, 98 / 255, 98 / 255), + rgb(82 / 255, 82 / 255, 82 / 255), + rgb(58 / 255, 58 / 255, 58 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "16" = tibble::lst(rgb(255 / 255, 255 / 255, 255 / 255), + rgb(247 / 255, 247 / 255, 247 / 255), + rgb(240 / 255, 240 / 255, 240 / 255), + rgb(230 / 255, 230 / 255, 230 / 255), + rgb(220 / 255, 220 / 255, 220 / 255), + rgb(207 / 255, 207 / 255, 207 / 255), + rgb(194 / 255, 194 / 255, 194 / 255), + rgb(178 / 255, 178 / 255, 178 / 255), + rgb(160 / 255, 160 / 255, 160 / 255), + rgb(142 / 255, 142 / 255, 142 / 255), + rgb(126 / 255, 126 / 255, 126 / 255), + rgb(110 / 255, 110 / 255, 110 / 255), + rgb(94 / 255, 94 / 255, 94 / 255), + rgb(78 / 255, 78 / 255,78 / 255), + rgb(57 / 255, 57 / 255, 57 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "17" = tibble::lst(rgb(255 / 255, 255 / 255, 255 / 255), + rgb(248 / 255, 248 / 255, 248 / 255), + rgb(241 / 255, 241 / 255, 241 / 255), + rgb(232 / 255, 232 / 255, 232 / 255), + rgb(222 / 255, 222 / 255, 222 / 255), + rgb(211 / 255, 211 / 255, 211 / 255), + rgb(199 / 255, 199 / 255, 199 / 255), + rgb(186 / 255, 186 / 255, 186 / 255), + rgb(169 / 255, 169 / 255, 169 / 255), + rgb(152 / 255, 152 / 255, 152 / 255), + rgb(136 / 255, 136 / 255, 136 / 255), + rgb(121 / 255, 121 / 255, 121 / 255), + rgb(106 / 255, 106 / 255, 106 / 255), + rgb(92 / 255, 92 / 255, 92 / 255), + rgb(76 / 255, 76 / 255,76 / 255), + rgb(55 / 255, 55 / 255, 55 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "18" = tibble::lst(rgb(255 / 255, 255 / 255, 255 / 255), + rgb(248 / 255, 248 / 255, 248 / 255), + rgb(242 / 255, 242 / 255, 242 / 255), + rgb(234 / 255, 234 / 255, 234 / 255), + rgb(225 / 255, 225 / 255, 225 / 255), + rgb(215 / 255, 215 / 255, 215 / 255), + rgb(203 / 255, 203 / 255, 203 / 255), + rgb(192 / 255, 192 / 255, 192 / 255), + rgb(177 / 255, 177 / 255, 177 / 255), + rgb(161 / 255, 161 / 255, 161 / 255), + rgb(145 / 255, 145 / 255, 145 / 255), + rgb(131 / 255, 131 / 255, 131 / 255), + rgb(117 / 255, 117 / 255, 117 / 255), + rgb(103 / 255, 103 / 255, 103 / 255), + rgb(89 / 255, 89 / 255, 89 / 255), + rgb(73 / 255, 73 / 255,73 / 255), + rgb(54 / 255, 54 / 255, 54 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "19" = tibble::lst(rgb(255 / 255, 255 / 255, 255 / 255), + rgb(249 / 255, 249 / 255, 249 / 255), + rgb(243 / 255, 243 / 255, 243 / 255), + rgb(236 / 255, 236 / 255, 236 / 255), + rgb(227 / 255, 227 / 255, 227 / 255), + rgb(218 / 255, 218 / 255, 218 / 255), + rgb(207 / 255, 207 / 255, 207 / 255), + rgb(196 / 255, 196 / 255, 196 / 255), + rgb(184 / 255, 184 / 255, 184 / 255), + rgb(169 / 255, 169 / 255, 169 / 255), + rgb(154 / 255, 154 / 255, 154 / 255), + rgb(140 / 255, 140 / 255, 140 / 255), + rgb(126 / 255, 126 / 255, 126 / 255), + rgb(113 / 255, 113 / 255, 113 / 255), + rgb(100 / 255, 100 / 255, 100 / 255), + rgb(87 / 255, 87 / 255, 87 / 255), + rgb(71 / 255, 71 / 255,71 / 255), + rgb(53 / 255, 53 / 255, 53 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "20" = tibble::lst(rgb(255 / 255, 255 / 255, 255 / 255), + rgb(249 / 255, 249 / 255, 249 / 255), + rgb(243 / 255, 243 / 255, 243 / 255), + rgb(237 / 255, 237 / 255, 237 / 255), + rgb(229 / 255, 229 / 255, 229 / 255), + rgb(220 / 255, 220 / 255, 220 / 255), + rgb(211 / 255, 211 / 255, 211 / 255), + rgb(200 / 255, 200 / 255, 200 / 255), + rgb(190 / 255, 190 / 255, 190 / 255), + rgb(176 / 255, 176 / 255, 176 / 255), + rgb(162 / 255, 162 / 255, 162 / 255), + rgb(148 / 255, 148 / 255, 148 / 255), + rgb(135 / 255, 135 / 255, 135 / 255), + rgb(122 / 255, 122 / 255, 122 / 255), + rgb(109 / 255, 109 / 255, 109 / 255), + rgb(97 / 255, 97 / 255, 97 / 255), + rgb(85 / 255, 85 / 255, 85 / 255), + rgb(69 / 255, 69 / 255, 69 / 255), + rgb(52 / 255, 52 / 255, 52 / 255), + rgb(37 / 255, 37 / 255, 37 / 255)), + "type" = "sequential" + ), + "YlOrRd" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(255 / 255, 237 / 255, 160 / 255), + rgb(240 / 255, 59 / 255, 32 / 255)), + "3" = tibble::lst(rgb(255 / 255, 237 / 255, 160 / 255), + rgb(254 / 255, 178 / 255, 76 / 255), + rgb(240 / 255, 59 / 255, 32 / 255)), + "4" = tibble::lst(rgb(255 / 255, 255 / 255, 178 / 255), + rgb(254 / 255, 204 / 255, 92 / 255), + rgb(253 / 255, 141 / 255, 60 / 255), + rgb(227 / 255, 26 / 255, 28 / 255)), + "5" = tibble::lst(rgb(255 / 255, 255 / 255, 178 / 255), + rgb(254 / 255, 204 / 255, 92 / 255), + rgb(253 / 255, 141 / 255, 60 / 255), + rgb(240 / 255, 59 / 255, 32 / 255), + rgb(189 / 255, 0 / 255, 38 / 255)), + "6" = tibble::lst(rgb(255 / 255, 255 / 255, 178 / 255), + rgb(254 / 255, 217 / 255, 118 / 255), + rgb(254 / 255, 178 / 255, 76 / 255), + rgb(253 / 255, 141 / 255, 60 / 255), + rgb(240 / 255, 59 / 255, 32 / 255), + rgb(189 / 255, 0 / 255, 38 / 255)), + "7" = tibble::lst(rgb(255 / 255, 255 / 255, 178 / 255), + rgb(254 / 255, 217 / 255, 118 / 255), + rgb(254 / 255, 178 / 255, 76 / 255), + rgb(253 / 255, 141 / 255, 60 / 255), + rgb(252 / 255,78 / 255, 42 / 255), + rgb(227 / 255, 26 / 255, 28 / 255), + rgb(177 / 255, 0 / 255, 38 / 255)), + "8" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(255 / 255, 237 / 255, 160 / 255), + rgb(254 / 255, 217 / 255, 118 / 255), + rgb(254 / 255, 178 / 255, 76 / 255), + rgb(253 / 255, 141 / 255, 60 / 255), + rgb(252 / 255, 78 / 255, 42 / 255), + rgb(227 / 255, 26 / 255, 28 / 255), + rgb(177 / 255, 0 / 255, 38 / 255)), + "9" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(255 / 255, 239 / 255, 165 / 255), + rgb(254 / 255, 221 / 255, 128 / 255), + rgb(254 / 255, 192 / 255, 91 / 255), + rgb(253 / 255, 159 / 255, 67 / 255), + rgb(253 / 255, 119 / 255, 52 / 255), + rgb(245 / 255, 67 / 255, 38 / 255), + rgb(220 / 255, 23 / 255, 29 / 255), + rgb(177 / 255, 0 / 255, 38 / 255)), + "10" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(255 / 255, 240 / 255, 169 / 255), + rgb(254 / 255, 225 / 255, 136 / 255), + rgb(254 / 255, 204 / 255, 104 / 255), + rgb(254 / 255, 174 / 255, 74 / 255), + rgb(253 / 255, 145 / 255, 61 / 255), + rgb(252 / 255, 101 / 255, 47 / 255), + rgb(240 / 255, 58 / 255, 35 / 255), + rgb(215 / 255, 20 / 255, 30 / 255), + rgb(177 / 255, 0 / 255, 38 / 255)), + "11" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(255 / 255, 242 / 255, 173 / 255), + rgb(254 / 255, 228 / 255, 143 / 255), + rgb(254 / 255, 213 / 255, 113 / 255), + rgb(254 / 255, 185 / 255, 84 / 255), + rgb(253 / 255, 159 / 255, 67 / 255), + rgb(253 / 255, 129 / 255, 56 / 255), + rgb(252 / 255, 85 / 255, 43 / 255), + rgb(237 / 255, 51 / 255, 33 / 255), + rgb(211 / 255, 18 / 255, 31 / 255), + rgb(177 / 255, 0 / 255, 38 / 255)), + "12" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(255 / 255, 243 / 255, 175 / 255), + rgb(255 / 255, 231 / 255, 148 / 255), + rgb(254 / 255, 218 / 255, 121 / 255), + rgb(254 / 255, 195 / 255, 95 / 255), + rgb(254 / 255, 171 / 255, 73 / 255), + rgb(253 / 255, 147 / 255, 62 / 255), + rgb(253 / 255, 115 / 255, 51 / 255), + rgb(249 / 255,74 / 255, 40 / 255), + rgb(233 / 255, 44 / 255, 31 / 255), + rgb(208 / 255, 17 / 255, 32 / 255), + rgb(177 / 255, 0 / 255, 38 / 255)), + "13" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(255 / 255, 244 / 255, 178 / 255), + rgb(255 / 255, 233 / 255, 153 / 255), + rgb(254 / 255, 221 / 255, 128 / 255), + rgb(254 / 255, 204 / 255, 104 / 255), + rgb(254 / 255, 181 / 255, 79 / 255), + rgb(253 / 255, 159 / 255, 67 / 255), + rgb(253 / 255, 136 / 255, 58 / 255), + rgb(252 / 255, 101 / 255, 47 / 255), + rgb(245 / 255, 67 / 255, 38 / 255), + rgb(231 / 255, 38 / 255, 30 / 255), + rgb(205 / 255, 15 / 255, 33 / 255), + rgb(177 / 255, 0 / 255, 38 / 255)), + "14" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(255 / 255, 245 / 255, 180 / 255), + rgb(255 / 255, 235 / 255, 156 / 255), + rgb(254 / 255, 224 / 255, 134 / 255), + rgb(254 / 255, 211 / 255, 111 / 255), + rgb(254 / 255, 190 / 255, 89 / 255), + rgb(254 / 255, 169 / 255, 72 / 255), + rgb(253 / 255, 149 / 255, 63 / 255), + rgb(253 / 255, 123 / 255, 54 / 255), + rgb(252 / 255, 89 / 255, 44 / 255), + rgb(242 / 255, 61 / 255, 36 / 255), + rgb(228 / 255, 31 / 255, 29 / 255), + rgb(203 / 255, 14 / 255, 33 / 255), + rgb(177 / 255, 0 / 255, 38 / 255)), + "15" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(255 / 255, 245 / 255, 181 / 255), + rgb(255 / 255, 236 / 255, 159 / 255), + rgb(254 / 255, 226 / 255, 139 / 255), + rgb(254 / 255, 216 / 255, 118 / 255), + rgb(254 / 255, 197 / 255, 97 / 255), + rgb(254 / 255, 178 / 255, 76 / 255), + rgb(253 / 255, 159 / 255, 67 / 255), + rgb(253 / 255, 140 / 255, 59 / 255), + rgb(253 / 255, 112 / 255, 50 / 255), + rgb(252 / 255,77 / 255, 42 / 255), + rgb(239 / 255, 56 / 255, 34 / 255), + rgb(227 / 255, 25 / 255, 27 / 255), + rgb(201 / 255, 13 / 255, 33 / 255), + rgb(177 / 255, 0 / 255, 38 / 255)), + "16" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(255 / 255, 246 / 255, 183 / 255), + rgb(255 / 255, 238 / 255, 162 / 255), + rgb(254 / 255, 228 / 255, 143 / 255), + rgb(254 / 255, 219 / 255, 123 / 255), + rgb(254 / 255, 204 / 255, 104 / 255), + rgb(254 / 255, 185 / 255, 84 / 255), + rgb(253 / 255, 168 / 255, 71 / 255), + rgb(253 / 255, 151 / 255, 64 / 255), + rgb(253 / 255, 129 / 255, 56 / 255), + rgb(252 / 255, 101 / 255, 47 / 255), + rgb(248 / 255,72 / 255, 40 / 255), + rgb(237 / 255, 51 / 255, 33 / 255), + rgb(223 / 255, 24 / 255, 28 / 255), + rgb(200 / 255, 12 / 255, 34 / 255), + rgb(177 / 255, 0 / 255, 38 / 255)), + "17" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(255 / 255, 247 / 255, 184 / 255), + rgb(255 / 255, 239 / 255, 165 / 255), + rgb(255 / 255, 230 / 255, 146 / 255), + rgb(254 / 255, 221 / 255, 128 / 255), + rgb(254 / 255, 209 / 255, 110 / 255), + rgb(254 / 255, 192 / 255, 91 / 255), + rgb(254 / 255, 175 / 255, 74 / 255), + rgb(253 / 255, 159 / 255, 67 / 255), + rgb(253 / 255, 143 / 255, 60 / 255), + rgb(253 / 255, 119 / 255, 52 / 255), + rgb(252 / 255, 91 / 255, 45 / 255), + rgb(245 / 255, 67 / 255, 38 / 255), + rgb(234 / 255, 46 / 255, 32 / 255), + rgb(220 / 255, 23 / 255, 29 / 255), + rgb(198 / 255, 11 / 255, 34 / 255), + rgb(177 / 255, 0 / 255, 38 / 255)), + "18" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(255 / 255, 247 / 255, 185 / 255), + rgb(255 / 255, 240 / 255, 167 / 255), + rgb(255 / 255, 232 / 255, 150 / 255), + rgb(254 / 255, 224 / 255, 132 / 255), + rgb(254 / 255, 214 / 255, 115 / 255), + rgb(254 / 255, 198 / 255, 98 / 255), + rgb(254 / 255, 182 / 255, 81 / 255), + rgb(253 / 255, 167 / 255, 71 / 255), + rgb(253 / 255, 152 / 255, 64 / 255), + rgb(253 / 255, 134 / 255, 57 / 255), + rgb(253 / 255, 110 / 255, 50 / 255), + rgb(252 / 255, 82 / 255, 42 / 255), + rgb(243 / 255, 62 / 255, 36 / 255), + rgb(232 / 255, 42 / 255, 31 / 255), + rgb(218 / 255, 21 / 255, 30 / 255), + rgb(197 / 255, 11 / 255, 34 / 255), + rgb(177 / 255, 0 / 255, 38 / 255)), + "19" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(255 / 255, 247 / 255, 186 / 255), + rgb(255 / 255, 240 / 255, 169 / 255), + rgb(255 / 255, 233 / 255, 153 / 255), + rgb(254 / 255, 225 / 255, 136 / 255), + rgb(254 / 255, 218 / 255, 120 / 255), + rgb(254 / 255, 204 / 255, 104 / 255), + rgb(254 / 255, 188 / 255, 87 / 255), + rgb(254 / 255, 174 / 255, 74 / 255), + rgb(253 / 255, 159 / 255, 67 / 255), + rgb(253 / 255, 145 / 255, 61 / 255), + rgb(253 / 255, 125 / 255, 54 / 255), + rgb(252 / 255, 101 / 255, 47 / 255), + rgb(250 / 255,75 / 255, 41 / 255), + rgb(240 / 255, 58 / 255, 35 / 255), + rgb(231 / 255, 38 / 255, 30 / 255), + rgb(215 / 255, 20 / 255, 30 / 255), + rgb(196 / 255, 10 / 255, 34 / 255), + rgb(177 / 255, 0 / 255, 38 / 255)), + "20" = tibble::lst(rgb(255 / 255, 255 / 255, 204 / 255), + rgb(255 / 255, 248 / 255, 187 / 255), + rgb(255 / 255, 241 / 255, 171 / 255), + rgb(255 / 255, 234 / 255, 155 / 255), + rgb(254 / 255, 227 / 255, 140 / 255), + rgb(254 / 255, 220 / 255, 124 / 255), + rgb(254 / 255, 208 / 255, 109 / 255), + rgb(254 / 255, 194 / 255, 93 / 255), + rgb(254 / 255, 180 / 255, 78 / 255), + rgb(253 / 255, 166 / 255,70 / 255), + rgb(253 / 255, 152 / 255, 64 / 255), + rgb(253 / 255, 138 / 255, 58 / 255), + rgb(253 / 255, 117 / 255, 52 / 255), + rgb(252 / 255, 93 / 255, 45 / 255), + rgb(248 / 255,71 / 255, 39 / 255), + rgb(238 / 255, 54 / 255, 34 / 255), + rgb(229 / 255, 33 / 255, 29 / 255), + rgb(213 / 255, 19 / 255, 31 / 255), + rgb(195 / 255, 9 / 255, 35 / 255), + rgb(177 / 255, 0 / 255, 38 / 255)), + "type" = "sequential" + ), + "PuRd" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(231 / 255, 225 / 255, 239 / 255), + rgb(221 / 255, 28 / 255, 119 / 255)), + "3" = tibble::lst(rgb(231 / 255, 225 / 255, 239 / 255), + rgb(201 / 255, 148 / 255, 199 / 255), + rgb(221 / 255, 28 / 255, 119 / 255)), + "4" = tibble::lst(rgb(241 / 255, 238 / 255, 246 / 255), + rgb(215 / 255, 181 / 255, 216 / 255), + rgb(223 / 255, 101 / 255, 176 / 255), + rgb(206 / 255, 18 / 255, 86 / 255)), + "5" = tibble::lst(rgb(241 / 255, 238 / 255, 246 / 255), + rgb(215 / 255, 181 / 255, 216 / 255), + rgb(223 / 255, 101 / 255, 176 / 255), + rgb(221 / 255, 28 / 255, 119 / 255), + rgb(152 / 255, 0 / 255, 67 / 255)), + "6" = tibble::lst(rgb(241 / 255, 238 / 255, 246 / 255), + rgb(212 / 255, 185 / 255, 218 / 255), + rgb(201 / 255, 148 / 255, 199 / 255), + rgb(223 / 255, 101 / 255, 176 / 255), + rgb(221 / 255, 28 / 255, 119 / 255), + rgb(152 / 255, 0 / 255, 67 / 255)), + "7" = tibble::lst(rgb(241 / 255, 238 / 255, 246 / 255), + rgb(212 / 255, 185 / 255, 218 / 255), + rgb(201 / 255, 148 / 255, 199 / 255), + rgb(223 / 255, 101 / 255, 176 / 255), + rgb(231 / 255, 41 / 255, 138 / 255), + rgb(206 / 255, 18 / 255, 86 / 255), + rgb(145 / 255, 0 / 255, 63 / 255)), + "8" = tibble::lst(rgb(247 / 255, 244 / 255, 249 / 255), + rgb(231 / 255, 225 / 255, 239 / 255), + rgb(212 / 255, 185 / 255, 218 / 255), + rgb(201 / 255, 148 / 255, 199 / 255), + rgb(223 / 255, 101 / 255, 176 / 255), + rgb(231 / 255, 41 / 255, 138 / 255), + rgb(206 / 255, 18 / 255, 86 / 255), + rgb(145 / 255, 0 / 255, 63 / 255)), + "9" = tibble::lst(rgb(247 / 255, 244 / 255, 249 / 255), + rgb(231 / 255, 225 / 255, 239 / 255), + rgb(212 / 255, 185 / 255, 218 / 255), + rgb(201 / 255, 148 / 255, 199 / 255), + rgb(223 / 255, 101 / 255, 176 / 255), + rgb(231 / 255, 41 / 255, 138 / 255), + rgb(206 / 255, 18 / 255, 86 / 255), + rgb(152 / 255, 0 / 255, 67 / 255), + rgb(103 / 255, 0 / 255, 31 / 255)), + "10" = tibble::lst(rgb(247 / 255, 243 / 255, 248 / 255), + rgb(234 / 255, 229 / 255, 241 / 255), + rgb(220 / 255, 202 / 255, 227 / 255), + rgb(208 / 255, 172 / 255, 211 / 255), + rgb(203 / 255, 143 / 255, 196 / 255), + rgb(220 / 255, 106 / 255, 178 / 255), + rgb(229 / 255, 66 / 255, 150 / 255), + rgb(220 / 255, 31 / 255, 114 / 255), + rgb(192 / 255, 13 / 255, 80 / 255), + rgb(145 / 255, 0 / 255, 63 / 255)), + "11" = tibble::lst(rgb(247 / 255, 243 / 255, 248 / 255), + rgb(235 / 255, 230 / 255, 241 / 255), + rgb(223 / 255, 208 / 255, 230 / 255), + rgb(210 / 255, 181 / 255, 216 / 255), + rgb(203 / 255, 155 / 255, 202 / 255), + rgb(213 / 255, 126 / 255, 187 / 255), + rgb(225 / 255, 91 / 255, 168 / 255), + rgb(230 / 255, 49 / 255, 141 / 255), + rgb(216 / 255, 27 / 255, 106 / 255), + rgb(187 / 255, 11 / 255, 78 / 255), + rgb(145 / 255, 0 / 255, 63 / 255)), + "12" = tibble::lst(rgb(247 / 255, 243 / 255, 248 / 255), + rgb(236 / 255, 231 / 255, 242 / 255), + rgb(225 / 255, 214 / 255, 233 / 255), + rgb(213 / 255, 188 / 255, 219 / 255), + rgb(206 / 255, 164 / 255, 207 / 255), + rgb(205 / 255, 140 / 255, 194 / 255), + rgb(219 / 255, 110 / 255, 180 / 255), + rgb(227 / 255, 78 / 255, 158 / 255), + rgb(228 / 255, 39 / 255, 133 / 255), + rgb(213 / 255, 24 / 255, 99 / 255), + rgb(183 / 255, 10 / 255,77 / 255), + rgb(145 / 255, 0 / 255, 63 / 255)), + "13" = tibble::lst(rgb(247 / 255, 243 / 255, 248 / 255), + rgb(237 / 255, 232 / 255, 243 / 255), + rgb(227 / 255, 218 / 255, 235 / 255), + rgb(216 / 255, 194 / 255, 223 / 255), + rgb(208 / 255, 172 / 255, 211 / 255), + rgb(201 / 255, 151 / 255, 200 / 255), + rgb(213 / 255, 126 / 255, 187 / 255), + rgb(223 / 255, 97 / 255, 172 / 255), + rgb(229 / 255, 66 / 255, 150 / 255), + rgb(224 / 255, 35 / 255, 124 / 255), + rgb(210 / 255, 22 / 255, 94 / 255), + rgb(180 / 255, 9 / 255, 76 / 255), + rgb(145 / 255, 0 / 255, 63 / 255)), + "14" = tibble::lst(rgb(247 / 255, 243 / 255, 248 / 255), + rgb(238 / 255, 233 / 255, 243 / 255), + rgb(229 / 255, 221 / 255, 237 / 255), + rgb(219 / 255, 200 / 255, 226 / 255), + rgb(210 / 255, 179 / 255, 215 / 255), + rgb(204 / 255, 159 / 255, 204 / 255), + rgb(206 / 255, 138 / 255, 193 / 255), + rgb(218 / 255, 113 / 255, 181 / 255), + rgb(226 / 255, 86 / 255, 164 / 255), + rgb(230 / 255, 53 / 255, 143 / 255), + rgb(221 / 255, 32 / 255, 117 / 255), + rgb(208 / 255, 20 / 255, 89 / 255), + rgb(177 / 255, 8 / 255, 75 / 255), + rgb(145 / 255, 0 / 255, 63 / 255)), + "15" = tibble::lst(rgb(247 / 255, 243 / 255, 248 / 255), + rgb(238 / 255, 234 / 255, 243 / 255), + rgb(230 / 255, 224 / 255, 238 / 255), + rgb(221 / 255, 204 / 255, 228 / 255), + rgb(211 / 255, 184 / 255, 217 / 255), + rgb(206 / 255, 166 / 255, 208 / 255), + rgb(201 / 255, 147 / 255, 198 / 255), + rgb(213 / 255, 126 / 255, 187 / 255), + rgb(223 / 255, 101 / 255, 176 / 255), + rgb(227 / 255, 76 / 255, 156 / 255), + rgb(230 / 255, 40 / 255, 138 / 255), + rgb(218 / 255, 30 / 255, 111 / 255), + rgb(205 / 255, 18 / 255, 85 / 255), + rgb(175 / 255,7 / 255,74 / 255), + rgb(145 / 255, 0 / 255, 63 / 255)), + "16" = tibble::lst(rgb(247 / 255, 243 / 255, 248 / 255), + rgb(239 / 255, 235 / 255, 244 / 255), + rgb(232 / 255, 226 / 255, 239 / 255), + rgb(223 / 255, 208 / 255, 230 / 255), + rgb(214 / 255, 190 / 255, 220 / 255), + rgb(208 / 255, 172 / 255, 211 / 255), + rgb(203 / 255, 155 / 255, 202 / 255), + rgb(207 / 255, 136 / 255, 192 / 255), + rgb(217 / 255, 114 / 255, 182 / 255), + rgb(225 / 255, 91 / 255, 168 / 255), + rgb(229 / 255, 66 / 255, 150 / 255), + rgb(227 / 255, 38 / 255, 130 / 255), + rgb(216 / 255, 27 / 255, 106 / 255), + rgb(201 / 255, 16 / 255, 84 / 255), + rgb(172 / 255, 7 / 255,73 / 255), + rgb(145 / 255, 0 / 255, 63 / 255)), + "17" = tibble::lst(rgb(247 / 255, 243 / 255, 248 / 255), + rgb(239 / 255, 235 / 255, 244 / 255), + rgb(232 / 255, 227 / 255, 240 / 255), + rgb(225 / 255, 212 / 255, 232 / 255), + rgb(216 / 255, 194 / 255, 223 / 255), + rgb(210 / 255, 178 / 255, 214 / 255), + rgb(205 / 255, 161 / 255, 206 / 255), + rgb(202 / 255, 145 / 255, 197 / 255), + rgb(213 / 255, 126 / 255, 187 / 255), + rgb(221 / 255, 104 / 255, 177 / 255), + rgb(226 / 255, 82 / 255, 161 / 255), + rgb(229 / 255, 56 / 255, 144 / 255), + rgb(224 / 255, 35 / 255, 124 / 255), + rgb(214 / 255, 25 / 255, 101 / 255), + rgb(198 / 255, 15 / 255, 83 / 255), + rgb(171 / 255, 6 / 255, 72 / 255), + rgb(145 / 255, 0 / 255, 63 / 255)), + "18" = tibble::lst(rgb(247 / 255, 243 / 255, 248 / 255), + rgb(240 / 255, 236 / 255, 244 / 255), + rgb(233 / 255, 228 / 255, 240 / 255), + rgb(226 / 255, 215 / 255, 234 / 255), + rgb(218 / 255, 199 / 255, 225 / 255), + rgb(211 / 255, 182 / 255, 216 / 255), + rgb(207 / 255, 167 / 255, 209 / 255), + rgb(202 / 255, 152 / 255, 201 / 255), + rgb(208 / 255, 135 / 255, 192 / 255), + rgb(217 / 255, 116 / 255, 182 / 255), + rgb(224 / 255, 95 / 255, 171 / 255), + rgb(228 / 255, 74 / 255, 155 / 255), + rgb(230 / 255, 46 / 255, 140 / 255), + rgb(222 / 255, 33 / 255, 119 / 255), + rgb(212 / 255, 24 / 255, 97 / 255), + rgb(194 / 255, 14 / 255, 81 / 255), + rgb(169 / 255, 6 / 255,72 / 255), + rgb(145 / 255, 0 / 255, 63 / 255)), + "19" = tibble::lst(rgb(247 / 255, 243 / 255, 248 / 255), + rgb(240 / 255, 236 / 255, 245 / 255), + rgb(234 / 255, 229 / 255, 241 / 255), + rgb(227 / 255, 218 / 255, 235 / 255), + rgb(220 / 255, 202 / 255, 227 / 255), + rgb(213 / 255, 187 / 255, 219 / 255), + rgb(208 / 255, 172 / 255, 211 / 255), + rgb(204 / 255, 158 / 255, 204 / 255), + rgb(203 / 255, 143 / 255, 196 / 255), + rgb(213 / 255, 126 / 255, 187 / 255), + rgb(220 / 255, 106 / 255, 178 / 255), + rgb(225 / 255, 87 / 255, 165 / 255), + rgb(229 / 255, 66 / 255, 150 / 255), + rgb(229 / 255, 39 / 255, 135 / 255), + rgb(220 / 255, 31 / 255, 114 / 255), + rgb(210 / 255, 22 / 255, 94 / 255), + rgb(192 / 255, 13 / 255, 80 / 255), + rgb(168 / 255, 5 / 255, 71 / 255), + rgb(145 / 255, 0 / 255, 63 / 255)), + "20" = tibble::lst(rgb(247 / 255, 243 / 255, 248 / 255), + rgb(241 / 255, 236 / 255, 245 / 255), + rgb(235 / 255, 229 / 255, 241 / 255), + rgb(229 / 255, 220 / 255, 236 / 255), + rgb(222 / 255, 205 / 255, 229 / 255), + rgb(215 / 255, 191 / 255, 221 / 255), + rgb(209 / 255, 177 / 255, 213 / 255), + rgb(205 / 255, 163 / 255, 206 / 255), + rgb(201 / 255, 149 / 255, 199 / 255), + rgb(208 / 255, 134 / 255, 191 / 255), + rgb(216 / 255, 117 / 255, 183 / 255), + rgb(223 / 255, 98 / 255, 173 / 255), + rgb(227 / 255, 80 / 255, 159 / 255), + rgb(229 / 255, 58 / 255, 145 / 255), + rgb(227 / 255, 37 / 255, 129 / 255), + rgb(218 / 255, 29 / 255, 110 / 255), + rgb(208 / 255, 20 / 255, 91 / 255), + rgb(189 / 255, 12 / 255, 79 / 255), + rgb(167 / 255, 5 / 255,71 / 255), + rgb(145 / 255, 0 / 255, 63 / 255)), + "type" = "sequential" + ), + "Blues" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(222 / 255, 235 / 255, 247 / 255), + rgb(49 / 255, 130 / 255, 189 / 255)), + "3" = tibble::lst(rgb(222 / 255, 235 / 255, 247 / 255), + rgb(158 / 255, 202 / 255, 225 / 255), + rgb(49 / 255, 130 / 255, 189 / 255)), + "4" = tibble::lst(rgb(239 / 255, 243 / 255, 255 / 255), + rgb(189 / 255, 215 / 255, 231 / 255), + rgb(107 / 255, 174 / 255, 214 / 255), + rgb(33 / 255, 113 / 255, 181 / 255)), + "5" = tibble::lst(rgb(239 / 255, 243 / 255, 255 / 255), + rgb(189 / 255, 215 / 255, 231 / 255), + rgb(107 / 255, 174 / 255, 214 / 255), + rgb(49 / 255, 130 / 255, 189 / 255), + rgb(8 / 255, 81 / 255, 156 / 255)), + "6" = tibble::lst(rgb(239 / 255, 243 / 255, 255 / 255), + rgb(198 / 255, 219 / 255, 239 / 255), + rgb(158 / 255, 202 / 255, 225 / 255), + rgb(107 / 255, 174 / 255, 214 / 255), + rgb(49 / 255, 130 / 255, 189 / 255), + rgb(8 / 255, 81 / 255, 156 / 255)), + "7" = tibble::lst(rgb(239 / 255, 243 / 255, 255 / 255), + rgb(198 / 255, 219 / 255, 239 / 255), + rgb(158 / 255, 202 / 255, 225 / 255), + rgb(107 / 255, 174 / 255, 214 / 255), + rgb(66 / 255, 146 / 255, 198 / 255), + rgb(33 / 255, 113 / 255, 181 / 255), + rgb(8 / 255, 69 / 255, 148 / 255)), + "8" = tibble::lst(rgb(247 / 255, 251 / 255, 255 / 255), + rgb(222 / 255, 235 / 255, 247 / 255), + rgb(198 / 255, 219 / 255, 239 / 255), + rgb(158 / 255, 202 / 255, 225 / 255), + rgb(107 / 255, 174 / 255, 214 / 255), + rgb(66 / 255, 146 / 255, 198 / 255), + rgb(33 / 255, 113 / 255, 181 / 255), + rgb(8 / 255, 69 / 255, 148 / 255)), + "9" = tibble::lst(rgb(247 / 255, 251 / 255, 255 / 255), + rgb(222 / 255, 235 / 255, 247 / 255), + rgb(198 / 255, 219 / 255, 239 / 255), + rgb(158 / 255, 202 / 255, 225 / 255), + rgb(107 / 255, 174 / 255, 214 / 255), + rgb(66 / 255, 146 / 255, 198 / 255), + rgb(33 / 255, 113 / 255, 181 / 255), + rgb(8 / 255, 81 / 255, 156 / 255), + rgb(8 / 255, 48 / 255, 107 / 255)), + "10" = tibble::lst(rgb(247 / 255, 250 / 255, 255 / 255), + rgb(227 / 255, 238 / 255, 248 / 255), + rgb(208 / 255, 226 / 255, 242 / 255), + rgb(184 / 255, 213 / 255, 234 / 255), + rgb(152 / 255, 198 / 255, 223 / 255), + rgb(113 / 255, 177 / 255, 215 / 255), + rgb(80 / 255, 155 / 255, 203 / 255), + rgb(53 / 255, 131 / 255, 190 / 255), + rgb(29 / 255, 102 / 255, 173 / 255), + rgb(7 / 255, 69 / 255, 147 / 255)), + "11" = tibble::lst(rgb(247 / 255, 250 / 255, 255 / 255), + rgb(229 / 255, 239 / 255, 249 / 255), + rgb(212 / 255, 228 / 255, 243 / 255), + rgb(194 / 255, 217 / 255, 237 / 255), + rgb(166 / 255, 205 / 255, 227 / 255), + rgb(133 / 255, 187 / 255, 219 / 255), + rgb(99 / 255, 168 / 255, 210 / 255), + rgb(70 / 255, 148 / 255, 199 / 255), + rgb(48 / 255, 126 / 255, 187 / 255), + rgb(27 / 255, 99 / 255, 171 / 255), + rgb(7 / 255, 69 / 255, 147 / 255)), + "12" = tibble::lst(rgb(247 / 255, 250 / 255, 255 / 255), + rgb(231 / 255, 240 / 255, 249 / 255), + rgb(215 / 255, 230 / 255, 244 / 255), + rgb(200 / 255, 220 / 255, 239 / 255), + rgb(176 / 255, 209 / 255, 231 / 255), + rgb(149 / 255, 196 / 255, 223 / 255), + rgb(117 / 255, 179 / 255, 216 / 255), + rgb(89 / 255, 161 / 255, 206 / 255), + rgb(63 / 255, 142 / 255, 196 / 255), + rgb(43 / 255, 121 / 255, 185 / 255), + rgb(26 / 255, 96 / 255, 168 / 255), + rgb(7 / 255, 69 / 255, 147 / 255)), + "13" = tibble::lst(rgb(247 / 255, 250 / 255, 255 / 255), + rgb(232 / 255, 241 / 255, 250 / 255), + rgb(218 / 255, 232 / 255, 245 / 255), + rgb(204 / 255, 222 / 255, 241 / 255), + rgb(184 / 255, 213 / 255, 234 / 255), + rgb(161 / 255, 203 / 255, 226 / 255), + rgb(133 / 255, 187 / 255, 219 / 255), + rgb(103 / 255, 171 / 255, 212 / 255), + rgb(80 / 255, 155 / 255, 203 / 255), + rgb(59 / 255, 137 / 255, 193 / 255), + rgb(39 / 255, 118 / 255, 183 / 255), + rgb(25 / 255, 94 / 255, 167 / 255), + rgb(7 / 255, 69 / 255, 147 / 255)), + "14" = tibble::lst(rgb(247 / 255, 250 / 255, 255 / 255), + rgb(233 / 255, 242 / 255, 250 / 255), + rgb(220 / 255, 233 / 255, 246 / 255), + rgb(207 / 255, 225 / 255, 242 / 255), + rgb(191 / 255, 216 / 255, 236 / 255), + rgb(170 / 255, 207 / 255, 229 / 255), + rgb(146 / 255, 195 / 255, 222 / 255), + rgb(119 / 255, 180 / 255, 216 / 255), + rgb(95 / 255, 165 / 255, 209 / 255), + rgb(73 / 255, 150 / 255, 200 / 255), + rgb(55 / 255, 133 / 255, 191 / 255), + rgb(36 / 255, 115 / 255, 182 / 255), + rgb(24 / 255, 92 / 255, 165 / 255), + rgb(7 / 255, 69 / 255, 147 / 255)), + "15" = tibble::lst(rgb(247 / 255, 250 / 255, 255 / 255), + rgb(234 / 255, 242 / 255, 251 / 255), + rgb(222 / 255, 235 / 255, 247 / 255), + rgb(210 / 255, 226 / 255, 243 / 255), + rgb(197 / 255, 218 / 255, 238 / 255), + rgb(178 / 255, 210 / 255, 231 / 255), + rgb(158 / 255, 202 / 255, 224 / 255), + rgb(133 / 255, 187 / 255, 219 / 255), + rgb(107 / 255, 173 / 255, 214 / 255), + rgb(87 / 255, 159 / 255, 205 / 255), + rgb(65 / 255, 146 / 255, 197 / 255), + rgb(51 / 255, 129 / 255, 189 / 255), + rgb(32 / 255, 113 / 255, 180 / 255), + rgb(23 / 255, 90 / 255, 164 / 255), + rgb(7 / 255, 69 / 255, 147 / 255)), + "16" = tibble::lst(rgb(247 / 255, 250 / 255, 255 / 255), + rgb(235 / 255, 243 / 255, 251 / 255), + rgb(223 / 255, 236 / 255, 247 / 255), + rgb(212 / 255, 228 / 255, 243 / 255), + rgb(201 / 255, 221 / 255, 240 / 255), + rgb(184 / 255, 213 / 255, 234 / 255), + rgb(166 / 255, 205 / 255, 227 / 255), + rgb(145 / 255, 194 / 255, 222 / 255), + rgb(121 / 255, 181 / 255, 216 / 255), + rgb(99 / 255, 168 / 255, 210 / 255), + rgb(80 / 255, 155 / 255, 203 / 255), + rgb(62 / 255, 141 / 255, 195 / 255), + rgb(48 / 255, 126 / 255, 187 / 255), + rgb(31 / 255, 109 / 255, 178 / 255), + rgb(22 / 255, 89 / 255, 163 / 255), + rgb(7 / 255, 69 / 255, 147 / 255)), + "17" = tibble::lst(rgb(247 / 255, 250 / 255, 255 / 255), + rgb(236 / 255, 243 / 255, 251 / 255), + rgb(225 / 255, 236 / 255, 248 / 255), + rgb(214 / 255, 229 / 255, 244 / 255), + rgb(204 / 255, 222 / 255, 241 / 255), + rgb(190 / 255, 215 / 255, 236 / 255), + rgb(173 / 255, 208 / 255, 230 / 255), + rgb(155 / 255, 200 / 255, 224 / 255), + rgb(133 / 255, 187 / 255, 219 / 255), + rgb(110 / 255, 175 / 255, 214 / 255), + rgb(92 / 255, 163 / 255, 207 / 255), + rgb(74 / 255, 151 / 255, 200 / 255), + rgb(59 / 255, 137 / 255, 193 / 255), + rgb(45 / 255, 123 / 255, 186 / 255), + rgb(30 / 255, 107 / 255, 176 / 255), + rgb(22 / 255, 87 / 255, 162 / 255), + rgb(7 / 255, 69 / 255, 147 / 255)), + "18" = tibble::lst(rgb(247 / 255, 250 / 255, 255 / 255), + rgb(236 / 255, 244 / 255, 251 / 255), + rgb(226 / 255, 237 / 255, 248 / 255), + rgb(216 / 255, 231 / 255, 245 / 255), + rgb(206 / 255, 224 / 255, 241 / 255), + rgb(195 / 255, 217 / 255, 238 / 255), + rgb(179 / 255, 210 / 255, 232 / 255), + rgb(162 / 255, 203 / 255, 226 / 255), + rgb(143 / 255, 193 / 255, 221 / 255), + rgb(122 / 255, 182 / 255, 217 / 255), + rgb(102 / 255, 170 / 255, 212 / 255), + rgb(86 / 255, 159 / 255, 205 / 255), + rgb(68 / 255, 147 / 255, 198 / 255), + rgb(55 / 255, 134 / 255, 192 / 255), + rgb(42 / 255, 120 / 255, 185 / 255), + rgb(30 / 255, 105 / 255, 175 / 255), + rgb(21 / 255, 86 / 255, 161 / 255), + rgb(7 / 255, 69 / 255, 147 / 255)), + "19" = tibble::lst(rgb(247 / 255, 250 / 255, 255 / 255), + rgb(237 / 255, 244 / 255, 251 / 255), + rgb(227 / 255, 238 / 255, 248 / 255), + rgb(218 / 255, 232 / 255, 245 / 255), + rgb(208 / 255, 226 / 255, 242 / 255), + rgb(199 / 255, 219 / 255, 239 / 255), + rgb(184 / 255, 213 / 255, 234 / 255), + rgb(169 / 255, 206 / 255, 228 / 255), + rgb(152 / 255, 198 / 255, 223 / 255), + rgb(133 / 255, 187 / 255, 219 / 255), + rgb(113 / 255, 177 / 255, 215 / 255), + rgb(96 / 255, 166 / 255, 209 / 255), + rgb(80 / 255, 155 / 255, 203 / 255), + rgb(64 / 255, 144 / 255, 197 / 255), + rgb(53 / 255, 131 / 255, 190 / 255), + rgb(39 / 255, 118 / 255, 183 / 255), + rgb(29 / 255, 102 / 255, 173 / 255), + rgb(20 / 255, 85 / 255, 160 / 255), + rgb(7 / 255, 69 / 255, 147 / 255)), + "20" = tibble::lst(rgb(247 / 255, 250 / 255, 255 / 255), + rgb(237 / 255, 245 / 255, 252 / 255), + rgb(228 / 255, 239 / 255, 249 / 255), + rgb(219 / 255, 233 / 255, 246 / 255), + rgb(210 / 255, 227 / 255, 243 / 255), + rgb(201 / 255, 221 / 255, 240 / 255), + rgb(189 / 255, 215 / 255, 236 / 255), + rgb(175 / 255, 209 / 255, 230 / 255), + rgb(160 / 255, 202 / 255, 225 / 255), + rgb(142 / 255, 193 / 255, 221 / 255), + rgb(124 / 255, 182 / 255, 217 / 255), + rgb(105 / 255, 172 / 255, 213 / 255), + rgb(90 / 255, 162 / 255, 207 / 255), + rgb(75 / 255, 151 / 255, 201 / 255), + rgb(61 / 255, 140 / 255, 195 / 255), + rgb(50 / 255, 128 / 255, 189 / 255), + rgb(37 / 255, 116 / 255, 182 / 255), + rgb(28 / 255, 101 / 255, 172 / 255), + rgb(20 / 255, 84 / 255, 160 / 255), + rgb(7 / 255, 69 / 255, 147 / 255)), + "type" = "sequential" + ), + "PuBuGn" = tibble::lst( + "1" = tibble::lst(), + "2" = tibble::lst(rgb(236 / 255, 226 / 255, 240 / 255), + rgb(28 / 255, 144 / 255, 153 / 255)), + "3" = tibble::lst(rgb(236 / 255, 226 / 255, 240 / 255), + rgb(166 / 255, 189 / 255, 219 / 255), + rgb(28 / 255, 144 / 255, 153 / 255)), + "4" = tibble::lst(rgb(246 / 255, 239 / 255, 247 / 255), + rgb(189 / 255, 201 / 255, 225 / 255), + rgb(103 / 255, 169 / 255, 207 / 255), + rgb(2 / 255, 129 / 255, 138 / 255)), + "5" = tibble::lst(rgb(246 / 255, 239 / 255, 247 / 255), + rgb(189 / 255, 201 / 255, 225 / 255), + rgb(103 / 255, 169 / 255, 207 / 255), + rgb(28 / 255, 144 / 255, 153 / 255), + rgb(1 / 255, 108 / 255, 89 / 255)), + "6" = tibble::lst(rgb(246 / 255, 239 / 255, 247 / 255), + rgb(208 / 255, 209 / 255, 230 / 255), + rgb(166 / 255, 189 / 255, 219 / 255), + rgb(103 / 255, 169 / 255, 207 / 255), + rgb(28 / 255, 144 / 255, 153 / 255), + rgb(1 / 255, 108 / 255, 89 / 255)), + "7" = tibble::lst(rgb(246 / 255, 239 / 255, 247 / 255), + rgb(208 / 255, 209 / 255, 230 / 255), + rgb(166 / 255, 189 / 255, 219 / 255), + rgb(103 / 255, 169 / 255, 207 / 255), + rgb(54 / 255, 144 / 255, 192 / 255), + rgb(2 / 255, 129 / 255, 138 / 255), + rgb(1 / 255, 100 / 255, 80 / 255)), + "8" = tibble::lst(rgb(255 / 255, 247 / 255, 251 / 255), + rgb(236 / 255, 226 / 255, 240 / 255), + rgb(208 / 255, 209 / 255, 230 / 255), + rgb(166 / 255, 189 / 255, 219 / 255), + rgb(103 / 255, 169 / 255, 207 / 255), + rgb(54 / 255, 144 / 255, 192 / 255), + rgb(2 / 255, 129 / 255, 138 / 255), + rgb(1 / 255, 100 / 255, 80 / 255)), + "9" = tibble::lst(rgb(255 / 255, 247 / 255, 251 / 255), + rgb(236 / 255, 226 / 255, 240 / 255), + rgb(208 / 255, 209 / 255, 230 / 255), + rgb(166 / 255, 189 / 255, 219 / 255), + rgb(103 / 255, 169 / 255, 207 / 255), + rgb(54 / 255, 144 / 255, 192 / 255), + rgb(2 / 255, 129 / 255, 138 / 255), + rgb(1 / 255, 108 / 255, 89 / 255), + rgb(1 / 255, 70 / 255, 54 / 255)), + "10" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(240 / 255, 230 / 255, 242 / 255), + rgb(220 / 255, 216 / 255, 234 / 255), + rgb(194 / 255, 202 / 255, 226 / 255), + rgb(159 / 255, 186 / 255, 217 / 255), + rgb(110 / 255, 171 / 255, 208 / 255), + rgb(72 / 255, 152 / 255, 196 / 255), + rgb(37 / 255, 137 / 255, 167 / 255), + rgb(4 / 255, 122 / 255, 124 / 255), + rgb(0 / 255, 100 / 255, 80 / 255)), + "11" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(241 / 255, 232 / 255, 243 / 255), + rgb(224 / 255, 219 / 255, 235 / 255), + rgb(203 / 255, 206 / 255, 228 / 255), + rgb(174 / 255, 192 / 255, 221 / 255), + rgb(136 / 255, 179 / 255, 213 / 255), + rgb(94 / 255, 163 / 255, 203 / 255), + rgb(60 / 255, 146 / 255, 193 / 255), + rgb(31 / 255, 135 / 255, 159 / 255), + rgb(4 / 255, 120 / 255, 120 / 255), + rgb(0 / 255, 100 / 255, 80 / 255)), + "12" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(242 / 255, 233 / 255, 243 / 255), + rgb(228 / 255, 221 / 255, 237 / 255), + rgb(210 / 255, 210 / 255, 230 / 255), + rgb(185 / 255, 198 / 255, 224 / 255), + rgb(155 / 255, 185 / 255, 216 / 255), + rgb(115 / 255, 172 / 255, 209 / 255), + rgb(82 / 255, 157 / 255, 200 / 255), + rgb(51 / 255, 142 / 255, 186 / 255), + rgb(24 / 255, 133 / 255, 152 / 255), + rgb(5 / 255, 118 / 255, 116 / 255), + rgb(0 / 255, 100 / 255, 80 / 255)), + "13" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(243 / 255, 234 / 255, 244 / 255), + rgb(231 / 255, 223 / 255, 238 / 255), + rgb(215 / 255, 213 / 255, 232 / 255), + rgb(194 / 255, 202 / 255, 226 / 255), + rgb(169 / 255, 190 / 255, 219 / 255), + rgb(136 / 255, 179 / 255, 213 / 255), + rgb(99 / 255, 166 / 255, 205 / 255), + rgb(72 / 255, 152 / 255, 196 / 255), + rgb(45 / 255, 140 / 255, 178 / 255), + rgb(17 / 255, 131 / 255, 146 / 255), + rgb(5 / 255, 116 / 255, 113 / 255), + rgb(0 / 255, 100 / 255, 80 / 255)), + "14" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(244 / 255, 235 / 255, 245 / 255), + rgb(233 / 255, 224 / 255, 239 / 255), + rgb(218 / 255, 215 / 255, 233 / 255), + rgb(201 / 255, 205 / 255, 228 / 255), + rgb(179 / 255, 195 / 255, 222 / 255), + rgb(152 / 255, 184 / 255, 216 / 255), + rgb(119 / 255, 173 / 255, 209 / 255), + rgb(89 / 255, 161 / 255, 202 / 255), + rgb(63 / 255, 147 / 255, 194 / 255), + rgb(40 / 255, 138 / 255, 170 / 255), + rgb(9 / 255, 130 / 255, 142 / 255), + rgb(5 / 255, 115 / 255, 110 / 255), + rgb(0 / 255, 100 / 255, 80 / 255)), + "15" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(245 / 255, 236 / 255, 245 / 255), + rgb(235 / 255, 225 / 255, 240 / 255), + rgb(222 / 255, 217 / 255, 234 / 255), + rgb(208 / 255, 209 / 255, 229 / 255), + rgb(187 / 255, 198 / 255, 224 / 255), + rgb(165 / 255, 189 / 255, 218 / 255), + rgb(136 / 255, 179 / 255, 213 / 255), + rgb(102 / 255, 169 / 255, 206 / 255), + rgb(80 / 255, 156 / 255, 199 / 255), + rgb(53 / 255, 144 / 255, 191 / 255), + rgb(35 / 255, 136 / 255, 164 / 255), + rgb(1 / 255, 128 / 255, 138 / 255), + rgb(5 / 255, 114 / 255, 108 / 255), + rgb(0 / 255, 100 / 255, 80 / 255)), + "16" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(246 / 255, 237 / 255, 245 / 255), + rgb(237 / 255, 227 / 255, 240 / 255), + rgb(224 / 255, 219 / 255, 235 / 255), + rgb(211 / 255, 211 / 255, 231 / 255), + rgb(194 / 255, 202 / 255, 226 / 255), + rgb(174 / 255, 192 / 255, 221 / 255), + rgb(150 / 255, 183 / 255, 215 / 255), + rgb(121 / 255, 174 / 255, 210 / 255), + rgb(94 / 255, 163 / 255, 203 / 255), + rgb(72 / 255, 152 / 255, 196 / 255), + rgb(49 / 255, 142 / 255, 184 / 255), + rgb(31 / 255, 135 / 255, 159 / 255), + rgb(2 / 255, 127 / 255, 134 / 255), + rgb(4 / 255, 113 / 255, 106 / 255), + rgb(0 / 255, 100 / 255, 80 / 255)), + "17" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(246 / 255, 237 / 255, 246 / 255), + rgb(238 / 255, 228 / 255, 241 / 255), + rgb(227 / 255, 220 / 255, 236 / 255), + rgb(215 / 255, 213 / 255, 232 / 255), + rgb(200 / 255, 205 / 255, 227 / 255), + rgb(181 / 255, 196 / 255, 223 / 255), + rgb(162 / 255, 187 / 255, 218 / 255), + rgb(136 / 255, 179 / 255, 213 / 255), + rgb(107 / 255, 170 / 255, 207 / 255), + rgb(86 / 255, 159 / 255, 201 / 255), + rgb(64 / 255, 148 / 255, 194 / 255), + rgb(45 / 255, 140 / 255, 178 / 255), + rgb(26 / 255, 133 / 255, 154 / 255), + rgb(3 / 255, 125 / 255, 130 / 255), + rgb(4 / 255, 112 / 255, 104 / 255), + rgb(0 / 255, 100 / 255, 80 / 255)), + "18" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(247 / 255, 238 / 255, 246 / 255), + rgb(239 / 255, 229 / 255, 241 / 255), + rgb(229 / 255, 221 / 255, 237 / 255), + rgb(217 / 255, 214 / 255, 233 / 255), + rgb(205 / 255, 207 / 255, 229 / 255), + rgb(188 / 255, 199 / 255, 224 / 255), + rgb(171 / 255, 191 / 255, 220 / 255), + rgb(148 / 255, 183 / 255, 215 / 255), + rgb(123 / 255, 174 / 255, 210 / 255), + rgb(98 / 255, 166 / 255, 205 / 255), + rgb(79 / 255, 155 / 255, 199 / 255), + rgb(57 / 255, 145 / 255, 192 / 255), + rgb(41 / 255, 138 / 255, 172 / 255), + rgb(22 / 255, 132 / 255, 150 / 255), + rgb(4 / 255, 123 / 255, 127 / 255), + rgb(4 / 255, 111 / 255, 103 / 255), + rgb(0 / 255, 100 / 255, 80 / 255)), + "19" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(247 / 255, 238 / 255, 246 / 255), + rgb(240 / 255, 230 / 255, 242 / 255), + rgb(231 / 255, 223 / 255, 238 / 255), + rgb(220 / 255, 216 / 255, 234 / 255), + rgb(209 / 255, 209 / 255, 230 / 255), + rgb(194 / 255, 202 / 255, 226 / 255), + rgb(177 / 255, 194 / 255, 222 / 255), + rgb(159 / 255, 186 / 255, 217 / 255), + rgb(136 / 255, 179 / 255, 213 / 255), + rgb(110 / 255, 171 / 255, 208 / 255), + rgb(91 / 255, 161 / 255, 202 / 255), + rgb(72 / 255, 152 / 255, 196 / 255), + rgb(52 / 255, 143 / 255, 188 / 255), + rgb(37 / 255, 137 / 255, 167 / 255), + rgb(17 / 255, 131 / 255, 146 / 255), + rgb(4 / 255, 122 / 255, 124 / 255), + rgb(4 / 255, 111 / 255, 102 / 255), + rgb(0 / 255, 100 / 255, 80 / 255)), + "20" = tibble::lst(rgb(255 / 255, 247 / 255, 250 / 255), + rgb(247 / 255, 239 / 255, 246 / 255), + rgb(240 / 255, 231 / 255, 242 / 255), + rgb(233 / 255, 224 / 255, 238 / 255), + rgb(222 / 255, 217 / 255, 235 / 255), + rgb(212 / 255, 211 / 255, 231 / 255), + rgb(199 / 255, 204 / 255, 227 / 255), + rgb(183 / 255, 197 / 255, 223 / 255), + rgb(168 / 255, 190 / 255, 219 / 255), + rgb(147 / 255, 182 / 255, 215 / 255), + rgb(124 / 255, 175 / 255, 210 / 255), + rgb(100 / 255, 167 / 255, 206 / 255), + rgb(84 / 255, 158 / 255, 200 / 255), + rgb(66 / 255, 149 / 255, 195 / 255), + rgb(48 / 255, 141 / 255, 183 / 255), + rgb(34 / 255, 136 / 255, 163 / 255), + rgb(12 / 255, 130 / 255, 143 / 255), + rgb(4 / 255, 121 / 255, 122 / 255), + rgb(4 / 255, 110 / 255, 100 / 255), + rgb(0 / 255, 100 / 255, 80 / 255)), + "type" = "sequential" + ) +) + + diff --git a/R/sits_filters.R b/R/sits_filters.R index 97c46384c..d7bcaccb6 100644 --- a/R/sits_filters.R +++ b/R/sits_filters.R @@ -35,40 +35,40 @@ #' @return result.tb a sits_table with same samples and the new bands #' @export sits_apply <- function(data.tb, fun, fun_index = NULL, bands_suffix = "") { - # get the bands in the data - bands <- sits_bands(data.tb) - ensurer::ensure_that(bands, length(.) > 0, err_desc = "sits_apply: at least one band should be provided.") - - data.tb$time_series <- data.tb$time_series %>% - purrr::map(function(ts.tb) { - ts_computed.lst <- dplyr::select(ts.tb, dplyr::one_of(bands)) %>% - purrr::map(function(band) { - result <- fun(band) - return(result) - }) - - # append bands names' suffixes - if (nchar(bands_suffix) != 0) - names(ts_computed.lst) <- ifelse(bands == "Index", "Index", paste0(bands, ".", bands_suffix)) - - # unlist if there are more than one result from `fun` - if (is.recursive(ts_computed.lst[[1]])) - ts_computed.lst <- unlist(ts_computed.lst, recursive = FALSE) - - # convert to tibble - ts_computed.tb <- tibble::as_tibble(ts_computed.lst) - ensurer::ensure_that(ts_computed.tb, (any(names(.) == "Index") | !is.null(fun_index)), - err_desc = "sits_apply: computed time series does not have `Index` column. - Add `Index` in `bands` argument or provide a function to `fun_index` - in order to compute a `Index` column.") - - if (!is.null(fun_index)) - ts_computed.tb <- dplyr::mutate(ts_computed.tb, Index = fun_index(ts.tb$Index)) - - return(dplyr::select(ts_computed.tb, Index, dplyr::everything())) - }) - - return(data.tb) + # get the bands in the data + bands <- sits_bands(data.tb) + ensurer::ensure_that(bands, length(.) > 0, err_desc = "sits_apply: at least one band should be provided.") + + data.tb$time_series <- data.tb$time_series %>% + purrr::map(function(ts.tb) { + ts_computed.lst <- dplyr::select(ts.tb, dplyr::one_of(bands)) %>% + purrr::map(function(band) { + result <- fun(band) + return(result) + }) + + # append bands names' suffixes + if (nchar(bands_suffix) != 0) + names(ts_computed.lst) <- ifelse(bands == "Index", "Index", paste0(bands, ".", bands_suffix)) + + # unlist if there are more than one result from `fun` + if (is.recursive(ts_computed.lst[[1]])) + ts_computed.lst <- unlist(ts_computed.lst, recursive = FALSE) + + # convert to tibble + ts_computed.tb <- tibble::as_tibble(ts_computed.lst) + ensurer::ensure_that(ts_computed.tb, (any(names(.) == "Index") | !is.null(fun_index)), + err_desc = "sits_apply: computed time series does not have `Index` column. + Add `Index` in `bands` argument or provide a function to `fun_index` + in order to compute a `Index` column.") + + if (!is.null(fun_index)) + ts_computed.tb <- dplyr::mutate(ts_computed.tb, Index = fun_index(ts.tb$Index)) + + return(dplyr::select(ts_computed.tb, Index, dplyr::everything())) + }) + + return(data.tb) } #' @title Lagged differences of a SITS band. #' @name sits_lag_diff @@ -80,20 +80,20 @@ sits_apply <- function(data.tb, fun, fun_index = NULL, bands_suffix = "") { #' @return result.tb a sits_table with same samples and the new bands #' @export sits_lag_diff <- function(data.tb, bands = NULL, differences = 1) { - if (is.null(bands)) - bands <- sits_bands(data.tb) - - ensurer::ensure_that(bands, length(.) > 0, err_desc = "sits_ts_diff: at least one band should be provided.") - - result.tb <- data.tb - - # compute differential - result.tb <- sits_apply(data.tb, - fun = function(band) diff(band, lag = 1, differences = differences), - fun_index = function(band) band[0:-differences], - bands_suffix = paste0("diff", differences)) - - return(result.tb) + if (is.null(bands)) + bands <- sits_bands(data.tb) + + ensurer::ensure_that(bands, length(.) > 0, err_desc = "sits_ts_diff: at least one band should be provided.") + + result.tb <- data.tb + + # compute differential + result.tb <- sits_apply(data.tb, + fun = function(band) diff(band, lag = 1, differences = differences), + fun_index = function(band) band[0:-differences], + bands_suffix = paste0("diff", differences)) + + return(result.tb) } #' @title Inerpolation function of sits_table's time series #' @name sits_linear_interp @@ -105,17 +105,17 @@ sits_lag_diff <- function(data.tb, bands = NULL, differences = 1) { #' @return result.tb a sits_table with same samples and the new bands #' @export sits_linear_interp <- function(data.tb, n = 23){ - # get the bands of the SITS tibble - bands <- sits_bands(data.tb) - ensurer::ensure_that(bands, length(.) > 0, err_desc = "sits_ts_approx: at least one band should be provided.") - - # compute linear approximation - result.tb <- sits_apply(data.tb, - fun = function(band) stats::approx(band, n = n, ties=mean)$y, - fun_index = function(band) as.Date(stats::approx(band, n = n, ties=mean)$y, - origin = "1970-01-01")) - - return(result.tb) + # get the bands of the SITS tibble + bands <- sits_bands(data.tb) + ensurer::ensure_that(bands, length(.) > 0, err_desc = "sits_ts_approx: at least one band should be provided.") + + # compute linear approximation + result.tb <- sits_apply(data.tb, + fun = function(band) stats::approx(band, n = n, ties=mean)$y, + fun_index = function(band) as.Date(stats::approx(band, n = n, ties=mean)$y, + origin = "1970-01-01")) + + return(result.tb) } #' @title Inerpolation function of sits_table's time series @@ -130,21 +130,21 @@ sits_linear_interp <- function(data.tb, n = 23){ #' @return result.tb a sits_table with same samples and the new bands #' @export sits_interp <- function(data.tb, fun = stats::approx, n = 23, ...){ - # get the bands of the SITS tibble - bands <- sits_bands(data.tb) - ensurer::ensure_that(bands, length(.) > 0, err_desc = "sits_interp: at least one band should be provided.") - - # compute linear approximation - result.tb <- sits_apply(data.tb, - fun = function(band) fun(band, n = n, ...)$y, - fun_index = function(band) as.Date(fun(band, n = n, ...)$y, - origin = "1970-01-01")) - - return(result.tb) + # get the bands of the SITS tibble + bands <- sits_bands(data.tb) + ensurer::ensure_that(bands, length(.) > 0, err_desc = "sits_interp: at least one band should be provided.") + + # compute linear approximation + result.tb <- sits_apply(data.tb, + fun = function(band) fun(band, n = n, ...)$y, + fun_index = function(band) as.Date(fun(band, n = n, ...)$y, + origin = "1970-01-01")) + + return(result.tb) } #' @title Remove missing values #' @name sits_missing_values -#' @author Gilberto Camarara, \email{gilberto.camara@inpe.br} +#' @author Gilberto Camara, \email{gilberto.camara@inpe.br} #' @description This function removes the missing values from an image time series #' @param data.tb a valid sits table #' @param mv a number indicating missing values in a time series. @@ -152,24 +152,24 @@ sits_interp <- function(data.tb, fun = stats::approx, n = 23, ...){ #' @export #' sits_missing_values <- function(data.tb, mv = NULL) { - # get the bands in the data - bands <- sits_bands(data.tb) - ensurer::ensure_that(bands, length(.) > 0, err_desc = "sits_missing_values: at least one band should be provided.") - - # copy the results - time_series <- data.tb$time_series - - # update missing values to NA - for (b in bands){ - time_series[,b][time_series[,b] == mv] <- NA - } - - # interpolate missing values - time_series[,bands] <- zoo::na.spline(time_series[,bands]) - - data.tb$time_series <- time_series - - return (data.tb) + # get the bands in the data + bands <- sits_bands(data.tb) + ensurer::ensure_that(bands, length(.) > 0, err_desc = "sits_missing_values: at least one band should be provided.") + + # copy the results + time_series <- data.tb$time_series + + # update missing values to NA + for (b in bands){ + time_series[,b][time_series[,b] == mv] <- NA + } + + # interpolate missing values + time_series[,bands] <- zoo::na.spline(time_series[,bands]) + + data.tb$time_series <- time_series + + return (data.tb) } @@ -183,13 +183,13 @@ sits_missing_values <- function(data.tb, mv = NULL) { #' @return result.tb a sits_table with same samples and the new bands #' @export sits_envelope <- function(data.tb, window_size = 1){ - # compute envelopes - result.tb <- sits_apply(data.tb, - fun = function(band) dtwclust::compute_envelope(band, window.size = window_size, error.check = FALSE), - fun_index = function(band) band) - - return(result.tb) - + # compute envelopes + result.tb <- sits_apply(data.tb, + fun = function(band) dtwclust::compute_envelope(band, window.size = window_size, error.check = FALSE), + fun_index = function(band) band) + + return(result.tb) + } #' Smooth the time series using Whittaker smoother (based on PTW package) @@ -204,12 +204,12 @@ sits_envelope <- function(data.tb, window_size = 1){ #' @return output.tb a tibble with smoothed sits time series #' @export sits_whittaker <- function (data.tb, lambda = 0.5, bands_suffix = "whit") { - result.tb <- sits_apply(data.tb, - fun = function(band) ptw::whit2(band, lambda = lambda), - fun_index = function(band) band, - bands_suffix = bands_suffix) - - return(result.tb) + result.tb <- sits_apply(data.tb, + fun = function(band) ptw::whit2(band, lambda = lambda), + fun_index = function(band) band, + bands_suffix = bands_suffix) + + return(result.tb) } #' Smooth the time series using Savitsky-Golay filter (based on signal package) @@ -225,12 +225,12 @@ sits_whittaker <- function (data.tb, lambda = 0.5, bands_suffix = "whit") { #' @return output.tb a tibble with smoothed sits time series #' @export sits_sgolay <- function (data.tb, order = 3, scale = 1, bands_suffix = "sg") { - result.tb <- sits_apply(data.tb, - fun = function(band) signal::sgolayfilt(band, p = order, ts = scale), - fun_index = function(band) band, - bands_suffix = bands_suffix) - - return(result.tb) + result.tb <- sits_apply(data.tb, + fun = function(band) signal::sgolayfilt(band, p = order, ts = scale), + fun_index = function(band) band, + bands_suffix = bands_suffix) + + return(result.tb) } diff --git a/R/sits_init.R b/R/sits_init.R index 8c519f11e..c8245dae1 100644 --- a/R/sits_init.R +++ b/R/sits_init.R @@ -20,49 +20,10 @@ utils::globalVariables(c(".", "%>%", "Index", "value", "variable", "cond", "year", "start_date", "end_date", "time_series", "name", "scale_factor", "missing_value", "original_label", "n_members", "count", - "segr", "frac", "total", "n")) + "segr", "frac", "total", "n", + "distance", "twdtw_distances", "predicted", "new_values")) # define the dependencies of the SITS package -#' @importFrom snow sendCall recvOneData clusterExport -#' @importFrom raster shapefile brick beginCluster endCluster getCluster returnCluster blockSize trim canProcessInMemory rasterTmpFile getValues pbCreate pbStep pbClose writeStart writeValues setValues writeStop -#' @importFrom kohonen supersom somgrid -#' @importFrom ensurer ensure_that ensure check check_that -#' @importFrom entropy entropy -#' @importFrom dplyr bind_rows contains distinct do filter inner_join left_join matches mutate num_range one_of rename rename_ rowwise select starts_with transmute if_else -#' @importFrom readr cols col_integer col_double col_date col_character read_csv write_lines -#' @importFrom tibble tibble as_tibble add_column add_row lst -#' @importFrom tidyr nest unnest drop_na -#' @importFrom purrr map map2 map_df is_null -#' @importFrom purrrlyr by_row -#' @importFrom ggplot2 ggplot aes geom_line labs scale_color_brewer scale_colour_hue -#' @importFrom jsonlite fromJSON toJSON -#' @importFrom lubridate as_date dyears days period yday year ymd -#' @importFrom methods new as -#' @importFrom mgcv gam predict.gam s -#' @importFrom parallel mcMap -#' @importFrom ptw whit2 -#' @importFrom reshape2 melt -#' @importFrom rfUtilities accuracy -#' @importFrom signal sgolayfilt -#' @importFrom sp bbox SpatialPoints CRS proj4string -#' @importFrom stats sd setNames predict as.formula -#' @importFrom stringr str_extract str_detect -#' @importFrom tools file_ext file_path_sans_ext -#' @importFrom utils head tail -#' @importFrom wtss WTSS timeSeries listCoverages describeCoverage -#' @importFrom zoo zoo fortify.zoo -#' @importFrom nnet multinom class.ind -#' @importFrom randomForest randomForest -#' @importFrom gbm gbm -#' @importFrom nnet multinom -#' @importFrom glmnet cv.glmnet -#' @importFrom MASS lda -#' @importFrom e1071 svm -#' @importFrom caret createDataPartition -#' @import magrittr -#' @import dtwclust -#' @import dtwSat -#' @import Rcpp #' @useDynLib sits, .registration = TRUE #' NULL diff --git a/R/sits_patterns.R b/R/sits_patterns.R index bcd97f663..b41adc083 100644 --- a/R/sits_patterns.R +++ b/R/sits_patterns.R @@ -27,12 +27,15 @@ #' (used only in `kohonen` or `kohonen-dendogram` methods). Default is 100. #' @param koh_alpha learning rate, a vector of two numbers indicating the amount of change. #' Default is to decline linearly from 0.05 to 0.01 over rlen updates. +#' @param unsupervised if TRUE, proceeds an unsupervised cluster followed by a relabel taking original label majority ( +#' this option has not any effect if method == "gam") #' @param show show the results of the clustering algorithm? (for clustering methods). +#' @param ... additional arguments to be passed to the method function. #' @return patterns.tb a SITS table with the patterns. #' @export sits_patterns <- function (samples.tb, method = "gam", bands = NULL, from = NULL, to = NULL, freq = 8, formula = y ~ s(x), n_clusters = 2, grouping_method = "ward.D2", min_clu_perc = 0.10, apply_gam = FALSE, - koh_xgrid = 5, koh_ygrid = 5, koh_rlen = 100, koh_alpha = c(0.05, 0.01), show = FALSE) { + koh_xgrid = 5, koh_ygrid = 5, koh_rlen = 100, koh_alpha = c(0.05, 0.01), unsupervised = FALSE, show = FALSE, ...) { # check the input exists ensurer::ensure_that(samples.tb, !purrr::is_null(.), err_desc = "sits_patterns: input data not provided") @@ -61,38 +64,40 @@ sits_patterns <- function (samples.tb, method = "gam", bands = NULL, from = NULL } switch(method, - "gam" = { patterns.tb <- .sits_patterns_gam (samples.tb, bands = bands, from = from, to = to, freq = freq, formula = formula) }, + "gam" = { patterns.tb <- .sits_patterns_gam (samples.tb, bands = bands, from = from, to = to, freq = freq, formula = formula, ...) }, "dendogram" = { patterns.tb <- sits_cluster (samples.tb, bands = bands, method = "dendogram", n_clusters = n_clusters, grouping_method = grouping_method, - return_members = apply_gam, unsupervised = FALSE, show = show) + return_members = apply_gam, unsupervised = unsupervised, show = show, ... = ...) }, "centroids" = { patterns.tb <- sits_cluster (samples.tb, bands = bands, method = "centroids", n_clusters = n_clusters, grouping_method = grouping_method, - return_members = apply_gam, unsupervised = FALSE, show = show) + return_members = apply_gam, unsupervised = unsupervised, show = show, ... = ...) }, "kohonen" = { patterns.tb <- sits_cluster (samples.tb, bands = bands, method = "kohonen", koh_xgrid = koh_xgrid, koh_ygrid = koh_ygrid, koh_rlen = koh_rlen, koh_alpha = koh_alpha, - return_members = apply_gam, unsupervised = FALSE, show = show) + return_members = apply_gam, unsupervised = unsupervised, show = show, ... = ...) }, "kohonen-dendogram" = { patterns.tb <- sits_cluster (samples.tb, bands = bands, method = "kohonen-dendogram", n_clusters = n_clusters, grouping_method = grouping_method, koh_xgrid = koh_xgrid, koh_ygrid = koh_ygrid, koh_rlen = koh_rlen, koh_alpha = koh_alpha, - return_members = apply_gam, unsupervised = FALSE, show = show) + return_members = apply_gam, unsupervised = unsupervised, show = show, ... = ...) }) # get only the significant clusters - if (method != "gam") - patterns.tb <- sits_significant_labels(patterns.tb, min_label_frac = min_clu_perc) + if (method != "gam"){ + if (!unsupervised) + patterns.tb <- sits_significant_labels(patterns.tb, min_label_frac = min_clu_perc) + } if (apply_gam) { # get cluster information before calling GAM... pat_labels.tb <- sits_labels(patterns.tb) # extract only significant clusters (cut line given by min_clu_perc parameter) - patterns.tb <- .sits_patterns_gam (patterns.tb, bands = bands, from = from, to = to, freq = freq, formula = formula) + patterns.tb <- .sits_patterns_gam (patterns.tb, bands = bands, from = from, to = to, freq = freq, formula = formula, ... = ...) # append cluster informations to the result patterns.tb <- dplyr::inner_join(pat_labels.tb, patterns.tb, by = "label") %>% dplyr::select(longitude, latitude, start_date, end_date, label, coverage, time_series, original_label, n_members = count) @@ -128,10 +133,10 @@ sits_patterns <- function (samples.tb, method = "gam", bands = NULL, from = NULL #' @param to end data of the estimated (month-day) #' @param freq int - the interval in days for the estimates to be generated #' @param formula the formula to be applied in the estimate +#' @param ... any additional parameters #' @return patterns.tb a SITS table with the patterns #' -#' -.sits_patterns_gam <- function (samples.tb, bands, from, to, freq, formula){ +.sits_patterns_gam <- function (samples.tb, bands, from, to, freq, formula, ...){ # create a tibble to store the results patterns.tb <- sits_table() diff --git a/R/sits_plot.R b/R/sits_plot.R index 4d97130a8..0906038fe 100644 --- a/R/sits_plot.R +++ b/R/sits_plot.R @@ -12,7 +12,7 @@ #' @param start_date the start date of the plot (used for showing classifications) #' @param end_date the end date of the plot (used for showing classifications) #' @param interval the interval between classifications (used for showing classifications) -#' @param overlap minimum overlapping between one match and the interval of classification +#' @param overlap minimum overlapping between one match and the interval of classification. For details see dtwSat::twdtwApply help. #' @param n_matches number of matches of a given label to be displayed #' @return data.tb tibble - the input SITS table (useful for chaining functions) #' @export @@ -259,5 +259,66 @@ sits_plot <- function (data.tb = NULL, type = "allyears", colors = "Dark2", labe sep = "") return (title) } +#' @title Plot a dendrogram +#' @name sits_plot_dendrogram +#' +#' @description Plot an enhanced dendrogram based on a cluster object usually found in `.sits_last_cluster` +#' +#' @param data.tb SITS tibble to extract labels +#' @param cluster_obj cluster object. Usually stored by `sits_cluster` function in `.sits_last_object` +#' @param cutree_height A dashed horizontal line to be drawed indicating the height of dendrogram cutting. +#' @param colors a color scheme as showed in `sits_color_name` function +#' @param ... Other parameters to be passed to graphics::plot() function +#' @export +sits_plot_dendrogram <- function(data.tb, + cluster_obj = NULL, + cutree_height = NULL, + colors = "RdYlGn", ...){ + # get cluster_obj + if (is.null(cluster_obj)) + cluster_obj <- sits_last_cluster() + + # ensures that a cluster object is informed or exists in .sits_last_cluster global variable. + ensurer::ensure_that(cluster_obj, !is.null(.), err_desc = "plot_dendrogram: no valid `cluster_obj` informed or found in `.sits_last_cluster`.") + + # get unique labels + data_labels <- data.tb$label + uniq_labels <- base::unique(data_labels) + + # warns if the number of available colors is insufficient to all labels + if (length(uniq_labels) > (length(.sits_brewerRGB[[sits_color_name(colors)]]) - 1)) + message("sits_plot_dendrogram: The number of labels is greater than the number of available colors.") + + # extract the dendrogram object + hclust_cl <- methods::S3Part(cluster_obj, strictS3 = TRUE) + dendrogram <- hclust_cl %>% + stats::as.dendrogram() + + # prepare labels color vector + cols <- character(length(data_labels)) + cols[] <- rgb(0/255, 0/255, 0/255, 0/255) + i <- 1 + seq(uniq_labels) %>% + purrr::map(function (i){ + cols[data_labels[hclust_cl$order] == uniq_labels[i]] <<- .sits_brewerRGB[[sits_color_name(colors)]][[length(uniq_labels)]][[i]] + i <<- i + 1 + }) + + # plot the dendrogram + dendrogram %>% + dendextend::set("labels", character(length = length(data_labels))) %>% + dendextend::set("branches_k_color", value = cols, k = length(data_labels)) %>% + graphics::plot(xlab = "Clusters", ylab = paste(tools::file_path_sans_ext(cluster_obj@method), + "linkage distance"), ...) + + + # plot cutree line + if (!is.null(cutree_height)) graphics::abline(h = cutree_height, lty = 2) + + # plot legend + graphics::legend("topright", + fill = as.character(.sits_brewerRGB[[sits_color_name(colors)]][[length(uniq_labels)]]), + legend = uniq_labels) +} diff --git a/R/sits_separability.R b/R/sits_separability.R new file mode 100644 index 000000000..6874086f1 --- /dev/null +++ b/R/sits_separability.R @@ -0,0 +1,236 @@ +#' @title compare clusters against original labels and computes a separability matrix. +#' @name sits_cluster_separability +#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br} +#' +#' @description Given a sits table with `original_label` column, computes a separability matrix +#' between the original labels (`original_label` column) and new labels. This is useful +#' to analyse the separability of samples for a given clustering algorithm. +#' +#' @param data.tb a SITS table with the samples to be validated +#' @return result.tb a tibble with separability matrix. +#' @export +sits_cluster_separability <- function (data.tb){ + ensurer::ensure_that(data.tb, !purrr::is_null(.), + err_desc = "sits_cluster_separability: SITS table not provided") + # do the input data have the `original_label` column? + ensurer::ensure_that(data.tb, "original_label" %in% colnames(data.tb), + err_desc = "sits_cluster_separability: informed SITS table has not an `original_label` column.") + + # verify if there is n_members column. If not exists initialize it with ones. + if (!any("n_members" %in% names(data.tb))) + data.tb$n_members <- data.tb$label %>% + purrr::map(function(label) return(tibble::tibble(original_label = label, n = 1))) + + result.tb <- data.tb %>% + tidyr::unnest_("n_members", .sep = ".") %>% + dplyr::group_by_("n_members.original_label", "label") %>% + dplyr::summarize(count = sum(n_members.n, na.rm = TRUE)) %>% + dplyr::select_(original_label="n_members.original_label", "label", "count") %>% + tidyr::spread_(key = "label", value = "count") %>% + dplyr::ungroup() + + return (result.tb) +} + +#' @title computes a separability measure from a clusterized SITS table data. +#' @name sits_separability_measure +#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br} +#' +#' @description Computes an measure of seggregation from SITS table based on mean relative Shannon Entropy, +#' where maximum entropy is 1.0 and minimum entropy is 0.0, from each cluster label against +#' original_label occorrences in it. To obtain this measure, first we compute Shannon +#' information entropy from counts of each cluster label. To get the relative entropy, we +#' divide this value by the maximum entropy. After that, we compute the weighted mean relative +#' to the frequency of each cluster label. The final values represents an average of +#' maximum entropy fraction. This is useful to assess the separability of samples for a given clustering algorithm. +#' +#' @param data.tb a SITS table with the samples to be validated. +#' @param measure an separability measure. For now, only "entropy" is implemented. +#' @param per_cluster (boolean) should return a total average separability measure, or a per cluster measure? +#' @return result a separability measure. +#' @export +sits_separability_measure <- function (data.tb, measure = "entropy", per_cluster = FALSE){ + ensurer::ensure_that(data.tb, !purrr::is_null(.), + err_desc = "sits_separability_measure: SITS table not provided.") + + # ensure that `measure` parameter is a valid option + ensurer::ensure_that(measure, . == "entropy", + err_desc = "sits_separability_measure: measure not supported.") + + # do the input data have the `original_label` column? + ensurer::ensure_that(data.tb, "original_label" %in% colnames(data.tb), + err_desc = "sits_separability_measure: informed SITS table has not an `original_label` column.") + + # do we have at least two original labels? + labels_count <- length(table(data.tb$original_label)) + if (labels_count == 1) + return (0.0) + + # verify if there is n_members column. If not exists initialize it with ones. + if (!any("n_members" %in% names(data.tb))) + data.tb$n_members <- data.tb$label %>% + purrr::map(function(label) return(tibble::tibble(original_label = label, n = 1))) + + + if (measure == "entropy") + # sum all n_members values grouping by original_label and label fields. After that computes a separability measure. + # further measures implementations must return two mandatory fields: segr and frac. The first is the measure itself, + # the second represents the fraction of cluster members. + result.tb <- data.tb %>% + tidyr::unnest_("n_members", .sep = ".") %>% + dplyr::group_by_("original_label", "label") %>% + dplyr::summarise(count = sum(n_members.n, na.rm = TRUE)) %>% + dplyr::ungroup() %>% + dplyr::group_by(label) %>% + dplyr::summarise(segr = entropy::entropy(count) / log(labels_count), frac = sum(count, na.rm = TRUE) / NROW(data.tb)) + + if (per_cluster) + return (result.tb) + + return (dplyr::summarise(result.tb, mean_segr = sum(segr * frac, na.rm = TRUE)) %>% .$mean_segr) + +} + +#' @title Do a cross combination of all elements of its input parameters. +#' @name .setup_separability_expr +#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br} +#' +#' @description Returns a tibble with all cross combinations of its input parameters informed as lists, vectors, or scalars. +#' This output is used to set the parameters' domain to be used in `sits_separability_experiment` function. +#' +#' @param method string - either 'dendogram', 'centroids', 'kohonen', or 'kohonen-dendogram'. +#' @param times how many times to run an parameterized experiment. +#' @param bands the bands to be clusterized. +#' @param n_clusters the number of clusters to be croped from hierarchical clustering (ignored in `kohonen` method). Default is 2. +#' @param grouping_method the agglomeration method to be used. Any `hclust` method (see `hclust`) (ignored in `kohonen` method). Default is 'ward.D2'. +#' @param koh_grid_length grid side dimension of the SOM grid (used only in `kohonen` or `kohonen-dendogram` methods). Defaul is 5. +#' @param koh_rlen the number of times the complete data set will be presented to the SOM grid +#' (used only in `kohonen` or `kohonen-denddogram` methods). Default is 100. +#' @param koh_alpha_from starting learning rate. Default is to decline linearly from 0.05 to `koh_alpha_to` over rlen updates. +#' @param koh_alpha_to ending learning rate. Default is to decline linearly from `koh_alpha_from` to 1.0 over rlen updates. +#' @return result.tb a tibble with all cross combinations of parameters' values. +.setup_separability_expr <- function(method, times, bands, n_clusters, grouping_method, + koh_grid_length, koh_rlen, koh_alpha_from, koh_alpha_to){ + + if (method == "dendogram" || method == "centroids") + return (tibble::as_tibble(expand.grid(method = "dendogram", bands = bands, n_clusters = n_clusters, + grouping_method = grouping_method, koh_grid_length = NA, + koh_rlen = NA, koh_alpha_from = NA, koh_alpha_to = NA, + times = 1, + KEEP.OUT.ATTRS = FALSE, stringsAsFactors = FALSE))) + else if (method == "kohonen") + return (tibble::as_tibble(expand.grid(method = "kohonen", bands = bands, n_clusters = NA, + grouping_method = NA, koh_grid_length = koh_grid_length, + koh_rlen = koh_rlen, koh_alpha_from = koh_alpha_from, koh_alpha_to = koh_alpha_to, + times = times, + KEEP.OUT.ATTRS = FALSE, stringsAsFactors = FALSE))) + else if (method == "kohonen-dendogram") + return (tibble::as_tibble(expand.grid(method = "kohonen-dendogram", bands = bands, n_clusters = n_clusters, + grouping_method = grouping_method, koh_grid_length = koh_grid_length, + koh_rlen = koh_rlen, koh_alpha_from = koh_alpha_from, koh_alpha_to = koh_alpha_to, + times = times, + KEEP.OUT.ATTRS = FALSE, stringsAsFactors = FALSE))) +} + +#' @title Runs a single separability experiment. +#' @name .exec_separability_expr +#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br} +#' +#' @description Runs an separability experiment with a single valued parameters and returns a list with experiment's result. +#' +#' @param data.tb a SITS table with the samples to be used in experiments. +#' @param method string - either 'dendogram', 'centroids', 'kohonen', or 'kohonen-dendogram'. +#' @param bands the bands to be clusterized. +#' @param n_clusters the number of clusters to be croped from hierarchical clustering (ignored in `kohonen` method). Default is 2. +#' @param grouping_method the agglomeration method to be used. Any `hclust` method (see `hclust`) (ignored in `kohonen` method). Default is 'ward.D2'. +#' @param koh_grid_length grid side dimension of the SOM grid (used only in `kohonen` or `kohonen-dendogram` methods). Defaul is 5. +#' @param koh_rlen the number of times the complete data set will be presented to the SOM grid +#' (used only in `kohonen` or `kohonen-denddogram` methods). Default is 100. +#' @param koh_alpha_from starting learning rate. Default is to decline linearly from 0.05 to `koh_alpha_to` over rlen updates. +#' @param koh_alpha_to ending learning rate. Default is to decline linearly from `koh_alpha_from` to 1.0 over rlen updates.#' @param method +#' @param times how many times to run an parameterized experiment. +#' @return result.lst a list with all cross combinations of parameters' values. +.exec_separability_expr <- function(data.tb, method, bands, n_clusters, grouping_method, koh_grid_length, + koh_rlen, koh_alpha_from, koh_alpha_to, times = 1) { + + separability <- 0.0 + etime <- as.difftime(0, units = "secs") + for (i in 1:times){ + stime <- Sys.time() + clusterized.tb <- sits_cluster(data.tb, method = method, grouping_method = grouping_method, + bands = bands, n_clusters = n_clusters, + show = FALSE, return_members = TRUE, unsupervised = TRUE, + koh_xgrid = koh_grid_length, koh_ygrid = koh_grid_length, + koh_rlen = koh_rlen, koh_alpha = c(koh_alpha_from, koh_alpha_to)) + etime <- Sys.time() - stime + etime + separability <- sits_separability_measure(clusterized.tb) + separability + + } + etime <- etime / times + separability <- separability / times + + result.lst <- list(method = method, + bands = paste0(bands, collapse = "&"), + n_clusters = n_clusters, + grouping_method = grouping_method, + koh_grid_length = koh_grid_length, + koh_rlen = koh_rlen, + koh_alpha_from = koh_alpha_from, + koh_alpha_to = koh_alpha_to, + separability = separability, + expr_time = etime) + + return (result.lst) +} + +#' @title Do a set of separability experiments based on a cross combination of all elements of its input parameters. +#' @name sits_separability_experiments +#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br} +#' +#' @description Runs a set of separability experiments according to informed parameters and returns a tibble with experiment's result. +#' +#' @param data.tb a SITS table with the samples to be used in experiments. +#' @param method string - either 'dendogram', 'centroids', 'kohonen', or 'kohonen-dendogram'. +#' @param times how many times to run an parameterized experiment. +#' @param bands the bands to be clusterized. +#' @param n_clusters the number of clusters to be croped from hierarchical clustering (ignored in `kohonen` method). Default is 2. +#' @param grouping_method the agglomeration method to be used. Any `hclust` method (see `hclust`) (ignored in `kohonen` method). Default is 'ward.D2'. +#' @param koh_grid_length grid side dimension of the SOM grid (used only in `kohonen` or `kohonen-dendogram` methods). Defaul is 5. +#' @param koh_rlen the number of times the complete data set will be presented to the SOM grid +#' (used only in `kohonen` or `kohonen-denddogram` methods). Default is 100. +#' @param koh_alpha_from starting learning rate. Default is to decline linearly from 0.05 to `koh_alpha_to` over rlen updates. +#' @param koh_alpha_to ending learning rate. Default is to decline linearly from `koh_alpha_from` to 1.0 over rlen updates. +#' @param .multiproc (Linux only) numbers of cores to be used in multiprocessing. +#' @return result.tb a tibble with all cross combinations of parameters' values. +#' @export +sits_separability_experiments <- function(data.tb, method = "kohonen-dendogram", times = 1, + bands = list("evi", "ndvi", c("evi", "ndvi")), + n_clusters = 5, + grouping_method = c("average", "ward.D2", "complete", "single"), + koh_grid_length = 7, + koh_rlen = 1200, + koh_alpha_from = 0.2, + koh_alpha_to = 0.04, .multiproc = 1){ + + # CAUTION: generates all parameters' combinations + parameters <- .setup_separability_expr(method = method, times = times, bands = bands, + n_clusters = n_clusters, grouping_method = grouping_method, + koh_grid_length = koh_grid_length, koh_rlen = koh_rlen, + koh_alpha_from = koh_alpha_from, koh_alpha_to = koh_alpha_to) + + # if .multiproc greater than 1, start parallel threads + if (.multiproc > 1) + result.lst <- parallel::mcMap(.exec_separability_expr, list(data.tb), parameters$method, parameters$bands, + parameters$n_clusters, parameters$grouping_method, + parameters$koh_grid_length, parameters$koh_rlen, + parameters$koh_alpha_from, parameters$koh_alpha_to, + mc.cores = .multiproc) + + else + result.lst <- Map(.exec_separability_expr, list(data.tb), parameters$method, parameters$bands, + parameters$n_clusters, parameters$grouping_method, + parameters$koh_grid_length, parameters$koh_rlen, parameters$koh_alpha_from, parameters$koh_alpha_to) + + # composes final result as a tibble + return (tibble::as_tibble(plyr::rbind.fill(purrr::map(result.lst, tibble::as_tibble)))) +} diff --git a/R/sits_stack_proc.R b/R/sits_stack_proc.R index 4c2d5f798..207828a16 100644 --- a/R/sits_stack_proc.R +++ b/R/sits_stack_proc.R @@ -1,256 +1,256 @@ -#' @title Relabel raster stack object -#' @name sits_stack_relabel -#' @author Victor Maus, \email{vwmaus1@@gmail.com} -#' -#' @include RcppExports.R -#' -#' @param x raster stack .... tbl raster or wms??? -#' @param old_values an integer vector with raster values (label) to be updated -#' @param new_values an integer vector with new values (label). It must have the same length as \code{old_label} -#' @param filename a character with the file name. Optional -#' @param progress a character. See \code{\link[raster]{pbCreate}}. Default is \code{'text'} -#' @param parallel perform parallel processing. Default is TRUE -#' @param ... other arguments to pass to \code{\link[raster]{beginCluster}} and \code{\link[raster]{writeStart}} -#' -#' @description This function updates categorical raster values (labels). -#' -#' @export -sits_stack_relabel <- function(x, - old_values, - new_values, - filename = "", - progress = 'text', - parallel = TRUE, ...) { - - if(length(new_values)!=length(old_values)) - stop("new_values must have the same length as old_values", call. = TRUE) - - if(parallel){ - raster::beginCluster(...) - our <- .apply_stack_parallel(x, - fun = .stack_relabel, - args.list = list(old_values, new_values), - filename = "", progress = 'text', ...) - raster::endCluster() - } else { - stop("Not implemented yet", call. = TRUE) - #out <- .apply_stack(x, fun = .stack_relabel, args.list = list(old_values, new_values), filename = "", progress = 'text', ...) - } - - return(our) - -} - -#' @title Relabel raster stack based on temporal transitions -#' @name sits_stack_transition_relabel -#' @author Victor Maus, \email{vwmaus1@@gmail.com} -#' -#' @param x raster stack object -#' @param A transition matrix. This gives the possible transitions among classes. -#' The first line and the first column of the table has the raster class followed -#' by the class label, such that .. The rows are the -#' Class(t) and columns Class(t-1), the cobination between a row (Class(t)) and a -#' column (Class(t-1)) gives the updated class number. -#' @param filename a character with the file name. Optional -#' @param progress a character. See \code{\link[raster]{pbCreate}}. Default is \code{'text'} -#' @param parallel perform parallel processing. Default is TRUE -#' @param ... other arguments to pass to \code{\link[raster]{beginCluster}} and \code{\link[raster]{writeStart}} -#' -#' @description This function updates categorical raster values (labels) based on the temporal transitions, -#' assuming Class(t) = f(Class(t-1), Class(t)) subject to the initial condition Class(t=1) = Class(t=1) -#' -#' @export -sits_stack_transition_relabel <- function(x, - A, - filename = "", - progress = 'text', - parallel = TRUE, ...) { - - if(parallel){ - raster::beginCluster(...) - our <- .apply_stack_parallel(x, - fun = .stack_transition_relabel, - args.list = list(A), - filename = "", progress = 'text', ...) - raster::endCluster() - } else { - stop("Not implemented yet", call. = TRUE) - #out <- .apply_stack(x, fun = .stack_transition_relabel, args.list = list(old_values, new_values), filename = "", progress = 'text', ...) - } - - return(our) - -} - - -#' @title Parallel processing of raster stack objects -#' @name .apply_stack_parallel -#' @author Victor Maus, \email{vwmaus1@@gmail.com} -#' -#' @param x raster stack -#' @param fun is a function to be applied to each chunk of the raster stack object and can -#' be defined by the user. The function must have four arguments: k (is the chunk index), -#' x (is the raster stack object), bs (is the chnuk information created by raster::blockSize), -#' and args.list is a list of other arguments used in the processing. The function must return -#' a matrix with the same dimensions as returned by raster::getValues for each chunk. -#' @param filename a character with the file name. Optional -#' @param progress a character. See \code{\link[raster]{pbCreate}}. Default is \code{'text'} -#' @param ... other arguments to pass to \code{\link[raster]{beginCluster}} and \code{\link[raster]{writeStart}} -#' -#' @description This function performs the parallel processing of raster stack objects. -#' -#' @noRd -.apply_stack_parallel <- function(x, fun, args.list, filename = "", progress = 'text', ...) { - - # Create output raster - out <- raster::brick(x, values = FALSE) - names(out) <- names(x) - - # Create cluster - cl <- raster::getCluster() - on.exit(raster::returnCluster()) - nodes <- length(cl) - - # Compute raster tiles - bs <- raster::blockSize(x, minblocks = nodes * 4) - bs$array_rows <- cumsum(c(1, bs$nrows * out@ncols)) - pb <- raster::pbCreate(bs$n, progress = progress) - - # Export global variables to cluster - # snow::clusterExport(cl, list = c("x", "bs"), envir = environment()) - - # Get all nodes going - for (k in 1:nodes) { - snow::sendCall(cl[[k]], fun, list(k, x, bs, args.list), tag = k) - } - - # If needed create raster files - filename <- raster::trim(filename) - if (!raster::canProcessInMemory(out) & filename == "") { - filename <- raster::rasterTmpFile() - } - - if (filename != "") { - out <- raster::writeStart(out, filename = filename, ... ) - } else { - vv <- matrix(out, ncol = nlayers(out)) - } - - # Process raster tiles - for (k in 1:bs$n) { - # receive results from a node - d <- snow::recvOneData(cl) - - # error? - if (! d$value$success) { - stop('cluster error') - } - - # which block is this? - b <- d$value$tag - # cat('received block: ',b," / ",bs$n,'\n'); flush.console(); - - if (filename != "") { - out <- raster::writeValues(out, d$value$value, bs$row[b]) - } else { - # cols <- bs$row[b]:(bs$row[b] + bs$nrows[b]-1) - # vv[,cols] <- matrix(d$value$value, nrow=out@ncols) - rows <- seq(from = bs$array_rows[b], by = 1, length.out = bs$nrows[b]*out@ncols) - vv[rows,] <- d$value$value - } - - # need to send more data? - ni <- nodes + k - if (ni <= bs$n) { - snow::sendCall(cl[[d$node]], fun, list(ni, x, bs, args.list), tag = ni) - } - - # Progess bar - raster::pbStep(pb, k) - - } - - # Create output raster - if (filename != "") { - out <- raster::writeStop(out) - } else { - out <- raster::setValues(out, as.vector(vv)) - } - - # Close cluster - raster::pbClose(pb) - - # Assign layers names - names(out) <- names(x) - - return(out) -} - -#' @title Relabel a chunk from raster stack object -#' @name .stack_relabel -#' @author Victor Maus, \email{vwmaus1@@gmail.com} -#' -#' @param k is the chunk index -#' @param x is the raster stack object -#' @param bs is is the chnuk information created by raster::blockSize -#' @param args.list is a list of other arguments used in the processing. -#' -#' @description Relabel a chunk from raster stack object according old label -#' -#' @noRd -.stack_relabel <- function(k, x, bs, args.list){ - - A <- tibble::tibble(old_values = args.list[[1]], new_values = args.list[[2]]) - - v <- raster::getValues(x, bs$row[k], bs$nrows[k]) - - v <- v %>% as.vector() %>% - tibble::as.tibble() %>% - dplyr::left_join(., y = A, by = c("value" = "old_values")) %>% - dplyr::mutate(new_values = dplyr::if_else(is.na(new_values), value, new_values)) %>% - .$new_values %>% - matrix(nrow = nrow(v)) - - return(v) - -} - -#' @title Relabel a chunk from raster stack object -#' @name .stack_transition_relabel -#' @author Victor Maus, \email{vwmaus1@@gmail.com} -#' -#' @param k is the chunk index -#' @param x is the raster stack object -#' @param bs is is the chnuk information created by raster::blockSize -#' @param args.list is a list of other arguments used in the processing. -#' -#' @description Relabel a chunk from raster stack object according to transition rules. -#' -#' @noRd -.stack_transition_relabel <- function(k, x, bs, args.list){ - - A <- args.list[[1]] - - cpp_fun <- function(v, A, I) { - .Call(`_sits_apply_first_order_transition_rules`, v, A, I) - } - - v <- raster::getValues(x, bs$row[k], bs$nrows[k]) - - # Skip NA - I <- tibble::as.tibble(v) %>% - dplyr::rowwise() %>% - dplyr::do(na = !anyNA(.)) %>% - unlist() - - # Update label - if(any(I)){ - A <- A %>% dplyr::select(-label) %>% as.matrix() - colnames(A) <- NULL - I <- as.vector(which(I)) - v <- cpp_fun(v, A, I) - } - - return(v) -} - +#' @title Relabel raster stack object +#' @name sits_stack_relabel +#' @author Victor Maus, \email{vwmaus1@@gmail.com} +#' +#' @include RcppExports.R +#' +#' @param x raster stack .... tbl raster or wms??? +#' @param old_values an integer vector with raster values (label) to be updated +#' @param new_values an integer vector with new values (label). It must have the same length as \code{old_label} +#' @param filename a character with the file name. Optional +#' @param progress a character. See \code{\link[raster]{pbCreate}}. Default is \code{'text'} +#' @param parallel perform parallel processing. Default is TRUE +#' @param ... other arguments to pass to \code{\link[raster]{beginCluster}} and \code{\link[raster]{writeStart}} +#' +#' @description This function updates categorical raster values (labels). +#' +#' @export +sits_stack_relabel <- function(x, + old_values, + new_values, + filename = "", + progress = 'text', + parallel = TRUE, ...) { + + if(length(new_values)!=length(old_values)) + stop("new_values must have the same length as old_values", call. = TRUE) + + if(parallel){ + raster::beginCluster(...) + our <- .apply_stack_parallel(x, + fun = .stack_relabel, + args.list = list(old_values, new_values), + filename = "", progress = 'text', ...) + raster::endCluster() + } else { + stop("Not implemented yet", call. = TRUE) + #out <- .apply_stack(x, fun = .stack_relabel, args.list = list(old_values, new_values), filename = "", progress = 'text', ...) + } + + return(our) + +} + +#' @title Relabel raster stack based on temporal transitions +#' @name sits_stack_transition_relabel +#' @author Victor Maus, \email{vwmaus1@@gmail.com} +#' +#' @param x raster stack object +#' @param A transition matrix. This gives the possible transitions among classes. +#' The first line and the first column of the table has the raster class followed +#' by the class label, such that .. The rows are the +#' Class(t) and columns Class(t-1), the cobination between a row (Class(t)) and a +#' column (Class(t-1)) gives the updated class number. +#' @param filename a character with the file name. Optional +#' @param progress a character. See \code{\link[raster]{pbCreate}}. Default is \code{'text'} +#' @param parallel perform parallel processing. Default is TRUE +#' @param ... other arguments to pass to \code{\link[raster]{beginCluster}} and \code{\link[raster]{writeStart}} +#' +#' @description This function updates categorical raster values (labels) based on the temporal transitions, +#' assuming Class(t) = f(Class(t-1), Class(t)) subject to the initial condition Class(t=1) = Class(t=1) +#' +#' @export +sits_stack_transition_relabel <- function(x, + A, + filename = "", + progress = 'text', + parallel = TRUE, ...) { + + if(parallel){ + raster::beginCluster(...) + our <- .apply_stack_parallel(x, + fun = .stack_transition_relabel, + args.list = list(A), + filename = "", progress = 'text', ...) + raster::endCluster() + } else { + stop("Not implemented yet", call. = TRUE) + #out <- .apply_stack(x, fun = .stack_transition_relabel, args.list = list(old_values, new_values), filename = "", progress = 'text', ...) + } + + return(our) + +} + + +#' @title Parallel processing of raster stack objects +#' @name .apply_stack_parallel +#' @author Victor Maus, \email{vwmaus1@@gmail.com} +#' +#' @param x raster stack +#' @param fun is a function to be applied to each chunk of the raster stack object and can +#' be defined by the user. The function must have four arguments: k (is the chunk index), +#' x (is the raster stack object), bs (is the chnuk information created by raster::blockSize), +#' and args.list is a list of other arguments used in the processing. The function must return +#' a matrix with the same dimensions as returned by raster::getValues for each chunk. +#' @param filename a character with the file name. Optional +#' @param progress a character. See \code{\link[raster]{pbCreate}}. Default is \code{'text'} +#' @param ... other arguments to pass to \code{\link[raster]{beginCluster}} and \code{\link[raster]{writeStart}} +#' +#' @description This function performs the parallel processing of raster stack objects. +#' +#' @noRd +.apply_stack_parallel <- function(x, fun, args.list, filename = "", progress = 'text', ...) { + + # Create output raster + out <- raster::brick(x, values = FALSE) + names(out) <- names(x) + + # Create cluster + cl <- raster::getCluster() + on.exit(raster::returnCluster()) + nodes <- length(cl) + + # Compute raster tiles + bs <- raster::blockSize(x, minblocks = nodes * 4) + bs$array_rows <- cumsum(c(1, bs$nrows * out@ncols)) + pb <- raster::pbCreate(bs$n, progress = progress) + + # Export global variables to cluster + # snow::clusterExport(cl, list = c("x", "bs"), envir = environment()) + + # Get all nodes going + for (k in 1:nodes) { + snow::sendCall(cl[[k]], fun, list(k, x, bs, args.list), tag = k) + } + + # If needed create raster files + filename <- raster::trim(filename) + if (!raster::canProcessInMemory(out) & filename == "") { + filename <- raster::rasterTmpFile() + } + + if (filename != "") { + out <- raster::writeStart(out, filename = filename, ... ) + } else { + vv <- matrix(out, ncol = nlayers(out)) + } + + # Process raster tiles + for (k in 1:bs$n) { + # receive results from a node + d <- snow::recvOneData(cl) + + # error? + if (! d$value$success) { + stop('cluster error') + } + + # which block is this? + b <- d$value$tag + # cat('received block: ',b," / ",bs$n,'\n'); flush.console(); + + if (filename != "") { + out <- raster::writeValues(out, d$value$value, bs$row[b]) + } else { + # cols <- bs$row[b]:(bs$row[b] + bs$nrows[b]-1) + # vv[,cols] <- matrix(d$value$value, nrow=out@ncols) + rows <- seq(from = bs$array_rows[b], by = 1, length.out = bs$nrows[b]*out@ncols) + vv[rows,] <- d$value$value + } + + # need to send more data? + ni <- nodes + k + if (ni <= bs$n) { + snow::sendCall(cl[[d$node]], fun, list(ni, x, bs, args.list), tag = ni) + } + + # Progess bar + raster::pbStep(pb, k) + + } + + # Create output raster + if (filename != "") { + out <- raster::writeStop(out) + } else { + out <- raster::setValues(out, as.vector(vv)) + } + + # Close cluster + raster::pbClose(pb) + + # Assign layers names + names(out) <- names(x) + + return(out) +} + +#' @title Relabel a chunk from raster stack object +#' @name .stack_relabel +#' @author Victor Maus, \email{vwmaus1@@gmail.com} +#' +#' @param k is the chunk index +#' @param x is the raster stack object +#' @param bs is is the chnuk information created by raster::blockSize +#' @param args.list is a list of other arguments used in the processing. +#' +#' @description Relabel a chunk from raster stack object according old label +#' +#' @noRd +.stack_relabel <- function(k, x, bs, args.list){ + + A <- tibble::tibble(old_values = args.list[[1]], new_values = args.list[[2]]) + + v <- raster::getValues(x, bs$row[k], bs$nrows[k]) + + v <- v %>% as.vector() %>% + tibble::as.tibble() %>% + dplyr::left_join(., y = A, by = c("value" = "old_values")) %>% + dplyr::mutate(new_values = dplyr::if_else(is.na(new_values), value, new_values)) %>% + .$new_values %>% + matrix(nrow = nrow(v)) + + return(v) + +} + +#' @title Relabel a chunk from raster stack object +#' @name .stack_transition_relabel +#' @author Victor Maus, \email{vwmaus1@@gmail.com} +#' +#' @param k is the chunk index +#' @param x is the raster stack object +#' @param bs is is the chnuk information created by raster::blockSize +#' @param args.list is a list of other arguments used in the processing. +#' +#' @description Relabel a chunk from raster stack object according to transition rules. +#' +#' @noRd +.stack_transition_relabel <- function(k, x, bs, args.list){ + + A <- args.list[[1]] + + cpp_fun <- function(v, A, I) { + .Call(`_sits_apply_first_order_transition_rules`, v, A, I) + } + + v <- raster::getValues(x, bs$row[k], bs$nrows[k]) + + # Skip NA + I <- tibble::as.tibble(v) %>% + dplyr::rowwise() %>% + dplyr::do(na = !anyNA(.)) %>% + unlist() + + # Update label + if(any(I)){ + A <- A %>% dplyr::select(-label) %>% as.matrix() + colnames(A) <- NULL + I <- as.vector(which(I)) + v <- cpp_fun(v, A, I) + } + + return(v) +} + diff --git a/R/sits_table.R b/R/sits_table.R index 390e05bec..cc19c166d 100644 --- a/R/sits_table.R +++ b/R/sits_table.R @@ -275,10 +275,11 @@ sits_group_bylatlong <- function (data.tb) { #' @description returns the labels and its respective counting and frequency. #' #' @param data.tb a valid sits table +#' @param from_members (Boolean) if n_members collumn is present, compute label's statistics from it. #' @return result.tb a tibble with the names of the labels and its absolute and relative frequency #' @export #' -sits_labels <- function (data.tb) { +sits_labels <- function (data.tb, from_members = FALSE) { # verify if there is original_label column. If not exists initialize it with empty string. if (!any("original_label" %in% names(data.tb))) @@ -286,15 +287,26 @@ sits_labels <- function (data.tb) { # verify if there is n_members column. If not exists initialize it with ones. if (!any("n_members" %in% names(data.tb))) - data.tb$n_members <- 1 + data.tb$n_members <- data.tb$label %>% + purrr::map(function(label) return(tibble::tibble(original_label = label, n = 1))) # compute frequency (absolute and relative) - result.tb <- data.tb %>% - dplyr::group_by(original_label, label) %>% - dplyr::summarize(count = sum(n_members, na.rm = TRUE)) %>% - dplyr::mutate(total = sum(count, na.rm = TRUE), frac = count / sum(count, na.rm = TRUE)) %>% - dplyr::ungroup() %>% - dplyr::select(label, count, original_label, total, frac) + if (from_members) + result.tb <- data.tb %>% + tidyr::unnest_("n_members", .sep = ".") %>% + dplyr::group_by_("n_members.original_label", "label") %>% + dplyr::summarize(count = sum(n_members.n, na.rm = TRUE)) %>% + dplyr::mutate(total = sum(count, na.rm = TRUE), frac = count / sum(count, na.rm = TRUE)) %>% + dplyr::ungroup() %>% + dplyr::select_("label", "count", original_label="n_members.original_label", "total", "frac") + else + result.tb <- data.tb %>% + tidyr::unnest_("n_members", .sep = ".") %>% + dplyr::group_by_("original_label", "label") %>% + dplyr::summarize(count = sum(n_members.n, na.rm = TRUE)) %>% + dplyr::mutate(total = sum(count, na.rm = TRUE), frac = count / sum(count, na.rm = TRUE)) %>% + dplyr::ungroup() %>% + dplyr::select_("label", "count", "original_label", "total", "frac") return (result.tb) } @@ -696,6 +708,41 @@ sits_relabel <- function (data.tb, conv){ return (out.tb) } +#' @title creates a conversion identity list for sits_relabel +#' @name sits_relabel_conv +#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br} +#' +#' @description Given a confusion matrix obtained in the validation +#' procedure, returns a identity conversion list of labels. +#' +#' @param file a JSON file contaning the result of a validation procedure +#' @return conv.lst a conversion list +#' @export +sits_relabel_conv <- function (file = NULL){ + # do the input file exist? + ensurer::ensure_that(file, !purrr::is_null(.), + err_desc = "sits_relabel: JSON file not provided") + + # get labels from JSON file + confusion.vec <- jsonlite::fromJSON (file) + + # what are the labels of the samples? + #labels <- rle(sort(ref.vec))$values + labels <- base::unique(confusion.vec) + + # Create an identity list + conv.lst <- labels %>% + purrr::map(function(lbl) lbl) + names(conv.lst) <- labels + + # conv.lst <- tibble::lst() + # for (i in 1:length(labels)) { + # lab <- labels[i] + # conv.lst[lab] <- lab + # } + + return (conv.lst) +} #' @title Spread matches from a sits matches tibble #' @name .sits_spread_matches #' @author Victor Maus, \email{vwmaus1@@gmail.com} diff --git a/README.md b/README.md new file mode 100644 index 000000000..7e158ab26 --- /dev/null +++ b/README.md @@ -0,0 +1,9 @@ +sits +====== + +[![Build Status](https://travis-ci.org/vwmaus/sits.png?branch=master)](https://travis-ci.org/vwmaus/sits) [![License](http://img.shields.io/badge/license-GPL%20%28%3E=%202%29-brightgreen.svg?style=flat)](http://www.gnu.org/licenses/gpl-2.0.html) + +### Satellite Image Time Series Analysis +A set of tools for working with satellite image time series. Supports data retrieval from a WTSS (web time series service). Provides different visualisation methods for image time series. Enables different clustering methods. Provides smoothing methods for noisy time series. Classifies time series using TWDTW method using the [dtwSat](https://github.com/vwmaus/dtwSat) package. + + diff --git a/inst/extdata/PosProcessamentoDTW/ProgramasR/Modelagem_classificacao_dados_simulados.R b/inst/extdata/PosProcessamentoDTW/ProgramasR/Modelagem_classificacao_dados_simulados.R new file mode 100644 index 000000000..11e40bded --- /dev/null +++ b/inst/extdata/PosProcessamentoDTW/ProgramasR/Modelagem_classificacao_dados_simulados.R @@ -0,0 +1,301 @@ +<<<<<<< HEAD:inst/extdata/PosProcessamentoDTW/ProgramasR/Modelagem_classificacao_dados_simulados.R + +library(graphics); +library(MASS); +library(plyr); +library(caret); +library(nnet); +library(e1071); + +rm(list=ls()); + +#--------- creating data ----------# + +set.seed(2104); +categorias <- c('Soy-Sorgo', 'Corn', 'Forest', 'Pasture', 'Water') + +nsamples <- c(200, 150, 200, 200, 150) +k <- length(nsamples); k + +mu <- matrix(c(0.001, 0.3, 0.6, 0.4, 0.8, + 0.4, 0.001, 0.4, 0.4, 0.8, + 0.6, 0.3, 0.001, 0.4, 0.8, + 0.4, 0.3, 0.4, 0.001, 0.8, + 0.5, 0.6, 0.4, 0.4, 0.001), nrow = k, ncol = k, byrow = T) +mu +Sigma <- diag(k) + matrix(0.1, nrow = k, ncol = k) +Sigma + +for (i in 1:k) +{ + u <- exp(mvrnorm(n=nsamples[i], mu[i,], Sigma));u + colmeans <- colSums(u)/nsamples[i] + print(colmeans) + if (i == 1) + { + dados <- data.frame(categoria = rep(categorias[i], nsamples[i]), u) + } + else { + dados <- rbind(dados, data.frame(categoria = rep(categorias[i], nsamples[i]), u)) + } +} +dados <- rename(dados, c('X1'='dist1', 'X2'='dist2', 'X3'='dist3', + 'X4'='dist4', 'X5'='dist5')) + +#------ splitting samples ---------# + +trainIndex <- createDataPartition(dados$categoria, p = .8, + list = FALSE, + times = 1) +head(trainIndex) + +dadosTrain <- dados[ trainIndex,] +dadosTest <- dados[-trainIndex,] + +#------ classificando por menor distancia ---------# + +categorias.dtw.pred <- rep(categorias[1], nrow(dadosTest)) +for (i in 1:nrow(dadosTest)) +{ + min_dist <- dadosTest[i,c('dist1')]; + if (min_dist > dadosTest[i,c('dist2')]) + { + min_dist <- dadosTest[i,c('dist2')]; + categorias.dtw.pred[i] <- categorias[2] + } + if (min_dist > dadosTest[i,c('dist3')]) + { + min_dist <- dadosTest[i,c('dist3')]; + categorias.dtw.pred[i] <- categorias[3] + } + if (min_dist > dadosTest[i,c('dist4')]) + { + min_dist <- dadosTest[i,c('dist4')]; + categorias.dtw.pred[i] <- categorias[4] + } + if (min_dist > dadosTest[i,c('dist5')]) + { + min_dist <- dadosTest[i,c('dist5')]; + categorias.dtw.pred[i] <- categorias[5] + } +} + +#-------- rodando os modelos --------------# + +categorias.qda <- qda(categoria ~ log(dist1)+log(dist2)+log(dist3)+log(dist4)+log(dist5), + data=dadosTrain) +categorias.lda <- lda(categoria ~ log(dist1)+log(dist2)+log(dist3)+log(dist4)+log(dist5), + data=dadosTrain) +categorias.mlr <- multinom(categoria ~ log(dist1)+log(dist2)+log(dist3)+log(dist4)+log(dist5), + data=dadosTrain) +categorias.svm1 <- svm(categoria ~ log(dist1)+log(dist2)+log(dist3)+log(dist4)+log(dist5), + data=dadosTrain, kernel = "radial", type="C-classification", k = 5) +categorias.svm2 <- svm(categoria ~ log(dist1)+log(dist2)+log(dist3)+log(dist4)+log(dist5), + data=dadosTrain, kernel = "linear", type="C-classification", k = 5) +categorias.svm3 <- svm(categoria ~ log(dist1)+log(dist2)+log(dist3)+log(dist4)+log(dist5), + data=dadosTrain, kernel = "polynomial", type="C-classification", k = 5) +categorias.svm4 <- svm(categoria ~ log(dist1)+log(dist2)+log(dist3)+log(dist4)+log(dist5), + data=dadosTrain, kernel = "sigmoid", type="C-classification", k = 5) + +categorias.lda +categorias.qda +categorias.mlr +categorias.svm1 +categorias.svm2 +categorias.svm3 +categorias.svm4 + +categorias.lda.pred <- predict(categorias.lda, newdata = dadosTest)$class +categorias.qda.pred <- predict(categorias.qda, newdata = dadosTest)$class +categorias.mlr.pred <- predict(categorias.mlr, newdata = dadosTest) +categorias.svm1.pred <- predict(categorias.svm1, newdata = dadosTest) +categorias.svm2.pred <- predict(categorias.svm2, newdata = dadosTest) +categorias.svm3.pred <- predict(categorias.svm3, newdata = dadosTest) +categorias.svm4.pred <- predict(categorias.svm4, newdata = dadosTest) + +table(categorias.dtw.pred, dadosTest$categoria) +table(categorias.lda.pred, dadosTest$categoria) +table(categorias.qda.pred, dadosTest$categoria) +table(categorias.mlr.pred, dadosTest$categoria) +table(categorias.svm1.pred, dadosTest$categoria) +table(categorias.svm2.pred, dadosTest$categoria) +table(categorias.svm3.pred, dadosTest$categoria) +table(categorias.svm4.pred, dadosTest$categoria) + +sum(diag(table(categorias.dtw.pred, dadosTest$categoria))) / nrow(dadosTest) +sum(diag(table(categorias.lda.pred, dadosTest$categoria))) / nrow(dadosTest) +sum(diag(table(categorias.qda.pred, dadosTest$categoria))) / nrow(dadosTest) +sum(diag(table(categorias.mlr.pred, dadosTest$categoria))) / nrow(dadosTest) +sum(diag(table(categorias.svm1.pred, dadosTest$categoria))) / nrow(dadosTest) +sum(diag(table(categorias.svm2.pred, dadosTest$categoria))) / nrow(dadosTest) +sum(diag(table(categorias.svm3.pred, dadosTest$categoria))) / nrow(dadosTest) +sum(diag(table(categorias.svm4.pred, dadosTest$categoria))) / nrow(dadosTest) + +#----------------------------------------------------------------------------# +#---- the end ----# +#----------------------------------------------------------------------------# +======= + +#install.packages(c('graphics', 'MASS', 'plyr', 'caret', 'e1071', 'glmnet')) + +library(graphics); +library(MASS); +library(plyr); +library(caret); +library(nnet); +library(e1071); +library(glmnet); + +rm(list=ls()); + +#--------- creating data ----------# + +set.seed(2104); +categorias <- c('Soy-Sorgo', 'Corn', 'Forest', 'Pasture', 'Water') + +nsamples <- c(200, 250, 200, 400, 150) +k <- length(nsamples); k + +mu <- matrix(c(0.001, 0.3, 0.6, 0.4, 0.8, + 0.4, 0.001, 0.4, 0.4, 0.8, + 0.6, 0.3, 0.001, 0.4, 0.8, + 0.4, 0.3, 0.4, 0.001, 0.8, + 0.5, 0.6, 0.4, 0.4, 0.001), nrow = k, ncol = k, byrow = T) +mu +Sigma <- 0.1*(diag(k) + matrix(0.01, nrow = k, ncol = k)) +Sigma + +for (i in 1:k) +{ + u <- exp(mvrnorm(n=nsamples[i], mu[i,], Sigma));u + colmeans <- colSums(u)/nsamples[i] + print(colmeans) + if (i == 1) + { + dados <- data.frame(categoria = rep(categorias[i], nsamples[i]), u) + } + else { + dados <- rbind(dados, data.frame(categoria = rep(categorias[i], nsamples[i]), u)) + } +} +dados <- rename(dados, c('X1'='dist1', 'X2'='dist2', 'X3'='dist3', + 'X4'='dist4', 'X5'='dist5')) + +#------ splitting samples ---------# + +trainIndex <- createDataPartition(dados$categoria, p = .8, + list = FALSE, + times = 1) +head(trainIndex) + +dadosTrain <- dados[ trainIndex,] +dadosTest <- dados[-trainIndex,] + +#-------- rodando os modelos --------------# + +categorias.qda <- qda(categoria ~ log(dist1)+log(dist2)+log(dist3)+log(dist4)+log(dist5), + data=dadosTrain) +categorias.lda <- lda(categoria ~ log(dist1)+log(dist2)+log(dist3)+log(dist4)+log(dist5), + data=dadosTrain) +categorias.mlr <- multinom(categoria ~ log(dist1)+log(dist2)+log(dist3)+log(dist4)+log(dist5), + data=dadosTrain) +categorias.svm1 <- svm(categoria ~ log(dist1)+log(dist2)+log(dist3)+log(dist4)+log(dist5), + data=dadosTrain, kernel = "radial", type="C-classification", k = 5) +categorias.svm2 <- svm(categoria ~ log(dist1)+log(dist2)+log(dist3)+log(dist4)+log(dist5), + data=dadosTrain, kernel = "linear", type="C-classification", k = 5) +categorias.svm3 <- svm(categoria ~ log(dist1)+log(dist2)+log(dist3)+log(dist4)+log(dist5), + data=dadosTrain, kernel = "polynomial", type="C-classification", k = 5) +categorias.svm4 <- svm(categoria ~ log(dist1)+log(dist2)+log(dist3)+log(dist4)+log(dist5), + data=dadosTrain, kernel = "sigmoid", type="C-classification", k = 5) + +categorias.lasso <- glmnet(y = data.matrix(dadosTrain[,1]), + x = data.matrix(dadosTrain[,c(2,3,4,5,6)]), + family="multinomial", alpha=1) +categorias.ridge <- glmnet(y = data.matrix(dadosTrain[,1]), + x = data.matrix(dadosTrain[,c(2,3,4,5,6)]), + family="multinomial", alpha=0) +categorias.elnet <- glmnet(y = data.matrix(dadosTrain[,1]), + x = data.matrix(dadosTrain[,c(2,3,4,5,6)]), + family="multinomial", alpha=.5) + +categorias.lda +categorias.qda +categorias.mlr +categorias.svm1 +categorias.svm2 +categorias.svm3 +categorias.svm4 +summary(categorias.lasso) +summary(categorias.ridge) +summary(categorias.elnet) + +categorias.lda.pred <- predict(categorias.lda, newdata = dadosTest)$class +categorias.qda.pred <- predict(categorias.qda, newdata = dadosTest)$class +categorias.mlr.pred <- predict(categorias.mlr, newdata = dadosTest) +categorias.svm1.pred <- predict(categorias.svm1, newdata = dadosTest) +categorias.svm2.pred <- predict(categorias.svm2, newdata = dadosTest) +categorias.svm3.pred <- predict(categorias.svm3, newdata = dadosTest) +categorias.svm4.pred <- predict(categorias.svm4, newdata = dadosTest) + +categorias.lasso.pred <- predict(categorias.lasso, s=categorias.lasso$lambda.1se, + newx=data.matrix(dadosTest[,c(2,3,4,5,6)])) +categorias.ridge.pred <- predict(categorias.ridge, s=categorias.ridge$lambda.1se, + newx=data.matrix(dadosTest[,c(2,3,4,5,6)])) +categorias.elnet.pred <- predict(categorias.elnet, s=categorias.elnet$lambda.1se, + newx=data.matrix(dadosTest[,c(2,3,4,5,6)])) + +#------ classificando por menor distancia ---------# + +categorias.dtw.pred <- categorias.lda.pred +for (i in 1:nrow(dadosTest)) +{ + categorias.dtw.pred[i] <- categorias[1] + min_dist <- dadosTest[i,c('dist1')]; + if (min_dist > dadosTest[i,c('dist2')]) + { + min_dist <- dadosTest[i,c('dist2')]; + categorias.dtw.pred[i] <- categorias[2] + } + if (min_dist > dadosTest[i,c('dist3')]) + { + min_dist <- dadosTest[i,c('dist3')]; + categorias.dtw.pred[i] <- categorias[3] + } + if (min_dist > dadosTest[i,c('dist4')]) + { + min_dist <- dadosTest[i,c('dist4')]; + categorias.dtw.pred[i] <- categorias[4] + } + if (min_dist > dadosTest[i,c('dist5')]) + { + min_dist <- dadosTest[i,c('dist5')]; + categorias.dtw.pred[i] <- categorias[5] + } +} + +dadosTest$categoriadtw <- categorias.dtw.pred + +#---- avaliando performance + +table(categorias.dtw.pred, dadosTest$categoria) +table(categorias.lda.pred, dadosTest$categoria) +table(categorias.qda.pred, dadosTest$categoria) +table(categorias.mlr.pred, dadosTest$categoria) +table(categorias.svm1.pred, dadosTest$categoria) +table(categorias.svm2.pred, dadosTest$categoria) +table(categorias.svm3.pred, dadosTest$categoria) +table(categorias.svm4.pred, dadosTest$categoria) + +sum(diag(table(categorias.dtw.pred, dadosTest$categoria))) / nrow(dadosTest) +sum(diag(table(categorias.lda.pred, dadosTest$categoria))) / nrow(dadosTest) +sum(diag(table(categorias.qda.pred, dadosTest$categoria))) / nrow(dadosTest) +sum(diag(table(categorias.mlr.pred, dadosTest$categoria))) / nrow(dadosTest) +sum(diag(table(categorias.svm1.pred, dadosTest$categoria))) / nrow(dadosTest) +sum(diag(table(categorias.svm2.pred, dadosTest$categoria))) / nrow(dadosTest) +sum(diag(table(categorias.svm3.pred, dadosTest$categoria))) / nrow(dadosTest) +sum(diag(table(categorias.svm4.pred, dadosTest$categoria))) / nrow(dadosTest) + +#----------------------------------------------------------------------------# +#---- the end ----# +#----------------------------------------------------------------------------# +>>>>>>> be7ae2d8abd519b5ff237e573e1f1325068836b3:PosProcessamentoDTW/ProgramasR/Modelagem_classificacao_dados_simulados.R diff --git a/inst/extdata/examples/.directory b/inst/extdata/examples/.directory new file mode 100644 index 000000000..04eba6e78 --- /dev/null +++ b/inst/extdata/examples/.directory @@ -0,0 +1,4 @@ +[Dolphin] +Timestamp=2017,6,13,19,26,25 +Version=3 +ViewMode=1 diff --git a/inst/extdata/examples/rester_processing.R b/inst/extdata/examples/rester_processing.R index dab89c92d..68daa8d00 100644 --- a/inst/extdata/examples/rester_processing.R +++ b/inst/extdata/examples/rester_processing.R @@ -1,33 +1,33 @@ -# R script 2017-06-28, relabel raster statstack and apply post-processing using transition rules -# by Victor Maus - -library(sits) -library(raster) - -# default tmp partition might be too small, set a tmp folder in a partition with enough space -rasterOptions(tmpdir = "./.tmp") - -# Stack raster fiels (assuming that files are ordered by time) -map_files <- dir(system.file("extdata/raster/twdtw_classification", package="sits"), - pattern = ".tif$", full.names = TRUE) - -map_stack <- stack(map_files) - -# Relabel raster stack - Parallel processing -old_v = c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23) -new_v = c(0, 4, 1, 2, 1, 3, 3, 5, 5, 6, 7, 7, 7, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17) -relabeled_map <- sits_stack_relabel(x = map_stack, old_values = old_v, new_values = new_v) - -# Read transition matrix. This gives the possible transitions among classes. -# The first line and the first column of the table has the raster class followed -# by the class label, such that .. The rows are the -# Class(t) and columns Class(t-1), the cobination between a row (Class(t)) and a -# column (Class(t-1)) gives the updated class number. -transition_matrix <- - readr::read_csv(system.file("extdata/raster/twdtw_classification/transition_matrix.csv", package="sits")) - -# Relabel temporal transitions - Parallel processing -post_proc_map <- sits_stack_transition_relabel(x = relabeled_map, A = transition_matrix) - - - +# R script 2017-06-28, relabel raster statstack and apply post-processing using transition rules +# by Victor Maus + +library(sits) +library(raster) + +# default tmp partition might be too small, set a tmp folder in a partition with enough space +rasterOptions(tmpdir = "./.tmp") + +# Stack raster fiels (assuming that files are ordered by time) +map_files <- dir(system.file("extdata/raster/twdtw_classification", package="sits"), + pattern = ".tif$", full.names = TRUE) + +map_stack <- stack(map_files) + +# Relabel raster stack - Parallel processing +old_v = c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23) +new_v = c(0, 4, 1, 2, 1, 3, 3, 5, 5, 6, 7, 7, 7, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17) +relabeled_map <- sits_stack_relabel(x = map_stack, old_values = old_v, new_values = new_v) + +# Read transition matrix. This gives the possible transitions among classes. +# The first line and the first column of the table has the raster class followed +# by the class label, such that .. The rows are the +# Class(t) and columns Class(t-1), the cobination between a row (Class(t)) and a +# column (Class(t-1)) gives the updated class number. +transition_matrix <- + readr::read_csv(system.file("extdata/raster/twdtw_classification/transition_matrix.csv", package="sits")) + +# Relabel temporal transitions - Parallel processing +post_proc_map <- sits_stack_transition_relabel(x = relabeled_map, A = transition_matrix) + + + diff --git a/inst/extdata/examples/sits_cerrado_pasture.R b/inst/extdata/examples/sits_cerrado_pasture.R index 79eba941c..af803f367 100644 --- a/inst/extdata/examples/sits_cerrado_pasture.R +++ b/inst/extdata/examples/sits_cerrado_pasture.R @@ -20,38 +20,38 @@ cerrado1.tb <- cerrado.tb[1:20,] #plots the first 10 time series (default: put the same places in a single plot) sits_plot (cerrado1.tb) -#plots the time series one by one -sits_plot (savanna.tb[1:10,], type = "one_by_one") - - #plots all time series of the same label and band together (shows dispersion) sits_plot (cerrado.tb, type = "together") -# interpolate the series using spline -cerrado1s.tb <- sits_interp(cerrado1.tb, stats::spline, n = 50) -sits_plot(cerrado1s.tb) - # smooth the time series using the Whittaker smoother -# Merge with original and plot -cerrado2.tb <- sits_interp(cerrado1.tb, stats::spline, n = 50) %>% - sits_whittaker(lambda = 5.0) %>% +# then rename the result, merge with original and plot + +cerrado1s.tb <- sits_interp(cerrado1.tb, stats::approx, n = 50) + +cerrado2.tb <- sits_interp(cerrado1.tb, stats::approx, n = 50) %>% + sits_whittaker(lambda = 5.0) + +cerrado2.tb %>% sits_merge(cerrado1s.tb) %>% sits_select(c("evi.whit", "evi")) %>% sits_plot() -# find a set of patterns to find the samples using the GAM -# Plot the result -pat_cerrado.tb <- sits_patterns(cerrado.tb, method = "gam") -sits_plot(pat_cerrado.tb, type = "patterns") +cerrado3.tb <- cerrado1.tb %>% + sits_interp (stats::approx, n = 50) %>% + sits_envelope() %>% + sits_whittaker(lambda = 0.5) -# assess the quality of the classification using TWDTW -# perform a cross-validation -cm_gam <- sits_cross_validate (cerrado.tb, method = "gam", bands = c("ndvi","evi"), times = 20, perc = 0.5) - -# cross validation results: -# Accuracy (PCC): 92.2654155495978% -# Cohen's Kappa: 0.8435 -# Users accuracy: Cerrado = 96.7% Pasture = 87.1% -# Producers accuracy: Cerrado = 89.7% Pasture = 95.8% +cerrado3.tb %>% + sits_merge(cerrado1s.tb) %>% + sits_select(c("evi.upper.whit", "evi.lower.whit", "evi")) %>% + sits_plot() +cerrado3.tb %>% + sits_merge(cerrado2.tb) %>% + sits_select(c("evi.upper.whit", "evi.lower.whit", "evi.whit")) %>% + sits_plot() +cerrado3.tb %>% + sits_merge(cerrado1s.tb) %>% + sits_select(c("evi.upper.whit", "evi")) %>% + sits_plot() diff --git a/inst/extdata/examples/sits_cerrado_pasture_KF.R b/inst/extdata/examples/sits_cerrado_pasture_KF.R index 030b9db85..cf54c7c6e 100644 --- a/inst/extdata/examples/sits_cerrado_pasture_KF.R +++ b/inst/extdata/examples/sits_cerrado_pasture_KF.R @@ -1,59 +1,59 @@ -#' @title Example of retrieval, plotting, filtering (Kalman filter), and classification of -#' Cerrado and Pasture temporal patterns -#' @name sits_cerrado_pasture -#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br} -#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br} -#' @author Alber Sánchez, \email{alber.sanchez@@inpe.br} -#' -#' @description Retrieves a set of 736 time series of cerrado and pasture, plots the data -#' and shows various processing functions of sits_plot. -#' -#' @param cerrado.tb a SITS tibble retrieved from a JSON file - -#install library -library (sits) -# get a local data set -cerrado.tb <- sits_getdata("./inst/extdata/samples/cerrado.json") - -# pick the first 10 time series -cerrado1.tb <- cerrado.tb[1:20,] - -#plots the first 10 time series (default: put the same places in a single plot) -sits_plot (cerrado1.tb) - -#plots all time series of the same label and band together (shows dispersion) -sits_plot (cerrado.tb, type = "together") - -# smooth the time series using the Kalman filter -# then rename the result, merge with original and plot - -cerrado1s.tb <- sits_interp(cerrado1.tb, stats::approx, n = 50) - -cerrado2.tb <- sits_interp(cerrado1.tb, stats::approx, n = 50) %>% - sits_kf() - -cerrado2.tb %>% - sits_merge(cerrado1s.tb) %>% - sits_select(c("evi.kf.estimation", "evi")) %>% - sits_plot() - -cerrado3.tb <- cerrado1.tb %>% - sits_interp (stats::approx, n = 50) %>% - sits_envelope() %>% - sits_kf() - -cerrado3.tb %>% - sits_merge(cerrado1s.tb) %>% - sits_select(c("evi.upper.kf.estimation", "evi.lower.kf.estimation", "evi")) %>% - sits_plot() - -cerrado3.tb %>% - sits_merge(cerrado2.tb) %>% - sits_select(c("evi.upper.kf.estimation", "evi.lower.kf.estimation", "evi.kf.estimation")) %>% - sits_plot() - -cerrado3.tb %>% - sits_merge(cerrado1s.tb) %>% - sits_select(c("evi.upper.kf.estimation", "evi")) %>% - sits_plot() - +#' @title Example of retrieval, plotting, filtering (Kalman filter), and classification of +#' Cerrado and Pasture temporal patterns +#' @name sits_cerrado_pasture +#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br} +#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br} +#' @author Alber Sánchez, \email{alber.sanchez@@inpe.br} +#' +#' @description Retrieves a set of 736 time series of cerrado and pasture, plots the data +#' and shows various processing functions of sits_plot. +#' +#' @param cerrado.tb a SITS tibble retrieved from a JSON file + +#install library +library (sits) +# get a local data set +cerrado.tb <- sits_getdata("./inst/extdata/samples/cerrado.json") + +# pick the first 10 time series +cerrado1.tb <- cerrado.tb[1:20,] + +#plots the first 10 time series (default: put the same places in a single plot) +sits_plot (cerrado1.tb) + +#plots all time series of the same label and band together (shows dispersion) +sits_plot (cerrado.tb, type = "together") + +# smooth the time series using the Kalman filter +# then rename the result, merge with original and plot + +cerrado1s.tb <- sits_interp(cerrado1.tb, stats::approx, n = 50) + +cerrado2.tb <- sits_interp(cerrado1.tb, stats::approx, n = 50) %>% + sits_kf() + +cerrado2.tb %>% + sits_merge(cerrado1s.tb) %>% + sits_select(c("evi.kf.estimation", "evi")) %>% + sits_plot() + +cerrado3.tb <- cerrado1.tb %>% + sits_interp (stats::approx, n = 50) %>% + sits_envelope() %>% + sits_kf() + +cerrado3.tb %>% + sits_merge(cerrado1s.tb) %>% + sits_select(c("evi.upper.kf.estimation", "evi.lower.kf.estimation", "evi")) %>% + sits_plot() + +cerrado3.tb %>% + sits_merge(cerrado2.tb) %>% + sits_select(c("evi.upper.kf.estimation", "evi.lower.kf.estimation", "evi.kf.estimation")) %>% + sits_plot() + +cerrado3.tb %>% + sits_merge(cerrado1s.tb) %>% + sits_select(c("evi.upper.kf.estimation", "evi")) %>% + sits_plot() + diff --git a/inst/extdata/examples/sits_cluster_TWDTW.R b/inst/extdata/examples/sits_cluster_TWDTW.R index e87ac5e09..8c2398755 100644 --- a/inst/extdata/examples/sits_cluster_TWDTW.R +++ b/inst/extdata/examples/sits_cluster_TWDTW.R @@ -1,27 +1,26 @@ -# R script 2017-05-02, verifying the separability between labels using TWDTW clustering and logistic weight -# by Victor Maus - -library(sits) - -# Load sample time series -mt.tb <- sits_getdata (file = system.file("extdata/samples/matogrosso.json", package="sits")) -sits_labels(mt.tb) - -# Remove insignificant labels -mt.tb <- sits_significant_labels(mt.tb, 0.02) -sits_labels(mt.tb) - -# Prunes the times series -mt.tb <- sits_prune(mt.tb) - -# Set TWDTW weight function -log_fun = logisticWeight(-0.1, 50) - -# TWDTW clustering -proc_time = - system.time(new_class.tb <- sits_cluster(mt.tb, method = "dendogram", bands = c("evi", "ndvi", "nir", "mir"), - dist_method = "TWDTW", show = TRUE, weight.fun = log_fun, span = 300)) - - -sits_labels(new_class.tb) - +# R script 2017-05-02, verifying the separability between labels using TWDTW clustering and logistic weight +# by Victor Maus + +library(sits) + +# Load sample time series +mt.tb <- sits_getdata (file = system.file("extdata/samples/matogrosso.json", package="sits")) +sits_labels(mt.tb) + +# Remove insignificant labels +mt.tb <- sits_significant_labels(mt.tb, 0.02) +sits_labels(mt.tb) + +# Prunes the times series +mt.tb <- sits_prune(mt.tb) + +# Set TWDTW weight function +log_fun = logisticWeight(-0.1, 50) + +# TWDTW clustering +proc_time = + system.time(new_class.tb <- sits_cluster(mt.tb, method = "dendogram", bands = c("evi", "ndvi", "nir", "mir"), + dist_method = "TWDTW", show = TRUE, weight.fun = log_fun, span = 300)) + + +sits_labels(new_class.tb) \ No newline at end of file diff --git a/inst/extdata/examples/sits_cluster_segregation.R b/inst/extdata/examples/sits_cluster_segregation.R index 07a878245..9bbdaee2a 100644 --- a/inst/extdata/examples/sits_cluster_segregation.R +++ b/inst/extdata/examples/sits_cluster_segregation.R @@ -1,29 +1,29 @@ -# R script 2017-03-09 -# sits package by Gilberto Câmara -# https://github.com/gilbertocamara/sits - -# Verifying the separability between labels using unsupervised clustering algorithm. - -library(sits) -mt.tb <- sits_getdata (file = system.file("extdata/samples/matogrosso.json", package="sits")) -sits_labels(mt.tb) - -mt.tb <- sits_significant_labels(mt.tb, 0.02) -sits_labels(mt.tb) - -mt.tb <- sits_prune(mt.tb) - -new_class.tb <- sits_cluster(data.tb = mt.tb, method = "kohonen-dendogram", grouping_method = "ward.D2", - bands = c("evi", "ndvi", "nir", "mir"), n_clusters = 15, - show = TRUE, return_members = TRUE, unsupervised = TRUE, - koh_xgrid = 10, koh_ygrid = 10, koh_rlen = 1200, koh_alpha = c(0.2, 0.04)) - -new_class.tb - -sits_labels(new_class.tb) - -sits_cluster_segregation(new_class.tb) - -sits_segregation_measure(new_class.tb, per_cluster = TRUE) - -sits_segregation_measure(new_class.tb, per_cluster = FALSE) +# R script 2017-03-09 +# sits package by Gilberto Câmara +# https://github.com/gilbertocamara/sits + +# Verifying the separability between labels using unsupervised clustering algorithm. + +library(sits) +mt.tb <- sits_getdata (file = system.file("extdata/samples/matogrosso.json", package="sits")) +sits_labels(mt.tb) + +mt.tb <- sits_significant_labels(mt.tb, 0.02) +sits_labels(mt.tb) + +mt.tb <- sits_prune(mt.tb) + +new_class.tb <- sits_cluster(data.tb = mt.tb, method = "kohonen-dendogram", grouping_method = "ward.D2", + bands = c("evi", "ndvi", "nir", "mir"), n_clusters = 15, + show = TRUE, return_members = TRUE, unsupervised = TRUE, + koh_xgrid = 10, koh_ygrid = 10, koh_rlen = 1200, koh_alpha = c(0.2, 0.04)) + +new_class.tb + +sits_labels(new_class.tb) + +sits_cluster_segregation(new_class.tb) + +sits_segregation_measure(new_class.tb, per_cluster = TRUE) + +sits_segregation_measure(new_class.tb, per_cluster = FALSE) diff --git a/inst/extdata/examples/sits_cluster_separability.R b/inst/extdata/examples/sits_cluster_separability.R new file mode 100644 index 000000000..7675fffb6 --- /dev/null +++ b/inst/extdata/examples/sits_cluster_separability.R @@ -0,0 +1,29 @@ +# R script 2017-03-09 +# sits package by Gilberto Câmara +# https://github.com/gilbertocamara/sits + +# Verifying the separability between labels using unsupervised clustering algorithm. + +library(sits) +mt.tb <- sits_getdata (file = system.file("extdata/samples/matogrosso.json", package="sits")) +sits_labels(mt.tb) + +mt.tb <- sits_significant_labels(mt.tb, 0.02) +sits_labels(mt.tb) + +mt.tb <- sits_prune(mt.tb) + +new_class.tb <- sits_cluster(data.tb = mt.tb, method = "kohonen-dendogram", grouping_method = "ward.D2", + bands = c("evi", "ndvi", "nir", "mir"), n_clusters = 15, + show = TRUE, return_members = TRUE, unsupervised = TRUE, + koh_xgrid = 10, koh_ygrid = 10, koh_rlen = 1200, koh_alpha = c(0.2, 0.04)) + +new_class.tb + +sits_labels(new_class.tb) + +sits_cluster_separability(new_class.tb) + +sits_separability_measure(new_class.tb, per_cluster = TRUE) + +sits_separability_measure(new_class.tb, per_cluster = FALSE) diff --git a/inst/extdata/examples/sits_clustering_examples.R b/inst/extdata/examples/sits_clustering_examples.R new file mode 100644 index 000000000..b64c5b767 --- /dev/null +++ b/inst/extdata/examples/sits_clustering_examples.R @@ -0,0 +1,28 @@ +library (sits) + +# select samples for pasture and savanna +cerrado.tb <- sits_getdata (file = system.file("extdata/samples/cerrado.json", package="sits")) + +pat_cerrado.tb <- sits_patterns(cerrado.tb) + +sits_plot(pat_cerrado.tb, type = "patterns") + +pat_cerrado2.tb <- sits_patterns(cerrado.tb, method = "dendogram") + +sits_plot(pat_cerrado2.tb, type = "patterns") + +pat_cerrado3.tb <- sits_patterns(cerrado.tb, method = "dendogram", apply_gam = TRUE) + +sits_plot(pat_cerrado3.tb, type = "patterns") + +savanna.tb <- dplyr::filter (cerrado.tb, label == "Cerrado") + +sits_plot (savanna.tb[1:10,], type = "one_by_one") + +sits_plot (savanna.tb[1:50,], type = "allyears") + +sits_plot (cerrado.tb[1:200,], type = "together") + +cerrado_pat.tb <- sits_patterns(cerrado.tb, method = "dendo&gam") + +sits_plot(cerrado_pat.tb, type = "patterns") diff --git a/inst/extdata/examples/sits_examples_TWDTW_3.R b/inst/extdata/examples/sits_examples_TWDTW_3.R index ab3db8b91..f95c4e37e 100644 --- a/inst/extdata/examples/sits_examples_TWDTW_3.R +++ b/inst/extdata/examples/sits_examples_TWDTW_3.R @@ -1,64 +1,64 @@ -# satellite image time series package (SITS) -# example of the classification of a time series -#devtools::install_github("gilbertocamara/sits") -library(sits) - -# Get information about the WTSS (web time series service) -# see WTSS paper for more information ("Web Services for Big Data") - -URL <- "http://www.dpi.inpe.br/tws/wtss" -wtss_inpe <- sits_infoWTSS(URL) - -# get information about a specific coverage -sits_coverageWTSS(URL,"mod13q1_512") - -# choose a coverage -coverage <- "mod13q1_512" -# recover all bands -bands <- c("ndvi", "evi", "nir") - -# a point -long <- -59.60500 -lat <- -10.23667 - -# obtain a time series from the WTSS server for this point -series.tb <- sits_getdata(longitude = long, latitude = lat, URL = URL, coverage = "mod13q1_512", bands = bands) - -# plot the “evi” band -series.tb %>% - sits_select (bands = "evi") %>% - sits_plot () - -# read a pattern table from a JSON file -patterns.tb <- sits_getdata(file = system.file("extdata/patterns/patterns_Damien_Ieda_Rodrigo_15classes_3bands_Water.json", package="sits")) -# plot patterns -sits_plot (patterns.tb, type = "patterns") - -# classify samples using TWDTW -results.tb <- sits_TWDTW(series.tb, patterns.tb, bands, alpha= -0.1, beta = 100, theta = 0.5, keep = TRUE) - -# # plot the classification -sits_plot(results.tb, type = "classification") -# # plot the alignments -sits_plot(results.tb, type = "alignments") -# # plot the matches for the class -sits_plot(results.tb, type = "matches", label = "SA", k = 4) - -long <- -55.01768 -lat <- -15.35588 - -# obtain a time series from the WTSS server for this point -series.tb <- sits_getdata(longitude = long, latitude = lat, URL = URL, coverage = "mod13q1_512", bands = bands) - -# plot the “evi” band -series.tb %>% - sits_select (bands = "evi") %>% - sits_plot () - -# classify samples using TWDTW -results.tb <- sits_TWDTW(series.tb, patterns.tb, bands, alpha= -0.1, beta = 100, theta = 0.5, keep = TRUE) - -# # plot the classification -sits_plot(results.tb, type = "classification") -# # plot the alignments -sits_plot(results.tb, type = "alignments") +# satellite image time series package (SITS) +# example of the classification of a time series +#devtools::install_github("gilbertocamara/sits") +library(sits) + +# Get information about the WTSS (web time series service) +# see WTSS paper for more information ("Web Services for Big Data") + +URL <- "http://www.dpi.inpe.br/tws/wtss" +wtss_inpe <- sits_infoWTSS(URL) + +# get information about a specific coverage +sits_coverageWTSS(URL,"mod13q1_512") + +# choose a coverage +coverage <- "mod13q1_512" +# recover all bands +bands <- c("ndvi", "evi", "nir") + +# a point +long <- -59.60500 +lat <- -10.23667 + +# obtain a time series from the WTSS server for this point +series.tb <- sits_getdata(longitude = long, latitude = lat, URL = URL, coverage = "mod13q1_512", bands = bands) + +# plot the “evi” band +series.tb %>% + sits_select (bands = "evi") %>% + sits_plot () + +# read a pattern table from a JSON file +patterns.tb <- sits_getdata(file = system.file("extdata/patterns/patterns_Damien_Ieda_Rodrigo_15classes_3bands_Water.json", package="sits")) +# plot patterns +sits_plot (patterns.tb, type = "patterns") + +# classify samples using TWDTW +results.tb <- sits_TWDTW(series.tb, patterns.tb, bands, alpha= -0.1, beta = 100, theta = 0.5, keep = TRUE) + +# # plot the classification +sits_plot(results.tb, type = "classification") +# # plot the alignments +sits_plot(results.tb, type = "alignments") +# # plot the matches for the class +sits_plot(results.tb, type = "matches", label = "SA", k = 4) + +long <- -55.01768 +lat <- -15.35588 + +# obtain a time series from the WTSS server for this point +series.tb <- sits_getdata(longitude = long, latitude = lat, URL = URL, coverage = "mod13q1_512", bands = bands) + +# plot the “evi” band +series.tb %>% + sits_select (bands = "evi") %>% + sits_plot () + +# classify samples using TWDTW +results.tb <- sits_TWDTW(series.tb, patterns.tb, bands, alpha= -0.1, beta = 100, theta = 0.5, keep = TRUE) + +# # plot the classification +sits_plot(results.tb, type = "classification") +# # plot the alignments +sits_plot(results.tb, type = "alignments") \ No newline at end of file diff --git a/inst/extdata/examples/sits_examples_TWDTW_4.R b/inst/extdata/examples/sits_examples_TWDTW_4.R index 4d7067069..84056befc 100644 --- a/inst/extdata/examples/sits_examples_TWDTW_4.R +++ b/inst/extdata/examples/sits_examples_TWDTW_4.R @@ -1,59 +1,58 @@ - -# example of the classification of a time series -#devtools::install_github("gilbertocamara/sits") -library(sits) - -# Get information about the WTSS (web time series service) -# see WTSS paper for more information ("Web Services for Big Data") - -URL <- "http://www.dpi.inpe.br/tws/wtss" -wtss_inpe <- sits_infoWTSS(URL) - -# get information about a specific coverage -sits_coverageWTSS(URL,"mod13q1_512") - - -# choose a coverage -coverage <- "mod13q1_512" -# recover all bands -bands <- c("ndvi", "evi", "nir") - -# a point in the transition forest pasture in Northern MT -long <- -58.60918 -lat <- -10.55992 - -# outro ponto interessante: -58.63919,-10.74036 - -# obtain a time series from the WTSS server for this point -series.tb <- sits_getdata(longitude = long, latitude = lat, URL = URL, coverage = "mod13q1_512", bands = bands) - -# smooth all the bands, plot them, and save the smoothed bands in a new table -series_s.tb <- series.tb %>% - sits_smooth() %>% - sits_rename (c("ndvi_smooth", "evi_smooth", "nir_smooth")) %>% - sits_plot() - -# merge the raw and smoothed series and plot the “red” and “red_smooth” bands -series.tb %>% - sits_merge(., series_s.tb) %>% - sits_select (bands = c("evi", "evi_smooth")) %>% - sits_plot() - -# retrieve patterns from a JSON file -matogrosso.tb <- sits_getdata(file = system.file("extdata/patterns/patterns_MatoGrosso_18052017.json", package="sits")) - -sits_plot (matogrosso.tb, type = "patterns") - -results.tb <- sits_TWDTW(series.tb, matogrosso.tb, bands, alpha= -0.1, beta = 100, theta = 0.5) - -# plot the results of the classification -sits_plot (results.tb, type = "classification") -sits_plot (results.tb, type = "alignments") - -matogrosso2.tb <- dplyr::filter (matogrosso.tb, label != "Sugarcane") - -results2.tb <- sits_TWDTW(series.tb, matogrosso2.tb, bands, alpha= -0.1, beta = 100, theta = 0.5) - -# plot the results of the classification -sits_plot (results2.tb, type = "classification") -sits_plot (results2.tb, type = "alignments") +# example of the classification of a time series +#devtools::install_github("gilbertocamara/sits") +library(sits) + +# Get information about the WTSS (web time series service) +# see WTSS paper for more information ("Web Services for Big Data") + +URL <- "http://www.dpi.inpe.br/tws/wtss" +wtss_inpe <- sits_infoWTSS(URL) + +# get information about a specific coverage +sits_coverageWTSS(URL,"mod13q1_512") + + +# choose a coverage +coverage <- "mod13q1_512" +# recover all bands +bands <- c("ndvi", "evi", "nir") + +# a point in the transition forest pasture in Northern MT +long <- -58.60918 +lat <- -10.55992 + +# outro ponto interessante: -58.63919,-10.74036 + +# obtain a time series from the WTSS server for this point +series.tb <- sits_getdata(longitude = long, latitude = lat, URL = URL, coverage = "mod13q1_512", bands = bands) + +# smooth all the bands, plot them, and save the smoothed bands in a new table +series_s.tb <- series.tb %>% + sits_smooth() %>% + sits_rename (c("ndvi_smooth", "evi_smooth", "nir_smooth")) %>% + sits_plot() + +# merge the raw and smoothed series and plot the “red” and “red_smooth” bands +series.tb %>% + sits_merge(., series_s.tb) %>% + sits_select (bands = c("evi", "evi_smooth")) %>% + sits_plot() + +# retrieve patterns from a JSON file +matogrosso.tb <- sits_getdata(file = system.file("extdata/patterns/patterns_MatoGrosso_18052017.json", package="sits")) + +sits_plot (matogrosso.tb, type = "patterns") + +results.tb <- sits_TWDTW(series.tb, matogrosso.tb, bands, alpha= -0.1, beta = 100, theta = 0.5) + +# plot the results of the classification +sits_plot (results.tb, type = "classification") +sits_plot (results.tb, type = "alignments") + +matogrosso2.tb <- dplyr::filter (matogrosso.tb, label != "Sugarcane") + +results2.tb <- sits_TWDTW(series.tb, matogrosso2.tb, bands, alpha= -0.1, beta = 100, theta = 0.5) + +# plot the results of the classification +sits_plot (results2.tb, type = "classification") +sits_plot (results2.tb, type = "alignments") \ No newline at end of file diff --git a/inst/extdata/examples/sits_fao_validation.R b/inst/extdata/examples/sits_fao_validation.R index de76b5261..4f25a1077 100644 --- a/inst/extdata/examples/sits_fao_validation.R +++ b/inst/extdata/examples/sits_fao_validation.R @@ -1,4 +1,3 @@ - # R script 2017-03-09 # sits package by Gilberto Câmara # https://github.com/gilbertocamara/sits diff --git a/inst/extdata/examples/sits_matogrosso_validation.R b/inst/extdata/examples/sits_matogrosso_validation.R index 29d10d3aa..a9280c094 100644 --- a/inst/extdata/examples/sits_matogrosso_validation.R +++ b/inst/extdata/examples/sits_matogrosso_validation.R @@ -17,57 +17,57 @@ matogrosso.tb <- sits_prune(matogrosso.tb) mt_cm <- sits_relabel (matogrosso.tb, file = system.file("extdata/results/conf_matrix.json", package = "sits")) -mt_conv.lst <- tibble::lst(Soja_Sorgo Soybean_Commerc - Soja_Nada Soybean_Fallow - Soja_Brachiaria Soybean_NonCommerc - Soja Soybean_Fallow - Pasto Pasture - Milheto_Alg NonCommerc_Cotton - SojaSA Soybean_Cotton - Soja_Milheto Soybean_NonCommerc - Cana Sugarcane - Soja_Milho Soybean_Commerc - Alg NonCommerc_Cotton - Arroz Rice - Milheto Millet - Soja_Milho.3 Soybean_Commerc - Soja_Sorgo.1 Soybean_Commerc - Soja_Brachiaria.2 Soybean_NonCommerc - Soja_Sorgo.2 Soybean_Commerc - Soja_Nada.3 Soybean_Fallow - Pasto.2 Pasture - SojaSA.1 Soybean_Cotton - Soja_Milho.2 Soybean_Commerc - Soja.1 Soybean_Fallow - Soja_Milheto.1 Soybean_NonCommerc - Soja_Nada.1 Soybean_Fallow - Soja_Brachiaria.3 Soybean_NonCommerc - Arroz.1 Rice - Soja_Nada.2 Soybean_Fallow - Arroz.2 Rice - Soja_Milho.1 Soybean_Commerc - Soja_Milheto.3 Soybean_NonCommerc - Soja.3 Soybean_Fallow - Pasto.1 Pasture - Cana.2 Sugarcane - Milheto_Alg.3 NonCommerc_Cotton - Cana.3 Sugarcane - Pasto.3 Pasture - Milheto_Alg.1 NonCommerc_Cotton - Cana.1 Sugarcane - Alg.2 NonCommerc_Cotton - Milheto.1 Millet - Alg.3 NonCommerc_Cotton - Alg.1 NonCommerc_Cotton - SojaSA.2 Soybean_Cotton - Milheto_Alg.2 NonCommerc_Cotton - Milheto.3 Millet - Soja_Brachiaria.1 Soybean_NonCommerc - Milheto.2 Millet - Soja_Milheto.2 Soybean_NonCommerc - SojaSA.3 Soybean_Cotton - Soja.2 Soybean_Fallow - Arroz.3 Rice +mt_conv.lst <- tibble::lst("Soja_Sorgo" = "Soybean_Commerc", + "Soja_Nada" = "Soybean_Fallow", + "Soja_Brachiaria" = "Soybean_NonCommerc", + "Soja" = "Soybean_Fallow", + "Pasto" = "Pasture", + "Milheto_Alg" = "NonCommerc_Cotton", + "SojaSA" = "Soybean_Cotton", + "Soja_Milheto" = "Soybean_NonCommerc", + "Cana" = "Sugarcane", + "Soja_Milho" = "Soybean_Commerc", + "Alg" = "NonCommerc_Cotton", + "Arroz" = "Rice", + "Milheto" = "Millet", + "Soja_Milho.3" = "Soybean_Commerc", + "Soja_Sorgo.1" = "Soybean_Commerc", + "Soja_Brachiaria.2" = "Soybean_NonCommerc", + "Soja_Sorgo.2" = "Soybean_Commerc", + "Soja_Nada.3" = "Soybean_Fallow", + "Pasto.2" = "Pasture", + "SojaSA.1" = "Soybean_Cotton", + "Soja_Milho.2" = "Soybean_Commerc", + "Soja.1" = "Soybean_Fallow", + "Soja_Milheto.1" = "Soybean_NonCommerc", + "Soja_Nada.1" = "Soybean_Fallow", + "Soja_Brachiaria.3" = "Soybean_NonCommerc", + "Arroz.1" = "Rice", + "Soja_Nada.2" = "Soybean_Fallow", + "Arroz.2" = "Rice", + "Soja_Milho.1" = "Soybean_Commerc", + "Soja_Milheto.3" = "Soybean_NonCommerc", + "Soja.3" = "Soybean_Fallow", + "Pasto.1" = "Pasture", + "Cana.2" = "Sugarcane", + "Milheto_Alg.3" = "NonCommerc_Cotton", + "Cana.3" = "Sugarcane", + "Pasto.3" = "Pasture", + "Milheto_Alg.1" = "NonCommerc_Cotton", + "Cana.1" = "Sugarcane", + "Alg.2" = "NonCommerc_Cotton", + "Milheto.1" = "Millet", + "Alg.3" = "NonCommerc_Cotton", + "Alg.1" = "NonCommerc_Cotton", + "SojaSA.2" = "Soybean_Cotton", + "Milheto_Alg.2" = "NonCommerc_Cotton", + "Milheto.3" = "Millet", + "Soja_Brachiaria.1" = "Soybean_NonCommerc", + "Milheto.2" = "Millet", + "Soja_Milheto.2" = "Soybean_NonCommerc", + "SojaSA.3" = "Soybean_Cotton", + "Soja.2" = "Soybean_Fallow", + "Arroz.3" = "Rice") mt_cm <- sits_relabel (matogrosso.tb, file = system.file("extdata/results/mt_cm.json", package = "sits"), diff --git a/inst/extdata/examples/sits_prodes_test_1.R b/inst/extdata/examples/sits_prodes_test_1.R index 89a6d0321..725a418ad 100644 --- a/inst/extdata/examples/sits_prodes_test_1.R +++ b/inst/extdata/examples/sits_prodes_test_1.R @@ -3,17 +3,6 @@ #devtools::install_github("gilbertocamara/sits") library(sits) -# Get information about the WTSS (web time series service) -# see WTSS paper for more information ("Web Services for Big Data") - -URL <- "http://www.dpi.inpe.br/tws/wtss" -wtss_inpe <- sits_infoWTSS(URL) - -# get information about a specific coverage -sits_coverageWTSS(URL,"mixl8mod") - -# choose a coverage -coverage <- "mixl8mod" # recover all bands bands <- c("ndvi", "evi") @@ -46,3 +35,4 @@ sits_plot (matches.tb, type = "alignments") class1.tb <- sits_TWDTW_classify (matches1.tb, start_date = "2013-08-01", end_date = "2017-07-31", interval = "12 month") sits_plot(matches.tb, type = "classification", start_date = "2013-08-01", end_date = "2017-07-31", interval = "12 month") +>>>>>>> d864f58c1aba6d813a3413858509369a47000989 diff --git a/inst/extdata/examples/sits_validation_examples.R b/inst/extdata/examples/sits_validation_examples.R index effd27f29..0f33b2c68 100644 --- a/inst/extdata/examples/sits_validation_examples.R +++ b/inst/extdata/examples/sits_validation_examples.R @@ -4,7 +4,7 @@ # comparing patterns obtained from Generalized Additive Models and from Clustering library(sits) -# mudança + # retrieve a set of samples from a JSON file cerrado.tb <- sits_getdata(file = system.file("extdata/samples/cerrado.json", package="sits")) @@ -20,18 +20,3 @@ cm_som <- sits_cross_validate (cerrado.tb, method = "koho&dogram", bands = c("ev min_clu_perc = 0.1, apply_gam = TRUE, tw_alpha = -0.1, tw_beta = 45) -# Accuracy (PCC): 94.1460506706408% -# Cohen's Kappa: 0.882 -# Users accuracy: -# Cerrado Pasture -# 96.2 91.7 -# -# Producers accuracy: -# Cerrado Pasture -# 93.1 95.5 -# Confusion matrix -# y -# x Cerrado Pasture -# Cerrado 17322 1286 -# Pasture 678 14264 - diff --git a/inst/extdata/patterns/.Rapp.history b/inst/extdata/patterns/.Rapp.history new file mode 100644 index 000000000..ee61c85bb --- /dev/null +++ b/inst/extdata/patterns/.Rapp.history @@ -0,0 +1 @@ +load("/Users/gilberto/sits/data/patterns/temporal_patterns_Cartaxo_Rodrigo_13class_6bands.RData") diff --git a/inst/extdata/patterns/patterns_Damien_Ieda_Rodrigo_15classes_3bands_Pasture3.json b/inst/extdata/patterns/patterns_Damien_Ieda_Rodrigo_15classes_3bands_Pasture3.json new file mode 100644 index 000000000..26bdbf121 --- /dev/null +++ b/inst/extdata/patterns/patterns_Damien_Ieda_Rodrigo_15classes_3bands_Pasture3.json @@ -0,0 +1,4114 @@ +[ + { + "longitude": 0, + "latitude": 0, + "start_date": "2000-09-13", + "end_date": "2001-08-23", + "label": "Cerrado", + "coverage": "mod13q1_512", + "time_series": [ + { + "Index": "2000-09-13", + "ndvi": 0.4635, + "evi": 0.2374, + "nir": 0.2011 + }, + { + "Index": "2000-09-21", + "ndvi": 0.5041, + "evi": 0.2639, + "nir": 0.2129 + }, + { + "Index": "2000-09-29", + "ndvi": 0.5423, + "evi": 0.2898, + "nir": 0.2243 + }, + { + "Index": "2000-10-07", + "ndvi": 0.5755, + "evi": 0.3142, + "nir": 0.2349 + }, + { + "Index": "2000-10-15", + "ndvi": 0.602, + "evi": 0.3365, + "nir": 0.2444 + }, + { + "Index": "2000-10-23", + "ndvi": 0.6207, + "evi": 0.3562, + "nir": 0.2525 + }, + { + "Index": "2000-10-31", + "ndvi": 0.6318, + "evi": 0.373, + "nir": 0.2594 + }, + { + "Index": "2000-11-08", + "ndvi": 0.6365, + "evi": 0.3869, + "nir": 0.2653 + }, + { + "Index": "2000-11-16", + "ndvi": 0.6367, + "evi": 0.3981, + "nir": 0.2704 + }, + { + "Index": "2000-11-24", + "ndvi": 0.6345, + "evi": 0.4066, + "nir": 0.275 + }, + { + "Index": "2000-12-02", + "ndvi": 0.6314, + "evi": 0.4129, + "nir": 0.2792 + }, + { + "Index": "2000-12-10", + "ndvi": 0.6287, + "evi": 0.4169, + "nir": 0.2831 + }, + { + "Index": "2000-12-18", + "ndvi": 0.6271, + "evi": 0.4191, + "nir": 0.2865 + }, + { + "Index": "2000-12-26", + "ndvi": 0.6267, + "evi": 0.4196, + "nir": 0.2891 + }, + { + "Index": "2001-01-03", + "ndvi": 0.6276, + "evi": 0.4189, + "nir": 0.2908 + }, + { + "Index": "2001-01-11", + "ndvi": 0.6295, + "evi": 0.4174, + "nir": 0.2914 + }, + { + "Index": "2001-01-19", + "ndvi": 0.6327, + "evi": 0.4157, + "nir": 0.2908 + }, + { + "Index": "2001-01-27", + "ndvi": 0.6373, + "evi": 0.4141, + "nir": 0.289 + }, + { + "Index": "2001-02-04", + "ndvi": 0.6433, + "evi": 0.4128, + "nir": 0.2863 + }, + { + "Index": "2001-02-12", + "ndvi": 0.6508, + "evi": 0.4118, + "nir": 0.2827 + }, + { + "Index": "2001-02-20", + "ndvi": 0.6592, + "evi": 0.4108, + "nir": 0.2785 + }, + { + "Index": "2001-02-28", + "ndvi": 0.668, + "evi": 0.4091, + "nir": 0.2738 + }, + { + "Index": "2001-03-08", + "ndvi": 0.676, + "evi": 0.4063, + "nir": 0.2687 + }, + { + "Index": "2001-03-16", + "ndvi": 0.6825, + "evi": 0.4018, + "nir": 0.2634 + }, + { + "Index": "2001-03-24", + "ndvi": 0.6867, + "evi": 0.3954, + "nir": 0.2579 + }, + { + "Index": "2001-04-01", + "ndvi": 0.6882, + "evi": 0.3874, + "nir": 0.2524 + }, + { + "Index": "2001-04-09", + "ndvi": 0.6873, + "evi": 0.3781, + "nir": 0.2469 + }, + { + "Index": "2001-04-17", + "ndvi": 0.6845, + "evi": 0.3684, + "nir": 0.2417 + }, + { + "Index": "2001-04-25", + "ndvi": 0.6804, + "evi": 0.3589, + "nir": 0.2368 + }, + { + "Index": "2001-05-03", + "ndvi": 0.6756, + "evi": 0.35, + "nir": 0.2323 + }, + { + "Index": "2001-05-11", + "ndvi": 0.6699, + "evi": 0.3417, + "nir": 0.2281 + }, + { + "Index": "2001-05-19", + "ndvi": 0.6629, + "evi": 0.3338, + "nir": 0.2239 + }, + { + "Index": "2001-05-27", + "ndvi": 0.6536, + "evi": 0.3255, + "nir": 0.2198 + }, + { + "Index": "2001-06-04", + "ndvi": 0.6412, + "evi": 0.3161, + "nir": 0.2153 + }, + { + "Index": "2001-06-12", + "ndvi": 0.6248, + "evi": 0.3051, + "nir": 0.2106 + }, + { + "Index": "2001-06-20", + "ndvi": 0.6043, + "evi": 0.2925, + "nir": 0.2056 + }, + { + "Index": "2001-06-28", + "ndvi": 0.5806, + "evi": 0.2786, + "nir": 0.2006 + }, + { + "Index": "2001-07-06", + "ndvi": 0.5553, + "evi": 0.2646, + "nir": 0.1962 + }, + { + "Index": "2001-07-14", + "ndvi": 0.5307, + "evi": 0.2517, + "nir": 0.1927 + }, + { + "Index": "2001-07-22", + "ndvi": 0.5087, + "evi": 0.2414, + "nir": 0.1907 + }, + { + "Index": "2001-07-30", + "ndvi": 0.4912, + "evi": 0.2345, + "nir": 0.1904 + }, + { + "Index": "2001-08-07", + "ndvi": 0.4788, + "evi": 0.2314, + "nir": 0.1919 + }, + { + "Index": "2001-08-15", + "ndvi": 0.4709, + "evi": 0.2316, + "nir": 0.1948 + }, + { + "Index": "2001-08-23", + "ndvi": 0.4665, + "evi": 0.2343, + "nir": 0.1987 + } + ] + }, + { + "longitude": 0, + "latitude": 0, + "start_date": "2006-09-14", + "end_date": "2007-08-29", + "label": "Crop_Cotton", + "coverage": "mod13q1_512", + "time_series": [ + { + "Index": "2006-09-14", + "ndvi": 0.3174, + "evi": 0.2001, + "nir": 0.2465 + }, + { + "Index": "2006-09-22", + "ndvi": 0.3438, + "evi": 0.2182, + "nir": 0.2499 + }, + { + "Index": "2006-09-30", + "ndvi": 0.3726, + "evi": 0.2392, + "nir": 0.2553 + }, + { + "Index": "2006-10-08", + "ndvi": 0.4051, + "evi": 0.2652, + "nir": 0.2645 + }, + { + "Index": "2006-10-16", + "ndvi": 0.4399, + "evi": 0.2954, + "nir": 0.2775 + }, + { + "Index": "2006-10-24", + "ndvi": 0.4741, + "evi": 0.3274, + "nir": 0.2932 + }, + { + "Index": "2006-11-01", + "ndvi": 0.502, + "evi": 0.3558, + "nir": 0.3089 + }, + { + "Index": "2006-11-09", + "ndvi": 0.5179, + "evi": 0.3752, + "nir": 0.3213 + }, + { + "Index": "2006-11-17", + "ndvi": 0.5179, + "evi": 0.3815, + "nir": 0.3279 + }, + { + "Index": "2006-11-25", + "ndvi": 0.5001, + "evi": 0.3728, + "nir": 0.3269 + }, + { + "Index": "2006-12-03", + "ndvi": 0.4686, + "evi": 0.3524, + "nir": 0.3195 + }, + { + "Index": "2006-12-11", + "ndvi": 0.4295, + "evi": 0.3261, + "nir": 0.3081 + }, + { + "Index": "2006-12-19", + "ndvi": 0.3929, + "evi": 0.3032, + "nir": 0.2975 + }, + { + "Index": "2006-12-27", + "ndvi": 0.3693, + "evi": 0.2936, + "nir": 0.2928 + }, + { + "Index": "2007-01-04", + "ndvi": 0.367, + "evi": 0.3046, + "nir": 0.2983 + }, + { + "Index": "2007-01-12", + "ndvi": 0.3898, + "evi": 0.3393, + "nir": 0.3162 + }, + { + "Index": "2007-01-20", + "ndvi": 0.4373, + "evi": 0.3963, + "nir": 0.3462 + }, + { + "Index": "2007-01-28", + "ndvi": 0.5051, + "evi": 0.4705, + "nir": 0.386 + }, + { + "Index": "2007-02-05", + "ndvi": 0.5851, + "evi": 0.5529, + "nir": 0.4308 + }, + { + "Index": "2007-02-13", + "ndvi": 0.6685, + "evi": 0.6342, + "nir": 0.4752 + }, + { + "Index": "2007-02-21", + "ndvi": 0.7467, + "evi": 0.7053, + "nir": 0.5135 + }, + { + "Index": "2007-03-01", + "ndvi": 0.8129, + "evi": 0.7593, + "nir": 0.541 + }, + { + "Index": "2007-03-09", + "ndvi": 0.8632, + "evi": 0.7929, + "nir": 0.5553 + }, + { + "Index": "2007-03-17", + "ndvi": 0.8958, + "evi": 0.8049, + "nir": 0.5552 + }, + { + "Index": "2007-03-25", + "ndvi": 0.9114, + "evi": 0.7975, + "nir": 0.5419 + }, + { + "Index": "2007-04-02", + "ndvi": 0.911, + "evi": 0.773, + "nir": 0.5177 + }, + { + "Index": "2007-04-10", + "ndvi": 0.8958, + "evi": 0.7346, + "nir": 0.4857 + }, + { + "Index": "2007-04-18", + "ndvi": 0.867, + "evi": 0.6852, + "nir": 0.449 + }, + { + "Index": "2007-04-26", + "ndvi": 0.8251, + "evi": 0.6268, + "nir": 0.4106 + }, + { + "Index": "2007-05-04", + "ndvi": 0.7711, + "evi": 0.5616, + "nir": 0.3729 + }, + { + "Index": "2007-05-12", + "ndvi": 0.707, + "evi": 0.4922, + "nir": 0.3377 + }, + { + "Index": "2007-05-20", + "ndvi": 0.6359, + "evi": 0.4218, + "nir": 0.3066 + }, + { + "Index": "2007-05-28", + "ndvi": 0.5626, + "evi": 0.3546, + "nir": 0.2807 + }, + { + "Index": "2007-06-05", + "ndvi": 0.4923, + "evi": 0.2946, + "nir": 0.2606 + }, + { + "Index": "2007-06-13", + "ndvi": 0.4303, + "evi": 0.2459, + "nir": 0.2468 + }, + { + "Index": "2007-06-21", + "ndvi": 0.3802, + "evi": 0.2108, + "nir": 0.2391 + }, + { + "Index": "2007-06-29", + "ndvi": 0.3438, + "evi": 0.1894, + "nir": 0.2366 + }, + { + "Index": "2007-07-07", + "ndvi": 0.3203, + "evi": 0.18, + "nir": 0.2381 + }, + { + "Index": "2007-07-15", + "ndvi": 0.3064, + "evi": 0.1784, + "nir": 0.2419 + }, 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"2016-06-12", + "ndvi": 0.6127, + "evi": 0.3921, + "nir": 0.3115 + }, + { + "Index": "2016-06-20", + "ndvi": 0.557, + "evi": 0.3459, + "nir": 0.2974 + }, + { + "Index": "2016-06-28", + "ndvi": 0.5047, + "evi": 0.3039, + "nir": 0.285 + }, + { + "Index": "2016-07-06", + "ndvi": 0.4603, + "evi": 0.2698, + "nir": 0.2752 + }, + { + "Index": "2016-07-14", + "ndvi": 0.4265, + "evi": 0.2449, + "nir": 0.268 + }, + { + "Index": "2016-07-22", + "ndvi": 0.404, + "evi": 0.2294, + "nir": 0.2632 + }, + { + "Index": "2016-07-30", + "ndvi": 0.3916, + "evi": 0.2216, + "nir": 0.2602 + }, + { + "Index": "2016-08-07", + "ndvi": 0.3871, + "evi": 0.2196, + "nir": 0.2583 + }, + { + "Index": "2016-08-15", + "ndvi": 0.388, + "evi": 0.2213, + "nir": 0.257 + }, + { + "Index": "2016-08-23", + "ndvi": 0.392, + "evi": 0.2249, + "nir": 0.256 + } + ] + } +] diff --git a/inst/extdata/raster/twdtw_classification/transition_matrix.csv b/inst/extdata/raster/twdtw_classification/transition_matrix.csv index 9f81ebcf7..cab0d51d6 100644 --- a/inst/extdata/raster/twdtw_classification/transition_matrix.csv +++ b/inst/extdata/raster/twdtw_classification/transition_matrix.csv @@ -1,11 +1,11 @@ -label,0.No_data,1.Cotton,2.Forest,3.Pasture,4.Savanna,5.Soybean_Commercial,6.Soybean_Cotton,7.Soybean,8.Soybean_Pasture,9.Water,10.Degraded_Forest,11.Secondary_Forest,12.Deforestation_Disturbance_Forest,13.Deforestation_Disturbance_Sec_Forest,14.Degraded_Savana,15.Secondary_Savana,16.Deforestation_Disturbance_Savana,17.Deforestation_Disturbance_Sec_Savana -0.No_data,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -1.Cotton,1,1,12,1,16,1,1,1,1,1,12,13,1,1,16,17,1,1 -2.Forest,2,11,2,11,2,11,11,11,11,2,10,11,10,11,10,11,10,11 -3.Pasture,3,3,12,3,3,3,3,3,3,3,12,13,3,3,16,17,3,3 -4.Savanna,4,15,4,15,15,15,15,15,15,9,10,11,4,4,14,15,14,15 -5.Soybean_Commercial,5,5,12,5,5,5,5,5,5,5,12,13,5,5,16,17,5,5 -6.Soybean_Cotton,6,6,12,6,6,6,6,6,6,6,12,13,6,6,16,17,14,15 -7.Soybean,7,7,12,7,7,7,7,7,7,7,12,13,7,7,16,17,7,7 -8.Soybean_Pasture,8,8,12,8,8,8,8,8,8,8,12,13,8,8,16,17,8,8 -9.Water,9,9,9,9,9,9,9,9,9,9,12,13,9,9,16,17,9,9 +label,0.No_data,1.Cotton,2.Forest,3.Pasture,4.Savanna,5.Soybean_Commercial,6.Soybean_Cotton,7.Soybean,8.Soybean_Pasture,9.Water,10.Degraded_Forest,11.Secondary_Forest,12.Deforestation_Disturbance_Forest,13.Deforestation_Disturbance_Sec_Forest,14.Degraded_Savana,15.Secondary_Savana,16.Deforestation_Disturbance_Savana,17.Deforestation_Disturbance_Sec_Savana +0.No_data,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +1.Cotton,1,1,12,1,16,1,1,1,1,1,12,13,1,1,16,17,1,1 +2.Forest,2,11,2,11,2,11,11,11,11,2,10,11,10,11,10,11,10,11 +3.Pasture,3,3,12,3,3,3,3,3,3,3,12,13,3,3,16,17,3,3 +4.Savanna,4,15,4,15,15,15,15,15,15,9,10,11,4,4,14,15,14,15 +5.Soybean_Commercial,5,5,12,5,5,5,5,5,5,5,12,13,5,5,16,17,5,5 +6.Soybean_Cotton,6,6,12,6,6,6,6,6,6,6,12,13,6,6,16,17,14,15 +7.Soybean,7,7,12,7,7,7,7,7,7,7,12,13,7,7,16,17,7,7 +8.Soybean_Pasture,8,8,12,8,8,8,8,8,8,8,12,13,8,8,16,17,8,8 +9.Water,9,9,9,9,9,9,9,9,9,9,12,13,9,9,16,17,9,9 diff --git a/inst/extdata/samples/savanna_pasture_samples_2.csv b/inst/extdata/samples/savanna_pasture_samples_2.csv index af4976f36..7788da6e0 100644 --- a/inst/extdata/samples/savanna_pasture_samples_2.csv +++ b/inst/extdata/samples/savanna_pasture_samples_2.csv @@ -1,181 +1,181 @@ -id,longitude,latitude,start_date,end_date,label -1,-54.2313,-14.0482,2000-09-13,2001-08-29,Savanna -1,-54.2313,-14.0482,2001-09-14,2002-08-29,Savanna -1,-54.2313,-14.0482,2002-09-14,2003-08-29,Savanna -1,-54.2313,-14.0482,2003-09-14,2004-08-28,Savanna -1,-54.2313,-14.0482,2004-09-13,2005-08-29,Savanna -1,-54.2313,-14.0482,2005-09-14,2006-08-29,Savanna -1,-54.2313,-14.0482,2006-09-14,2007-08-29,Savanna -1,-54.2313,-14.0482,2007-09-14,2008-08-28,Savanna -1,-54.2313,-14.0482,2008-09-13,2009-08-29,Savanna -1,-54.2313,-14.0482,2009-09-14,2010-08-29,Savanna -1,-54.2313,-14.0482,2010-09-14,2011-08-29,Savanna -1,-54.2313,-14.0482,2011-09-14,2012-08-28,Savanna -1,-54.2313,-14.0482,2012-09-13,2013-08-29,Savanna -1,-54.2313,-14.0482,2013-09-14,2014-08-29,Savanna -1,-54.2313,-14.0482,2014-09-14,2015-08-29,Savanna -2,-54.229,-14.0632,2000-09-13,2001-08-29,Savanna -2,-54.229,-14.0632,2001-09-14,2002-08-29,Savanna -2,-54.229,-14.0632,2002-09-14,2003-08-29,Savanna -2,-54.229,-14.0632,2003-09-14,2004-08-28,Savanna -2,-54.229,-14.0632,2004-09-13,2005-08-29,Savanna -2,-54.229,-14.0632,2005-09-14,2006-08-29,Savanna -2,-54.229,-14.0632,2006-09-14,2007-08-29,Savanna -2,-54.229,-14.0632,2007-09-14,2008-08-28,Savanna -2,-54.229,-14.0632,2008-09-13,2009-08-29,Savanna -2,-54.229,-14.0632,2009-09-14,2010-08-29,Savanna -2,-54.229,-14.0632,2010-09-14,2011-08-29,Savanna -2,-54.229,-14.0632,2011-09-14,2012-08-28,Savanna -2,-54.229,-14.0632,2012-09-13,2013-08-29,Savanna -2,-54.229,-14.0632,2013-09-14,2014-08-29,Savanna -2,-54.229,-14.0632,2014-09-14,2015-08-29,Savanna -3,-54.5421,-14.3098,2012-09-13,2013-08-29,Savanna -4,-52.6234,-15.3137,2005-09-14,2006-08-29,Savanna -4,-52.6234,-15.3137,2006-09-14,2007-08-29,Savanna -4,-52.6234,-15.3137,2007-09-14,2008-08-28,Savanna -4,-52.6234,-15.3137,2008-09-13,2009-08-29,Savanna -4,-52.6234,-15.3137,2009-09-14,2010-08-29,Savanna -4,-52.6234,-15.3137,2010-09-14,2011-08-29,Savanna -4,-52.6234,-15.3137,2011-09-14,2012-08-28,Savanna -4,-52.6234,-15.3137,2012-09-13,2013-08-29,Savanna -4,-52.6234,-15.3137,2013-09-14,2014-08-29,Savanna -5,-52.5824,-15.3168,2005-09-14,2006-08-29,Savanna -5,-52.5824,-15.3168,2006-09-14,2007-08-29,Savanna -5,-52.5824,-15.3168,2007-09-14,2008-08-28,Savanna -5,-52.5824,-15.3168,2008-09-13,2009-08-29,Savanna -5,-52.5824,-15.3168,2009-09-14,2010-08-29,Savanna -5,-52.5824,-15.3168,2010-09-14,2011-08-29,Savanna -5,-52.5824,-15.3168,2011-09-14,2012-08-28,Savanna -5,-52.5824,-15.3168,2012-09-13,2013-08-29,Savanna -5,-52.5824,-15.3168,2013-09-14,2014-08-29,Savanna -6,-52.6217,-15.0231,2005-09-14,2006-08-29,Savanna -6,-52.6217,-15.0231,2006-09-14,2007-08-29,Savanna -6,-52.6217,-15.0231,2007-09-14,2008-08-28,Savanna -6,-52.6217,-15.0231,2008-09-13,2009-08-29,Savanna -6,-52.6217,-15.0231,2009-09-14,2010-08-29,Savanna -6,-52.6217,-15.0231,2010-09-14,2011-08-29,Savanna -6,-52.6217,-15.0231,2011-09-14,2012-08-28,Savanna -6,-52.6217,-15.0231,2012-09-13,2013-08-29,Savanna -6,-52.6217,-15.0231,2013-09-14,2014-08-29,Savanna -7,-51.7751,-14.347,2006-09-14,2007-08-29,Savanna -7,-51.7751,-14.347,2007-09-14,2008-08-28,Savanna -7,-51.7751,-14.347,2008-09-13,2009-08-29,Savanna -7,-51.7751,-14.347,2009-09-14,2010-08-29,Savanna -7,-51.7751,-14.347,2010-09-14,2011-08-29,Savanna -7,-51.7751,-14.347,2011-09-14,2012-08-28,Savanna -7,-51.7751,-14.347,2012-09-13,2013-08-29,Savanna -7,-51.7751,-14.347,2013-09-14,2014-08-29,Savanna -8,-51.7824,-14.3609,2006-09-14,2007-08-29,Savanna -8,-51.7824,-14.3609,2007-09-14,2008-08-28,Savanna -8,-51.7824,-14.3609,2008-09-13,2009-08-29,Savanna -8,-51.7824,-14.3609,2009-09-14,2010-08-29,Savanna -8,-51.7824,-14.3609,2010-09-14,2011-08-29,Savanna -8,-51.7824,-14.3609,2011-09-14,2012-08-28,Savanna -8,-51.7824,-14.3609,2012-09-13,2013-08-29,Savanna -8,-51.7824,-14.3609,2013-09-14,2014-08-29,Savanna -9,-53.4066,-14.2578,2000-09-13,2001-08-29,Savanna -9,-53.4066,-14.2578,2001-09-14,2002-08-29,Savanna -9,-53.4066,-14.2578,2002-09-14,2003-08-29,Savanna -9,-53.4066,-14.2578,2003-09-14,2004-08-28,Savanna -9,-53.4066,-14.2578,2004-09-13,2005-08-29,Savanna -21,-57.794,-9.7573,2012-09-13,2013-08-29,Pasture -21,-57.794,-9.7573,2013-09-14,2014-08-29,Pasture -22,-55.9643,-10.0621,2001-09-14,2002-08-29,Pasture -22,-55.9643,-10.0621,2002-09-14,2003-08-29,Pasture -22,-55.9643,-10.0621,2003-09-14,2004-08-28,Pasture -22,-55.9643,-10.0621,2004-09-13,2005-08-29,Pasture -22,-55.9643,-10.0621,2005-09-14,2006-08-29,Pasture -22,-55.9643,-10.0621,2006-09-14,2007-08-29,Pasture -22,-55.9643,-10.0621,2007-09-14,2008-08-28,Pasture -22,-55.9643,-10.0621,2008-09-13,2009-08-29,Pasture -23,-55.9472,-10.0801,2001-09-14,2002-08-29,Pasture -23,-55.9472,-10.0801,2002-09-14,2003-08-29,Pasture -23,-55.9472,-10.0801,2003-09-14,2004-08-28,Pasture -23,-55.9472,-10.0801,2004-09-13,2005-08-29,Pasture -23,-55.9472,-10.0801,2005-09-14,2006-08-29,Pasture -23,-55.9472,-10.0801,2006-09-14,2007-08-29,Pasture -23,-55.9472,-10.0801,2007-09-14,2008-08-28,Pasture -23,-55.9472,-10.0801,2008-09-13,2009-08-29,Pasture -24,-55.9467,-10.0902,2001-09-14,2002-08-29,Pasture -24,-55.9467,-10.0902,2002-09-14,2003-08-29,Pasture -24,-55.9467,-10.0902,2003-09-14,2004-08-28,Pasture -24,-55.9467,-10.0902,2004-09-13,2005-08-29,Pasture -24,-55.9467,-10.0902,2005-09-14,2006-08-29,Pasture -24,-55.9467,-10.0902,2006-09-14,2007-08-29,Pasture -24,-55.9467,-10.0902,2007-09-14,2008-08-28,Pasture -24,-55.9467,-10.0902,2008-09-13,2009-08-29,Pasture -25,-55.8955,-10.081,2003-09-14,2004-08-28,Pasture -25,-55.8955,-10.081,2004-09-13,2005-08-29,Pasture -25,-55.8955,-10.081,2005-09-14,2006-08-29,Pasture -25,-55.8955,-10.081,2006-09-14,2007-08-29,Pasture -25,-55.8955,-10.081,2007-09-14,2008-08-28,Pasture -25,-55.8955,-10.081,2008-09-13,2009-08-29,Pasture -25,-55.8955,-10.081,2009-09-14,2010-08-29,Pasture -25,-55.8955,-10.081,2010-09-14,2011-08-29,Pasture -25,-55.8955,-10.081,2011-09-14,2012-08-28,Pasture -25,-55.8955,-10.081,2012-09-13,2013-08-29,Pasture -26,-51.985,-10.606,2010-09-14,2011-08-29,Pasture -26,-51.985,-10.606,2011-09-14,2012-08-28,Pasture -27,-51.9303,-10.6362,2010-09-14,2011-08-29,Pasture -27,-51.9303,-10.6362,2011-09-14,2012-08-28,Pasture -27,-51.9303,-10.6362,2012-09-13,2013-08-29,Pasture -28,-51.565,-12.5681,2000-09-13,2001-08-29,Pasture -28,-51.565,-12.5681,2001-09-14,2002-08-29,Pasture -28,-51.565,-12.5681,2002-09-14,2003-08-29,Pasture -28,-51.565,-12.5681,2003-09-14,2004-08-28,Pasture -28,-51.565,-12.5681,2004-09-13,2005-08-29,Pasture -28,-51.565,-12.5681,2005-09-14,2006-08-29,Pasture -28,-51.565,-12.5681,2006-09-14,2007-08-29,Pasture -28,-51.565,-12.5681,2007-09-14,2008-08-28,Pasture -28,-51.565,-12.5681,2008-09-13,2009-08-29,Pasture -28,-51.565,-12.5681,2009-09-14,2010-08-29,Pasture -28,-51.565,-12.5681,2010-09-14,2011-08-29,Pasture -28,-51.565,-12.5681,2011-09-14,2012-08-28,Pasture -28,-51.565,-12.5681,2012-09-13,2013-08-29,Pasture -28,-51.565,-12.5681,2013-09-14,2014-08-29,Pasture -28,-51.565,-12.5681,2014-09-14,2015-08-29,Pasture -29,-51.5718,-12.603,2000-09-13,2001-08-29,Pasture -29,-51.5718,-12.603,2001-09-14,2002-08-29,Pasture -29,-51.5718,-12.603,2002-09-14,2003-08-29,Pasture -29,-51.5718,-12.603,2003-09-14,2004-08-28,Pasture -29,-51.5718,-12.603,2004-09-13,2005-08-29,Pasture -29,-51.5718,-12.603,2005-09-14,2006-08-29,Pasture -29,-51.5718,-12.603,2006-09-14,2007-08-29,Pasture -29,-51.5718,-12.603,2007-09-14,2008-08-28,Pasture -29,-51.5718,-12.603,2008-09-13,2009-08-29,Pasture -29,-51.5718,-12.603,2009-09-14,2010-08-29,Pasture -29,-51.5718,-12.603,2010-09-14,2011-08-29,Pasture -29,-51.5718,-12.603,2011-09-14,2012-08-28,Pasture -29,-51.5718,-12.603,2012-09-13,2013-08-29,Pasture -29,-51.5718,-12.603,2013-09-14,2014-08-29,Pasture -29,-51.5718,-12.603,2014-09-14,2015-08-29,Pasture -30,-53.0647,-14.6894,2000-09-13,2001-08-29,Pasture -30,-53.0647,-14.6894,2001-09-14,2002-08-29,Pasture -30,-53.0647,-14.6894,2002-09-14,2003-08-29,Pasture -30,-53.0647,-14.6894,2003-09-14,2004-08-28,Pasture -30,-53.0647,-14.6894,2004-09-13,2005-08-29,Pasture -30,-53.0647,-14.6894,2005-09-14,2006-08-29,Pasture -30,-53.0647,-14.6894,2006-09-14,2007-08-29,Pasture -30,-53.0647,-14.6894,2007-09-14,2008-08-28,Pasture -30,-53.0647,-14.6894,2008-09-13,2009-08-29,Pasture -30,-53.0647,-14.6894,2009-09-14,2010-08-29,Pasture -30,-53.0647,-14.6894,2010-09-14,2011-08-29,Pasture -30,-53.0647,-14.6894,2011-09-14,2012-08-28,Pasture -30,-53.0647,-14.6894,2012-09-13,2013-08-29,Pasture -30,-53.0647,-14.6894,2013-09-14,2014-08-29,Pasture -30,-53.0647,-14.6894,2014-09-14,2015-08-29,Pasture -31,-53.0516,-14.7615,2000-09-13,2001-08-29,Pasture -31,-53.0516,-14.7615,2001-09-14,2002-08-29,Pasture -31,-53.0516,-14.7615,2002-09-14,2003-08-29,Pasture -31,-53.0516,-14.7615,2003-09-14,2004-08-28,Pasture -31,-53.0516,-14.7615,2004-09-13,2005-08-29,Pasture -31,-53.0516,-14.7615,2005-09-14,2006-08-29,Pasture -31,-53.0516,-14.7615,2006-09-14,2007-08-29,Pasture -31,-53.0516,-14.7615,2007-09-14,2008-08-28,Pasture -31,-53.0516,-14.7615,2008-09-13,2009-08-29,Pasture -31,-53.0516,-14.7615,2009-09-14,2010-08-29,Pasture -31,-53.0516,-14.7615,2010-09-14,2011-08-29,Pasture -31,-53.0516,-14.7615,2011-09-14,2012-08-28,Pasture -31,-53.0516,-14.7615,2012-09-13,2013-08-29,Pasture -31,-53.0516,-14.7615,2013-09-14,2014-08-29,Pasture +id,longitude,latitude,start_date,end_date,label +1,-54.2313,-14.0482,2000-09-13,2001-08-29,Savanna +1,-54.2313,-14.0482,2001-09-14,2002-08-29,Savanna +1,-54.2313,-14.0482,2002-09-14,2003-08-29,Savanna 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+31,-53.0516,-14.7615,2009-09-14,2010-08-29,Pasture +31,-53.0516,-14.7615,2010-09-14,2011-08-29,Pasture +31,-53.0516,-14.7615,2011-09-14,2012-08-28,Pasture +31,-53.0516,-14.7615,2012-09-13,2013-08-29,Pasture +31,-53.0516,-14.7615,2013-09-14,2014-08-29,Pasture 31,-53.0516,-14.7615,2014-09-14,2015-08-29,Pasture \ No newline at end of file diff --git a/man/sits_accuracy.Rd b/man/sits_accuracy.Rd index dd777b6ac..d045e37ce 100644 --- a/man/sits_accuracy.Rd +++ b/man/sits_accuracy.Rd @@ -10,6 +10,9 @@ sits_accuracy(results.tb) \item{results.tb}{a sits table with a set of lat/long/time locations with known and trusted labels and with the result of a classification method} } +\value{ +assessment a list containing overall accuracy, producers and users accuracy, and confusion matrix. +} \description{ To use this function, the input table should be a set of results containing both the label assigned by the user and the classification result. diff --git a/man/sits_cluster.Rd b/man/sits_cluster.Rd index 0cd7ccb8f..7d7666474 100644 --- a/man/sits_cluster.Rd +++ b/man/sits_cluster.Rd @@ -4,10 +4,10 @@ \alias{sits_cluster} \title{Cluster a set of satellite image time series} \usage{ -sits_cluster(data.tb = NULL, bands = NULL, method = "dendogram", - n_clusters = 2, dist_method = "dtw_basic", grouping_method = "ward.D2", - koh_xgrid = 5, koh_ygrid = 5, koh_rlen = 100, koh_alpha = c(0.05, - 0.01), return_members = FALSE, unsupervised = FALSE, show = TRUE, ...) +sits_cluster(data.tb, bands = NULL, method = "dendogram", n_clusters = 2, + dist_method = "dtw_basic", grouping_method = "ward.D2", koh_xgrid = 5, + koh_ygrid = 5, koh_rlen = 100, koh_alpha = c(0.05, 0.01), + return_members = FALSE, unsupervised = FALSE, show = FALSE, ...) } \arguments{ \item{data.tb}{a SITS tibble the list of time series to be clustered} @@ -40,7 +40,7 @@ This column may be useful to measure confusion between clusters' members. Defaul \item{show}{(boolean) should the results be shown? Default is TRUE.} -\item{...}{Other arguments to pass to the distance method \code{dist_method}, see \code{\link[dtwclust]{dtwclust}} for details.} +\item{...}{Other arguments to pass to the distance method \code{dist_method}, and cluster method. See \code{\link[dtwclust]{dtwclust}} for details.} } \value{ clusters.tb a SITS tibble with the clusters time series or cluster' members time series according to return_member parameter. diff --git a/man/sits_cluster_segregation.Rd b/man/sits_cluster_separability.Rd similarity index 58% rename from man/sits_cluster_segregation.Rd rename to man/sits_cluster_separability.Rd index 977c59b90..4ec8ed439 100644 --- a/man/sits_cluster_segregation.Rd +++ b/man/sits_cluster_separability.Rd @@ -1,19 +1,19 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/sits_cluster.R -\name{sits_cluster_segregation} -\alias{sits_cluster_segregation} -\title{compare clusters against original labels and computes a segregation matrix.} +% Please edit documentation in R/sits_separability.R +\name{sits_cluster_separability} +\alias{sits_cluster_separability} +\title{compare clusters against original labels and computes a separability matrix.} \usage{ -sits_cluster_segregation(data.tb) +sits_cluster_separability(data.tb) } \arguments{ \item{data.tb}{a SITS table with the samples to be validated} } \value{ -result.tb a tibble with segregation matrix. +result.tb a tibble with separability matrix. } \description{ -Given a sits table with `original_label` column, computes a segregation matrix +Given a sits table with `original_label` column, computes a separability matrix between the original labels (`original_label` column) and new labels. This is useful to analyse the separability of samples for a given clustering algorithm. } diff --git a/man/sits_cross_validate.Rd b/man/sits_cross_validate.Rd index ce12af6c9..c5d697ef6 100644 --- a/man/sits_cross_validate.Rd +++ b/man/sits_cross_validate.Rd @@ -8,9 +8,10 @@ sits_cross_validate(data.tb, method = "gam", bands = NULL, times = 100, perc = 0.1, from = NULL, to = NULL, freq = 8, formula = y ~ s(x), tw_alpha = -0.1, tw_beta = 100, tw_theta = 0.5, tw_span = 0, interval = "12 month", overlap = 0.5, n_clusters = 2, - grouping_method = "ward.D2", min_clu_perc = 0.1, apply_gam = FALSE, - koh_xgrid = 5, koh_ygrid = 5, koh_rlen = 100, koh_alpha = c(0.05, - 0.01), file = "./conf_matrix.json", .multicores = 1) + grouping_method = "ward.D2", unsupervised = FALSE, min_clu_perc = 0.1, + apply_gam = FALSE, koh_xgrid = 5, koh_ygrid = 5, koh_rlen = 100, + koh_alpha = c(0.05, 0.01), file = "./conf_matrix.json", .multicores = 1, + ...) } \arguments{ \item{data.tb}{a SITS tibble} @@ -47,6 +48,9 @@ sits_cross_validate(data.tb, method = "gam", bands = NULL, times = 100, \item{grouping_method}{the agglomeration method to be used. Any `hclust` method (see `hclust`) (ignored in `kohonen` method). Default is 'ward.D2'.} +\item{unsupervised}{if TRUE, proceeds an unsupervised cluster followed by a relabel taking original label majority ( +this option has not any effect if method == "gam")} + \item{min_clu_perc}{the minimum percentagem of valid cluster members, with reference to the total number of samples (for clustering methods)} \item{apply_gam}{apply gam method after a clustering algorithm (ignored if method is `gam`).} @@ -64,6 +68,8 @@ Default is to decline linearly from 0.05 to 0.01 over rlen updates.} \item{file}{file to save the results} \item{.multicores}{number of threads to process the validation (Linux only). Each process will run a whole partition validation (see `times` parameter).} + +\item{...}{any additional parameters to be passed to `sits_pattern` function.} } \value{ cm a validation assessment diff --git a/man/sits_labels.Rd b/man/sits_labels.Rd index 5963c531d..be2ef8920 100644 --- a/man/sits_labels.Rd +++ b/man/sits_labels.Rd @@ -4,10 +4,12 @@ \alias{sits_labels} \title{returns the labels' count of a sits table} \usage{ -sits_labels(data.tb) +sits_labels(data.tb, from_members = FALSE) } \arguments{ \item{data.tb}{a valid sits table} + +\item{from_members}{(Boolean) if n_members collumn is present, compute label's statistics from it.} } \value{ result.tb a tibble with the names of the labels and its absolute and relative frequency diff --git a/man/sits_last_cluster.Rd b/man/sits_last_cluster.Rd new file mode 100644 index 000000000..7c691a157 --- /dev/null +++ b/man/sits_last_cluster.Rd @@ -0,0 +1,18 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/sits_cluster.R +\name{sits_last_cluster} +\alias{sits_last_cluster} +\title{Last cluster object} +\usage{ +sits_last_cluster() +} +\value{ +.sits_last_cluster an object created by dtwclust or SOM +} +\description{ +Returns the content of global variable `.sits_last_cluster` that stores the last +cluster object generated by sits_cluster function. +} +\author{ +Rolf Simoes, \email{rolf.simoes@inpe.br} +} diff --git a/man/sits_max_colors.Rd b/man/sits_max_colors.Rd new file mode 100644 index 000000000..194fef576 --- /dev/null +++ b/man/sits_max_colors.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/sits_colors.R +\name{sits_max_colors} +\alias{sits_max_colors} +\title{Brewer color schemes} +\usage{ +sits_max_colors(name = NULL) +} +\arguments{ +\item{name}{name of the brewer color set} +} +\value{ +numeric number of available colors +} +\description{ +Number of colors available in brewer color schemes +} diff --git a/man/sits_missing_values.Rd b/man/sits_missing_values.Rd index c9633966c..7923ae822 100644 --- a/man/sits_missing_values.Rd +++ b/man/sits_missing_values.Rd @@ -18,5 +18,5 @@ result.tb a sits_table with same samples and the new bands This function removes the missing values from an image time series } \author{ -Gilberto Camarara, \email{gilberto.camara@inpe.br} +Gilberto Camara, \email{gilberto.camara@inpe.br} } diff --git a/man/sits_patterns.Rd b/man/sits_patterns.Rd index 8ee330941..0385ed056 100644 --- a/man/sits_patterns.Rd +++ b/man/sits_patterns.Rd @@ -8,7 +8,7 @@ sits_patterns(samples.tb, method = "gam", bands = NULL, from = NULL, to = NULL, freq = 8, formula = y ~ s(x), n_clusters = 2, grouping_method = "ward.D2", min_clu_perc = 0.1, apply_gam = FALSE, koh_xgrid = 5, koh_ygrid = 5, koh_rlen = 100, koh_alpha = c(0.05, - 0.01), show = FALSE) + 0.01), unsupervised = FALSE, show = FALSE, ...) } \arguments{ \item{samples.tb}{a table in SITS format with a set of labelled time series.} @@ -43,7 +43,12 @@ sits_patterns(samples.tb, method = "gam", bands = NULL, from = NULL, \item{koh_alpha}{learning rate, a vector of two numbers indicating the amount of change. Default is to decline linearly from 0.05 to 0.01 over rlen updates.} +\item{unsupervised}{if TRUE, proceeds an unsupervised cluster followed by a relabel taking original label majority ( +this option has not any effect if method == "gam")} + \item{show}{show the results of the clustering algorithm? (for clustering methods).} + +\item{...}{additional arguments to be passed to the method function.} } \value{ patterns.tb a SITS table with the patterns. diff --git a/man/sits_patterns_gam.Rd b/man/sits_patterns_gam.Rd index 54a9669c7..6a4f9f358 100644 --- a/man/sits_patterns_gam.Rd +++ b/man/sits_patterns_gam.Rd @@ -5,7 +5,7 @@ \alias{.sits_patterns_gam} \title{Create temporal patterns using a generalised additive model (gam)} \usage{ -.sits_patterns_gam(samples.tb, bands, from, to, freq, formula) +.sits_patterns_gam(samples.tb, bands, from, to, freq, formula, ...) } \arguments{ \item{samples.tb}{a table in SITS format with time series to be classified using TWTDW} @@ -19,6 +19,8 @@ \item{freq}{int - the interval in days for the estimates to be generated} \item{formula}{the formula to be applied in the estimate} + +\item{...}{any additional parameters} } \value{ patterns.tb a SITS table with the patterns diff --git a/man/sits_plot.Rd b/man/sits_plot.Rd index d1e686114..ade30f840 100644 --- a/man/sits_plot.Rd +++ b/man/sits_plot.Rd @@ -25,7 +25,7 @@ sits_plot(data.tb = NULL, type = "allyears", colors = "Dark2", \item{interval}{the interval between classifications (used for showing classifications)} -\item{overlap}{minimum overlapping between one match and the interval of classification} +\item{overlap}{minimum overlapping between one match and the interval of classification. For details see dtwSat::twdtwApply help.} } \value{ data.tb tibble - the input SITS table (useful for chaining functions) diff --git a/man/sits_plot_dendrogram.Rd b/man/sits_plot_dendrogram.Rd new file mode 100644 index 000000000..a58ed29d0 --- /dev/null +++ b/man/sits_plot_dendrogram.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/sits_plot.R +\name{sits_plot_dendrogram} +\alias{sits_plot_dendrogram} +\title{Plot a dendrogram} +\usage{ +sits_plot_dendrogram(data.tb, cluster_obj = NULL, cutree_height = NULL, + colors = "RdYlGn", ...) +} +\arguments{ +\item{data.tb}{SITS tibble to extract labels} + +\item{cluster_obj}{cluster object. Usually stored by `sits_cluster` function in `.sits_last_object`} + +\item{cutree_height}{A dashed horizontal line to be drawed indicating the height of dendrogram cutting.} + +\item{colors}{a color scheme as showed in `sits_color_name` function} + +\item{...}{Other parameters to be passed to graphics::plot() function} +} +\description{ +Plot an enhanced dendrogram based on a cluster object usually found in `.sits_last_cluster` +} diff --git a/man/sits_plot_title.Rd b/man/sits_plot_title.Rd index 1f84d51e2..75cb5fe38 100644 --- a/man/sits_plot_title.Rd +++ b/man/sits_plot_title.Rd @@ -1,18 +1,28 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/sits_plot.R +% Please edit documentation in R/sits_colors.R, R/sits_plot.R \name{sits_plot_title} \alias{sits_plot_title} +\alias{sits_color_name} +\alias{sits_plot_title} \alias{.sits_plot_title} -\title{Create a plot title to use with ggplot} +\title{Brewer color schemes} \usage{ +sits_color_name(name = NULL) + .sits_plot_title(row) } \arguments{ +\item{name}{name of the brewer color set} + \item{row}{data row with information} } \value{ +name string - a common color set name + title string - the title to be used in the plot } \description{ +brewer color schemes names to be used with `sits_plot_dendrogram` + creates a plot title from row information } diff --git a/man/sits_relabel_conv.Rd b/man/sits_relabel_conv.Rd new file mode 100644 index 000000000..c536d986f --- /dev/null +++ b/man/sits_relabel_conv.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/sits_table.R +\name{sits_relabel_conv} +\alias{sits_relabel_conv} +\title{creates a conversion identity list for sits_relabel} +\usage{ +sits_relabel_conv(file = NULL) +} +\arguments{ +\item{file}{a JSON file contaning the result of a validation procedure} +} +\value{ +conv.lst a conversion list +} +\description{ +Given a confusion matrix obtained in the validation +procedure, returns a identity conversion list of labels. +} +\author{ +Rolf Simoes, \email{rolf.simoes@inpe.br} +} diff --git a/man/sits_separability_experiments.Rd b/man/sits_separability_experiments.Rd new file mode 100644 index 000000000..10680dc2f --- /dev/null +++ b/man/sits_separability_experiments.Rd @@ -0,0 +1,45 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/sits_separability.R +\name{sits_separability_experiments} +\alias{sits_separability_experiments} +\title{Do a set of separability experiments based on a cross combination of all elements of its input parameters.} +\usage{ +sits_separability_experiments(data.tb, method = "kohonen-dendogram", + times = 1, bands = list("evi", "ndvi", c("evi", "ndvi")), + n_clusters = 5, grouping_method = c("average", "ward.D2", "complete", + "single"), koh_grid_length = 7, koh_rlen = 1200, koh_alpha_from = 0.2, + koh_alpha_to = 0.04, .multiproc = 1) +} +\arguments{ +\item{data.tb}{a SITS table with the samples to be used in experiments.} + +\item{method}{string - either 'dendogram', 'centroids', 'kohonen', or 'kohonen-dendogram'.} + +\item{times}{how many times to run an parameterized experiment.} + +\item{bands}{the bands to be clusterized.} + +\item{n_clusters}{the number of clusters to be croped from hierarchical clustering (ignored in `kohonen` method). Default is 2.} + +\item{grouping_method}{the agglomeration method to be used. Any `hclust` method (see `hclust`) (ignored in `kohonen` method). Default is 'ward.D2'.} + +\item{koh_grid_length}{grid side dimension of the SOM grid (used only in `kohonen` or `kohonen-dendogram` methods). Defaul is 5.} + +\item{koh_rlen}{the number of times the complete data set will be presented to the SOM grid +(used only in `kohonen` or `kohonen-denddogram` methods). Default is 100.} + +\item{koh_alpha_from}{starting learning rate. Default is to decline linearly from 0.05 to `koh_alpha_to` over rlen updates.} + +\item{koh_alpha_to}{ending learning rate. Default is to decline linearly from `koh_alpha_from` to 1.0 over rlen updates.} + +\item{.multiproc}{(Linux only) numbers of cores to be used in multiprocessing.} +} +\value{ +result.tb a tibble with all cross combinations of parameters' values. +} +\description{ +Runs a set of separability experiments according to informed parameters and returns a tibble with experiment's result. +} +\author{ +Rolf Simoes, \email{rolf.simoes@inpe.br} +} diff --git a/man/sits_segregation_measure.Rd b/man/sits_separability_measure.Rd similarity index 58% rename from man/sits_segregation_measure.Rd rename to man/sits_separability_measure.Rd index 2cbceee97..885147190 100644 --- a/man/sits_segregation_measure.Rd +++ b/man/sits_separability_measure.Rd @@ -1,18 +1,20 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/sits_cluster.R -\name{sits_segregation_measure} -\alias{sits_segregation_measure} -\title{computes a segregation measure from a clusterized SITS table data.} +% Please edit documentation in R/sits_separability.R +\name{sits_separability_measure} +\alias{sits_separability_measure} +\title{computes a separability measure from a clusterized SITS table data.} \usage{ -sits_segregation_measure(data.tb, per_cluster = FALSE) +sits_separability_measure(data.tb, measure = "entropy", per_cluster = FALSE) } \arguments{ -\item{data.tb}{a SITS table with the samples to be validated} +\item{data.tb}{a SITS table with the samples to be validated.} -\item{per_cluster}{(boolean) should return a total average segregation measure, or a per cluster measure?} +\item{measure}{an separability measure. For now, only "entropy" is implemented.} + +\item{per_cluster}{(boolean) should return a total average separability measure, or a per cluster measure?} } \value{ -result a segregation measure. +result a separability measure. } \description{ Computes an measure of seggregation from SITS table based on mean relative Shannon Entropy, diff --git a/man/sits_test_patterns.Rd b/man/sits_test_patterns.Rd index 8e041182f..d647e76a4 100644 --- a/man/sits_test_patterns.Rd +++ b/man/sits_test_patterns.Rd @@ -13,7 +13,7 @@ sits_test_patterns(data.tb, patterns.tb, bands, alpha = -0.1, beta = 100, \item{patterns.tb}{A sits tibble containing a set of patterns} -\item{bands}{The bands used for classification} +\item{bands}{the bands used for classification} \item{alpha}{(double) - the steepness of the logistic function used for temporal weighting} diff --git a/sits.Rproj b/sits.Rproj index d83c63ec5..d6ffecdff 100644 --- a/sits.Rproj +++ b/sits.Rproj @@ -1,7 +1,7 @@ Version: 1.0 RestoreWorkspace: Default -SaveWorkspace: Default +SaveWorkspace: Ask AlwaysSaveHistory: Default EnableCodeIndexing: Yes @@ -12,6 +12,9 @@ Encoding: UTF-8 RnwWeave: Sweave LaTeX: pdfLaTeX +AutoAppendNewline: Yes +StripTrailingWhitespace: Yes + BuildType: Package PackageUseDevtools: Yes PackageInstallArgs: --no-multiarch --with-keep.source diff --git a/src/RcppExports.o b/src/RcppExports.o deleted file mode 100644 index 9e09ffacc..000000000 Binary files a/src/RcppExports.o and /dev/null differ diff --git a/src/apply_first_order_transition_rules.cpp b/src/apply_first_order_transition_rules.cpp index 9269a520e..bdd1c6a25 100644 --- a/src/apply_first_order_transition_rules.cpp +++ b/src/apply_first_order_transition_rules.cpp @@ -1,28 +1,28 @@ -// Apply transitions rules to raster assuming Class(t) = f(Class(t-1), Class(t)) -// subject to the initial condition Class(t=1) = Class(t=1) -// Author Victor Maus, \email{vwmaus1@@gmail.com} -// June, 2017 - -#include -using namespace Rcpp; - -//[[Rcpp::export()]] -Rcpp::NumericMatrix apply_first_order_transition_rules(Rcpp::NumericMatrix v, - Rcpp::NumericMatrix A, - Rcpp::NumericVector I){ - - int nrow = I.size(), ncol = v.ncol(), ii, jj; - - for (int i = 0; i < nrow; i++) - { - for (int j = 1; j < ncol; j++) - { - ii = (int) v(I(i)-1, j ); - jj = (int) v(I(i)-1, j-1); - v(I(i)-1, j) = A(ii, jj); - } - } - - return v; -} - +// Apply transitions rules to raster assuming Class(t) = f(Class(t-1), Class(t)) +// subject to the initial condition Class(t=1) = Class(t=1) +// Author Victor Maus, \email{vwmaus1@@gmail.com} +// June, 2017 + +#include +using namespace Rcpp; + +//[[Rcpp::export()]] +Rcpp::NumericMatrix apply_first_order_transition_rules(Rcpp::NumericMatrix v, + Rcpp::NumericMatrix A, + Rcpp::NumericVector I){ + + int nrow = I.size(), ncol = v.ncol(), ii, jj; + + for (int i = 0; i < nrow; i++) + { + for (int j = 1; j < ncol; j++) + { + ii = (int) v(I(i)-1, j ); + jj = (int) v(I(i)-1, j-1); + v(I(i)-1, j) = A(ii, jj); + } + } + + return v; +} + diff --git a/src/apply_first_order_transition_rules.o b/src/apply_first_order_transition_rules.o deleted file mode 100644 index 404ef1d1b..000000000 Binary files a/src/apply_first_order_transition_rules.o and /dev/null differ diff --git a/src/sits.so b/src/sits.so deleted file mode 100755 index 58d0d7825..000000000 Binary files a/src/sits.so and /dev/null differ diff --git a/vignettes/.texpadtmp/sits_intro-blx.bib b/vignettes/.texpadtmp/sits_intro-blx.bib new file mode 100644 index 000000000..61af54832 --- /dev/null +++ b/vignettes/.texpadtmp/sits_intro-blx.bib @@ -0,0 +1,11 @@ +@Comment{$ biblatex control file $} +@Comment{$ biblatex version 2.5 $} +Do not modify this file! + +This is an auxiliary file used by the 'biblatex' package. +This file may safely be deleted. It will be recreated as +required. + +@Control{biblatex-control, + options = {2.5:0:0:1:0:0:1:1:0:0:0:0:1:1:3:1:79:+}, +} diff --git a/vignettes/.texpadtmp/sits_intro.aux b/vignettes/.texpadtmp/sits_intro.aux new file mode 100644 index 000000000..03198698c --- /dev/null +++ b/vignettes/.texpadtmp/sits_intro.aux @@ -0,0 +1,16 @@ +\relax +\bibstyle{biblatex} +\bibdata{sits_intro-blx,"./references-esensing} +\citation{biblatex-control} +\citation{Gorelick2012} +\citation{Baumann1998} +\citation{Stonebraker2013} +\citation{Vatsavai2012} +\citation{Nativi2015} +\citation{Planthaber2012} +\citation{Krcal2015} +\@writefile{toc}{\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax } +\@writefile{lof}{\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax } +\@writefile{lot}{\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax } +\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{}} +\newlabel{sec:introduction}{{1}{}} diff --git a/vignettes/.texpadtmp/sits_intro.bbl b/vignettes/.texpadtmp/sits_intro.bbl new file mode 100644 index 000000000..9858e5a05 --- /dev/null +++ b/vignettes/.texpadtmp/sits_intro.bbl @@ -0,0 +1,22 @@ +% $ biblatex auxiliary file $ +% $ biblatex version 2.5 $ +% Do not modify the above lines! +% +% This is an auxiliary file used by the 'biblatex' package. +% This file may safely be deleted. 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Alves and Sugawara, Luciana Miura and Mello, Marcio Pupin}, + journal = {Sustainability}, + volume = {4}, + number = {4}, + pages = {574--585}, + year = {2012}} + +@book{Adams2006, + author = {Adams, J.B. and Gillespie, A.R.}, + Publisher = {Cambridge University Press}, + title = {Remote Sensing of Landscapes with Spectral Images: A Physical Modeling Approach}, + year = {2006}} + +@article{Aguiar2007, + author = {Ana Aguiar and Gilberto C\^{a}mara and Maria Isabel Sobral Escada}, + journal = {Ecological Modelling}, + number = {2-4}, + pages = {169 - 188}, + title = {Spatial statistical analysis of land-use determinants in the Brazilian Amazonia:exploring intra-regional heterogeneity}, + volume = {209}, + year = {2007}} + +@article{Ahlqvist2005, + author = {O. Ahlqvist}, + journal = {International Journal of Geographical Information Science}, + number = {7}, + pages = {831-857}, + title = {Using uncertain conceptual spaces to translate between land cover categories}, + volume = {19}, + year = {2005}} + +@article{Ahlqvist2008, + author = {Ahlqvist, Ola}, + journal = {Environment and Planning B: Planning and Design}, + number = {1}, + pages = {169--186}, + title = {In search of classification that supports the dynamics of science: the {FAO} Land Cover Classification System and proposed modifications}, + volume = {35}, + year = {2008}} + +@book{Ahlqvist2015, + Address = {Boca Raton}, + author = {O. Ahlqvist and D. Varanka and S. Fritz and K. Janowicz}, + Publisher = {CRC Press}, + title = {Land Use Cover and Land Cover Semantics. Principles, Best Practices, and Prospects}, + year = {2015}} + + +@inproceedings{Amirian2014, + author = {Amirian, Pouria and Basiri, Anahid and Winstanley, Adam}, + editor = {Murgante, Beniamino and Misra, Sanjay}, + title = {Evaluation of Data Management Systems for Geospatial Big Data}, + booktitle = {14th International Conference in Computational Science and Its Applications -- ICCSA 2014}, + year = {2014}, + pages = {678--690}} + +@article{Aji2013, + author = {Aji, Ablimit and Wang, Fusheng and Vo, Hoang and Lee, Rubao and Liu, Qiaoling and Zhang, Xiaodong and Saltz, Joel}, + journal = {Proc. VLDB Endow.}, + number = {11}, + pages = {1009--1020}, + title = {Hadoop GIS: A High Performance Spatial Data Warehousing System over Mapreduce}, + volume = {6}, + year = {2013}} + +@article{Allen1983, + author = {Allen, James F.}, + journal = {Communications of the ACM}, + number = {11}, + pages = {832--843}, + title = {Maintaining Knowledge About Temporal Intervals}, + volume = {26}, + year = {1983}} + +@article{Allen1984, + author = {Allen, James F.}, + journal = {Artificial Intelligence}, + number = {2}, + pages = {123--154}, + title = {Towards a General Theory of Action and Time}, + volume = {23}, + year = {1984}} + +@article{Arvor2011, + author = {Arvor, Damien and Jonathan, Milton and Meirelles, Margareth Sim\~{o}es Penello and Dubreuil, Vincent and Durieux, Laurent}, + journal = {International Journal of Remote Sensing}, + number = {22}, + pages = {7847-7871}, + title = {Classification of {MODIS EVI} time series for crop mapping in the state of Mato Grosso, Brazil}, + volume = {32}, + year = {2011}} + +@article{Baker2016, + author = {Monya Baker}, + journal = {Nature}, + pages = {452--454}, + title = {Reproducibility crisis?}, + volume = {533}, + year = {2016}} + +@book{Barnes1984, + author = {J. Barnes}, + Publisher = {Princeton University Press}, + title = {Complete Works of Aristotle, volume 2: The Revised Oxford Translation}, + year = {1984}} + +@article{Baumann1998, + author = {Baumann, P. and Dehmel, A. and Furtado, P. and Ritsch, R. and Widmann, N.}, + journal = {ACM SIGMOD Record}, + number = {2}, + pages = {575--577}, + title = {{The multidimensional database system RasDaMan}}, + volume = {27}, + year = {1998}} + +@article{Baumann1999, + author = {Baumann, Peter}, + journal = {Proceedings of the 4th International Workshop on Next Generation Information Technologies and Systems}, + number = {Mdd}, + pages = {76--93}, + title = {{A Database Array Algebra for Spatio-Temporal Data and Beyond}}, + year = {1999}} + +@inproceedings{Baumann2010a, + author = {Baumann, Peter}, + title = {Beyond Rasters: Introducing the New OGC Web Coverage Service 2.0}, + booktitle = {Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems}, + series = {GIS '10}, + pages = {320--329}, + year = {2010}} + +@article{Baumann2010, + title = {The OGC web coverage processing service (WCPS) standard}, + author = {Baumann, Peter}, + journal = {GeoInformatica}, + volume = {14}, + number = {4}, + pages = {447--479}, + year = {2010}} + + +@inproceedings{Berndt1994, + author = {Berndt, Donald J. and Clifford, James}, + title = {Using Dynamic Time Warping to Find Patterns in Time Series}, + booktitle = {KDD Workshop}, + year = {1994}, + editor = {Fayyad, Usama M. and Uthurusamy, Ramasamy}, + pages = {359-370}, + publisher = {AAAI Press}, + isbn = {0-929280-73-3} +} + +@book{Bivand2013, + Address = {New York}, + author = {Roger S. Bivand and Edzer Pebesma and Virgilio Gomez-Rubio}, + Edition = {2}, + Publisher = {Springer}, + title = {Applied spatial data analysis with {R}}, + year = {2013}} + +@software{Bivand2015a, + title = {{\pkg{rgdal}: Bindings for the Geospatial Data Abstraction Library}}, + author = {Roger Bivand and Tim Keitt and Barry Rowlingson}, + year = {2015}, + note = {R package version 1.0-7}, + url = {http://CRAN.R-project.org/package=rgdal} +} + +@software{Bivand2015, + title = {{\pkg{maptools}: Tools for Reading and Handling Spatial Objects}}, + author = {Roger Bivand and Nicholas Lewin-Koh}, + year = {2015}, + note = {R package version 0.8-37}, + url = {http://CRAN.R-project.org/package=maptools} +} + +@article{Blaschke2010, + author = {T. Blaschke}, + journal = {\{ISPRS\} Journal of Photogrammetry and Remote Sensing}, + number = {1}, + pages = {2--16}, + title = {Object based image analysis for remote sensing}, + volume = {65}, + year = {2010}} + +@article{Brennan2013, + author = {Brennan, J and Lee, HJ and Yang, M and Folk, M and Pourmal, E}, + journal = {Earth Observer Newsletter}, + number = {April 2013}, + pages = {16--19}, + title = {{Working with NASA's HDF and HDF-EOS earth science data formats}}, + volume = {25}, + year = {2013}} + +@article{Broich2011, + author = {Broich, Mark and Hansen, Matthew C. and Potapov, Peter and Adusei, Bernard and Lindquist, Erik and Stehman, Stephen V.}, + title = {Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in {S}umatra and {K}alimantan, {I}ndonesia}, + year = {2011}, + volume = {13}, + number = {2}, + pages = {277-291}, + journal = {International Journal of Applied Earth Observation and Geoinformation}, +} + +@inproceedings{Brown2010, + Address = {New York, NY, USA}, + author = {Brown, Paul G.}, + Booktitle = {Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data}, + pages = {963--968}, + Publisher = {ACM}, + title = {Overview of {SciDB}: Large Scale Array Storage, Processing and Analysis}, + year = {2010}} + +@article{Brown2013, + author = {Brown, J. Christopher and Kastens, Jude H. and Coutinho, Alexandre Camargo and Victoria, Daniel de Castro and Bishop, Christopher R.}, + journal = {Remote Sensing of Environment}, + pages = {39-50}, + title = {Classifying multiyear agricultural land use data from {Mato Grosso} using time-series {MODIS} vegetation index data}, + volume = {130}, + year = {2013}} + +@inproceedings{Camara2001, + author = {Camara, Gilberto and Egenhofer, Max and Fonseca, Frederico and Monteiro, Antonio Miguel}, + Booktitle = {Spatial Information Theory: Foundations of Geographic Information Science. 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Zalta}, + Publisher = {Stanford University}, + title = {Events}, + year = {2015}} + +@article{Chapin2000, + author = {Chapin, F. 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Friedl and Damien Sulla-Menashe and Bin Tan and Annemarie Schneider and Navin Ramankutty and Adam Sibley and Xiaoman Huang}, + journal = {Remote Sensing of Environment}, + number = {1}, + pages = {168 - 182}, + title = {MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets}, + volume = {114}, + year = {2010}} + +@article{Fritz2013, + author = {Fritz, Steffen and See, Linda and others}, + journal = {Eos, Transactions American Geophysical Union}, + number = {3}, + pages = {31--32}, + title = {The need for improved maps of global cropland}, + volume = {94}, + year = {2013}} + +@article{Galford2008, + author = {Gillian L. Galford and John F. Mustard and Jerry Melillo and Aline Gendrin and Carlos C. Cerri and Carlos E.P. 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K. and Rausch, L. and Munger, J. and others}, + journal = {Science}, + number = {6220}, + pages = {377--378}, + title = {Brazil's Soy Moratorium}, + volume = {347}, + year = {2015}} + +@article{Giorgino2009, + author = {Toni Giorgino}, + journal = {Journal of Statistical Software}, + number = {7}, + pages = {1--24}, + title = {Computing and Visualizing Dynamic Time Warping Alignments in {R}: the dtw package}, + volume = {31}, + year = {2009}} + +@article{Godar2012, + author = {Javier Godar and Emilio Jorge Tizado and Benno Pokorny}, + journal = {Forest Ecology and Management}, + pages = {58-73}, + title = {Who is responsible for deforestation in the Amazon? A spatially explicit analysis along the Transamazon Highway in Brazil}, + volume = {267}, + year = {2012}} + +@article{Godar2014, + author = {Godar, Javier and Gardner, Toby A. and Tizado, E. Jorge and Pacheco, Pablo}, + journal = {Proceedings of the National Academy of Sciences}, + number = {43}, + pages = {15591-15596}, + title = {Actor-specific contributions to the deforestation slowdown in the Brazilian Amazon}, + volume = {111}, + year = {2014}} + +@article{Godar2015, + author = {Javier Godar and U. Martin Persson and E. 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Wulder}, +} +@inproceedings{Gorelick2012, + author = {Gorelick, Noel}, + Booktitle = {AGU Fall Meeting Abstracts}, + pages = {04}, + title = {Google Earth Engine}, + volume = {1}, + year = {2012}} + +@article{Goslee2011, + author = {Sarah Goslee}, + journal = {Journal of Statistical Software}, + number = {1}, + pages = {1--25}, + title = {Analyzing Remote Sensing Data in {R}: the landsat package}, + volume = {43}, + year = {2011}} + +@Report{Gray1999, + author = {Gray, Jim}, + title = {What Next? 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It will be recreated as +required. + +@Control{biblatex-control, + options = {2.5:0:0:1:0:0:1:1:0:0:0:0:1:1:3:1:79:+}, +} diff --git a/vignettes/intro/sits_intro-concordance.tex b/vignettes/intro/sits_intro-concordance.tex new file mode 100644 index 000000000..fbb1046cb --- /dev/null +++ b/vignettes/intro/sits_intro-concordance.tex @@ -0,0 +1,3 @@ +\Sconcordance{concordance:sits_intro.tex:sits_intro.Rnw:% +1 50 1 1 0 1 1 1 6 11 1 1 2 1 0 2 1 11 0 1 4 3 0 1 2 1 0 1 1 1 2 1 4 5 % +0 1 2 17 1} diff --git a/vignettes/intro/sits_intro.Rnw b/vignettes/intro/sits_intro.Rnw new file mode 100644 index 000000000..d8363c04f --- /dev/null +++ b/vignettes/intro/sits_intro.Rnw @@ -0,0 +1,105 @@ +%DEFINITIONS FOR THE PAPER +\documentclass[a4paper, 11pt]{article} +\usepackage[utf8]{inputenc} +\usepackage[T1]{fontenc} +\usepackage[top=3.0cm, bottom=2.43cm, right=3.0cm, left=3.0cm]{geometry} +%\usepackage{fontspec} % include OTF fonts, requires XeTeX +%\defaultfontfeatures{Ligatures=TeX} +\usepackage{filecontents} +\usepackage[natbib=true, style=numeric, backend=biber]{biblatex} +\addbibresource{references-esensing.bib} +%\setcitestyle{open={(},close={)}} +\usepackage{subfigure} % multiple figures in LaTeX +\usepackage{wrapfig} +%\usepackage{comment} +\usepackage{multirow} % multiplerows in tables +\setlength{\parindent}{0.25in} +\setlength{\parskip}{1.3em} +\renewcommand{\baselinestretch}{1.2} +\usepackage{titlesec} +\usepackage{booktabs} % Better horizontal rules in tables +\usepackage[all,defaultlines=3]{nowidow} % avoids widow and orphan sentences +\usepackage{amsmath} % good math formulas +\usepackage{relsize} % increase size of formulas +\usepackage{subfiles} % to handle multiple documents +%microtype package +\usepackage[activate={true,nocompatibility}, final, tracking=true,kerning=false,spacing=false]{microtype} +\usepackage{authblk} +\renewcommand\Authfont{\fontsize{11}{13}\selectfont} +\renewcommand\Affilfont{\fontsize{10}{12}\itshape} +\titleformat*{\section}{\large\bfseries} +\pagenumbering{gobble}% Remove page numbers (and reset to 1) + +%END OF DEFINITIONS + +% Paper title + +\title{\bfseries Satellite image time series analysis with the sits package} +%no date +\date{\vspace{-5ex}} +\author[1]{\normalsize Gilberto Camara} +\author[2]{\normalsize Victor Maus} +\author[1]{\normalsize Luiz Fernando Assis} +\author[1]{\normalsize Rolf Simoes} +\author[1]{\normalsize Adeline Maciel} +\author[1]{\normalsize Gilberto Ribeiro de Queiroz} + +\affil[1]{\small Image Processing Division, National Institute for Space Research (INPE), Av dos Astronautas 1758, Sao Jose dos Campos, 12227-001 Brazil. \break + Email: \{gilberto.camara\}@inpe.br} +\affil[2]{\small International Institute for Applied System Analyis, Schlossplatz 2, 2631 Laxenburg, Austria} + +\begin{document} +\SweaveOpts{concordance=TRUE} +<>= +Sys.setenv(TEXINPUTS="~/sits/vignettes/intro", + BIBINPUTS="~/sits/vignettes/bib", + BSTINPUTS="~/sits/vignettes/bib") +@ + +\fontfamily{put}\selectfont % Utopia +\renewcommand*{\bibfont}{\small} +\maketitle + +%1. Introduction: +%2. Time Series for LUCC Classification +%3. Land +%4. A framework for data modelling on land use semantics + +A satellite image time series is... + +<<>>= +library(sits) +URL <- "http://www.dpi.inpe.br/tws/wtss" +sits_infoWTSS(URL) +# then, configure the WTSS service +inpe <- sits_configWTSS (URL, + coverage = "mod13q1_512", + bands = c("ndvi", "evi", "nir")) +# a complicated point +long <- -55.51810 +lat <- -11.63884 + +series.tb <- sits_getdata(longitude = long, latitude = lat, wtss = inpe) + +series.tb %>% + sits_select (bands = "evi") %>% + sits_plot () +@ + + +which shows the time series for the "evi" band +%\input{introduction.Rnw} +\section{Introduction} +\label{sec:introduction} + +%Stating the problem +Earth observation (EO) satellites produce vast amounts of geospatial data. The Landsat archive holds over five million images of the Earth's surface, with about 1 PB of data. New satellites from Europe, USA, China, Brazil, and India generate yearly as much data as one Landsat satellite in a decade. Most space agencies have adopted an open data policy, making unprecedented amounts of satellite data available for research and operational use. This data deluge has brought about a major challenge for Geoinformatics research: \textit{How to design and build technologies that allow the EO community to analyse big data sets?} + +When scientists use big EO data they face the burden of organising thousands of files, downloaded from the space agencies archives. To manage such large data sets, researchers need stable and efficient solutions that support their analytical tasks. To choose a solution is hard because the technology for large data handling and analytics is evolving. Alternatives include MapReduce-based solutions such as Google Earth Engine \cite{Gorelick2012}, object-relational DBMS extensions such as Rasdaman \cite{Baumann1998} and distributed multidimensional array databases such as SciDB \cite{Stonebraker2013}. Since each of these architectures takes on a different approach, understanding the benefits and drawbacks of each one helps researchers choose what best fits their needs. + +Given the diversity of options, researchers would gain from documented experience that helps them to assess how proposed big data architectures fit the needs of geospatial data analysis. Recent papers describe algorithms required for EO analysis \cite{Vatsavai2012} \cite{Nativi2015} and report case studies using specific architectures \cite{Planthaber2012}\cite{Krcal2015}. However, to make progress on big geospatial data analysis, we need to engage the large community of remote sensing researchers. In this paper, we consider how big EO data architectures can support the needs of data analytics. Our paper examines ways to cut the effort required for researchers to develop and validate algorithms for extracting information for big EO data. + +We take the viewpoint that architectures should serve applications, and not the other way around. To clarify the researcher's problem, we consider the needs for an important EO application: land use and land cover change (LUCC) analysis. We propose an architecture based on open-source tools that combines array databases with statistical analysis. We evaluate this design to assess how it meets the needs of EO scientists and compared with other approaches that aim to meet these needs. This paper also puts forward a set of criteria to build researcher-friendly architectures for big EO data analysis. +%\bibliographyfile{/Users/gilberto/sits/vignettes/bib/references-esensing.bib} +\printbibliography +\end{document} diff --git a/vignettes/intro/sits_intro.bbl b/vignettes/intro/sits_intro.bbl new file mode 100644 index 000000000..c8dbc7ec1 --- /dev/null +++ b/vignettes/intro/sits_intro.bbl @@ -0,0 +1,167 @@ +% $ biblatex auxiliary file $ +% $ biblatex bbl format version 2.5 $ +% Do not modify the above lines! +% +% This is an auxiliary file used by the 'biblatex' package. +% This file may safely be deleted. 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+%\setcitestyle{open={(},close={)}} +\usepackage{subfigure} % multiple figures in LaTeX +\usepackage{wrapfig} +%\usepackage{comment} +\usepackage{multirow} % multiplerows in tables +\setlength{\parindent}{0.25in} +\setlength{\parskip}{1.3em} +\renewcommand{\baselinestretch}{1.2} +\usepackage{titlesec} +\usepackage{booktabs} % Better horizontal rules in tables +\usepackage[all,defaultlines=3]{nowidow} % avoids widow and orphan sentences +\usepackage{amsmath} % good math formulas +\usepackage{relsize} % increase size of formulas +\usepackage{subfiles} % to handle multiple documents +%microtype package +\usepackage[activate={true,nocompatibility}, final, tracking=true,kerning=false,spacing=false]{microtype} +\usepackage{authblk} +\renewcommand\Authfont{\fontsize{11}{13}\selectfont} +\renewcommand\Affilfont{\fontsize{10}{12}\itshape} +\titleformat*{\section}{\large\bfseries} +\pagenumbering{gobble}% Remove page numbers (and reset to 1) + +%END OF DEFINITIONS + +% Paper title + +\title{\bfseries Satellite image time series analysis with the sits package} +%no date +\date{\vspace{-5ex}} +\author[1]{\normalsize Gilberto Camara} +\author[2]{\normalsize Victor Maus} +\author[1]{\normalsize Luiz Fernando Assis} +\author[1]{\normalsize Rolf Simoes} +\author[1]{\normalsize Adeline Maciel} +\author[1]{\normalsize Gilberto Ribeiro de Queiroz} + +\affil[1]{\small Image Processing Division, National Institute for Space Research (INPE), Av dos Astronautas 1758, Sao Jose dos Campos, 12227-001 Brazil. \break + Email: \{gilberto.camara\}@inpe.br} +\affil[2]{\small International Institute for Applied System Analyis, Schlossplatz 2, 2631 Laxenburg, Austria} + +\usepackage{Sweave} +\begin{document} +\input{sits_intro-concordance} + +\fontfamily{put}\selectfont % Utopia +\renewcommand*{\bibfont}{\small} +\maketitle + +%1. Introduction: +%2. Time Series for LUCC Classification +%3. Land +%4. A framework for data modelling on land use semantics + +A satellite image time series is... + +\begin{Schunk} +\begin{Sinput} +> library(sits) +> URL <- "http://www.dpi.inpe.br/tws/wtss" +> sits_infoWTSS(URL) +\end{Sinput} +\begin{Soutput} +----------------------------------------------------------- +The WTSS server URL is http://www.dpi.inpe.br/tws/wtss +Available coverages: +itobi +merge +mod13q1_512 +------------------------------------------------------------ +\end{Soutput} +\begin{Sinput} +> # then, configure the WTSS service +> inpe <- sits_configWTSS (URL, ++ coverage = "mod13q1_512", ++ bands = c("ndvi", "evi", "nir")) +> # a complicated point +> long <- -55.51810 +> lat <- -11.63884 +> series.tb <- sits_getdata(longitude = long, latitude = lat, wtss = inpe) +> series.tb %>% ++ sits_select (bands = "evi") %>% ++ sits_plot () +\end{Sinput} +\end{Schunk} + + +which shows the time series for the "evi" band +%\input{introduction.Rnw} +\section{Introduction} +\label{sec:introduction} + +%Stating the problem +Earth observation (EO) satellites produce vast amounts of geospatial data. The Landsat archive holds over five million images of the Earth's surface, with about 1 PB of data. New satellites from Europe, USA, China, Brazil, and India generate yearly as much data as one Landsat satellite in a decade. Most space agencies have adopted an open data policy, making unprecedented amounts of satellite data available for research and operational use. This data deluge has brought about a major challenge for Geoinformatics research: \textit{How to design and build technologies that allow the EO community to analyse big data sets?} + +When scientists use big EO data they face the burden of organising thousands of files, downloaded from the space agencies archives. To manage such large data sets, researchers need stable and efficient solutions that support their analytical tasks. To choose a solution is hard because the technology for large data handling and analytics is evolving. Alternatives include MapReduce-based solutions such as Google Earth Engine \cite{Gorelick2012}, object-relational DBMS extensions such as Rasdaman \cite{Baumann1998} and distributed multidimensional array databases such as SciDB \cite{Stonebraker2013}. Since each of these architectures takes on a different approach, understanding the benefits and drawbacks of each one helps researchers choose what best fits their needs. + +Given the diversity of options, researchers would gain from documented experience that helps them to assess how proposed big data architectures fit the needs of geospatial data analysis. Recent papers describe algorithms required for EO analysis \cite{Vatsavai2012} \cite{Nativi2015} and report case studies using specific architectures \cite{Planthaber2012}\cite{Krcal2015}. However, to make progress on big geospatial data analysis, we need to engage the large community of remote sensing researchers. In this paper, we consider how big EO data architectures can support the needs of data analytics. Our paper examines ways to cut the effort required for researchers to develop and validate algorithms for extracting information for big EO data. + +We take the viewpoint that architectures should serve applications, and not the other way around. To clarify the researcher's problem, we consider the needs for an important EO application: land use and land cover change (LUCC) analysis. We propose an architecture based on open-source tools that combines array databases with statistical analysis. We evaluate this design to assess how it meets the needs of EO scientists and compared with other approaches that aim to meet these needs. This paper also puts forward a set of criteria to build researcher-friendly architectures for big EO data analysis. +%\bibliographyfile{/Users/gilberto/sits/vignettes/bib/references-esensing.bib} +\printbibliography +\end{document}