Merge branch 'dev'

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: biodivMapR
 Title: biodivMapR: an R package for a- and ß-diversity mapping using remotely-sensed images
-Version: 1.10.6
+Version: 1.11.0
 Authors@R: c(person(given = "Jean-Baptiste", 
 					family = "Feret", 
 					email = "jb.feret@teledetection.fr", 
@@ -20,7 +20,6 @@ LazyData: true
 Imports: 
     dissUtils,
     data.table,
-    emstreeR,
     fields,
     future,
     future.apply,
@@ -37,9 +36,10 @@ Imports:
     tools,
     vegan,
     zip,
-    progress,
     progressr,
-    cli
+    cli,
+    ape,
+    terra
 RoxygenNote: 7.2.3
 Suggests: 
     knitr,

NAMESPACE

@@ -1,7 +1,9 @@
 # Generated by roxygen2: do not edit by hand
 
 export(ENVI_type2bytes)
+export(Get_FunctionalMetrics_From_Traits)
 export(IQR_outliers)
+export(RowToLinear)
 export(WeightedCoordsNN)
 export(Write_Big_Image)
 export(Write_Image_NativeRes)
@@ -19,15 +21,15 @@ export(compute_ALPHA_SSD_per_window_list)
 export(compute_ALPHA_per_window)
 export(compute_BCdiss)
 export(compute_BETA_FromPlots)
-export(compute_FUNCT)
+export(compute_FD)
+export(compute_Functional_metrics)
 export(compute_NN_from_ordination)
 export(compute_SSD)
 export(compute_alpha_metrics)
 export(compute_beta_metrics)
 export(compute_spectral_species)
 export(compute_spectral_species_FieldPlots)
 export(continuumRemoval)
-export(coordPix_kernel)
 export(create_hdr)
 export(create_mask_from_threshold)
 export(define_output_directory)
@@ -43,6 +45,7 @@ export(extract_samples_from_image)
 export(filter_PCA)
 export(filter_prior_CR)
 export(getBCdiss)
+export(getFD)
 export(get_HDR_name)
 export(get_SSpath)
 export(get_Shannon)
@@ -78,6 +81,7 @@ export(read_ENVI_header)
 export(read_bin_subset)
 export(read_image_bands)
 export(read_image_subset)
+export(repmat)
 export(revert_resolution_HDR)
 export(rm_invariant_bands)
 export(select_PCA_components)
@@ -97,25 +101,24 @@ export(write_raster)
 import(cli)
 import(stars)
 import(tools)
+importFrom(ape,mst)
 importFrom(data.table,data.table)
 importFrom(data.table,rbindlist)
 importFrom(data.table,setorder)
 importFrom(dissUtils,diss)
-importFrom(emstreeR,ComputeMST)
 importFrom(fields,rdist)
 importFrom(future,multisession)
 importFrom(future,plan)
 importFrom(future,sequential)
 importFrom(future.apply,future_lapply)
 importFrom(geometry,convhulln)
 importFrom(labdsv,pco)
-importFrom(matlab,ndims)
+importFrom(matlab,meshgrid)
 importFrom(matlab,ones)
 importFrom(matlab,padarray)
 importFrom(matrixStats,rowAnys)
 importFrom(matrixStats,rowSds)
 importFrom(mmand,erode)
-importFrom(progress,progress_bar)
 importFrom(progressr,handlers)
 importFrom(progressr,progressor)
 importFrom(progressr,with_progress)
@@ -144,11 +147,15 @@ importFrom(stars,write_stars)
 importFrom(stats,IQR)
 importFrom(stats,as.dist)
 importFrom(stats,cov)
+importFrom(stats,dist)
 importFrom(stats,kmeans)
+importFrom(stats,na.omit)
 importFrom(stats,prcomp)
 importFrom(stats,quantile)
 importFrom(stats,sd)
 importFrom(stringr,str_count)
+importFrom(terra,rast)
+importFrom(terra,values)
 importFrom(tools,file_path_sans_ext)
 importFrom(utils,file.edit)
 importFrom(utils,read.table)

R/Lib_ContinuumRemoval.R

@@ -189,7 +189,7 @@ filter_prior_CR <- function(Minit, Spectral_Bands) {
   # Minit[Minit<0] <- Minit + 100.0
   Minit[Minit < 0] <- 0
   # eliminate invariant spectra
-  SD <- rowSds(Minit)
+  SD <- matrixStats::rowSds(Minit)
   elim <- which(SD == 0 | is.na(SD))
   keep <- which(!SD == 0 & !is.na(SD))
   nbi0 <- nrow(Minit)
@@ -212,27 +212,31 @@ filter_prior_CR <- function(Minit, Spectral_Bands) {
   return(my_list)
 }
 
-#
-# @param MM
-# @param nbi
-# @param nbj
-#
-# @return
+#' get max index for each row and convert into linear index
+#' @param MM numeric.
+#' @param nbi numeric.
+#' @param nbj numeric.
+#'
+#' @return MaxCont
+#' @export
+
 RowToLinear <- function(MM, nbi, nbj) {
   adj <- seq(1:nbi)
   MaxCont <- ((MM - 1) * (nbi)) + adj
   return(MaxCont)
 }
 
-# R equivalent of repmat (matlab)
-#
-# @param X initial matrix
-# @param m nb of replications in row dimension
-# @param n nb of replications in column dimension
-#
-# @return
+#' R equivalent of repmat (matlab)
+#'
+#' @param X initial matrix
+#' @param m nb of replications in row dimension
+#' @param n nb of replications in column dimension
+#'
+#' @return matrix with values replicated and tiles
+#' @export
+
 repmat <- function(X, m, n) {
   mx <- dim(X)[1]
   nx <- dim(X)[2]
-  matrix(t(matrix(X, mx, nx * n)), mx * m, nx * n, byrow = T)
+  return(matrix(t(matrix(X, mx, nx * n)), mx * m, nx * n, byrow = T))
 }

R/Lib_ImageProcess.R

@@ -562,7 +562,7 @@ extract.big_raster <- function(ImPath, rowcol, MaxRAM=.50){
 #' @param kernel numeric.
 #' @param MaxRAM numeric.
 #'
-#' @importFrom matlab ones
+#' @importFrom matlab padarray meshgrid
 #' @importFrom raster raster brick nbands ncell values
 #' @importFrom mmand erode
 #' @importFrom data.table data.table rbindlist setorder
@@ -603,9 +603,9 @@ get_random_subset_from_image <- function(ImPath, MaskPath, nb_partitions,
   if(is.matrix(kernel)){
     mask[which(is.na(c(mask)))] <- 0
     # erode mask with kernel, to keep valid central pixels and neighbours
-    mask = matlab::padarray(mask, c(1,1), padval=0, direction='both')
-    mask = mmand::erode(mask, (kernel!=0)*1)
-    mask = mask[2:(nrow(mask)-1), 2:(ncol(mask)-1)]
+    mask <- matlab::padarray(mask, c(1,1), padval=0, direction='both')
+    mask <- mmand::erode(mask, (kernel!=0)*1)
+    mask <- mask[2:(nrow(mask)-1), 2:(ncol(mask)-1)]
   }
 
   # get a list of samples among unmasked pixels
@@ -659,7 +659,7 @@ get_random_subset_from_image <- function(ImPath, MaskPath, nb_partitions,
   # remove NA and Inf pixels
   if(any(is.na(Sample_Sel) | is.infinite(Sample_Sel))){
     print('Removing pixels with NA values.')
-    rmrows <- rowAnys(is.na(Sample_Sel) | is.infinite(Sample_Sel))
+    rmrows <- matrixStats::rowAnys(is.na(Sample_Sel) | is.infinite(Sample_Sel))
     rmpix <- unique(samplePixIndex$id[rmrows])
     Sample_Sel = Sample_Sel[!(samplePixIndex$id %in% rmpix),]
     samplePixIndex = samplePixIndex[!(samplePixIndex$id %in% rmpix)]
@@ -805,7 +805,7 @@ mean_filter <- function(Image, SizeFilt,NA_remove = FALSE) {
   }
   ImageSmooth <- array(0,c(nbi,nbj,dim(Image)[3]))
   for (band in 1: dim(Image)[3]){
-    E <- padarray(Image[,,band], c(SizeFilt, SizeFilt), "symmetric", "both")
+    E <- matlab::padarray(Image[,,band], c(SizeFilt, SizeFilt), "symmetric", "both")
     ImageSmooth_tmp <- matrix(0, nrow = nbi, ncol = nbj)
     Mat2D <- MatSun <- matrix(0, nrow = ((2 * SizeFilt) + 1)^2, ncol = nbj)
     spl <- split(1:nbj, 1:((2 * SizeFilt) + 1))
@@ -1338,7 +1338,6 @@ write_ENVI_header <- function(HDR, HDRpath) {
 #' @param Image_Format list. description of data format corresponding to ENVI type
 #'
 #' @return None
-#' @importFrom progress progress_bar
 #' @export
 
 Write_Big_Image <- function(ImgWrite,ImagePath,HDR,Image_Format){
@@ -1350,9 +1349,7 @@ Write_Big_Image <- function(ImgWrite,ImagePath,HDR,Image_Format){
   )
   close(fidOUT)
   # for each piece of image
-  pb <- progress_bar$new(
-    format = 'Writing raster [:bar] :percent in :elapsedfull',
-    total = nbPieces, clear = FALSE, width= 100)
+  message(paste('Writing raster', ImagePath, sep = ' '))
 
   for (i in 1:nbPieces) {
     # read image and mask data
@@ -1382,7 +1379,6 @@ Write_Big_Image <- function(ImgWrite,ImagePath,HDR,Image_Format){
       writeBin(c(as.integer(ImgChunk)), fidOUT, size = Image_Format$Bytes, endian = .Platform$endian, useBytes = FALSE)
     }
     close(fidOUT)
-    pb$tick()
   }
   rm(ImgWrite)
   rm(ImgChunk)

R/Lib_MapAlphaDiversity.R

@@ -403,8 +403,9 @@ convert_PCA_to_SSD <- function(ReadWrite, Spectral_Species_Path,
 #' @param Mask_Chunk numeric. 3D image chunk of mask (optional)
 #
 #' @return list of alpha diversity metrics for each iteration
-#' @import cli
 #' @importFrom vegan fisher.alpha
+#' @import cli
+#' @importFrom progressr progressor handlers with_progress
 #' @importFrom snow splitList
 #' @importFrom future plan multisession sequential
 #' @importFrom future.apply future_lapply

R/Lib_MapBetaDiversity.R

@@ -497,7 +497,7 @@ compute_BCdiss <- function(SSDList, pcelim=0.02) {
   SSD <- lapply(SSDList,FUN = normalize_SSD, pcelim = pcelim)
   # matrix of bray curtis dissimilarity (size = nb kernels x nb kernels)
   # Here use the package "dissUtils" to compute dissimilarity matrices sequentially
-  MatBC <- diss(SSD[[1]], SSD[[2]], method = 'braycurtis')
+  MatBC <- dissUtils::diss(SSD[[1]], SSD[[2]], method = 'braycurtis')
   # EDIT 06-Feb-2019: added this to fix problem when empty kernels occur, leading to NA BC value
   BCNA <- which(is.na(MatBC) == TRUE)
   if (length(BCNA) > 0) {