clean package

NAMESPACE

@@ -12,12 +12,13 @@ export(Map.Spectral.Species)
 export(Perform.PCA.Image)
 export(Perform.Radiometric.Filtering)
 export(Select.Components)
-import(future)
 import(raster)
 import(tools)
 importFrom(dissUtils,diss)
 importFrom(fields,rdist)
+importFrom(future,multiprocess)
 importFrom(future,plan)
+importFrom(future,sequential)
 importFrom(future.apply,future_lapply)
 importFrom(labdsv,pco)
 importFrom(matlab,padarray)
@@ -25,4 +26,5 @@ importFrom(matrixStats,rowSds)
 importFrom(raster,writeRaster)
 importFrom(rgdal,readOGR)
 importFrom(snow,splitRows)
+importFrom(stats,kmeans)
 importFrom(stringr,str_count)

R/Lib_CheckConvertData.R

@@ -23,7 +23,7 @@
 #' @import raster
 #' @import tools
 #' @export
-Convert.Raster2BIL <- function(Raster.Path, Sensor = "unknown", Output.Directory = FALSE, Convert.Integer = TRUE, Multiplying.Factor = 1, Multiplying.Factor.Last = 1) {
+raster2BIL <- function(Raster.Path, Sensor = "unknown", Output.Directory = FALSE, Convert.Integer = TRUE, Multiplying.Factor = 1, Multiplying.Factor.Last = 1) {
 
   # get directory and file name of original image
   Input.File <- basename(Raster.Path)
@@ -159,7 +159,7 @@ Convert.Raster2BIL <- function(Raster.Path, Sensor = "unknown", Output.Directory
 #' @param Mask is the raster a mask?
 #'
 #' @export
-Check.Data.Format <- function(Raster.Path, Mask = FALSE) {
+check_data <- function(Raster.Path, Mask = FALSE) {
   HDR.Path <- Get.HDR.Name(Raster.Path)
   # check if the hdr file exists
   if (file.exists(HDR.Path)) {

R/Lib_ContinuumRemoval.R

@@ -17,7 +17,7 @@
 #
 # @return samples from image and updated number of pixels to sampel if necessary
 #' @importFrom snow splitRows
-#' @import future
+#' @importFrom future plan multiprocess sequential
 #' @importFrom future.apply future_lapply
 Apply.Continuum.Removal <- function(Spectral.Data, Spectral, nbCPU = 1) {
   if (!length(Spectral$WaterVapor) == 0) {

R/Lib_FilterData.R

@@ -14,7 +14,7 @@
 #' @param Image.Path Path of the image to be processed
 #' @param Mask.Path Path of the mask corresponding to the image
 #' @param Output.Dir output directory
-#' @param TypePCA Type of PCA (PCA, SPCA, NLPCA...)
+#' @param TypePCA Type of PCA: "PCA" or "SPCA"
 #' @param NDVI.Thresh NDVI threshold applied to produce a mask (select pixels with NDVI>NDVI.Thresh)
 #' @param Blue.Thresh Blue threshold applied to produce a mask (select pixels with Blue refl < Blue.Thresh --> filter clouds) refl expected between 0 and 10000
 #' @param NIR.Thresh NIR threshold applied to produce a mask (select pixels with NIR refl < NIR.Thresh) refl expected between 0 and 10000

R/Lib_MapAlphaDiversity.R

@@ -26,7 +26,7 @@
 #' @param Index.Alpha character. Either 'Shannon', 'Simpson' or 'Fisher'.
 #'
 #' @export
-Map.Alpha.Diversity <- function(Input.Image.File, Output.Dir, Spatial.Unit,
+alpha_div <- function(Input.Image.File, Output.Dir, Spatial.Unit,
                                 TypePCA = "SPCA", nbclusters = 50,
                                 MinSun = 0.25, pcelim = 0.02,
                                 Index.Alpha = "Shannon", FullRes = TRUE,
@@ -153,7 +153,7 @@ Map.Alpha.Diversity.TestnbCluster <- function(ImNames, Output.Dir, Spatial.Unit,
 # @param MinSun minimum proportion of sunlit pixels required to consider plot
 #
 # @return list of mean and SD of alpha diversity metrics
-#' @import future
+#' @importFrom future plan multiprocess sequential
 #' @importFrom future.apply future_lapply
 Compute.Alpha.Diversity <- function(Spectral.Species.Path, Spatial.Unit, nbclusters, MinSun, pcelim, nbCPU = FALSE, MaxRAM = FALSE, Index.Alpha = "Shannon") {
   ##      read SpectralSpecies file and write distribution per spatial unit   ##

R/Lib_MapBetaDiversity.R

@@ -90,7 +90,6 @@ Map.Beta.Diversity.TestnbCluster <- function(Input.Image.File, Output.Dir, Spati
 # @param MinSun minimum proportion of sunlit pixels required
 #
 # @return
-#' @importFrom future plan
 Get.Sunlit.Pixels <- function(ImPathSunlit, MinSun) {
 
   # Filter out spatial units showing poor illumination
@@ -126,6 +125,7 @@ Get.Sunlit.Pixels <- function(ImPathSunlit, MinSun) {
 # @param MatBCdist BC dissimilarity matrix
 #
 # @return BetaNMDS.sel
+#' @importFrom future plan multiprocess sequential
 #' @importFrom future.apply future_lapply
 Compute.NMDS <- function(MatBCdist) {
   nbiterNMDS <- 4
@@ -169,7 +169,7 @@ Compute.NMDS <- function(MatBCdist) {
 #
 # @return Ordination.est coordinates of each spatial unit in ordination space
 #' @importFrom snow splitRows
-#' @import future
+#' @importFrom future plan multiprocess sequential
 #' @importFrom future.apply future_lapply
 Ordination.to.NN <- function(Beta.Ordination.sel, SSD.Path, Sample.Sel, coordTotSort, NbIter, nbclusters, pcelim, nbCPU = FALSE) {
   nb.Sunlit <- dim(coordTotSort)[1]

R/Lib_MapSpectralSpecies.R

@@ -15,7 +15,7 @@
 #'
 #' @param Input.Image.File Path and name of the image to be processed
 #' @param Output.Dir output directory
-#' @param TypePCA Type of PCA (PCA, SPCA, NLPCA...)
+#' @param TypePCA Type of PCA: "PCA" or "SPCA"
 #' @param PCA.Files path for PCA file
 #' @param nbCPU numeric. Number of CPUs to use in parallel.
 #' @param MaxRAM numeric. MaxRAM maximum size of chunk in GB to limit RAM allocation when reading image file.
@@ -80,8 +80,9 @@ Map.Spectral.Species <- function(Input.Image.File, Output.Dir, PCA.Files, TypePC
 # @param nbclusters number of clusters used in kmeans
 #
 # @return list of centroids and parameters needed to center/reduce data
-#' @import future
+#' @importFrom future plan multiprocess sequential
 #' @importFrom future.apply future_lapply
+#' @importFrom stats kmeans
 Init.Kmeans <- function(dataPCA, Pix.Per.Iter, NbIter, nbclusters, nbCPU = 1) {
   m0 <- apply(dataPCA, 2, function(x) min(x))
   M0 <- apply(dataPCA, 2, function(x) max(x))
@@ -92,7 +93,8 @@ Init.Kmeans <- function(dataPCA, Pix.Per.Iter, NbIter, nbclusters, nbCPU = 1) {
   # multiprocess kmeans clustering
   plan(multiprocess, workers = nbCPU) ## Parallelize using four cores
   Schedule.Per.Thread <- ceiling(length(dataPCA) / nbCPU)
-  res <- future_lapply(dataPCA, FUN = kmeans, centers = nbclusters, iter.max = 50, nstart = 10, algorithm = c("Hartigan-Wong"), future.scheduling = Schedule.Per.Thread)
+  res <- future_lapply(dataPCA, FUN = kmeans, centers = nbclusters, iter.max = 50, nstart = 10,
+                       algorithm = c("Hartigan-Wong"), future.scheduling = Schedule.Per.Thread)
   plan(sequential)
   Centroids <- list(NbIter)
   for (i in (1:NbIter)) {
@@ -177,7 +179,7 @@ Apply.Kmeans <- function(PCA.Path, PC.Select, ImPathShade, Kmeans.info, Spectral
 #
 # @return
 #' @importFrom snow splitRows
-#' @import future
+#' @importFrom future plan multiprocess sequential
 #' @importFrom future.apply future_lapply
 Compute.Spectral.Species <- function(PCA.Path, ImPathShade, Spectral.Species.Path, Location.RW, PC.Select, Kmeans.info, nbCPU = 1) {
 

R/Lib_Validation_biodivMapR.R

@@ -11,39 +11,40 @@
 # ==============================================================================
 
 #' get projection of a raster or a vector
-#' @param Path.File path for a raster or vector (shapefile)
-#' @param Type 'raster' or 'vector'
+#' @param file path for a raster or vector (shapefile)
+#' @param type 'raster' or 'vector'
 #' @return projection
+#' @importFrom raster raster shapefile projection
 #' @importFrom rgdal readOGR
 #' @import tools
 #' @export
-Get.Projection = function(Path.File,Type = 'raster'){
+projection.file <- function(file, type = 'raster'){
   if (Type == 'raster'){
-    obj  = raster(Path.File)
+    obj <- raster(file)
   } else if (Type == 'vector'){
-    Shp.Path      = dirname(Path.File)
-    Shp.Name      = file_path_sans_ext(basename(Path.File))
+    Shp.Path      = dirname(file)
+    Shp.Name      = file_path_sans_ext(basename(file))
     obj  = readOGR(dsn = Shp.Path,layer = Shp.Name,verbose = FALSE)
   }
-  Projection  = projection(obj)
-  return(Projection)
+  projstr <- projection(obj)
+  return(projstr)
 }
 
 #' Get list of shapefiles in a directory
 #'
-#' @param Path.Shp path of the directory containing shapefiles
+#' @param x character or list. Directory containing shapefiles
 #' @return list of shapefiles names
 #' @export
-Get.List.Shp = function(Path.Shp){
-  if(typeof(Path.Shp)=='list'){
+list.shp <- function(x){
+  if(typeof(x)=='list'){
     List.Shp = c()
     ii = 0
-    for (shp in Path.Shp){
+    for (shp in x){
       ii = ii+1
-      List.Shp[ii]  = grep(dir(shp, pattern = '.shp', full.names = TRUE, ignore.case = FALSE,include.dirs = FALSE), pattern='.xml', inv=T, value=T)
+      List.Shp[ii]  = dir(shp, pattern = '.shp$', full.names = TRUE, ignore.case = FALSE,include.dirs = FALSE)
     }
   } else if(typeof(Path.Shp)=='character'){
-    List.Shp  = grep(dir(Path.Shp, pattern = '.shp', full.names = TRUE, ignore.case = FALSE,include.dirs = FALSE), pattern='.xml', inv=T, value=T)
+    List.Shp  = dir(Path.Shp, pattern = '.shp', full.names = TRUE, ignore.case = FALSE,include.dirs = FALSE)
   }
   return(List.Shp)
 }
@@ -162,7 +163,7 @@ Get.Alpha.Metrics = function(Distrib){
 #' @importFrom rgdal readOGR
 #' @import tools
 #' @export
-Get.Diversity.From.Plots = function(Raster, Plots,NbClusters = 50,Name.Plot = FALSE){
+diversity_from_plots = function(Raster, Plots,NbClusters = 50,Name.Plot = FALSE){
   # get hdr from raster
   HDR           = read.ENVI.header(paste(Raster,'.hdr',sep=''))
   nbRepetitions = HDR$bands

TODO.md

-1- link raw data included in data-raw, or put it in a directory publicly available where the package will be proposed.
+# TODO
 
-2- hide all functions except:
-- Check.Data.Format
-- Convert.Raster2BIL
-- Perform.Radiometric.Filtering
-- Perform.PCA.Images
-- Select.Components
-- Map.Spectral.Species
-- Map.Alpha.Diversity
-- Map.Beta.Diversity
-- Get.List.Shp
-- Get.Projection
-- Get.Diversity.From.Plots
+1- put use case data in another repository/ftp to reduce package size, include smaller data for tests
 
-3- add manual help based on 'Main_DiversityMapping.R'
+2- clean code (optimize):
+  - rename functions without upper case and dots
+  - reduce dependcies (matlab)
 
-4- produce a binary/compiled packages for version beta
+3- include tests
+
+4- submit to CRAN
 
-5- put the binary package ready to be installed in a directory publicly available where the data is stored as well.
 
-7- CRAN & nettoyage du code
 

man/Map.Spectral.Species.Rd

@@ -14,7 +14,7 @@ Map.Spectral.Species(Input.Image.File, Output.Dir, PCA.Files,
 
 \item{PCA.Files}{path for PCA file}
 
-\item{TypePCA}{Type of PCA (PCA, SPCA, NLPCA...)}
+\item{TypePCA}{Type of PCA: "PCA" or "SPCA"}
 
 \item{nbclusters}{number of clusters defined in k-Means}
 

man/Perform.Radiometric.Filtering.Rd

@@ -15,7 +15,7 @@ Perform.Radiometric.Filtering(Image.Path, Mask.Path, Output.Dir,
 
 \item{Output.Dir}{output directory}
 
-\item{TypePCA}{Type of PCA (PCA, SPCA, NLPCA...)}
+\item{TypePCA}{Type of PCA: "PCA" or "SPCA"}
 
 \item{NDVI.Thresh}{NDVI threshold applied to produce a mask (select pixels with NDVI>NDVI.Thresh)}