diff functions.r @ 0:9adccd3da70c draft default tip

planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/functions.r	Mon Jan 09 13:37:37 2023 +0000
@@ -0,0 +1,79 @@
+#Rscript
+
+#########################
+##      Functions      ##
+#########################
+
+#####Packages : raster
+#               sp
+#               ggplot2
+
+####Set paramaters for all tools using BiodivMapR
+
+# path for the Mask raster corresponding to image to process
+# expected to be in ENVI HDR format, 1 band, integer 8bits
+# expected values in the raster: 0 = masked, 1 = selected
+# set to FALSE if no mask available
+input_mask_file <- FALSE
+
+# relative or absolute path for the Directory where results will be stored
+# For each image processed, a subdirectory will be created after its name
+output_dir <- "RESULTS"
+
+# SPATIAL RESOLUTION
+# resolution of spatial units for alpha and beta diversity maps (in pixels), relative to original image
+# if Res.Map = 10 for images with 10 m spatial resolution, then spatial units will be 10 pixels x 10m = 100m x 100m surfaces
+# rule of thumb: spatial units between 0.25 and 4 ha usually match with ground data
+# too small window_size results in low number of pixels per spatial unit, hence limited range of variation of diversity in the image
+window_size <- 10
+
+# PCA FILTERING: Set to TRUE if you want second filtering based on PCA outliers to be processed. Slower
+filterpca <- TRUE
+
+################################################################################
+##                    DEFINE PARAMETERS FOR METHOD                            ##
+################################################################################
+nbcpu <- 4
+maxram <- 0.5
+nbclusters <- 50
+
+################################################################################
+##                              PROCESS IMAGE                                 ##
+################################################################################
+# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels
+ndvi_thresh <- 0.5
+blue_thresh <- 500
+nir_thresh  <- 1500
+continuum_removal <- TRUE
+
+
+
+#### Convert raster to dataframe
+
+# Convert raster to SpatialPointsDataFrame
+convert_raster <- function(data_raster) {
+r_pts <- raster::rasterToPoints(data_raster, spatial = TRUE)
+
+# reproject sp object
+geo_prj <- "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"
+r_pts <- sp::spTransform(r_pts, sp::CRS(geo_prj))
+
+
+# Assign coordinates to @data slot, display first 6 rows of data.frame
+r_pts@data <- data.frame(r_pts@data, longitude = sp::coordinates(r_pts)[, 1],
+                         latitude = sp::coordinates(r_pts)[, 2])
+
+return(r_pts@data)
+}
+
+
+#### Potting indices
+
+plot_indices <- function(data, titre) {
+  graph_indices <- ggplot2::ggplot(data) +
+  ggplot2::geom_point(ggplot2::aes_string(x = data[, 2], y = data[, 3], color = data[, titre]), shape = "square", size = 2) + ggplot2::scale_colour_gradient(low = "blue", high = "orange", na.value = "grey50") +
+  ggplot2::xlab("Longitude") + ggplot2::ylab("Latitude") +
+  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1), plot.title = ggplot2::element_text(color = "black", size = 12, face = "bold")) + ggplot2::ggtitle(titre)
+
+ggplot2::ggsave(paste0(titre, ".png"), graph_indices, width = 12, height = 10, units = "cm")
+}