only select radar variables by ther names or alt names in df_to_mat_list

R/as.vpts.R

@@ -92,10 +92,11 @@ check_multivalue_attributes <- function(data) {
   rcs <- first_values$rcs
   sd_vvp_threshold <- first_values$sd_vvp_threshold
 
-  # Convert dataframe
-  maskvars <- c("radar", "rcs", "sd_vvp_threshold", "radar_latitude", "radar_longitude", "radar_height", "radar_wavelength", "source_file", "datetime", "height", "sunrise", "sunset", "day")
+  # Define set of radar variables that must be present at each height (including alternatives)
+  radvars <- c("u", "v", "w", "ff", "dd", "sd_vvp", "gap", "eta", "dens", "dbz", "dbz_all", "n", "n_dbz", "n_all", "n_dbz_all")
+  radvars <- c(radvars, unlist(vpts_schema$fields$nameAlternatives[vpts_schema$fields$name %in% maskvars]))
 
-  data <- df_to_mat_list(data, maskvars, vpts_schema)
+  data <- df_to_mat_list(data, radvars)
 
   # Create vpts object
   output <- list(

R/read_vpts.R

@@ -93,8 +93,13 @@ read_vpts <- function(files, data_frame = FALSE, ...) {
 read_vpts_csv <- function(files, data_frame = FALSE) {
 
   suppressMessages(
-  data <- readr::read_csv(files, show_col_types = FALSE)
+  data <- readr::read_csv(files, show_col_types = FALSE,
+  col_types = cols(
+     .default = col_guess(),  
+     `...1` = col_skip()      # Skip unnamed columns
+    ))
   )
+
   #Validate the data
   validate_vpts(data)
 

R/utils.R

@@ -275,13 +275,12 @@ tibble_to_mat <- function(tibble) {
 #' @returns A named list of matrices ordered according to radvars
 #' @keywords internal
 #' @noRd
-df_to_mat_list <- function(data, maskvars, schema) {
+df_to_mat_list <- function(data, radvars) {
   datetime <- height <- variable <- fields <- NULL
-  radvars <- schema$fields$name
-  radvars <- radvars[!radvars %in% maskvars]
+  all_vars <- c(radvars, "datetime", "height")
 
   tbls_lst <- data %>%
-    dplyr::select(c(setdiff(colnames(data), maskvars), "datetime", "height")) %>%
+    select(all_of(intersect(all_vars, names(data)))) %>%
     tidyr::pivot_longer(-c(datetime, height), names_to = "variable", values_to = "value") %>%
     dplyr::group_by(variable) %>%
     dplyr::group_split()