Nothing
monx_wrapper <- function(op, var, infile, outfile, nc34, overwrite, verbose, p = NULL,
nc = NULL) {
calc_time_start <- Sys.time()
check_variable(var)
if (is.null(nc)) check_infile(infile)
check_outfile(outfile)
outfile <- correct_filename(outfile)
check_overwrite(outfile, overwrite)
check_nc_version(nc34)
##### extract data from file #####
file_data <- read_file(infile, var, nc = nc)
if (op > 2) {
file_data$variable$prec <- "float"
}
if (op == 7) {
if (length(p) > 1) {
p <- p[1]
}
if (p < 0 || p > 1) {
if (verbose) message("Your given p-value is outside [0,1]. The default will be used (0.95).")
p <- 0.95
}
}
date_time <- get_date_time(file_data$dimension_data$t, file_data$time_info$units)
months_all <- date_time$months
months_unique <- sort(unique(months_all))
years_all <- date_time$years
years_unique <- sort(unique(years_all))
nmonmeans <- length(years_unique) * length(months_unique)
mul <- months_all * years_all
testnum <- mul
test_count <- 0
test <- -999
for (i in seq_along(mul)) {
if (sum(testnum == mul[i]) >= 1) {
test_count <- test_count + 1
test <- cbind(test, mul[i])
testnum[testnum == mul[i]] <- -999
}
}
# Use placeholder for result so that it can be calculated later without the
# need to have all input data in memory concurrently.
data_placeholder <- array(
file_data$variable$attributes$missing_value,
dim = c(length(file_data$dimension_data$x),
length(file_data$dimension_data$y),
test_count)
)
time_bnds <- get_time_bounds_mul(
file_data$dimension_data$t, test, test_count, mul
)
vars_data <- list(result = data_placeholder, time_bounds = time_bnds)
nc_format <- get_nc_version(nc34)
cmsaf_info <- switch(
op,
paste0("cmsafops::monmax for variable ", file_data$variable$name),
paste0("cmsafops::monmin for variable ", file_data$variable$name),
paste0("cmsafops::monmean for variable ", file_data$variable$name),
paste0("cmsafops::monsum for variable ", file_data$variable$name),
paste0("cmsafops::monsd for variable ", file_data$variable$name),
paste0("cmsafops::monvar for variable ", file_data$variable$name),
paste0("cmsafops::monpctl with p = ", p, " for variable ", file_data$variable$name),
paste0("cmsafops::monavg for variable ", file_data$variable$name),
)
time_data <- time_bnds[1, ]
##### prepare output #####
global_att_list <- names(file_data$global_att)
global_att_list <- global_att_list[toupper(global_att_list) %in% toupper(GLOBAL_ATT_DEFAULT)]
global_attributes <- file_data$global_att[global_att_list]
dims <- define_dims(file_data$grid$is_regular,
file_data$dimension_data$x,
file_data$dimension_data$y,
time_data,
NB2,
file_data$time_info$units)
vars <- define_vars(file_data$variable, dims, nc_format$compression)
write_output_file(
outfile,
nc_format$force_v4,
vars,
vars_data,
file_data$variable$name,
file_data$grid$vars, file_data$grid$vars_data,
cmsaf_info,
file_data$time_info$calendar,
file_data$variable$attributes,
global_attributes
)
##### calculate and write result #####
nc_out <- nc_open(outfile, write = TRUE)
dummy_vec <- seq_along(months_all)
count <- 1
for (j in seq_len(test_count)) {
mon_dummy <- which(mul == test[j + 1])
if (length(mon_dummy) < 1) {
if (verbose) message(paste0("length of month ", j, " not sufficient"))
next()
}
startt <- min(dummy_vec[mon_dummy])
countt <- length(mon_dummy)
if (!is.null(nc)) nc_in <- nc
else nc_in <- nc_open(infile)
dum_dat <- ncvar_get(nc_in, file_data$variable$name, start = c(1, 1, startt), count = c(-1, -1, countt), collapse_degen = FALSE)
switch(op,
{
if (verbose) message(paste0("apply monthly maximum ", count))
data <- do.call(pmax, c(na.rm = TRUE, lapply(seq_len(dim(dum_dat)[3]), function(i) dum_dat[, , i])))
},
{
if (verbose) message(paste0("apply monthly minimum ", count))
data <- do.call(pmin, c(na.rm = TRUE, lapply(seq_len(dim(dum_dat)[3]), function(i) dum_dat[, , i])))
},
{
if (verbose) message(paste0("apply monthly mean ", count))
data <- rowMeans(dum_dat, dims = 2, na.rm = TRUE)
},
{
if (verbose) message(paste0("apply monthly sum ", count))
data <- rowSums(dum_dat, dims = 2, na.rm = TRUE) * ifelse(rowSums(is.na(dum_dat), dims = 2) == dim(dum_dat)[3], NA, 1)
},
{
if (verbose) message(paste0("apply monthly standard deviation ", count))
data <- apply(dum_dat, c(1, 2), stats::sd, na.rm = TRUE)
},
{
if (verbose) message(paste0("apply monthly variance ", count))
data <- apply(dum_dat, c(1, 2), stats::var, na.rm = TRUE)
},
{
if (verbose) message(paste0("apply monthly percentile ", count))
data = apply(dum_dat, c(1, 2), stats::quantile, probs = p, names = FALSE, na.rm = TRUE)
},
{
if (verbose) message(paste0("apply monthly average ", count))
data <- rowMeans(dum_dat, dims = 2, na.rm = FALSE)
}
)
data[is.na(data)] <- file_data$variable$attributes$missing_value
ncvar_put(nc_out, vars[[1]], data, start = c(1, 1, count), count = c(-1, -1, 1))
count <- count + 1
if (is.null(nc)) nc_close(nc_in)
}
nc_close(nc_out)
calc_time_end <- Sys.time()
if (verbose) message(get_processing_time_string(calc_time_start, calc_time_end))
}
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