inst/dev/as_tibble.R

In r-gris/ncdump: Extract Metadata from 'NetCDF' Files as Data Frames

# # A tibble: 3 × 7
# id  name   len unlim group_index group_id create_dimvar
# <int> <chr> <int> <lgl>       <int>    <int>         <lgl>
# 1     0   lon   192 FALSE           1    65536          TRUE
# 2     1   lat    94 FALSE           1    65536          TRUE
# 3     3  time   365  TRUE           1    65536          TRUE
# > print(raster(x))
# File runof.sfc.gauss.1997.nc (NC_FORMAT_CLASSIC):
#   
#   2 variables (excluding dimension variables):
#   double time_bnds[nbnds,time]   
# long_name: Time Boundaries
# short runof[lon,lat,time]   
# long_name: Daily Accumulation of Water Runoff at Surface

# print(len_dims)
# # A tibble: 4 × 7
# id  name   len unlim group_index group_id create_dimvar
# <int> <chr> <int> <lgl>       <int>    <int>         <lgl>
# 1     0  time     1 FALSE           1    65536          TRUE
# 2     1  zlev     1 FALSE           1    65536          TRUE
# 3     2   lat   720 FALSE           1    65536          TRUE
# 4     3   lon  1440 FALSE           1    65536          TRUE
# >  tib <- list()
# > print(raster(x))
# File /rdsi/PRIVATE/raad/data/eclipse.ncdc.noaa.gov/pub/OI-daily-v2/NetCDF/1981/AVHRR/avhrr-only-v2.19810901.nc (NC_FORMAT_CLASSIC):
#   
#   4 variables (excluding dimension variables):
#   short sst[lon,lat,zlev,time]   
# long_name: Daily sea surface temperature
# units: degrees C
# _FillValue: -999
#' @name nc_as_tibble
#'x <- raadtools::sstfiles()$fullname[1]
## OISST
#' nc_as_tibble(x, "sst") %>% ggplot() + aes(lon, lat, fill = sst) + geom_raster()
## OISST anom
#' nc_as_tibble(x, "anom") %>% 
#'   filter(between(lon, 130, 180), between(lat, -70, -30)) %>% 
#' ggplot() + aes(lon, lat, fill = anom) + geom_raster()
#' 
#' 
#' x <- raadtools::currentsfiles()$fullname[1]
#' library(dplyr)
#' library(tibble)
#' ## OISST anom
#' library(ggplot2)
#' nc_as_tibble(x, "u") %>% bind_cols(var_as_tibble(x, "v")) %>% 
#'   filter(between(lon, 130, 180), between(lat, -70, -30)) %>% 
#'   ggplot() + aes(lon, lat, fill = sqrt(u * u + v * v)) + geom_raster()
#' 
#' 
#' f <-  "/rdsi/PRIVATE/raad/data/ftp.cdc.noaa.gov/Datasets/ncep.reanalysis2.dailyavgs/gaussian_grid/runof.sfc.gauss.1997.nc"
#' a <- nc_as_tibble(f, "runof")
#' ggplot(a %>% filter(time == 1726872), aes(lon, lat, fill = runof)) + geom_raster()
#' 
#' ggplot(a %>% filter(lon == 300, !is.na(runof)), aes(time, group = lat, y = runof, colour = factor(lat))) + 
#' geom_line() + guides(colour  = FALSE) + 
#' scale_y_log10()



## here we want the table from a variable with all coordinate values
## WIP
ok_var <- function(x, varname, ...) UseMethod("ok_var")
ok_var.character <- function(x, varname, ...) {
  nc <- ncdump::NetCDF(x)
  ok_var(nc, varname)
}
ok_var.NetCDF <- function(x, varname, ...) {
  avail <- x$variable$name
  varname %in% avail
  
}

var_as_tibble <- function(x, varname) {
  ## all the file metadata
  nc <- ncdump::NetCDF(x)
  yes <- ok_var(nc, varname)
  if (!yes) stop(sprintf("varname %s not found, available variable names are:\n %s", varname, paste(nc$variable$name, collapse = ", ")))
  con <- ncdf4::nc_open(x)
  tibble::as_tibble(setNames(list(as.vector(ncdf4::ncvar_get(con, varname))), varname))
}



#' @importFrom tibble tibble as_tibble
#' @importFrom dplyr %>% filter
dimvals_as_tibble <- function(x, varname) {
  nc <- ncdump::NetCDF(x)
  yes <- ok_var(nc, varname)
  stopifnot(yes)
  ## this dimorder is crucial, should be specified in the dump
  dims <- nc$variable %>% dplyr::filter(name == varname) %>% 
    dplyr::select(id) %>% inner_join(nc$vardim %>% mutate(dim_order = row_number())) %>%  arrange(dim_order)
  all_dimvals <- dims %>% transmute(id = dimids, dim_order) %>% inner_join(nc$dimvals %>% mutate(row_num = row_number()), "id") 
  ## we must filter on create_dimvar
  len_dims <- dims %>% transmute(id = dimids, dim_order) %>% inner_join(nc$dimension %>% dplyr::filter(create_dimvar)) #%>% arrange(desc(id))
print(len_dims)
 tib <- list()
 
 ## TODO: this is the wrong order, it must follow the order of NetCDF(file)$vardim  
  ## work through each dimension, with the right array logic for each
 total_prod <- prod(len_dims$len)
  prod_dims <- 1
  for (i in seq_len(nrow(len_dims))) {
    vd <- all_dimvals %>% filter(id == len_dims$id[i]) %>% arrange(row_num) %>% dplyr::select(vals)
    tib[[len_dims$name[i]]] <- rep(vd$vals, each = prod_dims, length.out = total_prod)
    prod_dims <- prod_dims * len_dims$len[i]
  }
  tib <- as_tibble(tib)
  tib
}

nc_as_tibble <- function(x, varname, ...) {
  dplyr::bind_cols(var_as_tibble(x, varname), dimvals_as_tibble(x, varname))
}