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A grid in NetCDF is a particular shape and size available for array variables, and consists of sets of dimensions. To activate a grid is to set the context for downstream operations, for querying, summarizing and reading data. There's no sense in performing these operations on more than one grid at a time, but multiple variables may exist in a single grid. There may be only one significant grid in a source or many, individual dimensions are themselves grids.


activate(.data, what, ..., select_var = NULL)

# S3 method for tidync
activate(.data, what, ..., select_var = NULL)

# S3 method for tidync


# S3 method for default

active(x) <- value

# S3 method for default
active(x) <- value



NetCDF object


name of a grid or variable


reserved, currently ignored


optional argument to set selected state of variable/s by name


NetCDF object


name of grid or variable to be active


NetCDF object


There may be more than one grid and one is always activated by default. A grid may be activated by name in the form of 'D1,D0' where one or more numbered dimensions indicates the grid. The grid definition names are printed as part of the summary of in the tidync object and may be obtained directly with hyper_grids() on the tidync object.

Activation of a grid sets the context for downstream operations (slicing and reading data) from NetCDF, and as there may be several grids in a single source activation allows a different choice of available variables. By default the largest grid is activated. Once activated, all downstream tasks apply to the set of variables that exist on that grid.

If activate() is called with a variable name, it puts the variable first. The function active() gets and sets the active grid. To restrict ultimate read to particular variables use the select_var argument to hyper_filter(), hyper_tibble() and hyper_tbl_cube().

Scalar variables are not currently available to tidync, and it's not obvious how activation would occur for scalars, but in future perhaps activate("S") could be the right way forward.

See also

hyper_filter hyper_tibble hyper_tbl_cube


if (!tolower([["sysname"]]) == "sunos") {
 l3file <- ""
 rnc <- tidync(system.file("extdata", "oceandata", l3file,
 package = "tidync"))
 activate(rnc, "palette")

 ## extract available grid names
#> # A tibble: 4 × 4
#>   grid  ndims nvars active
#>   <chr> <int> <int> <lgl> 
#> 1 D1,D0     2     1 TRUE  
#> 2 D3,D2     2     1 FALSE 
#> 3 D0        1     1 FALSE 
#> 4 D1        1     1 FALSE