On Thu, Jan 5, 2012 at 3:29 PM, Tom Roche <tom_ro...@pobox.com> wrote:
>
> How to programmatically (i.e., without no or minimal handcoding) copy
> a netCDF file? (Without calling
>
> > system("cp whatever wherever")
>
> [...]
>
> So I'm wondering, can anyone point me to, or provide, code that copies
> a netCDF file both
>
> * completely: all coordinate variables, all data variables and their
> attributes, and all global attributes, such that
>
> $ diff -wB <( ncdump -h source.nc ) <( ncdump -h target.nc ) | wc -l
> 0
>
> * programmatically: no or minimal hand-coding of, e.g., attribute
> names and values, missing-value value.
>
> ? If not, can this be done in principle, or are there steps that must
> (at least currently) necessarily be hand-coded?
>
> Hi Tom,
yes, this can be done in principle, although it would be a pain. Mostly
because a netcdf file is a surprisingly complicated object, so asking for R
script that copies one "programmatically" is actually asking quite a lot.
You might think that it should be as easy as "grab THIS var ... then grab
THAT var .. then write them both to the output file." But that fails,
because vars have dims, and in classic netcdf files the dims are identified
by name. What if one var has a lon dim with 360 entries, and the other has
a lon dim with 128 entries? Putting them both into the same file will give
an error.
What I'd suggest is keeping focused on your goal. Since "cp file1.nc
file2.nc" accomplished a complete copy of your netcdf file in a few seconds
of typing, simply copying the file generally isn't the point of an R
script. If I wanted to copy a var from an existing file to a new file,
manipulating it along the way, I'd do something like this (untested code
off the top of my head):
varname = 'temperature'
file_in = 'data1.nc'
file_out = 'data2.nc'
# Get var to copy
ncid_in = open.ncdf( file1 )
var = ncid_in$var[[varname]]
# Make new output dims that are copies of input dims
ndims = var$ndims
dim_out = list()
for( idim in 1:ndims ) {
dim_in = var$dim[[idim]]
dim_out[[idim]] = dim.def.ncdf( dim_in$name, dim_in$units, dim_in$vals,
unlim=dim_in$unlim )
}
# Make output var that is copy of input var
var_out = var.def.ncdf( var$name, var$units, dim_out, var$missval )
ncid_out = create.ncdf( file_out, var_out )
# Loop over timesteps to avoid running out of memory
sz = var$varsize
nt = sz[ndims]
for( it in 1:nt ) {
# Goal of following lines is to construct a 'start' array that is
(1,1,...,it) and a count array
# that is (nx,ny,....,1)
start = array(1,ndims-1)
count = sz[1:(ndims-1)]
start = c(start, it) # Get just this timestep
count = c(count, 1) # Get just this timestep
data = get.var.ncdf( ncid_in, var, start=start, count=count )
# ... Manipulate data here ...
put.var.ncdf( ncid_out, var_out, data, start=start, count=count )
sync.ncdf( ncid_out ) # always a good idea to keep your file sync'ed
}
close.ncdf( ncid_out )
Hope that gets you started,
--Dave
--
David W. Pierce
Division of Climate, Atmospheric Science, and Physical Oceanography
Scripps Institution of Oceanography, La Jolla, California, USA
(858) 534-8276 (voice) / (858) 534-8561 (fax) dpie...@ucsd.edu
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