hgreatrex wrote:
> Hi,
>
> ...
> I've managed to extract the lat / lon variables, so I know that I could
> work
> out manually which pixels correspond to which location. However, it's easy
> to make a mistake in such a calculation, so I thought I would check here
> before reinventing the wheel.
Hi hgreatrex,
this is an example of how I approach this problem, which works for me but
may or may not work for you. :)
CCD20 = open.ncdf("ccd1983_01-dk1_20.nc")
lat = CCD20$dim$lat$vals # NOTE: dim values are CACHED, don't read them
lon = CCD20$dim$lon$vals
lower_left_lon_lat = c(5,30)
upper_right_lon_lat = c(10,40)
ix0 = wherenearest( lower_left_lon_lat[1], lon )
ix1 = wherenearest( upper_right_lon_lat[1], lon )
iy0 = wherenearest( lower_left_lon_lat[2], lat )
iy1 = wherenearest( upper_right_lon_lat[2], lat )
countx = ix1 - ix0 + 1
county = iy1 - iy0 + 1
z = get.var.ncdf( CCD20, "data", start=c(ix0,iy0), count=c(countx,county)
Obviously, you can easily wrap into a separate function an extended
version of 'get.var.ncdf' that takes lat/lon values rather than indices.
The 'wherenearest(val,matrix)' function is probably obvious, but is
something like this:
dist = abs(matrix-val)
index = which.min(dist)
return( index )
Hope that helps,
--Dave
-------------------------------------------------------------------
David W. Pierce
Division of Climate, Atmospheric Science, and Physical Oceanography
Scripps Institution of Oceanography
(858) 534-8276 (voice) / (858) 534-8561 (fax) dpie...@ucsd.edu
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
