Opens an existing netCDF file for reading (or, optionally, writing).
Name of the existing netCDF file to be opened.
If FALSE (default), then the file is opened read-only. If TRUE, then writing to the file is allowed.
When invoked, this function reads in the values of all dimensions from
the associated variables. This can be slow for a large file with a long unlimited
dimension. If set to FALSE, the values for the unlimited dimension are not
automatically read in (they can be read in later, manually, using
If TRUE, then messages are printed out during execution of this function.
If TRUE, then GMT files are automatically detected. Does not yet do anything.
If TRUE, then NO dimensional values are automatically read in from the file. (Use this if there are so many dimensional values that a out-of-memory error is generated).
This routine opens an existing netCDF file for reading (or, if write=TRUE, for writing).
To create a new netCDF file, use
In addition to simply opening the file, information about the file and its contents
is read in and stored in the returned object, which is of class
This class has the following user-accessible fields, all of which are read-only: 1) filename,
which is a character string holding the name of the file; 2) ndims, which is an
integer holding the number of dimensions in the file; 3) nvars, which is an integer
holding the number of the variables in the file that are NOT coordinate variables
(aka dimensional variables); 4) natts, which is an integer holding the number of
global attributes; 5) unlimdimid, which is an integer holding the dimension id of
the unlimited dimension, or -1 if there is none; 6) dim, which is a list of
objects of class
ncdim4; 7) var, which is a list of objects of class
ncvar4; 8) writable, which is TRUE or FALSE, depending on whether the file
was opened with write=TRUE or write=FALSE.
The concept behind the R interface to a netCDF file is that the
returned by this function, as well as the list of
ncdim4 objects contained
in the ncdf object's "dim" list and the
ncvar4 objects contained in the
ncdf object's "var" list, completely describe the netCDF file. I.e., they hold
the entire contents of the file's metadata. Therefore, there are no R interfaces
to the explicit netCDF query functions, such as "nc_inq_nvars" or "nc_inq_natts".
The upshot is, look in the ncdf4 object or its children to get information about
the netCDF file. (Note: the
ncdim4 object is described in the help
ncvar4 object is described
in the help file for
Missing values: R uses "NA" as a missing value. Netcdf files have various
standards for indicating a missing value. The most common is that a variable
will have an attribute named "_FillValue" indicating the value that should
be interpreted as a missing value. (For example, the _FillValue attribute might
have the value of 1.e30, indicating that any data in the netcdf file with
a value of 1.e30 should be interpreted as a missing value.)
If the "_FillValue" attribute is found, then the ncdf4
package transparently maps all the netcdf file's missing values to NA's; this
is the most common case.
(The attribute "missing_value" is also recognized if there is no "_FillValue
attribute.) If the netcdf file does not have a missing value, then the ncdf4
package assigns a default missing value of 1.e30 to the netcdcf file so that R NA's,
which are always possible in the R environment, can be sensibly handled
in the netcdf file. On rare occasions this can cause problems with non-compliant
or incorrect netcdf files that implicitly use some particular value,
for example 9.96921e+36, to indicate a missing value but without
setting a proper _FillValue attribute. The best way to fix such netcdf files is to explicitly
put in the correct _FillValue attribute using an
An object of class
ncdf4 that has the fields described above.
David W. Pierce email@example.com
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# Define an integer dimension dimState <- ncdim_def( "StateNo", "count", 1:50 ) # Make an integer variable. Note that an integer variable can have # a double precision dimension, or vice versa; there is no fixed # relationship between the precision of the dimension and that of the # associated variable. We just make an integer variable here for # illustration purposes. varPop <- ncvar_def("Pop", "count", dimState, -1, longname="Population", prec="integer") # Create a netCDF file with this variable ncnew <- nc_create( "states_population.nc", varPop ) # Write some values to this variable on disk. popAlabama <- 4447100 ncvar_put( ncnew, varPop, popAlabama, start=1, count=1 ) # Add source info metadata to file ncatt_put( ncnew, 0, "source", "Census 2000 from census bureau web site") nc_close(ncnew) # Now open the file and read its data ncold <- nc_open("states_population.nc") data <- ncvar_get(ncold) print("here is the data in the file:") print(data) nc_close( ncold ) # Clean up example file.remove( "states_population.nc" )
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