nc_open: Open a netCDF File

In ncdf4: Interface to Unidata netCDF (Version 4 or Earlier) Format Data Files

Open a netCDF File

Description

Opens an existing netCDF file for reading (or, optionally, writing).

Usage

 nc_open( filename, write=FALSE, readunlim=TRUE, verbose=FALSE, 
 	auto_GMT=TRUE, suppress_dimvals=FALSE, return_on_error=FALSE )

Arguments

filename	Name of the existing netCDF file to be opened.
write	If FALSE (default), then the file is opened read-only. If TRUE, then writing to the file is allowed.
readunlim	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 ncvar_get()).
verbose	If TRUE, then messages are printed out during execution of this function.
auto_GMT	If TRUE, then GMT files are automatically detected. Does not yet do anything.
suppress_dimvals	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).
return_on_error	If TRUE, then nc_open always returns, and returned list element $error will be TRUE if an error was encountered and FALSE if no error was encountered. If return_on_error is FALSE (the default), nc_open halts with an error message if an error is encountered.

Details

This routine opens an existing netCDF file for reading (or, if write=TRUE, for writing). To create a new netCDF file, use nc_create instead.

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 ncdf4. 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 ncdf4 object 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 file for ncdim_def; the ncvar4 object is described in the help file for ncvar_def).

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.

Some netcdf files specify both a "_FillValue" and a "missing_value" attribute for a variable. If these two attributes have the same value, then everything is fine. If they have different values, I consider this a malformed netcdf file and I suggest you contact the person who made your netcdf file to fix it. In this event you can set the "raw_datavals" flag in the ncvar_get() call and handle the conflicting missing values however you want.

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 ncatt_put call.

Value

An object of class ncdf4 that has the fields described above.

Author(s)

David W. Pierce [e-mail]

References

http://dwpierce.com/software

See Also

ncdim_def, ncvar_def, ncatt_put.

Examples

## Not run: 
# 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" )

## End(Not run)

ncdf4 documentation built on Sept. 11, 2024, 8:16 p.m.