def set_variable(self, varname, dtype='f4', dims=None, chunksize=None, fill=None, zlib=False, **kwargs):
"""
Define (create) a variable in a netCDF object. No data is written to the
variable yet. Give the variable's dimensions as a tuple of dimension names.
Dimensions must have been previously created with self.netcdf_object.createDimension
(e.g. see set_timelatlon()).
Recommended ordering of dimensions is:
time, height or depth (Z), latitude (Y), longitude (X).
Any other dimensions should be defined before (placed to the left of) the
spatio-temporal coordinates.
To create a scalar variable, use an empty tuple for the dimensions.
Variables can be renamed with the 'renameVariable' method of the netCDF
object.
Specify compression with zlib=True (default = False).
Specify the chunksize with a sequence (tuple, list) of the same length
as dims (i.e., the number of dimensions) where each element of chunksize
corresponds to the size of the chunk along the corresponding dimension.
There are some tips and tricks associated with chunking - see
http://data.auscover.org.au/node/73 for an overview.
The default behaviour is to create a floating-point (f4) variable
with dimensions ('time','latitude','longitude'), with no chunking and
no compression.
"""
netcdf_builder.set_variable(self.netcdf_object, varname, dtype=dtype, dims=dims, chunksize=chunksize, fill=fill, zlib=zlib, **kwargs)
def asciigrid_to_nc(arcfilename,fileroot):
"""
The main routine that calls the calls other routines to prepare the data
and metadata and create the netCDF file.
"""
# Read ascii grid file
asciihead,asciidata,asciitail = split_asciigrid(arcfilename)
meta = arcasciihdr_to_dict(asciihead)
# Create numpy components
latvec,lonvec,datadict = set_latlon(meta)
data,datadict = asciigrid_to_numpy(asciidata,meta,datadict)
# Resample or edit array
# Add a default no_data value if required.
miss = -999.0
if 'nodata_value' in meta: miss = float(meta['nodata_value'])
datadict['missing'] = miss
#data,latvec,lonvec,datadict = resample_array(data,latvec,lonvec,datadict)
# Prepare time, variable name and metadata
d1,d2,datadict = set_datetime(arcfilename,datadict)
datadict = set_varname(arcfilename,datadict)
attributes = set_attributes(arcfilename,meta,datadict)
# Netcdf options
# http://netcdf4-python.googlecode.com/svn/trunk/docs/netCDF4-module.html
nc_format = 'NETCDF4_CLASSIC'
nc_compress = True
debug = False
# Write netcdf file
if os.path.exists(fileroot+'.nc'): os.remove(fileroot+'.nc')
varname = datadict['varname']
vartype = datadict['datatype']
fillval = datadict['missing']
timevec = [d1]
ncobj = nb.ncopen(fileroot+'.nc','w',format=nc_format)
nb.set_timelatlon(ncobj,None,len(latvec),len(lonvec)) # unlimited time
nb.set_variable(ncobj,varname,dtype=vartype,fill=fillval,zlib=nc_compress)
nb.set_variable(ncobj,'crs',dims=(),dtype="i4") # Grid mapping container
nb.add_time(ncobj,timevec)
nb.add_data(ncobj,'latitude',latvec)
nb.add_data(ncobj,'longitude',lonvec)
if debug:
print varname,data.shape
nb.show_dimensions(ncobj)
# nb.add_data should work but is presently broken. Use direct method
#nb.add_data(ncobj,varname,data)
#ncobj.variables[varname][0,:,:] = data # 2D numpy array
ncobj.variables[varname][:] = data # 3D numpy array
nb.set_attributes(ncobj,attributes)
nb.ncclose(ncobj)
print 'Wrote:',fileroot+'.nc'
# Write metadata to json
if os.path.exists(fileroot+'.json'): os.remove(fileroot+'.json')
jh.json_dump(attributes,fileroot+'.json')
def set_variable(self,
varname,
dtype='f4',
dims=None,
chunksize=None,
fill=None,
zlib=False,
**kwargs):
"""
Define (create) a variable in a netCDF object. No data is written to the
variable yet. Give the variable's dimensions as a tuple of dimension names.
Dimensions must have been previously created with self.netcdf_object.createDimension
(e.g. see set_timelatlon()).
Recommended ordering of dimensions is:
time, height or depth (Z), latitude (Y), longitude (X).
Any other dimensions should be defined before (placed to the left of) the
spatio-temporal coordinates.
To create a scalar variable, use an empty tuple for the dimensions.
Variables can be renamed with the 'renameVariable' method of the netCDF
object.
Specify compression with zlib=True (default = False).
Specify the chunksize with a sequence (tuple, list) of the same length
as dims (i.e., the number of dimensions) where each element of chunksize
corresponds to the size of the chunk along the corresponding dimension.
There are some tips and tricks associated with chunking - see
http://data.auscover.org.au/node/73 for an overview.
The default behaviour is to create a floating-point (f4) variable
with dimensions ('time','latitude','longitude'), with no chunking and
no compression.
"""
netcdf_builder.set_variable(self.netcdf_object,
varname,
dtype=dtype,
dims=dims,
chunksize=chunksize,
fill=fill,
zlib=zlib,
**kwargs)
def asciigrid_to_nc(arcfilename, fileroot):
"""
The main routine that calls the calls other routines to prepare the data
and metadata and create the netCDF file.
"""
# Read ascii grid file
asciihead, asciidata, asciitail = split_asciigrid(arcfilename)
meta = arcasciihdr_to_dict(asciihead)
# Create numpy components
latvec, lonvec, datadict = set_latlon(meta)
data, datadict = asciigrid_to_numpy(asciidata, meta, datadict)
# Resample or edit array
# Add a default no_data value if required.
miss = -999.0
if 'nodata_value' in meta: miss = float(meta['nodata_value'])
datadict['missing'] = miss
#data,latvec,lonvec,datadict = resample_array(data,latvec,lonvec,datadict)
# Prepare time, variable name and metadata
d1, d2, datadict = set_datetime(arcfilename, datadict)
datadict = set_varname(arcfilename, datadict)
attributes = set_attributes(arcfilename, meta, datadict)
# Netcdf options
# http://netcdf4-python.googlecode.com/svn/trunk/docs/netCDF4-module.html
nc_format = 'NETCDF4_CLASSIC'
nc_compress = True
debug = False
# Write netcdf file
if os.path.exists(fileroot + '.nc'): os.remove(fileroot + '.nc')
varname = datadict['varname']
vartype = datadict['datatype']
fillval = datadict['missing']
timevec = [d1]
ncobj = nb.ncopen(fileroot + '.nc', 'w', format=nc_format)
nb.set_timelatlon(ncobj, None, len(latvec), len(lonvec)) # unlimited time
nb.set_variable(ncobj,
varname,
dtype=vartype,
fill=fillval,
zlib=nc_compress)
nb.set_variable(ncobj, 'crs', dims=(),
dtype="i4") # Grid mapping container
nb.add_time(ncobj, timevec)
nb.add_data(ncobj, 'latitude', latvec)
nb.add_data(ncobj, 'longitude', lonvec)
if debug:
print varname, data.shape
nb.show_dimensions(ncobj)
# nb.add_data should work but is presently broken. Use direct method
#nb.add_data(ncobj,varname,data)
#ncobj.variables[varname][0,:,:] = data # 2D numpy array
ncobj.variables[varname][:] = data # 3D numpy array
nb.set_attributes(ncobj, attributes)
nb.ncclose(ncobj)
print 'Wrote:', fileroot + '.nc'
# Write metadata to json
if os.path.exists(fileroot + '.json'): os.remove(fileroot + '.json')
jh.json_dump(attributes, fileroot + '.json')
