cf.write

cf.write(fields, filename, fmt='NETCDF3_CLASSIC', overwrite=True, verbose=False, cfa_options=None, mode='w', least_significant_digit=None, endian='native', compress=0, fletcher32=False, no_shuffle=False, datatype=None, single=False, double=False, reference_datetime=None, variable_attributes=None, HDF_chunksizes=None, unlimited=None)[source]

Write fields to a CF-netCDF or CFA-netCDF file.

NetCDF dimension and variable names will be taken from variables’ ncvar attributes and the domain attribute nc_dimensions if present, otherwise they are inferred from standard names or set to defaults. NetCDF names may be automatically given a numerical suffix to avoid duplication.

Output netCDF file global properties are those which occur in the set of CF global properties and non-standard data variable properties and which have equal values across all input fields.

Logically identical field components are only written to the file once, apart from when they need to fulfil both dimension coordinate and auxiliary coordinate roles for different data variables.

See also

cf.read

Parameters:
fields: (arbitrarily nested sequence of) cf.Field or cf.FieldList

The field or fields to write to the file.

filename: str

The output netCDF file. Various type of expansion are applied to the file names:

Expansion Description
Tilde An initial component of ~ or ~user is replaced by that user‘s home directory.
Environment variable Substrings of the form $name or ${name} are replaced by the value of environment variable name.

Where more than one type of expansion is used in the same string, they are applied in the order given in the above table.

Example: If the environment variable MYSELF has been set to the “david”, then '~$MYSELF/out.nc' is equivalent to '~david/out.nc'.

fmt: str, optional

The format of the output file. One of:

fmt Description
'NETCDF3_CLASSIC' Output to a CF-netCDF3 classic format file
'NETCDF3_64BIT' Output to a CF-netCDF3 64-bit offset format file
'NETCDF4_CLASSIC' Output to a CF-netCDF4 classic format file
'NETCDF4' Output to a CF-netCDF4 format file
'CFA3' Output to a CFA-netCDF3 classic format file
'CFA4' Output to a CFA-netCDF4 format file

By default the fmt is 'NETCDF3_CLASSIC'. Note that the netCDF3 formats may be slower than any of the other options.

overwrite: bool, optional

If False then raise an exception if the output file pre-exists. By default a pre-existing output file is over written.

verbose: bool, optional

If True then print one-line summaries of each field written.

cfa_options: dict, optional

A dictionary giving parameters for configuring the output CFA-netCDF file:

Key Value
'base'
  • If None (the default) then file names within CFA-netCDF files are stored with absolute paths.
  • If set to an empty string then file names within CFA-netCDF files are given relative to the directory or URL base containing the output CFA-netCDF file.
  • If set to a string then file names within CFA-netCDF files are given relative to the directory or URL base described by the value. For example: '../archive'.

By default no parameters are specified.

mode: str, optional

Specify the mode of write access for the output file. One of:

mode Description
'w' Open a new file for writing to. If it exists and overwrite is True then the file is deleted prior to being recreated.

By default the file is opened with write access mode 'w'.

endian: str, optional

The endian-ness of the output file. Valid values are 'little', 'big' or 'native'. By default the output is native endian.

compress: int, optional

Regulate the speed and efficiency of compression. Must be an integer between 0 and 9. 0 means no compression; 1 is the fastest, but has the lowest compression ratio; 9 is the slowest but best compression ratio. The default value is 0. An exception is raised if compression is requested for a netCDF3 output file format.

least_significant_digit: int, optional

Truncate the input field data arrays. For a positive integer, N the precision that is retained in the compressed data is ‘10 to the power -N’. For example, a value of 2 will retain a precision of 0.01. In conjunction with the compress parameter this produces ‘lossy’, but significantly more efficient compression.

fletcher32: bool, optional

If True then the Fletcher-32 HDF5 checksum algorithm is activated to detect compression errors. Ignored if compress is 0.

no_shuffle: bool, optional

If True then the HDF5 shuffle filter (which de-interlaces a block of data before compression by reordering the bytes by storing the first byte of all of a variable’s values in the chunk contiguously, followed by all the second bytes, and so on) is turned off. By default the filter is applied because if the data array values are not all wildly different, using the filter can make the data more easily compressible. Ignored if compress is 0.

datatype: dict, optional

Specify data type conversions to be applied prior to writing data to disk. Arrays with data types which are not specified remain unchanged. By default, input data types are preserved. Data types defined by numpy.dtype objects in a dictionary whose are input data types with values of output data types.

Example:

To convert 64-bit floats and 64-bit integers to their 32-bit counterparts: datatype={numpy.dtype('float64'): numpy.dtype('float32'), numpy.dtype('int64'): numpy.dtype('int32')}.

single: bool, optional

Write 64-bit floats as 32-bit floats and 64-bit integers as 32-bit integers. By default, input data types are preserved. Note that single=True is exactly equivalent to datatype={numpy.dtype('float64'): numpy.dtype('float32'), numpy.dtype('int64'): numpy.dtype('int32')}.

double: bool, optional

Write 32-bit floats as 64-bit floats and 32-bit integers as 64-bit integers. By default, input data types are preserved. Note that double=True is exactly equivalent to datatype={numpy.dtype('float32'): numpy.dtype('float64'), numpy.dtype('int32'): numpy.dtype('int64')}.

HDF_chunksizes: dict, optional

Manually specify HDF5 chunks for the field data arrays.

Chunking refers to a storage layout where a data array is partitioned into fixed-size multi-dimensional chunks when written to a netCDF4 file on disk. Chunking is ignored if the field is written to a netCDF3 format file.

A chunk has the same rank as the data array, but with fewer (or no more) elements along each axes. The chunk is defined by a dictionary whose keys identify axes with values of the chunks size for those axes.

If a given chunk size for an axis is larger than the axis size for any field, then the size of the axis at the time of writing to disk will be used instead.

If chunk sizes have been specified for some but not all axes, then the each unset chunk size is assumed to be the full size of the axis for each field.

If no chunk sizes have been set for any axes then the netCDF default chunk is used (http://www.unidata.ucar.edu/software/netcdf/docs/netcdf_perf_chunking.html).

If any chunk sizes have already been set on a field with the cf.Field.HDF_chunks method then these are used in instead.

A detailed discussion of HDF chunking and I/O performance is available at https://www.hdfgroup.org/HDF5/doc/H5.user/Chunking.html and http://www.unidata.ucar.edu/software/netcdf/workshops/2011/nc4chunking. Basically, you want the chunks for each dimension to match as closely as possible the size and shape of the data block that users will read from the file.

unlimited: sequence of str, optional

Create a unlimited dimensions (dimensions that can be appended to). A dimension is identified by either a standard name; one of T, Z, Y, X denoting time, height or horixontal axes (as defined by the CF conventions); or the value of an arbitrary CF property preceeded by the property name and a colon. For example:

Multiple unlimited axes may be defined by specifying more than one –unlimited option. Note, however, that only netCDF4 formats support multiple unlimited dimensions. For example, to set the time and Z dimensions to be unlimited you could use –unlim- ited=time –unlimited=Z

An example of defining an axis by an arbitrary CF property could be –unlimited=long_name:pseudo_level
Returns:

None
Raises:
IOError:

If overwrite is False and the output file pre-exists.
Examples: 
>>> f
[<CF Field: air_pressure(30, 24)>,
 <CF Field: u_compnt_of_wind(19, 29, 24)>,
 <CF Field: v_compnt_of_wind(19, 29, 24)>,
 <CF Field: potential_temperature(19, 30, 24)>]
>>> write(f
, ‘file’)
>>> type(f)
<clas
s ‘cf.field.FieldList’>
>>> type(g)
<clas
s ‘cf.field.Field’>
>>> cf.write([f, g], 'file.nc', verbose=True)
[<CF Field: air_pressure(30, 24)>,
 <CF Field: u_compnt_of_wind(19, 29, 24)>,
 <CF Field: v_compnt_of_wind(19, 29, 24)>,
 <CF Field: potential_temperature(19, 30, 24)>]