xarray.DataArray.to_netcdf (original) (raw)

DataArray.to_netcdf(path=None, mode='w', format=None, group=None, engine=None, encoding=None, unlimited_dims=None, compute=True, invalid_netcdf=False, auto_complex=None)[source]#

Write DataArray contents to a netCDF file.

Parameters:

• path (str, path-like or None, optional) – Path to which to save this dataset. File-like objects are only supported by the scipy engine. If no path is provided, this function returns the resulting netCDF file as bytes; in this case, we need to use scipy, which does not support netCDF version 4 (the default format becomes NETCDF3_64BIT).
• mode ({"w", "a"}, default: "w") – Write (‘w’) or append (‘a’) mode. If mode=’w’, any existing file at this location will be overwritten. If mode=’a’, existing variables will be overwritten.
• format ({"NETCDF4", "NETCDF4_CLASSIC", "NETCDF3_64BIT", "NETCDF3_CLASSIC"}, optional) – File format for the resulting netCDF file:
  • NETCDF4: Data is stored in an HDF5 file, using netCDF4 API features.
  • NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only netCDF 3 compatible API features.
  • NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format, which fully supports 2+ GB files, but is only compatible with clients linked against netCDF version 3.6.0 or later.
  • NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not handle 2+ GB files very well.
    All formats are supported by the netCDF4-python library. scipy.io.netcdf only supports the last two formats.
    The default format is NETCDF4 if you are saving a file to disk and have the netCDF4-python library available. Otherwise, xarray falls back to using scipy to write netCDF files and defaults to the NETCDF3_64BIT format (scipy does not support netCDF4).
• group (str, optional) – Path to the netCDF4 group in the given file to open (only works for format=’NETCDF4’). The group(s) will be created if necessary.
• engine ({"netcdf4", "scipy", "h5netcdf"}, optional) – Engine to use when writing netCDF files. If not provided, the default engine is chosen based on available dependencies, with a preference for ‘netcdf4’ if writing to a file on disk.
• encoding (dict, optional) – Nested dictionary with variable names as keys and dictionaries of variable specific encodings as values, e.g.,{"my_variable": {"dtype": "int16", "scale_factor": 0.1, "zlib": True}, ...}
  The h5netcdf engine supports both the NetCDF4-style compression encoding parameters {"zlib": True, "complevel": 9} and the h5py ones {"compression": "gzip", "compression_opts": 9}. This allows using any compression plugin installed in the HDF5 library, e.g. LZF.
• unlimited_dims (iterable of Hashable, optional) – Dimension(s) that should be serialized as unlimited dimensions. By default, no dimensions are treated as unlimited dimensions. Note that unlimited_dims may also be set viadataset.encoding["unlimited_dims"].
• compute (bool, default: True) – If true compute immediately, otherwise return adask.delayed.Delayed object that can be computed later.
• invalid_netcdf (bool, default: False) – Only valid along with engine="h5netcdf". If True, allow writing hdf5 files which are invalid netcdf as described inh5netcdf/h5netcdf.

Returns:

store (bytes or Delayed or None) –

• bytes if path is None
• dask.delayed.Delayed if compute is False
• None otherwise

Notes

Only xarray.Dataset objects can be written to netCDF files, so the xarray.DataArray is converted to a xarray.Dataset object containing a single variable. If the DataArray has no name, or if the name is the same as a coordinate name, then it is given the name"__xarray_dataarray_variable__".

[netCDF4 backend only] netCDF4 enums are decoded into the dataarray dtype metadata.