Source code for

from __future__ import absolute_import, division, print_function, \

import numpy
import netCDF4
from datetime import datetime
import sys

default_format = 'NETCDF3_64BIT'
default_engine = None
default_char_dim_name = 'StrLen'
default_fills = netCDF4.default_fillvals

[docs] def write_netcdf(ds, fileName, fillValues=None, format=None, engine=None, char_dim_name=None): """ Write an xarray.Dataset to a file with NetCDF4 fill values and the given name of the string dimension. Also adds the time and command-line to the history attribute. Parameters ---------- ds : xarray.Dataset The dataset to save fileName : str The path for the NetCDF file to write fillValues : dict, optional A dictionary of fill values for different NetCDF types. Default is ````, which can be modified but which defaults to ``netCDF4.default_fillvals`` format : {'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_64BIT', 'NETCDF3_CLASSIC'}, optional The NetCDF file format to use. Default is ````, which can be modified but which defaults to ``'NETCDF3_64BIT'`` engine : {'netcdf4', 'scipy', 'h5netcdf'}, optional The library to use for NetCDF output. The default is the same as in :py:meth:`xarray.Dataset.to_netcdf` and depends on ``format``. You can override the default by setting ```` char_dim_name : str, optional The name of the dimension used for character strings, or None to let xarray figure this out. Default is ````, which can be modified but which defaults to ``'StrLen'`` """ if format is None: format = default_format if fillValues is None: fillValues = default_fills if engine is None: engine = default_engine if char_dim_name is None: char_dim_name = default_char_dim_name encodingDict = {} variableNames = list(ds.data_vars.keys()) + list(ds.coords.keys()) for variableName in variableNames: isNumeric = numpy.issubdtype(ds[variableName].dtype, numpy.number) if isNumeric and numpy.any(numpy.isnan(ds[variableName])): dtype = ds[variableName].dtype for fillType in fillValues: if dtype == numpy.dtype(fillType): encodingDict[variableName] = \ {'_FillValue': fillValues[fillType]} break else: encodingDict[variableName] = {'_FillValue': None} isString = numpy.issubdtype(ds[variableName].dtype, numpy.bytes_) if isString and char_dim_name is not None: encodingDict[variableName] = {'char_dim_name': char_dim_name} update_history(ds) if 'Time' in ds.dims: # make sure the Time dimension is unlimited because MPAS has trouble # reading Time otherwise ds.encoding['unlimited_dims'] = {'Time'} ds.to_netcdf(fileName, encoding=encodingDict, format=format, engine=engine)
def update_history(ds): '''Add or append history to attributes of a data set''' thiscommand ="%a %b %d %H:%M:%S %Y") + ": " + \ " ".join(sys.argv[:]) if 'history' in ds.attrs: newhist = '\n'.join([thiscommand, ds.attrs['history']]) else: newhist = thiscommand ds.attrs['history'] = newhist