xarray.backends.NetCDF4DataStore

class xarray.backends.NetCDF4DataStore(manager, group=None, mode=None, lock=CombinedLock([<SerializableLock: dd093d29-a7dd-4140-bb84-6a350ce1e675>, <SerializableLock: 1949245a-d4c3-4bb1-9eb2-ce98401b371d>]), autoclose=False)

Store for reading and writing data via the Python-NetCDF4 library.

This store supports NetCDF3, NetCDF4 and OpenDAP datasets.

__init__(manager, group=None, mode=None, lock=CombinedLock([<SerializableLock: dd093d29-a7dd-4140-bb84-6a350ce1e675>, <SerializableLock: 1949245a-d4c3-4bb1-9eb2-ce98401b371d>]), autoclose=False)

Methods

__init__(manager[, group, mode, lock, autoclose])
close(**kwargs)
encode(variables, attributes)	Encode the variables and attributes in this store
encode_attribute(a)	encode one attribute
encode_variable(variable)	encode one variable
get_attrs()
get_dimensions()
get_encoding()
get_variables()
load()	This loads the variables and attributes simultaneously.
open(filename[, mode, format, group, ...])
open_store_variable(name, var)
prepare_variable(name, variable[, ...])
set_attribute(key, value)
set_attributes(attributes)	This provides a centralized method to set the dataset attributes on the data store.
set_dimension(name, length[, is_unlimited])
set_dimensions(variables[, unlimited_dims])	This provides a centralized method to set the dimensions on the data store.
set_variable(k, v)
set_variables(variables, check_encoding_set, ...)	This provides a centralized method to set the variables on the data store.
store(variables, attributes[, ...])	Top level method for putting data on this store, this method:
store_dataset(dataset)	in stores, variables are all variables AND coordinates in xarray.Dataset variables are variables NOT coordinates, so here we pass the whole dataset in instead of doing dataset.variables
sync()

Attributes

autoclose
format
is_remote
lock
ds