xarray.backends.NetCDF4DataStore

class xarray.backends.NetCDF4DataStore(manager, group=None, mode=None, lock=CombinedLock([<SerializableLock: c238dc10-d44d-483c-b77f-631d742881f2>, <SerializableLock: bd6ae7c2-242b-408d-83d9-f0622d2b9384>]), autoclose=False)[source]

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: c238dc10-d44d-483c-b77f-631d742881f2>, <SerializableLock: bd6ae7c2-242b-408d-83d9-f0622d2b9384>]), autoclose=False)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(manager[, group, mode, lock, autoclose])

Initialize self.

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

ds

format

is_remote

lock