xarray.backends.H5NetCDFStore

class xarray.backends.H5NetCDFStore(manager, group=None, mode=None, lock=<SerializableLock: d46e9890-1bbf-469c-9bf2-bd87ed3034b2>, autoclose=False)

Store for reading and writing data via h5netcdf

__init__(manager, group=None, mode=None, lock=<SerializableLock: d46e9890-1bbf-469c-9bf2-bd87ed3034b2>, autoclose=False)

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(k[,d])
get_attrs()
get_dimensions()
get_encoding()
get_variables()
items()
keys()
load()	This loads the variables and attributes simultaneously.
open(filename[, mode, format, group, lock, …])
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()
values()

Attributes

attrs
autoclose
dimensions
ds
format
is_remote
lock
variables