xarray.backends.ScipyDataStore

class xarray.backends.ScipyDataStore(filename_or_obj, mode='r', format=None, group=None, mmap=None, lock=None)

Store for reading and writing data via scipy.io.netcdf_file.

This store has the advantage of being able to be initialized with a StringIO object, allow for serialization without writing to disk.

It only supports the NetCDF3 file-format.

__init__(filename_or_obj, mode='r', format=None, group=None, mmap=None, lock=None)

Methods

__init__(filename_or_obj[, mode, format, ...])
close()
encode(variables, attributes)	Encode the variables and attributes in this store
encode_attribute(a)	encode one attribute
encode_variable(variable[, name])	encode one variable
get_attrs()
get_child_store(group)	Get a store corresponding to the indicated child group.
get_dimensions()
get_encoding()
get_parent_dimensions()
get_variables()
load()	This loads the variables and attributes simultaneously.
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()	Write all buffered data to disk.

Attributes

ds
lock