ūüćĺ Xarray is now 10 years old! ūüéČ

xarray.backends.ScipyDataStore

xarray.backends.ScipyDataStore#

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

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

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)[source]#

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)

encode one variable

get_attrs()

get_dimensions()

get_encoding()

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()

Attributes

ds