cf.Data.compute

Data.compute(persist=None, _force_to_memory=True, _cache_elements=True)

A view of the computed data.

In-place changes to the returned array might affect the underlying Dask array, depending on how the that Dask array has been defined.

The returned array has the same mask hardness and fill value as the data.

Compare with array.

Performance

compute causes all delayed operations to be computed.

Added in version (cfdm): 1.11.2.0

See also

array, datetime_array, sparse_array, persist, cf.persist_data

Parameters:

persist: None or bool, optional

Control the persistence of computed data. Persisting turns the underlying lazy dask array into an equivalent chunked dask array, but now with the results fully computed and cached memory. This can avoid the expense of re-reading the data from disk, or re-computing it, when the data is accessed on multiple occasions.

If persist is None (the default) then the value of persist will be taken from the cf.persist_data function. If persist is True then the data is persisted, regardless of value returned by cf.persist_data. If persist is False then the data is not persisted, regardless of value returned by cf.persist_data.

Added in version (cfdm): 1.13.1.0

_force_to_memory: bool, optional

If True (the default) then force the data resulting from computing the returned Dask graph to be in memory. If False then the data resulting from computing the Dask graph may or may not be in memory, depending on the nature of the stack.

_cache_elements: bool, optional

If True (the default) then create a cache of selected elements from the computed array in memory, if the type of the array allows it. See cache_elements for details.

Added in version (cfdm): 1.13.0.0

Returns:

An in-memory view of the data

Examples

>>> d = cf.Data([1, 2, 3.0], 'km')
>>> d.compute()
array([1., 2., 3.])

>>> from scipy.sparse import csr_array
>>> d = cf.Data(csr_array((2, 3)))
>>> d.compute()
<2x3 sparse array of type '<class 'numpy.float64'>'
        with 0 stored elements in Compressed Sparse Row format>
>>> d.array
array([[0., 0., 0.],
       [0., 0., 0.]])
>>> d.compute().toarray()
array([[0., 0., 0.],
       [0., 0., 0.]])

>>> f = cf.example_field(0)
>>> cf.write(f, 'file.nc')
>>> f = cf.read('file.nc')[0]
>>> print(f.data.compute())
[[0.007 0.034 0.003 0.014 0.018 0.037 0.024 0.029]
 [0.023 0.036 0.045 0.062 0.046 0.073 0.006 0.066]
 [0.11  0.131 0.124 0.146 0.087 0.103 0.057 0.011]
 [0.029 0.059 0.039 0.07  0.058 0.072 0.009 0.017]
 [0.006 0.036 0.019 0.035 0.018 0.037 0.034 0.013]]
>>> f.data.compute(_force_to_memory=False)
<CF PyfiveArray(5, 8): file.nc, q(5, 8)>