cf.Data.count_masked

Data.count_masked(split_every=None)

Count the masked elements of the data.

See also

count

Parameters

split_every: int or dict, optional

Determines the depth of the recursive aggregation. If set to or more than the number of input chunks, the aggregation will be performed in two steps, one partial collapse per input chunk and a single aggregation at the end. If set to less than that, an intermediate aggregation step will be used, so that any of the intermediate or final aggregation steps operates on no more than split_every inputs. The depth of the aggregation graph will be \(log_{split_every}(input chunks along reduced axes)\). Setting to a low value can reduce cache size and network transfers, at the cost of more CPU and a larger dask graph.

By default, dask heuristically decides on a good value. A default can also be set globally with the split_every key in dask.config. See dask.array.reduction for details.

Returns

Data

The count of missing elements.

Examples

>>> d = cf.Data(numpy.arange(12).reshape(3, 4))
>>> print(d.array)
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
>>> d.count_masked()
<CF Data(1, 1): [[0]]>

>>> d[0, :] = cf.masked
>>> print(d.array)
[[-- -- -- --]
 [ 4  5  6  7]
 [ 8  9 10 11]]
>>> d.count_masked()
<CF Data(1, 1): [[4]]>