masked_array(data, mask=False, fill_value=None, **kwargs)
This docstring was copied from numpy.ma.masked_array.
Some inconsistencies with the Dask version may exist.
Masked values of True exclude the corresponding element from any computation.
Construction:
x = MaskedArray(data, mask=nomask, dtype=None, copy=False, subok=True,
ndmin=0, fill_value=None, keep_mask=True, hard_mask=None,
shrink=True, order=None)
Input data.
Mask. Must be convertible to an array of booleans with the same shape as :None:None:`data`. True indicates a masked (i.e. invalid) data.
Data type of the output. If :None:None:`dtype` is None, the type of the data argument ( data.dtype
) is used. If :None:None:`dtype` is not None and different from data.dtype
, a copy is performed.
Whether to copy the input data (True), or to use a reference instead. Default is False.
Whether to return a subclass of :None:None:`MaskedArray` if possible (True) or a plain :None:None:`MaskedArray`. Default is True.
Minimum number of dimensions. Default is 0.
Value used to fill in the masked values when necessary. If None, a default based on the data-type is used.
Whether to combine :None:None:`mask` with the mask of the input data, if any (True), or to use only :None:None:`mask` for the output (False). Default is True.
Whether to use a hard mask or not. With a hard mask, masked values cannot be unmasked. Default is False.
Whether to force compression of an empty mask. Default is True.
Specify the order of the array. If order is 'C', then the array will be in C-contiguous order (last-index varies the fastest). If order is 'F', then the returned array will be in Fortran-contiguous order (first-index varies the fastest). If order is 'A' (default), then the returned array may be in any order (either C-, Fortran-contiguous, or even discontiguous), unless a copy is required, in which case it will be C-contiguous.
An array class with possibly masked values.
The mask
can be initialized with an array of boolean values with the same shape as data
.
>>> data = np.arange(6).reshape((2, 3)) # doctest: +SKIP
... np.ma.MaskedArray(data, mask=[[False, True, False], # doctest: +SKIP
... [False, False, True]]) masked_array( data=[[0, --, 2], [3, 4, --]], mask=[[False, True, False], [False, False, True]], fill_value=999999)
Alternatively, the mask
can be initialized to homogeneous boolean array with the same shape as data
by passing in a scalar boolean value:
>>> np.ma.MaskedArray(data, mask=False) # doctest: +SKIP masked_array( data=[[0, 1, 2], [3, 4, 5]], mask=[[False, False, False], [False, False, False]], fill_value=999999)This example is valid syntax, but we were not able to check execution
>>> np.ma.MaskedArray(data, mask=True) # doctest: +SKIP masked_array( data=[[--, --, --], [--, --, --]], mask=[[ True, True, True], [ True, True, True]], fill_value=999999, dtype=int64)
The recommended practice for initializing mask
with a scalar boolean value is to use True
/ False
rather than np.True_
/ np.False_
. The reason is :None:attr:`nomask` is represented internally as np.False_
.
See :>>> np.False_ is np.ma.nomask # doctest: +SKIP True
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