NumPy参考 >数组对象 >Masked array operations >numpy.ma.is_mask > numpy.ma.resize
numpy.ma.
resize
(x, new_shape)[source]¶Return a new masked array with the specified size and shape.
This is the masked equivalent of the numpy.resize
function. The new
array is filled with repeated copies of x (in the order that the
data are stored in memory). If x is masked, the new array will be
masked, and the new mask will be a repetition of the old one.
See also
numpy.resize
Equivalent function in the top level NumPy module.
Examples
>>> import numpy.ma as ma
>>> a = ma.array([[1, 2] ,[3, 4]])
>>> a[0, 1] = ma.masked
>>> a
masked_array(
data=[[1, --],
[3, 4]],
mask=[[False, True],
[False, False]],
fill_value=999999)
>>> np.resize(a, (3, 3))
masked_array(
data=[[1, 2, 3],
[4, 1, 2],
[3, 4, 1]],
mask=False,
fill_value=999999)
>>> ma.resize(a, (3, 3))
masked_array(
data=[[1, --, 3],
[4, 1, --],
[3, 4, 1]],
mask=[[False, True, False],
[False, False, True],
[False, False, False]],
fill_value=999999)
A MaskedArray is always returned, regardless of the input type.
>>> a = np.array([[1, 2] ,[3, 4]])
>>> ma.resize(a, (3, 3))
masked_array(
data=[[1, 2, 3],
[4, 1, 2],
[3, 4, 1]],
mask=False,
fill_value=999999)