resize(x, new_shape)
This is the masked equivalent of the numpy.resize
function. The new array is filled with repeated copies of :None:None:`x` (in the order that the data are stored in memory). If :None:None:`x` is masked, the new array will be masked, and the new mask will be a repetition of the old one.
Return a new masked array with the specified size and shape.
numpy.resize
Equivalent function in the top level NumPy module.
>>> import numpy.ma as maThis example is valid syntax, but we were not able to check execution
... 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)This example is valid syntax, but we were not able to check execution
>>> 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.
This example is valid syntax, but we were not able to check execution>>> a = np.array([[1, 2] ,[3, 4]])See :
... ma.resize(a, (3, 3)) masked_array( data=[[1, 2, 3], [4, 1, 2], [3, 4, 1]], mask=False, fill_value=999999)
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.ma.core.MaskedArray.resize
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