numpy.ma.masked_array.reshape¶

masked_array.reshape(*s, **kwargs)[source]¶

Give a new shape to the array without changing its data.

Returns a masked array containing the same data, but with a new shape. The result is a view on the original array; if this is not possible, a ValueError is raised.

Parameters:

Parameters:	shape : int or tuple of ints The new shape should be compatible with the original shape. If an integer is supplied, then the result will be a 1-D array of that length. order : {‘C’, ‘F’}, optional Determines whether the array data should be viewed as in C (row-major) or FORTRAN (column-major) order.
Returns:	reshaped_array : array A new view on the array.

shape : int or tuple of ints

The new shape should be compatible with the original shape. If an integer is supplied, then the result will be a 1-D array of that length.

order : {‘C’, ‘F’}, optional

Determines whether the array data should be viewed as in C (row-major) or FORTRAN (column-major) order.

Returns:

reshaped_array : array

A new view on the array.

See also

reshape: Equivalent function in the masked array module.
numpy.ndarray.reshape: Equivalent method on ndarray object.
numpy.reshape: Equivalent function in the NumPy module.

Notes

The reshaping operation cannot guarantee that a copy will not be made, to modify the shape in place, use a.shape = s

Examples

>>> x = np.ma.array([[1,2],[3,4]], mask=[1,0,0,1])
>>> print x
[[-- 2]
 [3 --]]
>>> x = x.reshape((4,1))
>>> print x
[[--]
 [2]
 [3]
 [--]]