numpy.ma.resize¶

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)

numpy.ma.reshape numpy.ma.swapaxes