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# numpy.matrix.getfield

`matrix.``getfield`(dtype, offset)

  Returns a field of the given array as a certain type.

  A field is a view of the array data with each itemsize determined by the given type and the offset into the current array, i.e. from `offset * dtype.itemsize` to `(offset+1) * dtype.itemsize`.

  Parameters : | dtype : str String denoting the data type of the field. offset : int Number of dtype.itemsize‘s to skip before beginning the element view.

  Examples

      >>> x = np.diag([1.+1.j]*2)
      >>> x
      array([[ 1.+1.j,  0.+0.j],
             [ 0.+0.j,  1.+1.j]])
      >>> x.dtype
      dtype('complex128')

      >>> x.getfield('complex64', 0) # Note how this != x
      array([[ 0.+1.875j,  0.+0.j   ],
             [ 0.+0.j   ,  0.+1.875j]], dtype=complex64)

      >>> x.getfield('complex64',1) # Note how different this is than x
      array([[ 0. +5.87173204e-39j,  0. +0.00000000e+00j],
             [ 0. +0.00000000e+00j,  0. +5.87173204e-39j]], dtype=complex64)

      >>> x.getfield('complex128', 0) # == x
      array([[ 1.+1.j,  0.+0.j],
             [ 0.+0.j,  1.+1.j]])

  If the argument dtype is the same as x.dtype, then offset != 0 raises a ValueError:

      >>> x.getfield('complex128', 1)
      Traceback (most recent call last):
        File "<stdin>", line 1, in <module>
      ValueError: Need 0 <= offset <= 0 for requested type but received offset = 1

      >>> x.getfield('float64', 0)
      array([[ 1.,  0.],
             [ 0.,  1.]])

      >>> x.getfield('float64', 1)
      array([[  1.77658241e-307,   0.00000000e+000],
             [  0.00000000e+000,   1.77658241e-307]])

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