lib.xnumpy.structured: New utility to create structured view of an array
Structured creates view of the array interpreting its minor axis as fully covered by a dtype. It is similar to arr.view(dtype) + corresponding reshape, but does not have limitations of ndarray.view(). For example: In [1]: a = np.arange(3*3, dtype=np.int32).reshape((3,3)) In [2]: a Out[2]: array([[0, 1, 2], [3, 4, 5], [6, 7, 8]], dtype=int32) In [3]: b = a[:2,:2] In [4]: b Out[4]: array([[0, 1], [3, 4]], dtype=int32) In [5]: dtxy = np.dtype([('x', np.int32), ('y', np.int32)]) In [6]: dtxy Out[6]: dtype([('x', '<i4'), ('y', '<i4')]) In [7]: b.view(dtxy) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-66-af98529aa150> in <module>() ----> 1 b.view(dtxy) ValueError: To change to a dtype of a different size, the array must be C-contiguous In [8]: structured(b, dtxy) Out[8]: array([(0, 1), (3, 4)], dtype=[('x', '<i4'), ('y', '<i4')]) Structured always creates view and never copies data. Here is original context where separately playing with .shape and .dtype was not enough, since it was creating array copy and OOM'ing the machine: klaus/wendelin@cbe4938b
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lib/tests/test_xnumpy.py
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