On Sun, Feb 2, 2014 at 10:06 AM, Charles R Harris <charlesr.har...@gmail.com > wrote:
> Sebastian has done a lot of work to refactor/rationalize numpy indexing. > The changes are extensive enough that it would be good to have more public > review, so here is the release note. > > The NumPy indexing has seen a complete rewrite in this version. This makes >> most advanced integer indexing operations much faster and should have no >> other implications. >> However some subtle changes and deprecations were introduced in advanced >> indexing operations: >> >> * Boolean indexing into scalar arrays will always return a new 1-d >> array. >> This means that ``array(1)[array(True)]`` gives ``array([1])`` and >> not the original array. >> * Advanced indexing into one dimensional arrays used to have >> (undocumented) >> special handling regarding repeating the value array in assignments >> when the shape of the value array was too small or did not match. >> Code using this will raise an error. For compatibility you can use >> ``arr.flat[index] = values``, which uses the old code branch. >> * The iteration order over advanced indexes used to be always C-order. >> In NumPy 1.9. the iteration order adapts to the inputs and is not >> guaranteed (with the exception of a *single* advanced index which is >> never reversed for compatibility reasons). This means that the result >> is >> undefined if multiple values are assigned to the same element. >> An example for this is ``arr[[0, 0], [1, 1]] = [1, 2]``, which may >> set ``arr[0, 1]`` to either 1 or 2. >> * Equivalent to the iteration order, the memory layout of the advanced >> indexing result is adapted for faster indexing and cannot be >> predicted. >> * All indexing operations return a view or a copy. No indexing operation >> will return the original array object. >> * In the future Boolean array-likes (such as lists of python bools) >> will always be treated as Boolean indexes and Boolean scalars >> (including >> python `True`) will be a legal *boolean* index. At this time, this is >> already the case for scalar arrays to allow the general >> ``positive = a[a > 0]`` to work when ``a`` is zero dimensional. >> * In NumPy 1.8 it was possible to use `array(True)` and `array(False)` >> equivalent to 1 and 0 if the result of the operation was a scalar. >> This will raise an error in NumPy 1.9 and, as noted above, treated as >> a >> boolean index in the future. >> * All non-integer array-likes are deprecated, object arrays of custom >> integer like objects may have to be cast explicitly. >> * The error reporting for advanced indexing is more informative, however >> the error type has changed in some cases. (Broadcasting errors of >> indexing arrays are reported as `IndexError`) >> * Indexing with more then one ellipsis (`...`) is deprecated. >> > > Thoughts? > > The PR is #3798 <https://github.com/numpy/numpy/pull/3798> if you want to test it. Chuck
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