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convolve.py: use `arr[tuple(seq)]` instead of `arr[seq]` -- with numpy 1.14.0

See original GitHub issue

Hi. I get such warnings when using numpy 1.14.0:

/home/nima/.local/lib/python3.5/site-packages/astropy/convolution/convolve.py:692: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  bigimwt[arrayslices] = 1.0 - nanmaskarray * interpolate_nan
/home/nima/.local/lib/python3.5/site-packages/astropy/convolution/convolve.py:700: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  bigimwt[arrayslices] = wtsm.real[arrayslices]
/home/nima/.local/lib/python3.5/site-packages/astropy/convolution/convolve.py:735: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  result = rifft[arrayslices].real

Rolling back to numpy 1.10 seems to solve the problem.

Issue Analytics

State:
Created 5 years ago
Comments:14 (14 by maintainers)

Top GitHub Comments

4reactions

mhvkcommented, Sep 14, 2018

This is a recent change in numpy, with the intent to make indexing with a list always return the items in that list, i.e., that only with a tuple you get indexes for each axis. One can add tuple(arrayslices) or perhaps ensure arrayslices is a tuple in the first place.

3reactions

astrofrogcommented, Sep 14, 2018

Maybe a topic for the coordination meeting - under what conditions do we envisage ever breaking backward compatibility in future? The reason Python 3 was bad is because they packaged all the breaks in a single release. In a way, Numpy’s approach is less impactful - just break one thing per release 😉

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