I've put up a PR to deal with the numpy scalar integer powers at
https://github.com/numpy/numpy/pull/8221. Note that for now everything goes
through the np.power function.
NumPy-Discussion mailing list
On Thu, Oct 27, 2016 at 11:35 PM, Benjamin Root
> Perhaps the numexpr package might be safer? Not exactly meant for this
> situation (meant for optimizations), but the evaluator is pretty darn safe.
It would not be able to evaluate something like 'np.arange(50)'
It is important to bear in mind where the code is being run - if this is
something running on a researcher’s own system, they almost certainly have lots
of other ways of messing it up. These kind of security vulnerabilities are
normally only relevant when you are running code that came from
Matthew has made what looks like a very nice implementation of padding
in np.diff in https://github.com/numpy/numpy/pull/8206. I raised two
general questions about desired behaviour there that Matthew thought
we should put out on the mailiing list as well. This indeed seemed a
good opportunity to