FWIW, you probably _don't_ want to use `ndarray` directly. Normally, you want to use the `np.array` factory function...
>>> import numpy as np >>> a = np.ndarray([0, 1, 2]) >>> a array([], shape=(0, 1, 2), dtype=float64) Aside from that, my main problem with this proposal is that it seems to only be relevant when used in third party code. There _is_ some precedence for this (for example rich comparisons and the matrix multiplication operator) -- However, these are all _operators_ so third party code can hook into it using the provided hook methods. This proposal is different in that it _isn't_ proposing an operator, so there isn't any object on which to define a magic hook method. I think that it was mentioned that it might be possible for a user to _register_ a callable that would then be used when this syntax was envoked -- But having a global setting like that leads to contention. What if I want to use this syntax with `np.ndarray` but some other third party code (that I want to use _with_ numpy_ tries to hook into the syntax as well? All of a sudden, my script stops working as soon as I import a new third party module. I _do_ think that this might be a valid proposal for some of the more domain specific python variants (e.g. IPython) which have a pre-processing layer on top of the rest of the language. It might be worth trying to float this idea in one of their ideas mailing lists/issue trackers. On Wed, Oct 19, 2016 at 1:10 PM, Todd <toddr...@gmail.com> wrote: > On Wed, Oct 19, 2016 at 3:55 PM, Joseph Jevnik <joe...@gmail.com> wrote: > >> You could add or prototype this with quasiquotes ( >> http://quasiquotes.readthedocs.io/en/latest/). You just need to be able >> to parse the body of your expression as a string into an array. Here is a >> quick example with a parser that only accepts 2d arrays: >> >> ``` >> # coding: quasiquotes >> >> import numpy as np >> from quasiquotes import QuasiQuoter >> >> >> @object.__new__ >> class array(QuasiQuoter): >> def quote_expr(self, expr, frame, col_offset): >> return np.array([ >> eval('[%s]' % d, frame.f_globals, frame.f_locals) >> for d in expr.split('||') >> ]) >> >> >> def f(): >> a = 1 >> b = 2 >> c = 3 >> return [$array| a, b, c || 4, 5, 6 |] >> >> >> if __name__ == '__main__': >> print(f()) >> ``` >> > > Interesting project, thanks! If there is any actual interest in this that > might be a good way to prototype it. > > >> Personally I am not sold on replacing `[` and `]` with `|` because I like >> that you can visually see where dimensions are closed. >> >> > Yes, that issue occurred to me. But assuming a rectangular matrix, I had > trouble coming up with a good example that is clearer than what you could > do with this syntax. For simple arrays it isn't needed, and complicated > arrays are large so picking out the "[" and "]" becomes visually harder at > least for me. Do you have a specific example that you think would be > clearer than what is possible with this syntax? > > Of course that is more of an issue with jagged arrays, but numpy doesn't > support those and I am not aware of any plans to add them (dynd is another > story). > > Also keep in mind that this would supplement the existing approach， it > doesn't replace it. np.ndarray() would stay around just like list() stays > around for cases where it makes sense. > > > _______________________________________________ > Python-ideas mailing list > Python-ideas@python.org > https://mail.python.org/mailman/listinfo/python-ideas > Code of Conduct: http://python.org/psf/codeofconduct/ > -- [image: pattern-sig.png] Matt Gilson // SOFTWARE ENGINEER E: m...@getpattern.com // P: 603.892.7736 We’re looking for beta testers. Go here <https://www.getpattern.com/meetpattern> to sign up!
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