[Python-ideas] Python multi-dimensional array constructor

[Matt Gilson]

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 <toddrjen at gmail.com> wrote:

> On Wed, Oct 19, 2016 at 3:55 PM, Joseph Jevnik <joejev at 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.
>
>
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