On 2014/07/06, 11:43 AM, Nathaniel Smith wrote:
On Sun, Jul 6, 2014 at 9:35 PM, Daniel da Silva <var.mail.daniel@gmail.com> wrote:
The idea is that there be a short-hand for creating arrays as there is for matrices:
np.mat('.2 .7 .1; .3 .5 .2; .1 .1 .9')
It was suggested in GitHub issue #4817 in light that it would be beneficial to beginners and to presenters during demonstrations. In GitHub pull request #484, I implemented this as the np.arr function.
Does anyone have any feedback on the API details? Some examples from my implementation follow.
np.arr('3; 4; 5') array([[3], [4], [5]])
np.arr('3; 4; 5', dtype=float) array([[ 3.], [ 4.], [ 5.]])
np.arr('1 0 0; 0 1 0; 0 0 1') array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
np.arr('4, 5; 6, 7') array([[4, 5], [6, 7]])
It occurs to me that np.mat always returns a 2d matrix, but for arrays there are more options.
What should np.arr('1 2 3') return? a 1d array or a 2d row vector?
I would say 1d array. This is numpy, not numpy.matrix.
(Maybe np.arr('1 2 3;') should give the row-vector?)
Yes, it is reasonable that a semicolon should trigger 2d.
Should there be some way to write 3d or higher-d arrays?
No, there should not. This is for quick demos and that sort of thing. It is not a substitute for np.array(). (I'm not entirely convinced np.arr() is a good idea at all; but if it is, it must be kept simple.) A possible downside for beginners is that this might delay their understanding that the commas are needed for np.array([1, 2, 3]). Eric
-n