Dag Sverre Seljebotn wrote: > Robert Bradshaw wrote: >> Currently the way to do numerics in Sage is to import scipy and numpy >> (because they really have created a good stack), and turn off >> preparsing (because those type issues get really annoying). At this >> point, it may become unclear why one is using Sage instead of the >> SciPy stack itself. The fact that most Sage examples and discussion >> revolves around "esoteric" things like Rings and Categories and >> Modular Forms just promotes this view. >> >> Dag, if you put together a list of why Sage isn't good enough for >> you, that would be very interesting. I wonder if many things on that >> list would be desirable for non-numerical use as well. > > It's dangerous to ask this of me -- it's roughly in order of priority so > feel free to stop when you got what you wanted :- ) > > (Any of this might have been changed recently, I haven't upgraded for a > while to be honest). > > The minimum to make me start using Sage: > > 1) I must be able to use NumPy together with the preparser (it's just > too much hassle to turn it on and off, and it kind of defeats the > purpose.). That is, with the preparser on, I should be able to run most > NumPy-using code without changes. (I don't think is a difficult to > achieve, but certainly didn't look at it in detail.) > > One example is: > > sage: np.arange(4) > array([0, 1, 2, 3], dtype=object) > > This is not what I want, and I can never remember to pass in > dtype=np.int64. I don't think it makes sense either -- passing in > np.arange(int(4)) gives the desired behaviour, and a Python int and a > Sage Integer are equally far from an np.int64 anyway. > > > I think that's all, actually -- this would stop Sage from getting in my > way. But this is on my strongly wanted-list too: > > 2) sin, sqrt etc. should understand, act on, and return NumPy arrays > (probably by calling corresponding functions in numpy) > > 3) It would be nice with better plotting support for NumPy arrays, so > that I don't have to use pylab directly, but haven't given any thought > to what I want here. > > 4) Not sure if this can be done in a reasonable way, but: I'd like to > not have to use Python ints/floats at all, it's just nicer to know that > I have a Sage element. So ideally > > sage: a=np.arange(5, dtype=np.uint8) > sage: a[2]/a[4] > 1/2 > > 5) #4571 (cimport numpy in notebook), obviously. > > 6) One of the things drawing me towards using Sage is the "attach" > feature. However, it is too limited for my use (and so makes me > frustrated, so that it is better not to use it at all). Something being > a combination for an "attach" and a pure Python "import" is what I want; > that is, "import" with automatically calling "reload" on change. (The > recent work in pyximport may come into play here; if the sources are in > Cython.) > > 7) Make NumPy dtypes for all Sage rings. I think that NumPy is quite > extensible in this respect and that it shouldn't be too difficult to > have arrays over Sage rings. > > sage: a = np.arange(10, dtype=ZZ.numpy()) # or dtype=ZZ if possible > > 8) For syntax consistency etc., a simple function to create SIMD arrays. > > sage: x = array(RDF, 20, 20, 20) # 20x20x20 array > > Whether a wrapper is returned or a raw NumPy array, it is important to > have elementwise operators and allow slices. Whether a slice should be a > view, a copy, or sometimes a view and sometimes a copy (like in NumPy > :-( ) seems to be debatable here though. > > 9) Long-term, linear algebra in a data processing context Done Right > (According To Myself). I.e.: > > sage: parent(img) > Array of shape (100, 100, 3) over uint8 > sage: parent(weights) > Array of shape (100, 100, 3) over Real Double Field > > Now, componentwise multiply the two the proper way; treating img as a > vector and the reweighting as an operator matrix: > > sage: op = diagonal_matrix(weights); parent(op) > Diagonal MatrixSpace of (100, 100, 3) by (100, 100, 3) matrices > over Real Double Field > > Access as six-dimensional array; first three indices is the row, the > next three indices the column: > > sage: op[1,2,3,4,5,6] > 0.5
Sorry, this would always be 0. op[1,2,3,1,2,3] could be 0.5 though. > > sage: result = op * vector(img) > sage: result > Vector space of dimension (100, 100, 3) over uint8 > > (Here, * is the linear algebra *, while result contains the elementwise > product of weights and img). > -- Dag Sverre --~--~---------~--~----~------------~-------~--~----~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~----------~----~----~----~------~----~------~--~---
