On Jan 12, 1:36 am, Timothy Hochberg [EMAIL PROTECTED] wrote:
I believe that you need to look at __array_finalize__ and __array_priority__
(and there may be one other thing as well, I can't remember; it's late).
Search for __array_finalize__ and that will probably help get you started.
Well
Basilisk96 wrote:
On Jan 12, 1:36 am, Timothy Hochberg [EMAIL PROTECTED] wrote:
I believe that you need to look at __array_finalize__ and __array_priority__
(and there may be one other thing as well, I can't remember; it's late).
Search for __array_finalize__ and that will probably help get
Thanks Stefan and Colin,
The subclass documentation made this a little clearer now. Instead of
using a super() call in __new__, I now do this:
#construct a matrix based on the input
ret = _N.matrix(data, dtype=dtype)
#promote it to Vector
ret = ret.view(cls)
The second statement
On Jan 11, 2008, Colin J. Williams wrote:
You make a good case that it's good not
to need to ponder what sort of
vector you are dealing with.
My guess is that the answer to your
question is no but I would need to
play with your code to see that. My
feeling is that, at the bottom of
the
On Jan 11, 2008 9:59 PM, Basilisk96 [EMAIL PROTECTED] wrote:
On Jan 11, 2008, Colin J. Williams wrote:
You make a good case that it's good not
to need to ponder what sort of
vector you are dealing with.
My guess is that the answer to your
question is no but I would need to
play
Basilisk96 wrote:
Hello folks,
In the course of a project that involved heavy use of geometry and
linear algebra, I found it useful to create a Vector subclass of
numpy.matrix (represented as a column vector in my case).
Why not consider a matrix with a shape
of (1, n) as a row vector and
Yes, that certainly meets the need.
In previous applications, I never thought to use Vector because it was
sufficient for me to adopt the convention of a column vector to
represent XY points. That way, I could do things like c * R * u + t.
Isn't that simple? Not only is it easy on the eyes, but
