[SciPy-user] array vs matrix, converting code from matlab
wbaxter at gmail.com
Fri Apr 21 00:03:42 CDT 2006
On 4/21/06, David Cournapeau <david at ar.media.kyoto-u.ac.jp> wrote:
> Robert Kern wrote:
> > David Cournapeau wrote:
> This looks exactly like what I am looking for. My problem for my
> function is the following (pseudo code):
> foo(x, mu, va):
> if mu and va scalars:
> call scalar_implementation
> return result
> if mu and va rank 1:
> call scalar implementation on each element
> if mu rank 1 and va rank 2:
> call matrix implementation
To handle the first two cases (scalar, and call scalar on every element),
you should be able to use 'numpy.frompyfunc' to create a version of your
scalar function that automatically works that way.
uf = numpy.frompyfunc(plusone,1,1)
array([2, 3, 4], dtype=object)
and assumed all arguments are always rank 2, even if they are "scalar"
> (size 1), a bit like in numpy.linalg, if I understood
> correctly (calling numpy.linalg.inv(1) does not work). It looks like
> those atleast* methods should do the work.
> Actually, my problem is pretty similar to implementing wrapper around
> numpy.linalg.inv which works in scalar case and rank 1 (assuming rank 1
> means diagonal) cases. Are those atleast* functions expensive ? For
> small size arrays, I don't care too much, but in the case of a big array
> of rank 1 converted to a rank 2 array, does those function need to copy
> the data ?
Looks like atleast_1d doesn't copy the data, so yes, it should be fast.
a = numpy.array([1,2,3,4])
b = numpy.atleast_2d(a)
array([1, 2, 3, 4])
array([[1, 2, 3, 4]])
array([1, 0, 3, 4])
array([[1, 0, 3, 4]])
William V. Baxter III
Kono Dens Building Rm 302
1-8-8 Wakabayashi Setagaya-ku
Tokyo, Japan 154-0023
+81 (3) 3422-3380
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the SciPy-user