On Sat, Sep 27, 2008 at 10:01 PM, Nathan Bell <[EMAIL PROTECTED]> wrote:
> On Sun, Sep 28, 2008 at 12:34 AM, Geoffrey Irving <[EMAIL PROTECTED]> wrote:
>>
>> Is there an efficient way to implement a nonuniform gather operation
>> in numpy? Specifically, I want to do something like
>>
>> n,m = 100,
All,
In a recent post, I was pointing to a bug in numpy.ma. I think I can
understand what happens, but I'd need a couple of clarifications.
When multiplying a 0d ndarrray or one of its subclass by a numpy scalar, we
end up with a numpy scalar cast to the highest type, right ?
So, np.float64(1)*n
Hello,
I have two lists of numpy matrices : LM = [M_i, i=1..N] and LN = [N_i, i =1..N]
and I would like to compute the list of the products : LP = [M_i * N_i,
i=1..N].
I can do :
P=[]
for i in range(N) :
P.append(LM[i]*LN[i])
But this is not vectorized. Is there a faster solution ?
Can
Hi Rob & All,
On Sat, Sep 27, 2008 at 4:05 PM, Rob Clewley wrote:
> Hi Andrea,
>
>>I was wondering if someone had any suggestions/references/snippets
>> of code on how to find the minimum distance between 2 paths in 3D.
>> Basically, for every path, I have I series of points (x, y, z) and I
>>
