To make the last point more concrete the implementation could look
something like this (note that I didn't test it and that it still
takes some work):
def bmat(obj, ldict=None, gdict=None):
return matrix(stack(obj, ldict, gdict))
def stack(obj, ldict=None, gdict=None):
# the old bmat
Stefan van der Walt ste...@sun.ac.za writes:
On 2014-10-27 15:26:58, D. Michael McFarland dm...@dmmcf.net wrote:
What I would like to ask about is the situation this illustrates, where
both NumPy and SciPy provide similar functionality (sometimes identical,
to judge by the documentation). Is
Just to throw in my two cents here. I feel that sometimes, features are
tried out first elsewhere (possibly in scipy) and then brought down into
numpy after sufficient shakedown time. So, in some cases, I wonder if the
numpy version is actually more refined than the scipy version? Of course,
there
On Fri, Oct 31, 2014 at 11:07 AM, Benjamin Root ben.r...@ou.edu wrote:
Just to throw in my two cents here. I feel that sometimes, features are
tried out first elsewhere (possibly in scipy) and then brought down into
numpy after sufficient shakedown time. So, in some cases, I wonder if the
On Fri, Oct 31, 2014 at 3:07 PM, Benjamin Root ben.r...@ou.edu wrote:
Just to throw in my two cents here. I feel that sometimes, features are
tried out first elsewhere (possibly in scipy) and then brought down into
numpy after sufficient shakedown time. So, in some cases, I wonder if the
numpy
