):
max_B[i,j] = B[tmax_idx[i,j],i,j]
As you know, this is reasonably fast for modest-sized arrays,
but is far more expensive for large arrays.
Thanks in advance for your help.
Sincerely,
Daran Rife
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Hi Robert,
This solution works beautifully! Thanks for sending it
along. I need to learn and understand more about fancy
indexing for multi-dimensional arrays, especially your
clever trick of np.newaxis for broadcasting.
Daran
--
Hello list,
This didn't seem to get through last time round,
How about a solution inspired by recipe 18.1 in the Python Cookbook,
2nd Ed:
import numpy as np
a = [(x0,y0), (x1,y1), ...]
l = a.tolist()
l.sort()
unique = [x for i, x in enumerate(l) if not i or x != b[l-1]]
a_unique = np.asarray(unique)
Performance of this approach should be highly scalable.
15, 2008, at 5:24 PM, Daran Rife wrote:
How about a solution inspired by recipe 18.1 in the Python Cookbook,
2nd Ed:
import numpy as np
a = [(x0,y0), (x1,y1), ...]
l = a.tolist()
l.sort()
unique = [x for i, x in enumerate(l) if not i or x != b[l-1]]
a_unique = np.asarray(unique
Ordinarily I avoid becoming involved in such acrimony, but I take this
single
opportunity to state clearly that I find Linda Seltzer's behavior utterly
rude
and childish.
Having been a member of this mailing list for over 6 years, I take
exception
to the pointless ranting and vitriolic comments
Yep, I was referring to: http://pysclint.sourceforge.net/pycdf/
Regarding the issue of deciding which netCDF interface to adopt
as the standard, although it is unlikely we'll ever get consensus
on this, I have tried several of the netCDF interfaces, including
the one in Scientific, and have found
