Be sure to file a ticket...
-Travis
On May 30, 2012, at 9:05 PM, chris farrow wrote:
Hi all,
I encountered an odd bug today that I wanted to bring to everyone's
attention. First the code:
import numpy as np
shape = (8, 8)
dtype = np.dtype(np.uint8)
image = np.random.randint(0,
Hey Val,
Well it doesn't matter what I do, but specifically I do factor =
sum(data_array[start_point:start_point+length_data]) and then
data[array[start_point:start_point+length_data]) /= factor. and that for every
star_point and length data.
How to do this fast?
Cheers
Wolfgang
On
Hi Wolfgang,
I thought maybe there is a trick for your specific operation.
Your array stacking is a simple case of the group-by operation and
normalization is aggregation followed by update.
I believe group-by and aggregation are on the NumPy todo-list.
You may have to write a small extension
Will copying slices always work correctly w/r to aliasing?
That is, will:
u[a:b] = u[c:d]
always work (assuming the ranges of a:b, d:d are equal, or course)
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On Thu, May 31, 2012 at 7:30 AM, Neal Becker ndbeck...@gmail.com wrote:
That is, will:
u[a:b] = u[c:d]
always work (assuming the ranges of a:b, d:d are equal, or course)
It works most of the time. This thread shows you how to find an
example where it does not work:
