On Thu, Jun 7, 2012 at 5:24 PM, Andreas Hilboll li...@hilboll.de wrote:
Hi,
I just noticed that there's a PPA for NumPy/SciPy on Launchpad:
https://launchpad.net/~scipy/+archive/ppa
However, it's painfully outdated. Does anyone know of its status? Is it
'official'? Are there any plans
Hi all,
I am reading a datagram which contains within it a type. The type
dictates the structure of the datagram. I want to put this into a numpy
structure, one of which is:
np.zeros(1,dtype=('2uint8,uint8,uint8,uint32,8uint8,504uint8,8uint8,504uint8'))
As I don't know what I'm getting until
Robert Kern wrote:
On Thu, Jun 7, 2012 at 7:55 PM, Neal Becker ndbeck...@gmail.com wrote:
In [3]: u = np.arange(10)
In [4]: u
Out[4]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
In [5]: u[-2:]
Out[5]: array([8, 9])
In [6]: u[-2:2]
Out[6]: array([], dtype=int64)
I would argue for
On 6/8/2012 9:14 AM, Neal Becker wrote:
The fact that this proposed numpy behavior would not match python list
behavior
holds little weight for me.
It is not just Python behavior for lists.
It is the semantics for all sequence types.
Breaking this would be appalling.
Alan Isaac
On 06/07/2012 12:55 PM, Neal Becker wrote:
In [3]: u = np.arange(10)
In [4]: u
Out[4]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
In [5]: u[-2:]
Out[5]: array([8, 9])
In [6]: u[-2:2]
Out[6]: array([], dtype=int64)
I would argue for consistency it would be desirable for this to return
[8,
On 08/06/12 14:14, Neal Becker wrote:
The fact that this proposed numpy behavior would not match python list
behavior
holds little weight for me. I would still favor this change, unless it added
significant overhead. My opinion, of course.
It holds enormous weight for me. My opinion is
On 08/06/12 14:14, Neal Becker wrote:
The fact that this proposed numpy behavior would not match python list
behavior
holds little weight for me. I would still favor this change, unless it added
significant overhead. My opinion, of course.
As a Joe User, I think using the [-2:2] syntax
On Fri, Jun 8, 2012 at 11:31 AM, Bob Cowdery b...@bobcowdery.plus.com wrote:
Hi all,
I am reading a datagram which contains within it a type. The type
dictates the structure of the datagram. I want to put this into a numpy
structure, one of which is:
Hi,
While reviewing the Theano op that wrap numpy.fill_diagonal, we found
an unexpected behavior of it:
# as expected for square matrix
a=numpy.zeros((5,5))
numpy.fill_diagonal(a, 10)
print a
# as expected long rectangular matrix
a=numpy.zeros((3,5))
numpy.fill_diagonal(a, 10)
print a
[[
