On 11/23/12 8:00 PM, Chris Barker - NOAA Federal wrote:
On Thu, Nov 22, 2012 at 6:20 AM, Francesc Alted franc...@continuum.io wrote:
As Nathaniel said, there is not a difference in terms of *what* is
computed. However, the methods that you suggested actually differ on
*how* they are computed,
Hi,
I try to update values in a single field of numpy record array based on
a condition defined in another array. I found that that the result
depends on the order in which I apply the boolean indices/field names.
For example:
cond = np.zeros(5, dtype=np.bool)
cond[2:] = True
X =
On 11/28/12 1:47 PM, Bartosz wrote:
Hi,
I try to update values in a single field of numpy record array based on
a condition defined in another array. I found that that the result
depends on the order in which I apply the boolean indices/field names.
For example:
cond = np.zeros(5,
Thanks for answer, Francesc.
I understand now that fancy indexing returns a copy of a recarray. Is
it also true for standard ndarrays? If so, I do not understand why
X['a'][cond]=-1 should work.
Cheers,
Bartosz
On Wed 28 Nov 2012 03:05:37 PM CET, Francesc Alted wrote:
On 11/28/12 1:47 PM,
Hey Bartosz,
On 11/28/12 3:26 PM, Bartosz wrote:
Thanks for answer, Francesc.
I understand now that fancy indexing returns a copy of a recarray. Is
it also true for standard ndarrays? If so, I do not understand why
X['a'][cond]=-1 should work.
Yes, that's a good question. No, in this case
I got it. Thanks! Now I see why this is non-trivial to fix it.
However, it might be also a source of very-hard-to-find bugs. It might
be worth discussing this non-intuitive example in the documentation.
Cheers,
Bartosz
Thanks for answer, Francesc.
I understand now that fancy indexing
On Wed, 2012-11-28 at 11:11 -0500, Skipper Seabold wrote:
On Tue, Nov 27, 2012 at 11:16 AM, Sebastian Berg
sebast...@sipsolutions.net wrote:
On Mon, 2012-11-26 at 13:54 -0500, Skipper Seabold wrote:
I discovered this because scipy.optimize.fmin_powell appears
to
On Wed, Nov 28, 2012 at 12:31 PM, Sebastian Berg sebast...@sipsolutions.net
wrote:
Maybe a strict matrix product would make sense too, but the dot function
behavior cannot be changed in any case, so its pointless to argue about
it. Just make sure your arrays are 2-d (or matrices) if you want
I have tried to install the 1.6.2 win32 superpack on my Windows 7 Pro (64
bit) system which has ActiveState ActivePython 2.7.2.5 (64 bit) installed.
However, I get an error that Python 2.7 is required and can't be found in
the Registry.
I only need numpy as it is a pre-requisite for another
On Wed, Nov 28, 2012 at 10:16 PM, Jim O'Brien j...@jgssebl.net wrote:
**
I have tried to install the 1.6.2 win32 superpack on my Windows 7 Pro (64
bit) system which has ActiveState ActivePython 2.7.2.5 (64 bit) installed.
However, I get an error that Python 2.7 is required and can't be found
Ralf,
Thanks.
I downloaded the 1.6.2 release for win64 and tried to install.
I am still being told that it requires 2.7 and that was not found in the
registry.
I know I have Python 2.7 as other packages find it just fine.
Is there a way to get around the check that is done by the
Forget the last post.
I was one the wrong machine!
The 64 bit release installed fine.
Regards,
Jim
_
From: numpy-discussion-boun...@scipy.org
[mailto:numpy-discussion-boun...@scipy.org] On Behalf Of Ralf Gommers
Sent: Wed, Nov 28, 2012 2:32 PM
To: Discussion of Numerical Python
I have a file with thousands of lines like this:
Signal was returned in 204 microseconds
Signal was returned in 184 microseconds
Signal was returned in 199 microseconds
Signal was returned in 4274 microseconds
Signal was returned in 202 microseconds
Signal was returned in 189 microseconds
I try
On 29.11.2012, at 1:21AM, Robert Love wrote:
I have a file with thousands of lines like this:
Signal was returned in 204 microseconds
Signal was returned in 184 microseconds
Signal was returned in 199 microseconds
Signal was returned in 4274 microseconds
Signal was returned in 202
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