Keith Goodman wrote:
Here's one way:
a.flat[i + a.shape[1] * np.arange(a.shape[0])]
array([0, 3, 5, 6, 9])
I'm afraid I made my example a little too simple. In retrospect, what I really
want is to be able to use a 2D index array i, like this:
a = np.array([[ 0, 1, 2, 3],
On Fri, Aug 6, 2010 at 6:01 AM, Martin Spacek nu...@mspacek.mm.st wrote:
Keith Goodman wrote:
Here's one way:
a.flat[i + a.shape[1] * np.arange(a.shape[0])]
array([0, 3, 5, 6, 9])
I'm afraid I made my example a little too simple. In retrospect, what I really
want is to be able
On Fri, Aug 6, 2010 at 3:01 AM, Martin Spacek nu...@mspacek.mm.st wrote:
Keith Goodman wrote:
Here's one way:
a.flat[i + a.shape[1] * np.arange(a.shape[0])]
array([0, 3, 5, 6, 9])
I'm afraid I made my example a little too simple. In retrospect, what I really
want is to be able
On Thu, Aug 5, 2010 at 2:55 PM, josef.p...@gmail.com wrote:
On Thu, Aug 5, 2010 at 3:43 PM, Gökhan Sever gokhanse...@gmail.com
wrote:
Hello,
There is a nice e-mailing trend tool for Gmail users
at http://code.google.com/p/mail-trends/
It is a command line tool producing an html output
Hi,
I found what looks like a bug in histogram, when the option normed=True
is used together with non-uniform bins.
Consider this example:
import numpy as np
data = np.array([1, 2, 3, 4])
bins = np.array([.5, 1.5, 4.5])
bin_widths = np.diff(bins)
(counts, dummy) = np.histogram(data, bins)
On Fri, Aug 6, 2010 at 11:13 AM, Vincent Davis vinc...@vincentdavis.net wrote:
On Thu, Aug 5, 2010 at 2:55 PM, josef.p...@gmail.com wrote:
On Thu, Aug 5, 2010 at 3:43 PM, Gökhan Sever gokhanse...@gmail.com wrote:
Hello,
There is a nice e-mailing trend tool for Gmail users
at
On Fri, Aug 6, 2010 at 11:46 AM, Nils Becker n.bec...@amolf.nl wrote:
Hi,
I found what looks like a bug in histogram, when the option normed=True
is used together with non-uniform bins.
Consider this example:
import numpy as np
data = np.array([1, 2, 3, 4])
bins = np.array([.5, 1.5,
2010/8/5, David Warde-Farley d...@cs.toronto.edu:
I've been having a similar problem compiling NumPy with MKL on a cluster
with a site-wide license. Dag's site.cfg fails to config if I use 'iomp5' in
it, since (at least with this version, 11.1) libiomp5 is located in
Hi,
@ http://new.scipy.org/download.html numpy and scipy links for Fedora is
broken.
Could you update the links with these?
https://admin.fedoraproject.org/pkgdb/acls/name/numpy
https://admin.fedoraproject.org/pkgdb/acls/name/numpy
https://admin.fedoraproject.org/pkgdb/acls/name/scipy
Thanks.
On Fri, Aug 6, 2010 at 10:16 AM, josef.p...@gmail.com wrote:
On Fri, Aug 6, 2010 at 11:13 AM, Vincent Davis vinc...@vincentdavis.net
wrote:
On Thu, Aug 5, 2010 at 2:55 PM, josef.p...@gmail.com wrote:
On Thu, Aug 5, 2010 at 3:43 PM, Gökhan Sever gokhanse...@gmail.com
wrote:
Hello,
Hi again,
first a correction: I posted
I believe np.histogram(data, bins, normed=True) effectively does :
np.histogram(data, bins, normed=False) / (bins[-1] - bins[0]).
However, it _should_ do
np.histogram(data, bins, normed=False) / bins_widths
but there is a normalization missing; it
On 2010-08-06 13:11, Martin Spacek wrote:
Josef, I'd forgotten you could use None to increase the dimensionality of an
array. Neat. And, somehow, it's almost twice as fast as the Cython version!:
timeit a[np.arange(a.shape[0])[:, None], i]
10 loops, best of 3: 5.76 us per loop
I just
On Fri, Aug 6, 2010 at 4:53 PM, Nils Becker n.bec...@amolf.nl wrote:
Hi again,
first a correction: I posted
I believe np.histogram(data, bins, normed=True) effectively does :
np.histogram(data, bins, normed=False) / (bins[-1] - bins[0]).
However, it _should_ do
np.histogram(data, bins,
On Jul 24, 2010, at 2:42 PM, Thomas Robitaille wrote:
Hi,
If I create a structured array with vector columns:
array = np.array(zip([[1,2],[1,2],[1,3]]),dtype=[('a',float,2)])
then examine the type of the column, I get:
array.dtype[0]
dtype(('float64',(2,)))
Then, if I try and
14 matches
Mail list logo
