On Mon, Mar 24, 2014 at 8:33 PM, Nathaniel Smith n...@pobox.com wrote:
On Mon, Mar 24, 2014 at 11:58 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Mon, Mar 24, 2014 at 5:56 PM, Nathaniel Smith n...@pobox.com wrote:
On Sat, Mar 22, 2014 at 6:13 PM, Nathaniel Smith n...@pobox.com
On 3/25/2014 5:13 PM, Colin J. Williams wrote:
avoid the use of an additional operator which would only be used with numpy.
http://legacy.python.org/dev/peps/pep-0465/#but-isn-t-matrix-multiplication-a-pretty-niche-requirement
Alan Isaac
___
Hi Carl,
I installed Python 2.7.6 64 bits on a windows server instance from
rackspace cloud and then ran get-pip.py and then could successfully
install the numpy and scipy wheel packages from your google drive
folder. I tested dot products and scipy.linalg.svd and they work as
expected.
Then I
On Tue, Mar 25, 2014 at 9:47 PM, Matthew Brett matthew.br...@gmail.comwrote:
Hi,
On Tue, Mar 25, 2014 at 4:38 PM, Julian Taylor
jtaylor.deb...@googlemail.com wrote:
Hello,
I'm happy to announce the of Numpy 1.8.1.
This is a bugfix only release supporting Python 2.6 - 2.7 and 3.2 -
On 26.03.2014 16:27, Olivier Grisel wrote:
Hi Carl,
I installed Python 2.7.6 64 bits on a windows server instance from
rackspace cloud and then ran get-pip.py and then could successfully
install the numpy and scipy wheel packages from your google drive
folder. I tested dot products and
I am working on solving a recent recreational mathematical problem on
Project Euler http://projecteuler.net . I have a solution, which works
fine for small N up to 10^5 but it takes too long to compute for the actual
problem, where N is of the order 2*10^7. The problem is nested loops, and I
am
On Thu, Mar 27, 2014 at 1:18 AM, Slaunger slaun...@gmail.com wrote:
I am working on solving a recent recreational mathematical problem on
Project Euler http://projecteuler.net . I have a solution, which works
fine for small N up to 10^5 but it takes too long to compute for the actual
On Wed, Mar 26, 2014 at 3:48 PM, Slaunger slaun...@gmail.com wrote:
I am working on solving a recent recreational mathematical problem on
Project Euler http://projecteuler.net . I have a solution, which works
fine for small N up to 10^5 but it takes too long to compute for the actual
problem,
Jaidev Deshpande wrote
Can you provide a link to the problem itself?
--
JD
I'd rather not state the problem number since it should not be so easy to
search for it and find this thread, but I can state that at the the time
being, it is the problem with the highest problem number (released
jseabold wrote
IIUC,
[~/]
[1]: np.logical_and([True, False, True], [False, False, True])
[1]: array([False, False, True], dtype=bool)
You can avoid looping over k since they're all the same length
[~/]
[3]: np.logical_and([[True, False],[False, True],[False, True]],
[[False, False],
On Wed, Mar 26, 2014 at 7:34 PM, Julian Taylor
jtaylor.deb...@googlemail.com wrote:
as for using openblas by default in binary builds, no.
pthread openblas build is now fork safe which is great but it is still
not reliable enough for a default.
E.g. the current latest release 0.2.8 still has
On Wed, Mar 26, 2014 at 1:28 PM, Slaunger slaun...@gmail.com wrote:
See if you can make sense of the following. It is a little cryptic, but it
works:
f_change = np.array([2, 3, 39, 41, 58, 59, 65, 66, 93, 102, 145])
g_change = np.array([2, 94, 101, 146, 149])
N = 150
if len(f_change) % 2 :
On 26.03.2014 21:41, Nathaniel Smith wrote:
On Wed, Mar 26, 2014 at 7:34 PM, Julian Taylor
jtaylor.deb...@googlemail.com wrote:
as for using openblas by default in binary builds, no.
pthread openblas build is now fork safe which is great but it is still
not reliable enough for a default.
On Wed, Mar 26, 2014 at 4:28 PM, Slaunger slaun...@gmail.com wrote:
jseabold wrote
IIUC,
[~/]
[1]: np.logical_and([True, False, True], [False, False, True])
[1]: array([False, False, True], dtype=bool)
You can avoid looping over k since they're all the same length
[~/]
[3]:
My understanding of Carl's effort is that the long term goal is to
have official windows whl packages for both numpy and scipy published
on PyPI with a builtin BLAS / LAPACK implementation so that users can
do `pip install scipy` under windows and get something that just works
without have to
Jaime Fernández del Río wrote
On Wed, Mar 26, 2014 at 1:28 PM, Slaunger lt;
Slaunger@
gt; wrote:
See if you can make sense of the following. It is a little cryptic, but it
works:
f_change = np.array([2, 3, 39, 41, 58, 59, 65, 66, 93, 102, 145])
g_change = np.array([2, 94, 101, 146,
On 26.03.2014 22:17, Olivier Grisel wrote:
The problem with ATLAS is that you need to select the number of thread
at build time AFAIK. But we could set it to a reasonable default (e.g.
4 threads) for the default windows package.
You have to set the number of threads at build time with
jseabold wrote
Well, yes, if you work with the pure f_k and g_k that is true, but this
two-dimensional array will have 4*10^14 elements and will exhaust my
memory.
That is why I have found a more efficient method for finding only the
much
fewer changes_at elements for each k, and these
2014-03-26 22:31 GMT+01:00 Julian Taylor jtaylor.deb...@googlemail.com:
On 26.03.2014 22:17, Olivier Grisel wrote:
The problem with ATLAS is that you need to select the number of thread
at build time AFAIK. But we could set it to a reasonable default (e.g.
4 threads) for the default windows
On Wed, Mar 26, 2014 at 2:23 PM, Slaunger slaun...@gmail.com wrote:
Jaime Fernández del Río wrote
You saved my evening! Actually, my head has been spinning about this
problem
the last three evenings without having been able to nail it down.
I had to quit Project Euler about 5 years ago
Without looking ahead, here is what I came up with; but I see more elegant
solutions have been found already.
import numpy as np
def as_dense(f, length):
i = np.zeros(length+1, np.int)
i[f[0]] = 1
i[f[1]] = -1
return np.cumsum(i)[:-1]
def as_sparse(d):
diff =
On Wed, Mar 26, 2014 at 2:23 PM, Slaunger slaun...@gmail.com wrote:
Only I did not know about the append and insert methods. Very, very nice!
(I
only knew concatenate, which would be clumsy for just appending one
element),
Sorry -- I dont have the time to actually figure out what you are
What is the status of:
https://github.com/numpy/numpy/blob/master/doc/neps/missing-data.rst
and of missing data in Numpy, more generally?
Is np.ma.array still the state-of-the-art way to handle missing data? Or
has something better and more comprehensive been put together?
On Wed, Mar 26, 2014 at 7:22 PM, T J tjhn...@gmail.com wrote:
What is the status of:
https://github.com/numpy/numpy/blob/master/doc/neps/missing-data.rst
For what it's worth this NEP was written in 2011 by mwiebe who made
258 numpy commits in 2011, 1 in 2012, and 3 in 2014. According to
Hi,
Can I check what is stopping us building official numpy binary wheels
for Windows using the Intel Math Kernel Library?
* We'd need developer licenses, but those sound like they would be
easy to come by
* We'd have to add something to the license for the wheel on the lines
of the Canopy
Hi,
On Wed, Mar 26, 2014 at 4:48 PM, Matthew Brett matthew.br...@gmail.com wrote:
Hi,
Can I check what is stopping us building official numpy binary wheels
for Windows using the Intel Math Kernel Library?
* We'd need developer licenses, but those sound like they would be
easy to come by
*
On Wed, Mar 26, 2014 at 5:43 PM, alex argri...@ncsu.edu wrote:
On Wed, Mar 26, 2014 at 7:22 PM, T J tjhn...@gmail.com wrote:
What is the status of:
https://github.com/numpy/numpy/blob/master/doc/neps/missing-data.rst
For what it's worth this NEP was written in 2011 by mwiebe who made
Hi,
On Wed, Mar 26, 2014 at 3:02 PM, Chris Barker chris.bar...@noaa.gov wrote:
On Wed, Mar 26, 2014 at 8:56 AM, Charles R Harris
charlesr.har...@gmail.com wrote:
5 seconds waiting on a home internet connection and a numpy install
Nice.
That's pretty neat. Now if we can get the
I've often wondered the particulars of the MKL; I have licensed via
Enthought and distributed compiled works to client(s), and often use
C. Gohkle's distros myself.
- Ray
At 05:29 PM 3/26/2014, you wrote:
Hi,
On Wed, Mar 26, 2014 at 4:48 PM, Matthew Brett
matthew.br...@gmail.com wrote:
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