Re: [Numpy-discussion] Resolving the associativity/precedence debate for @

On Mon, Mar 24, 2014 at 8:33 PM, Nathaniel Smith wrote: > On Mon, Mar 24, 2014 at 11:58 PM, Charles R Harris > wrote: >> On Mon, Mar 24, 2014 at 5:56 PM, Nathaniel Smith wrote: >>> >>> On Sat, Mar 22, 2014 at 6:13 PM, Nathaniel Smith wrote: >>> > After 88 emails we don't have a conclusion in th

Re: [Numpy-discussion] NumPy-Discussion Digest, Vol 90, Issue 83

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 ___ NumPy-Di

Re: [Numpy-discussion] Default builds of OpenBLAS development branch are now fork safe

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 un

Re: [Numpy-discussion] ANN: NumPy 1.8.1 release

On Tue, Mar 25, 2014 at 9:47 PM, Matthew Brett wrote: > Hi, > > On Tue, Mar 25, 2014 at 4:38 PM, Julian Taylor > 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 - 3.4. > > > > More than 48 issues have been f

Re: [Numpy-discussion] Default builds of OpenBLAS development branch are now fork safe

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

[Numpy-discussion] Is there a pure numpy recipe for this?

I am working on solving a recent recreational mathematical problem on Project Euler . 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

Re: [Numpy-discussion] Is there a pure numpy recipe for this?

On Thu, Mar 27, 2014 at 1:18 AM, Slaunger wrote: > I am working on solving a recent recreational mathematical problem on > Project Euler . 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

Re: [Numpy-discussion] Is there a pure numpy recipe for this?

On Wed, Mar 26, 2014 at 3:48 PM, Slaunger wrote: > I am working on solving a recent recreational mathematical problem on > Project Euler . 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 o

Re: [Numpy-discussion] Is there a pure numpy recipe for this?

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

Re: [Numpy-discussion] Is there a pure numpy recipe for this?

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]], > [[Fals

Re: [Numpy-discussion] Default builds of OpenBLAS development branch are now fork safe

On Wed, Mar 26, 2014 at 7:34 PM, Julian Taylor 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 one crash bug on > dgemv[1]

Re: [Numpy-discussion] Is there a pure numpy recipe for this?

On Wed, Mar 26, 2014 at 1:28 PM, Slaunger 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 : f_change = np.a

Re: [Numpy-discussion] Default builds of OpenBLAS development branch are now fork safe

On 26.03.2014 21:41, Nathaniel Smith wrote: > On Wed, Mar 26, 2014 at 7:34 PM, Julian Taylor > 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

Re: [Numpy-discussion] Is there a pure numpy recipe for this?

On Wed, Mar 26, 2014 at 4:28 PM, Slaunger 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]: np.logica

Re: [Numpy-discussion] Default builds of OpenBLAS development branch are now fork safe

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 insta

Re: [Numpy-discussion] Is there a pure numpy recipe for this?

Jaime Fernández del Río wrote > On Wed, Mar 26, 2014 at 1:28 PM, Slaunger < > Slaunger@ > > 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,

Re: [Numpy-discussion] Default builds of OpenBLAS development branch are now fork safe

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

Re: [Numpy-discussion] Is there a pure numpy recipe for this?

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

Re: [Numpy-discussion] Default builds of OpenBLAS development branch are now fork safe

2014-03-26 22:31 GMT+01:00 Julian Taylor : > 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 ha

Re: [Numpy-discussion] Is there a pure numpy recipe for this?

On Wed, Mar 26, 2014 at 2:23 PM, Slaunger 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 because it was t

Re: [Numpy-discussion] ANN: NumPy 1.8.1 release

On Wed, Mar 26, 2014 at 8:56 AM, Charles R Harris wrote: > > 5 seconds waiting on a home internet connection and a numpy install >> Nice. >> >> > That's pretty neat. Now if we can get the windows versions to be as easy. > > Indeed -- where are we on that? Wasn't there more or less a consensu

Re: [Numpy-discussion] Is there a pure numpy recipe for this?

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 = np.diff(np.con

Re: [Numpy-discussion] Is there a pure numpy recipe for this?

On Wed, Mar 26, 2014 at 2:23 PM, Slaunger 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 doing, but::: no

[Numpy-discussion] Missing Data

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? _

Re: [Numpy-discussion] Missing Data

On Wed, Mar 26, 2014 at 7:22 PM, T J 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 github, in the la

[Numpy-discussion] Windows wheels using MKL?

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 licens

Re: [Numpy-discussion] Windows wheels using MKL?

Hi, On Wed, Mar 26, 2014 at 4:48 PM, Matthew Brett 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 > * We'd have to ad

Re: [Numpy-discussion] Missing Data

On Wed, Mar 26, 2014 at 5:43 PM, alex wrote: > On Wed, Mar 26, 2014 at 7:22 PM, T J 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 201

Re: [Numpy-discussion] ANN: NumPy 1.8.1 release

Hi, On Wed, Mar 26, 2014 at 3:02 PM, Chris Barker wrote: > On Wed, Mar 26, 2014 at 8:56 AM, Charles R Harris > wrote: >> >> >>> 5 seconds waiting on a home internet connection and a numpy install >>> Nice. >>> >> >> That's pretty neat. Now if we can get the windows versions to be as easy. >>

Re: [Numpy-discussion] Windows wheels using MKL?

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 wrote: > Hi, > > Can I check what