Re: [Numpy-discussion] zoom FFT with numpy?

On 3/14/07, Ray Schumacher <[EMAIL PROTECTED]> wrote: On 3/14/07, "Charles R Harris" wrote: > Sounds like you want to save cpu cycles. > How much you can save will depend > on the ratio of the bandwidth to the nyquist. The desired band is rather narrow, as the goal is to determine the f of a p

Re: [Numpy-discussion] zoom FFT with numpy?

On 15/03/07, Ray Schumacher <[EMAIL PROTECTED]> wrote: > The desired band is rather narrow, as the goal is to determine the f of a > peak that always occurs in a narrow band of about 1kHz around 7kHz > > >2) frequency shift, {low pass}, and downsample By this I would take it to mean, multiply by

Re: [Numpy-discussion] in place random generation

Charles R Harris wrote: > > > On 3/14/07, *Daniel Mahler* <[EMAIL PROTECTED] > > wrote: > > On 3/12/07, Travis Oliphant <[EMAIL PROTECTED] > > wrote: > > > I'm not convinced that the broadcasting is causing the slow-downs. > > Cur

Re: [Numpy-discussion] zoom FFT with numpy?

On 3/14/07, "Charles R Harris" wrote: > Sounds like you want to save cpu cycles. > How much you can save will depend > on the ratio of the bandwidth to the nyquist. The desired band is rather narrow, as the goal is to determine the f of a peak that always occurs in a narrow band of about 1kHz

Re: [Numpy-discussion] zoom FFT with numpy?

On 3/14/07, Ray S <[EMAIL PROTECTED]> wrote: We'd like to do what most call a "zoom FFT"; we only are interested in the frequencies of say, 6kHZ to 9kHz with a given N, and so the computations from DC to 6kHz are wasted CPU time. Can this be done without additional numpy pre-filtering computatio

Re: [Numpy-discussion] in place random generation

On 3/14/07, Daniel Mahler <[EMAIL PROTECTED]> wrote: On 3/12/07, Travis Oliphant <[EMAIL PROTECTED]> wrote: > I'm not convinced that the broadcasting is causing the slow-downs. > Currently, the code has two path-ways. One gets called when the inputs > are scalars which is equivalent to the old

Re: [Numpy-discussion] Nonblocking Plots with Matplotlib

On 3/15/07, Bill Baxter <[EMAIL PROTECTED]> wrote: > Thanks, Sebastian. I'll take a look at Pyro. Hadn't heard of it. > I'm using just xmlrpclib with pickle right now. I took a look at Pyro -- it looks nice. The only thing I couldn't find, though, is how decouple the wx GUI on the server side fr

Re: [Numpy-discussion] in place random generation

On 3/12/07, Travis Oliphant <[EMAIL PROTECTED]> wrote: > I'm not convinced that the broadcasting is causing the slow-downs. > Currently, the code has two path-ways. One gets called when the inputs > are scalars which is equivalent to the old code and the second gets > called when broadcasting is

Re: [Numpy-discussion] Nonblocking Plots with Matplotlib

Thanks, Sebastian. I'll take a look at Pyro. Hadn't heard of it. I'm using just xmlrpclib with pickle right now. --bb On 3/15/07, Sebastian Haase <[EMAIL PROTECTED]> wrote: > Hey Bill, > what are you using to communicate with the server ? > May I recommend looking at Pyro ! > (Python remote obj

[Numpy-discussion] zoom FFT with numpy?

We'd like to do what most call a "zoom FFT"; we only are interested in the frequencies of say, 6kHZ to 9kHz with a given N, and so the computations from DC to 6kHz are wasted CPU time. Can this be done without additional numpy pre-filtering computations? If explicit filtering is needed to "base

Re: [Numpy-discussion] Which dtype are supported by numexpr ?

El dc 14 de 03 del 2007 a les 16:10 -0400, en/na David M. Cooke va escriure: > On Wed, 14 Mar 2007 13:02:10 +0100 > Francesc Altet <[EMAIL PROTECTED]> wrote: > > > The info above is somewhat inexact. I was talking about the enhanced > > numexpr version included in PyTables 2.0 (see [1]). The origi

[Numpy-discussion] numexpr efficency depends on the size of the computing kernel

Hi, Now that I'm commanding my old AMD Duron machine, I've made some benchmarks just to prove that the numexpr computing is not influenced by the size of the CPU cache, but I failed miserably (and Tim was right: there is a dependency of the numexpr efficency on CPU cache size). Provided that the

Re: [Numpy-discussion] Which dtype are supported by numexpr ?

On Wed, 14 Mar 2007 13:02:10 +0100 Francesc Altet <[EMAIL PROTECTED]> wrote: > The info above is somewhat inexact. I was talking about the enhanced > numexpr version included in PyTables 2.0 (see [1]). The original version of > numexpr (see [2]) doesn't have support for int64 on 32-bit platforms a

Re: [Numpy-discussion] import numpy segmentation fault

If I recall correctly, there's a bug in numpy 1.0.1 on Linux-x86-64 that causes this segfault. This is fixed in the latest SVN version of numpy, so if you can grab that, it should work. I can't find the trac ticket, but I ran into this some weeks ago. Zach On Mar 14, 2007, at 1:36 PM, Cory

Re: [Numpy-discussion] Matlab -> NumPy translation and indexing

On Wed, Mar 14, 2007 at 04:11:43PM +0100, Sturla Molden wrote: > On 3/14/2007 2:46 PM, Robert Cimrman wrote: > > a = [] > > while ... > > a.append( scalar ) > > a = array( a ) > While it may help, growing Python lists is also an O(N) process. > One can reduce the amount of allocations by preal

[Numpy-discussion] import numpy segmentation fault

Hi there, I have just installed numpy-1.0.1 from source, which seemed to go fine. However when I try to "import numpy" I get a segmentation fault. A have a 64 bit machine running RedHat Enterprise Linux and Python 2.34 Any clues greatly appreciated. Cheers, Cory. __

Re: [Numpy-discussion] arrayrange

I would use something like this: t = linspace(0, durSecs, durSecs*SRate) Do you know the 'Numpy Example List' http://www.scipy.org/Numpy_Example_List Regards Eike. PS: Ah, you did subscribe. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org

Re: [Numpy-discussion] Putting some random back into the top-level?

On 3/14/07, Timothy Hochberg <[EMAIL PROTECTED]> wrote: > > > On 3/14/07, Sebastian Haase <[EMAIL PROTECTED]> wrote: > > Hi, > > Please remind me what's wrong with pylab's > > rand and randn ! > > I just learned about their existence recently and thought > > they seem quite handy and should go dir

Re: [Numpy-discussion] Multiplying Each Column of a Matrix by a Vector (Element-Wise)

Alexander Michael wrote: > Is there a clever way to multiply each column of a matrix by a vector > *element-wise*? That is, the equivalent of (from some 1D v and 2D m): > > r = numpy.empty_like(m) > for i in range(m.shape[-1]): > r[...,i] = v*m[...,i] numpy.multiply(m, v[:,numpy.newaxis])

Re: [Numpy-discussion] arrayrange

Miguel Oliveira, Jr. wrote: > Hello, > > I've got a few codes that use "arrayrange" within numpy. It happens > that the new version of numpy apparently doesn't recognize > "arrayrange"... I've tried to change it to "arange", but it doesn't > work... So, for example, the code below used to cr

[Numpy-discussion] Multiplying Each Column of a Matrix by a Vector (Element-Wise)

Is there a clever way to multiply each column of a matrix by a vector *element-wise*? That is, the equivalent of (from some 1D v and 2D m): r = numpy.empty_like(m) for i in range(m.shape[-1]): r[...,i] = v*m[...,i] Thanks, Alex ___ Numpy-discussion

Re: [Numpy-discussion] Putting some random back into the top-level?

On 3/14/07, Sebastian Haase <[EMAIL PROTECTED]> wrote: Hi, Please remind me what's wrong with pylab's rand and randn ! I just learned about their existence recently and thought they seem quite handy and should go directly into (the top-level of) numpy. Functions that have the same name and do t

Re: [Numpy-discussion] Nonblocking Plots with Matplotlib

Hey Bill, what are you using to communicate with the server ? May I recommend looking at Pyro ! (Python remote objects) It would allow you to get your proxy objects. And also handles exception super clean and easy. I have used it for many years ! It's very stable ! (If you run into problems, take

Re: [Numpy-discussion] cannot pickle large numpy objects when memory resources are already stressed

El dc 14 de 03 del 2007 a les 09:46 -0700, en/na Travis Oliphant va escriure: > Glen W. Mabey wrote: > > >Hello, > > > >After running a simulation that took 6 days to complete, my script > >proceeded to attempt to write the results out to a file, pickled. > > > >The operation failed even though th

Re: [Numpy-discussion] Matlab -> NumPy translation and indexing

On 3/14/07, Sturla Molden <[EMAIL PROTECTED]> wrote: On 3/14/2007 2:46 PM, Robert Cimrman wrote: > a = [] > while ... > a.append( scalar ) > a = array( a ) While it may help, growing Python lists is also an O(N) process. This may just be a terminology problem, but just to be clear, append

Re: [Numpy-discussion] Questions regarding migrating from Numeric to numpy

vinjvinj wrote: >I'm in the process of migrating from Numeric to numpy. In some of my >code I have the following: > >a = zeros(num_elements, PyObject) >b = zeros(num_elements, PyObject) > > PyObject --> object -Travis ___ Numpy-discussion mailing li

Re: [Numpy-discussion] Migrating from numeric to numpy

vinjvinj wrote: >So far my migration seems to be going well. I have one problem: > >I've been using the scipy_base.insert and scipy_base.extract functions >and the behavior in numpy is not the same. > >a = [0, 0, 0, 0] >mask = [0, 0, 0, 1] >c = [10] > >numpy.insert(a, mask, c) > >would change a so

Re: [Numpy-discussion] Putting some random back into the top-level?

Hi, Please remind me what's wrong with pylab's rand and randn ! I just learned about their existence recently and thought they seem quite handy and should go directly into (the top-level of) numpy. Functions that have the same name and do the same thing don't conflict either ;-) -Sebastian On

[Numpy-discussion] Questions regarding migrating from Numeric to numpy

I'm in the process of migrating from Numeric to numpy. In some of my code I have the following: a = zeros(num_elements, PyObject) b = zeros(num_elements, PyObject) a is an array of python string objects and b is an array holding mx.DateTime objects. What do I have to do to migrate this over to nu

[Numpy-discussion] Migrating from numeric to numpy

So far my migration seems to be going well. I have one problem: I've been using the scipy_base.insert and scipy_base.extract functions and the behavior in numpy is not the same. a = [0, 0, 0, 0] mask = [0, 0, 0, 1] c = [10] numpy.insert(a, mask, c) would change a so that a = [0, 0, 0, 10] Thi

Re: [Numpy-discussion] compiling numarrray on Cygwin

Hi Duhaime, are you having a *very* good reason that you want to compile numarray instead of the newer and better numpy. Essentially everyone here has switched either from numarray or from numeric to numpy. You won't get much help with numarray and problems might not ever get fixed. However, peo

Re: [Numpy-discussion] cannot pickle large numpy objects when memory resources are already stressed

On Wed, Mar 14, 2007 at 09:46:46AM -0700, Travis Oliphant wrote: > Perhaps when the new bytes type is added to Python we will have a way to > view a memory area as a bytes object and be able to make a pickle > without creating that extra copy in memory. Perhaps this is an aspect that could be me

Re: [Numpy-discussion] problem with installing scipy

On 3/2/07, nevin <[EMAIL PROTECTED]> wrote: > I am trying to use scipy optimize module but I am having problem when > I try to import it: > > >>> import numpy > >>> import scipy > >>> from scipy import optimize > Traceback (most recent call last): > File "", line 1, in ? > File "/Library/Frameworks

Re: [Numpy-discussion] cannot pickle large numpy objects when memory resources are already stressed

Glen W. Mabey wrote: >Hello, > >After running a simulation that took 6 days to complete, my script >proceeded to attempt to write the results out to a file, pickled. > >The operation failed even though there was 1G of RAM free (4G machine). >I've since reconsidered using the pickle format for st

[Numpy-discussion] arrayrange

Hello, I've got a few codes that use "arrayrange" within numpy. It happens that the new version of numpy apparently doesn't recognize "arrayrange"... I've tried to change it to "arange", but it doesn't work... So, for example, the code below used to create a sine wave file, but it's not wo

Re: [Numpy-discussion] Matlab -> NumPy translation and indexing

On 3/14/2007 2:46 PM, Robert Cimrman wrote: > a = [] > while ... > a.append( scalar ) > a = array( a ) While it may help, growing Python lists is also an O(N) process. One can reduce the amount of allocations by preallocating an ndarray of a certain size (e.g. 1024 scalars), filling it up, an

Re: [Numpy-discussion] Matlab -> NumPy translation and indexing

On 3/14/07, David Koch <[EMAIL PROTECTED]> wrote: On 3/14/07, Sven Schreiber <[EMAIL PROTECTED]> wrote: > > > > If you want a 1d-array in the end you could try empty(0) to start with, > and then do hstack((A, your_scalar)) or something like that. Depending on what your generating routine loo

Re: [Numpy-discussion] Matlab -> NumPy translation and indexing

On 3/14/07, Sven Schreiber <[EMAIL PROTECTED]> wrote: If you want a 1d-array in the end you could try empty(0) to start with, and then do hstack((A, your_scalar)) or something like that. Yeah, that works - odd, I thought concatenate((a,b),0) == hstack((a,b)) Thanks /David ___

Re: [Numpy-discussion] Matlab -> NumPy translation and indexing

David Koch schrieb: > > In Python, I tried: > > A = empty((0,0)) > while > A = concatenate((A, array([someScalarValue])), 1) > end > > which returns an error since the shape of the empty A does not match the > vector I want to concatenate with. Any way to get around this without > havi

Re: [Numpy-discussion] Matlab -> NumPy translation and indexing

David Koch wrote: > Hi, > > so one thing I came across now is the following, very simple: > > Matlab: > A = [] > while >A = [A some_scalar_value] > end > > > In Python, I tried: > > A = empty((0,0)) > while >A = concatenate((A, array([someScalarValue])), 1) > end > > which r

Re: [Numpy-discussion] Matlab -> NumPy translation and indexing

Hi, so one thing I came across now is the following, very simple: Matlab: A = [] while A = [A some_scalar_value] end In Python, I tried: A = empty((0,0)) while A = concatenate((A, array([someScalarValue])), 1) end which returns an error since the shape of the empty A does not

Re: [Numpy-discussion] 3-10x speedup in bincount()

Hi Stephen, I'd de glad to test your bincount function for histogramming purposes. David 2007/3/14, Stephen Simmons <[EMAIL PROTECTED]>: Well, there were no responses to my earlier email proposing changes to numpy.bincount() to make it faster and more flexible. Does this mean noone is using b

Re: [Numpy-discussion] Vectorized ufuncs with OS X frameworks

Hi Eric, A Divendres 09 Març 2007 15:32, Eric Brown escrigué: > Hi All, > > I have a set of large arrays to which I have to do exp functions > over and over again. I am wondering if there is any benefit to > teaching numpy how to use the vForce frameworks to do this function. > > Does

Re: [Numpy-discussion] [PATCH] a fix for compiling numexpr with MSVC Toolkit 2003

A Dissabte 10 Març 2007 19:18, Timothy Hochberg escrigué: > On 3/9/07, David M. Cooke <[EMAIL PROTECTED]> wrote: > > On Fri, 9 Mar 2007 16:11:28 + > > > > [EMAIL PROTECTED] wrote: > > > Hi, > > > > > > I've done a patch for allowing compiling the last version of numexpr > > > > with > > > > > t

Re: [Numpy-discussion] Which dtype are supported by numexpr ?

A Divendres 09 Març 2007 18:56, Francesc Altet escrigué: > A Divendres 09 Març 2007 18:40, Sebastian Haase escrigué: > > Which dtypes are supported by numexpr ? > > Well, numexpr does support any dtype that is homogeneous, except 'uint64'. > This is because internally all the unsigned types are upc

Re: [Numpy-discussion] nd_image.affine_transform edge effects

Hi James On Fri, Mar 09, 2007 at 08:44:34PM -0300, James Turner wrote: > Last year I wrote a program that uses the affine_transform() > function in numarray to resample and co-add datacubes with WCS > offsets in 3D. This function makes it relatively easy to handle > N-D offsets and rotations with

Re: [Numpy-discussion] efficient norm of a vector

thanks. I hadn't seen it. anyway, from very rough benchmarks I did, the quickest and easiest way of computing the euclidean norm of a 1D array is: n = sqrt(dot(x,x.conj())) much faster than: n = sqrt(sum(abs(x)**2)) and much much faster than: n = scipy.linalg.norm(x) regards, lorenzo. On 3/14/