Re: [Numpy-discussion] PEP: named axis

ay. In my opinion, the meta-data-management should be done by another (sub-?) class. This way, numpy-arrays are simple enough for new users (as I was roughly two years ago...). I would be very interested in a class that *uses* numpy-arrays to provide a datastructure for physical data with coo

Re: [Numpy-discussion] histogramdd memory needs

Hi, > 2) Is there a way to use another algorithm (at the cost of performance) >> > that uses less memory during calculation so that I can generate bigger >> > histograms? > > > You could work through your array block by block. Simply fix the range and > generate an histogram for each slice of 10

[Numpy-discussion] histogramdd memory needs

) that uses less memory during calculation so that I can generate bigger histograms? My numpy version is '1.0.4.dev3937' Thanks, Lars -- Dipl.-Ing. Lars Friedrich Photonic Measurement Technology Department of Microsystems Engineering -- IMTEK University of Freiburg Georges-Köhler-A

Re: [Numpy-discussion] fourier with single precision

Thank you for your comments! I will try this fftw3-scipy approach and see how much faster I can get. Maybe this is enough for me...? Lars ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-disc

Re: [Numpy-discussion] fourier with single precision

Hello, thanks for your comments. If I got you right, I should look for a FFT-code that uses SSE (what does this actually stand for?), which means that it vectorizes 32bit-single-operations into larger chunks that make efficient use of recent CPUs. You mentioned FFTW and MKL. Is this www.fftw.o

Re: [Numpy-discussion] fourier with single precision

Hello, David Cournapeau wrote: > As far as I can read from the fft code in numpy, only double is > supported at the moment, unfortunately. Note that you can get some speed > by using scipy.fftpack methods instead, if scipy is an option for you. What I understood is that numpy uses FFTPACK's alg

[Numpy-discussion] fourier with single precision

Hello, is there a way to tell numpy.fft.fft2 to use complex64 instead of complex128 as output dtype to speed the up transformation? Thanks Lars ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/nump

Re: [Numpy-discussion] tofile speed

Hello, I tried the following: ### start code a = N.random.rand(100) myFile = file('test.bin', 'wb') for i in range(100): a.tofile(myFile) myFile.close() ### end code And this gives roughly 50 MB/s on my office-machine but only 6.5 MB/s on the machine that I was report

Re: [Numpy-discussion] tofile speed

Hello everyone, thank you for the replies. Sebastian, the chunk size is roughly 4*10^6 samples, with two byte per sample, this is about 8MB. I can vary this size, but increasing it only helps for much smaller values. For example, when I use a size of 100 Samples, I am much too slow. It gets be

[Numpy-discussion] tofile speed

(harddisk). Currently I can stream with roughly 4 Mbyte/s, which is quite fast, I guess. However, if anyone can point me to a way to write my data to harddisk faster, I would be very happy! Thanks Lars -- Dipl.-Ing. Lars Friedrich Photonic Measurement Technology Department of Microsystems E

Re: [Numpy-discussion] inconsistent mgrid results

ehaviour is different from arange, I think it is not very intentional. But maybe there is a good reason for this behaviour? I am using numpy, version 1.0.1. Maybe the behaviour was already changed in more recent versions? Thank you for any comment Lars Friedrich -- Dipl.-Ing. Lars Friedrich O