Mailman 3 June 2006 - NumPy-Discussion

eig hangs
by Robert Hetland June 12, 2006

June 12, 2006

I set up a linux machine without BLAS, LAPACK, ATLAS, hoping that lapack_lite would take over. For the moment, I am not concerned about speed -- I just want something that will work with small matricies. I installed numpy, and it passes all of the tests OK, but it hangs when doing eig: u, v = linalg.eig(rand(10,10)) # ....lots of nothing.... Do you *need* the linear algebra libraries for eig? BTW, inverse seems to work fine. -Rob ----- Rob Hetland, Assistant Professor Dept of Oceanography, Texas A&M University p: 979-458-0096, f: 979-845-6331 e: hetland(a)tamu.edu, w: http://pong.tamu.edu

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ImportError: cannot import name inverse_fft
by Nils Wagner June 12, 2006

June 12, 2006

matplotlib data path /usr/lib64/python2.4/site-packages/matplotlib/mpl-data $HOME=/home/nwagner loaded rc file /home/nwagner/matplotlibrc matplotlib version 0.87.3 verbose.level helpful interactive is False platform is linux2 numerix numpy 0.9.9.2603 Traceback (most recent call last): File "cascade.py", line 3, in ? from pylab import plot, show, xlim, ylim, subplot, xlabel, ylabel, title, legend,savefig,clf,scatter File "/usr/lib64/python2.4/site-packages/pylab.py", line 1, in ? from matplotlib.pylab import * File "/usr/lib64/python2.4/site-packages/matplotlib/pylab.py", line 198, in ? import mlab #so I can override hist, psd, etc... File "/usr/lib64/python2.4/site-packages/matplotlib/mlab.py", line 74, in ? from numerix.fft import fft, inverse_fft ImportError: cannot import name inverse_fft

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wradmyv
by Obrien Noah June 12, 2006

June 12, 2006

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[OT] scipy-user not working?
by Emanuele Olivetti June 12, 2006

June 12, 2006

Hi, I've tried to send a message twice to scipy-user since friday without success (messages don't come back to me but I don't receive any message from scipy-user too and they don't appear in archives). Note that since friday there are no new messages from that list. Is scipy-user working? TIA Emanuele

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scipy.io.loadmat can't handle structs from octave
by Brian Blais June 12, 2006

June 12, 2006

Hello, I am trying to load some .mat files in python, that were saved with octave. I get some weird things with strings, and structs fail altogether. Am I doing something wrong? Python 2.4, Scipy '0.4.9.1906', numpy 0.9.8, octave 2.1.71, running Linux. thanks, Brian Blais here is what I tried: Numbers are ok: ========OCTAVE========== >> a=rand(4) a = 0.617860 0.884195 0.032998 0.217922 0.207970 0.753992 0.333966 0.905661 0.048432 0.290895 0.353919 0.958442 0.697213 0.616851 0.426595 0.371364 >> save -mat-binary pythonfile.mat a =========PYTHON=========== In [13]:d=io.loadmat('pythonfile.mat') In [14]:d Out[14]: {'__header__': 'MATLAB 5.0 MAT-file, written by Octave 2.1.71, 2006-06-09 14:23:54 UTC', '__version__': '1.0', 'a': array([[ 0.61785957, 0.88419484, 0.03299807, 0.21792207], [ 0.20796989, 0.75399171, 0.33396634, 0.90566095], [ 0.04843219, 0.29089527, 0.35391921, 0.95844178], [ 0.69721313, 0.61685075, 0.42659485, 0.37136358]])} Strings are weird (turns to all 1's) ========OCTAVE========== >> a='hello' a = hello >> save -mat-binary pythonfile.mat a =========PYTHON=========== In [15]:d=io.loadmat('pythonfile.mat') In [16]:d Out[16]: {'__header__': 'MATLAB 5.0 MAT-file, written by Octave 2.1.71, 2006-06-09 14:24:13 UTC', '__version__': '1.0', 'a': '11111'} Cell arrays are fine (except for strings): ========OCTAVE========== >> a={5 [1,2,3] 'this'} a = { [1,1] = 5 [1,2] = 1 2 3 [1,3] = this } >> save -mat-binary pythonfile.mat a =========PYTHON=========== In [17]:d=io.loadmat('pythonfile.mat') In [18]:d Out[18]: {'__header__': 'MATLAB 5.0 MAT-file, written by Octave 2.1.71, 2006-06-09 14:24:51 UTC', '__version__': '1.0', 'a': array([5.0, [ 1. 2. 3.], 1111], dtype=object)} Structs crash: ========OCTAVE========== >> clear a >> a.hello=5 a = { hello = 5 } >> a.this=[1,2,3] a = { hello = 5 this = 1 2 3 } >> save -mat-binary pythonfile.mat a =========PYTHON=========== In [19]:d=io.loadmat('pythonfile.mat') --------------------------------------------------------------------------- exceptions.AttributeError Traceback (most recent call last) /home/bblais/octave/work/mouse/<console> /usr/lib/python2.4/site-packages/scipy/io/mio.py in loadmat(name, dict, appendmat, basename) 751 if not (0 in test_vals): # MATLAB version 5 format 752 fid.rewind() --> 753 thisdict = _loadv5(fid,basename) 754 if dict is not None: 755 dict.update(thisdict) /usr/lib/python2.4/site-packages/scipy/io/mio.py in _loadv5(fid, basename) 688 try: 689 var = var + 1 --> 690 el, varname = _get_element(fid) 691 if varname is None: 692 varname = '%s_%04d' % (basename,var) /usr/lib/python2.4/site-packages/scipy/io/mio.py in _get_element(fid) 676 677 # handle miMatrix type --> 678 el, name = _parse_mimatrix(fid,numbytes) 679 return el, name 680 /usr/lib/python2.4/site-packages/scipy/io/mio.py in _parse_mimatrix(fid, bytes) 597 result[i].__dict__[element] = val 598 result = squeeze(transpose(reshape(result,tupdims))) --> 599 if rank(result)==0: result = result.item() 600 601 # object is like a structure with but with a class name AttributeError: mat_struct instance has no attribute 'item' -- ----------------- bblais(a)bryant.edu http://web.bryant.edu/~bblais

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speed of numpy vs matlab on dot product
by JJ June 11, 2006

June 11, 2006

Hello. I am a new user to scipy, thinking about crossing over from Matlab. I have a new AMD 64 machine and just installed fedora 5 and scipy. It is a dual boot machine with windows XP. I did a small test to compare the speed of matlab (in 32 bit windows, Matlab student v14) to the speed of scipy (in fedora, 64 bit). I generated two random matrices of 10,000 by 2,000 elements and then took their dot product. The scipy code was: python import numpy import scipy a = scipy.random.normal(0,1,[10000,2000]) b = scipy.random.normal(0,1,[10000,2000]) c = scipy.dot(a,scipy.transpose(b)) I timed the last line of the code and compared it to the equivalent code in Matlab. The results were that Matlab took 3.3 minutes and scipy took 11.5 minutes. Thats a factor of three. I am surprised with the difference and am wondering if there is anything I can do to speed up scipy. I installed scipy, blas, atlas, numpy and lapack from source, just as the instructions on the scipy web site suggested (or as close to the instructions as I could). The only thing odd was that when installing numpy, I received messages that the atlas libraries could not be found. However, it did locate the lapack libraries. I dont know why it could not find the atlas libraries, as I told it exactly where to find them. It did not give the message that it was using the slower default libraries. I also tried compiling after an export ATLAS = statement, but that did not make a difference. Wherever I could, I complied it specifically for the 64 bit machine. I used the current gcc compiler. The ATLAS notes suggested that the speed problems with the 2.9+ compilers had been fixed. Any ideas on where to look for a speedup? If the problem is that it could not locate the atlas ibraries, how might I assure that numpy finds the atlas libraries. I can recompile and send along the results if it would help. Thanks. John PS. I first sent this to the scipy mailing list, but it didnt seem to make it there. __________________________________________________ Do You Yahoo!? Tired of spam? Yahoo! Mail has the best spam protection around http://mail.yahoo.com

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fromiter
by Tim Hochberg June 10, 2006

June 10, 2006

I finally got around to cleaning up and checking in fromiter. As Travis suggested, this version does not require that you specify count. From the docstring: fromiter(...) fromiter(iterable, dtype, count=-1) returns a new 1d array initialized from iterable. If count is nonegative, the new array will have count elements, otherwise it's size is determined by the generator. If count is specified, it allocates the full array ahead of time. If it is not, it periodically reallocates space for the array, allocating 50% extra space each time and reallocating back to the final size at the end (to give realloc a chance to reclaim any extra space). Speedwise, "fromiter(iterable, dtype, count)" is about twice as fast as "array(list(iterable),dtype=dtype)". Omitting count slows things down by about 15%; still much faster than using "array(list(...))". It also is going to chew up more memory than if you include count, at least temporarily, but still should typically use much less than the "array(list(...))" approach. -tim

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Re: [Numpy-discussion] adaptive thresholding: get adacent cells for each pixel
by Alex Liberzon June 10, 2006

June 10, 2006

Not sure, but my Google desktop search of "medfilt" (the name of Matlab function) brought me to: info_signal.py - N-dimensional order filter. medfilt -N-dimensional median filter If it's true, then it is the 2D median filter. Regarding the neighbouring cells, I found the iterator on 2D ranges on the O'Reily Cookbook by Simon Wittber very useful for my PyPIV (Particle Image Velocimetry, which works by correlation of 2D blocks of two successive images): http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/334971 def blocks(size, box=(1,1)): """ Iterate over a 2D range in 2D increments. Returns a 4 element tuple of top left and bottom right coordinates. """ box = list(box) pos = [0,0] yield tuple(pos + box) while True: if pos[0] >= size[0]-box[0]: pos[0] = 0 pos[1] += box[1] if pos[1] >= size[1]: raise StopIteration else: pos[0] += box[0] topleft = pos bottomright = [min(x[1]+x[0],x[2]) for x in zip(pos,box,size)] yield tuple(topleft + bottomright) if __name__ == "__main__": for c in blocks((100,100),(99,10)): print c for c in blocks((10,10)): print c HIH, Alex

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adaptive thresholding: get adacent cells for each pixel
by stephen emslie June 10, 2006

June 10, 2006

I'm just starting with numpy (via scipy) and I'm wanting to perform adaptive thresholding (http://www.cee.hw.ac.uk/hipr/html/adpthrsh.html) on an image. Basically that means that I need to get a threshold for each pixel by examining the pixels around it. In numpy this translates to finding the adjacent cells for each cell (not including the value of the cell we are examining) and getting the mean, or median of those cells. I've written something that works, but is terribly slow. How would someone with more experience get the adjacent cells for each cell minus the cell being examined? Thanks Stephen Emslie

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June 10, 2006

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