Mailman 3 March 2006 - SciPy-User

[ANN] SciPy 0.4.8 released
by Ed Schofield March 18, 2006

March 18, 2006

=========================== SciPy 0.4.8 Scientific tools for Python =========================== I'm pleased to announce the release of SciPy 0.4.8. This release adds support for the latest NumPy (version 0.9.6) and adds Peter Verveer's multi-dimensional image processing package. It also has enhancements for sparse matrices and maximum entropy modelling, fixes bugs in linear algebra, simulated annealing, statistics, least squares, sparse matrices, and has seen extensive code cleanups. It is available for download from http://www.scipy.org/Download as a source tarball for Linux/Solaris/OS X/BSD/Windows (64-bit and 32-bit) and as an executable installer for Win32. More information on SciPy is available at http://www.scipy.org/ =========================== SciPy is an Open Source library of scientific tools for Python. It contains a variety of high-level science and engineering modules, including modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, genetic algorithms, ODE solvers, special functions, and more.

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Complex Numbers in ODEINT and BVP's
by Ganesh V March 18, 2006

March 18, 2006

Hi! I want to use complex numbers in the odeint package. Let me make it more precise. I have an eigenvalue inside the problem that I find by other means. (Related to acoustics). Now that can be complex. Is it possible in the current scipy ? In my case it was a Linear ODE, and hence I split into separate ones for real and imaginary ones and soved it. The other question is about Boundary value problem solvers. i.e can odeint or anything else solve Boundary value problems instead of initial value problems, or do I have to recourse to an iterative procedure myself instead of the solver doing it? (AFAIK matlab solves it in a similar way, assuming a derivative value at 0, and then iterating over it to meet the boundary value at the other end) Bye! -- Ganesh V Undergraduate student, Department of Aerospace Engineering, IIT Madras, India. My homepage --> http://www.ae.iitm.ac.in/~ae03b007

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difference between NumPy and Scipy
by linda.s March 17, 2006

March 17, 2006

i saw the news of NumPy release and SciPy release. Numpy is said to be the core of Scipy. But why they have different releases? for example: NumPy 0.9.6 released! (2006-03-14) See the Download and Release Notes pages. SciPy 0.4.6 released! (2006-02-16) See the Download and Release Notes pages. NumPy 0.9.5 released! (2006-02-16) See the Download and Release Notes pages.

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Call for posters to gmane.comp.python.devel newsgroup
by Travis Oliphant March 17, 2006

March 17, 2006

I'm trying to start discussions on python dev about getting a simple object into Python that at least exposes the array interface and/or has the basic C-structure of NumPy arrays. Please voice your support and comments on the newsgroup. The more people that respond, the more the python developers will see that it's not just my lonely voice asking for things to change. Perhaps it will help somebody with more time to get a PEP written up. I doubt we will make it into Python 2.5, unless somebody steps up in the next month, but it will help for Python 2.6 Thanks, -Travis

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scipy.test problem
by Hanno Klemm March 17, 2006

March 17, 2006

Hi there, I am new to scipy and have the following installation problem: I work on a dual processor Xeon box, and try to install scipy. After various unsuccessful attempts, I now compiled BLAS, LAPACK ATLAS and FFTW3.1 from scratch, then installed numpy-0.9.5 and then installed scipy-0.4.6 under python 2.4.2. The installation went fine but after import scipy scipy.test(level=1) I get the following error message: ====================================================================== FAIL: check_simple (scipy.linalg.decomp.test_decomp.test_eig) ---------------------------------------------------------------------- Traceback (most recent call last): File "/scratch/python2.4/lib/python2.4/site-packages/scipy/linalg/tests/test_decomp.py", line 89, in check_simple assert_array_almost_equal(w,exact_w) File "/scratch/python2.4/lib/python2.4/site-packages/numpy/testing/utils.py", line 183, in assert_array_almost_equal assert cond,\ AssertionError: Arrays are not almost equal (mismatch 66.6666666667%): Array 1: [ 9.4371906+0.j -0.1153653+0.j -0.3218254+0.j] Array 2: [ 9.3218254 0. -0.3218254] ====================================================================== FAIL: check_simple (scipy.linalg.decomp.test_decomp.test_eigvals) ---------------------------------------------------------------------- Traceback (most recent call last): File "/scratch/python2.4/lib/python2.4/site-packages/scipy/linalg/tests/test_decomp.py", line 37, in check_simple assert_array_almost_equal(w,exact_w) File "/scratch/python2.4/lib/python2.4/site-packages/numpy/testing/utils.py", line 183, in assert_array_almost_equal assert cond,\ AssertionError: Arrays are not almost equal (mismatch 66.6666666667%): Array 1: [ 9.4371906+0.j -0.1153653+0.j -0.3218254+0.j] Array 2: [ 9.3218254 0. -0.3218254] ====================================================================== FAIL: check_simple_tr (scipy.linalg.decomp.test_decomp.test_eigvals) ---------------------------------------------------------------------- Traceback (most recent call last): File "/scratch/python2.4/lib/python2.4/site-packages/scipy/linalg/tests/test_decomp.py", line 45, in check_simple_tr assert_array_almost_equal(w,exact_w) File "/scratch/python2.4/lib/python2.4/site-packages/numpy/testing/utils.py", line 183, in assert_array_almost_equal assert cond,\ AssertionError: Arrays are not almost equal (mismatch 66.6666666667%): Array 1: [ 9.4371906+0.j -0.1153653+0.j -0.3218254+0.j] Array 2: [ 9.3218254 0. -0.3218254] ---------------------------------------------------------------------- Ran 1067 tests in 2.250s FAILED (failures=3) <unittest.TextTestRunner object at 0x2aa03923d0> Could anybody please tell me what to do with that? If that is important: I compiled everything with gcc. gcc --version gives: gcc (GCC) 3.2.3 20030502 (Red Hat Linux 3.2.3-52) In order to get scipy to use the atlas libraries I had to add an option -fPIC in the compiler flags in atlas and friends. Any help would be much appreciated! Thanks in advance, Hanno -- Hanno Klemm klemm(a)phys.ethz.ch

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ValueError: need more than 1 value to unpack
by Nils Wagner March 17, 2006

March 17, 2006

Is this a bug ? A_csc = csc_matrix((3,3),Complex128) File "/usr/lib64/python2.4/site-packages/scipy/sparse/sparse.py", line 521, in __init__ (M, N) = dims ValueError: need more than 1 value to unpack Nils

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Interesting link for ideas
by Travis Oliphant March 17, 2006

March 17, 2006

Here's a link to PyVox which might be an interesting source for code to be brought into SciPy. A lot of the code is "re-invent the wheel" type stuff, but there are some interesting algorithms that we may be able to borrow from to add to the ndimage library. http://www.med.upenn.edu/bbl/downloads/pyvox/index.shtml -Travis

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nd_image
by joris＠ster.kuleuven.ac.be March 16, 2006

March 16, 2006

[TO]: So, what we have in SciPy is "the official" home for the numpy [TO]: version of his package. Sorry, I am a bit lost here. So nd_image was a numarray library, and is now available via scipy where it works with numpy arrays (correct?). At the moment, the library is unmaintained though, regardless whether it would be part of scipy or numpy. Correct? If so, why exactly would it be better to put it under scipy rather than under numpy? Some people don't need scipy for their work and are happy with numpy alone. It used to be rather easy for them to use nd_image with numarray, but if I understand you correctly, they would now have to install an entire new package, which for scipy is still quite a challenge compared to numpy. Joris Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

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linspace
by Nils Wagner March 16, 2006

March 16, 2006

Hi all, linspace is useful for one-dimensional objects. Is there something similar for surfaces ? I would like to build a uniform mesh on a cube (-R,R)^3 I mean collocation points uniformly distributed on each side of the cube. Nils

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Re: [SciPy-user] non contiguous numpy arrays
by Gary S. Thompson March 16, 2006

March 16, 2006

>Hi > I have read the stuff on the numpy interface and understand most of >if(?) ;-) However I want to interface numpy to an array which is stored >as a series of data blocks which are not contiguous in memory... Help! > >so for example I hava 3d matrix of 1024*512*128 where data is stored in >blocks within memory which are 16x16x16 floats an read in from disk on >demand... > >regards >gary > > >>i >>> I have read the stuff on the numpy interface and understand most of >>>if(?) ;-) However I want to interface numpy to an array which is stored >>>as a series of data blocks which are not contiguous in memory... Help! >>> >>> >>> >> >> >>>so for example I hava 3d matrix of 1024*512*128 where data is stored in >>>blocks within memory which are 16x16x16 floats an read in from disk on >>>demand... >>> >>> >>> >> >> > The memory model for numpy arrays requires that you can access the > next element in each dimension by striding a "fixed-number" of bytes. > In other words, to be a single ndarray, element i,j,k of the array, B, > must be at start_of_data + i*B.stride[0] + j*B.stride[1] + > k*B.stride[2] So, in your case, do these 16x16x16 blocks of floats all > exist at arbitrary memory locations? yep afraid so > If so, then I don't see how you can map that to a single ndarray. You > could, however, write a class that wraps each block as a separate > sub-array and do the indexing calculations yourself to determine which > sub-block the data is stored in. But, I don't see a way to use a > single ndarray to access memory layed out like that. Perhaps I still > don't understand what you are doing. Are you using a memory map? -Travis no ;-) no memory maps I believe So the problem I am trying to overcome is that the program I am using looks at large data matrices (100's of megabyte). Therefore it uses these submatrix formats to keep memory usage down if you are only lookiung at particular regions. Mentally I was thinking of this as being a bit like a sparse matrix but with all the data present So is there sparse matrix supportfor numpy? Now this was something that wasn't clear do you access each value in the numpy array by a direct memory reference or by a function call when you want to use it... regards gary -- ------------------------------------------------------------------- Dr Gary Thompson Astbury Centre for Structural Molecular Biology, University of Leeds, Astbury Building, Leeds, LS2 9JT, West-Yorkshire, UK Tel. +44-113-3433024 email: garyt(a)bmb.leeds.ac.uk Fax +44-113-2331407 -------------------------------------------------------------------

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