Mailman 3 October 2006 - SciPy-Dev

Question about Kaiser Implementation in firwin
by Buehler, Eric (AGRE) 04 Oct '08

04 Oct '08

Hello, I have been using the signal.firwin FIR design tool and I have a question about the implementation. Scipy-0.4.8 Numpy-0.9.6 I am generating a kaiser window with the following parameters: N = 128 Cutoff = ~4e7 width = .3 All of the special kaiser generation parameters in the function follow Oppenheim and Schafer well. Even the sinc function for linear phase is follows exactly. However, the last line of the firwin function is causing me some heartburn. filter_design.py 1538 win = get_window(window,N,fftbins=1) 1539 alpha = N//2 1540 m = numpy.arange(0,N) 1541 h = win*special.sinc(cutoff*(m-alpha)) 1542 return h / sum(h) Line 1542 of filter_design.py, "return h / sum(h)", normalizes the function where it doesn't seem necessary, at least in the kaiser window case. Without the normalization, the kaiser window already returns a value of 1 at the zero frequency point. This normalization scales all of the data, making the window difficult to use in the frequency domain. Can someone point me to the rationale for this line? Looking at the code, this seems to be a pretty recent change (within the last year/year and a half). Thanks, Eric Buehler eric <dot> buehler <at> smiths-aerospace <dot> com ****************************************** The information contained in, or attached to, this e-mail, may contain confidential information and is intended solely for the use of the individual or entity to whom they are addressed and may be subject to legal privilege. If you have received this e-mail in error you should notify the sender immediately by reply e-mail, delete the message from your system and notify your system manager. Please do not copy it for any purpose, or disclose its contents to any other person. The views or opinions presented in this e-mail are solely those of the author and do not necessarily represent those of the company. The recipient should check this e-mail and any attachments for the presence of viruses. The company accepts no liability for any damage caused, directly or indirectly, by any virus transmitted in this email. ******************************************

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new function regrid for fitpack2
by John Travers 03 Jan '07

03 Jan '07

Hi all, I've just submitted a ticket (#286) with patches to add support for the regrid function from dierckx (fitpack) to the scipy interface. regrid is used for fitting smoothing or interpolating splines to rectangular mesh data. It is more efficient and stable than using surfit (which is designed for scattered data). The patch makes use of the newstyle fitpack2 interface based on hand coded fitpack.pyf. The added class is RectBivariateSpline?. A simple test case is included. Can somebody tell me why the dierckx function surfit is currently used in interp2d? As far as I can tell from the documentation (and the fitpack author's book) surfit shouldn't be used for interpolation. It is designed for smoothing scattered data. I say this as the reason I implemented the regrid function is because interp2d wouldn't work for my data. regrid does (and *is* designed for interpolation or smoothing of rectangular data). Can I suggest that interp2d uses regrid instead (or do people need to interpolate scattered data, in which case a different library alltogether would be required)? I'm willing to work out the required patches to make these changes if people think I should. Best regards, John Travers

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Ebuilds and Attribute error
by Martin Höfling 01 Nov '06

01 Nov '06

Hi there, first of all, i've created subversion ebuilds for numpy and scipy so if anyone is interested in them just tell me. It seems as if there' something wrong with numy: I tried from the tutorial in the documentation. In [1]: from scipy import * In [2]: x,y = mgrid[-1:1:20j,-1:1,20j] --------------------------------------------------------------------------- exceptions.AttributeError Traceback (most recent call last) /home/martin/<console> /usr/lib/python2.4/site-packages/numpy/lib/index_tricks.py in __getitem__(self, key) 129 typ = int 130 for k in range(len(key)): --> 131 step = key[k].step 132 start = key[k].start 133 if start is None: start=0 AttributeError: 'complex' object has no attribute 'step' I tried also to revert back to numpy 1.0, no change. Best wishes, Martin -- HTML erhöht den Informationsgehalt eines Postings *immer* um ein paar unvorteilhafte Informationen über den Verfasser. (Thore Tams, de.soc.netzkultur, 17.5.1999)

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Citing_SciPy link
by Lisandro Dalcin 31 Oct '06

31 Oct '06

I cannot access the following link http://www.scipy.org/Citing_SciPy How should I cite SciPy project? -- Lisandro Dalcín --------------- Centro Internacional de Métodos Computacionales en Ingeniería (CIMEC) Instituto de Desarrollo Tecnológico para la Industria Química (INTEC) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) PTLC - Güemes 3450, (3000) Santa Fe, Argentina Tel/Fax: +54-(0)342-451.1594

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Re: [SciPy-dev] [Numpy-discussion] Conversion about getting array interface into Python
by Lisandro Dalcin 31 Oct '06

31 Oct '06

On 10/30/06, Travis Oliphant <oliphant.travis(a)ieee.org> wrote: > > If anybody has a desire to see the array interface into Python, please > help by voicing an opinion on python-dev in the discussion about adding > data-type objects to Python. There are a few prominent people who > don't get why applications would need to share data-type information > about memory areas. I need help giving reasons why. > > -Travis Python-Dev is sometimes a hard place to get into. I remember posting some proposals or even bugs (considered bugs for me and others) and getting rather crude responses. Travis said "a few prominent people...". I consider Travis a *very* prominent people in Python world. His **terrific** contribution to NumPy reveals a really smart, always-looking-ahead way of doing things. However, I've seen before strong and disparate opposition to his proposals in Python-Dev. Perhaps the reason for this is simple: few Python core developers are involved in scientific computing and do not have a clear idea of what it is needed for this. I really believe that NumPy/Scipy community should try to raise his voice on Python-Dev. Many NumPy/Scipy users/developers really want to run high-performance Python code. Python is being used in supercomputers, some applications taking advantage of Python (SPaSM) have even won the Gordon Bell Performance Prize. An 25 Tflop/s application involving Python programing language is really a good example of what can be achieved with Python and compiled code interaction. In short, I fully support Travis in his initiative to standardize access to low level binary data, and encourage others like me who really want this to post to Python-Dev. From my part, I will try to post my reasons in connection with my (small) experience developing MPI for Python. Regars, -- Lisandro Dalcín --------------- Centro Internacional de Métodos Computacionales en Ingeniería (CIMEC) Instituto de Desarrollo Tecnológico para la Industria Química (INTEC) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) PTLC - Güemes 3450, (3000) Santa Fe, Argentina Tel/Fax: +54-(0)342-451.1594

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scipy.linalg shadowed
by Bart Vandereycken 30 Oct '06

30 Oct '06

Hi, the scipy.linalg module is shadowed by numpy.linalg > import scipy > scipy.linalg.__file__ '.../lib/python/numpy/linalg/__init__.pyc' import scipy.linalg works fine Reading http://permalink.gmane.org/gmane.comp.python.scientific.devel/4431 , I see that "del linalg" is not present in scipy/__init__.py Bart

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PRIMME: PReconditioned Iterative MultiMethod Eigensolver
by Nils Wagner 30 Oct '06

30 Oct '06

Hi all, A new eigensolver is available (*NA Digest, V. 06, # 43*). PRIMME is released under the Lesser GPL license. Is this licence compatible wrt scipy ? Nils From: Andreas Stathopoulos <andreas(a)cs.wm.edu> Date: Thu, 19 Oct 2006 01:50:09 -0400 Subject: PRIMME Eigensolver PRIMME: PReconditioned Iterative MultiMethod Eigensolver http://www.cs.wm.edu/~andreas/software/ We are pleased to announce the release of PRIMME Version 1.1 featuring enhanced functionality and a year-long testing by several groups. PRIMME is a C library for the solution of large, sparse, real symmetric and complex Hermitian eigenvalue problems. It implements a main iteration similar to Davidson/Jacobi-Davidson, but with the appropriate choice of parameters it can transform to most known, and even yet undiscovered, preconditioned eigensolvers. These include the nearly optimal GD+k and JDQMR methods. PRIMME implements one of the most comprehensive sets of eigenvalue techniques, including block methods, locking, locally optimal restarting, dynamic stopping criteria for inner iteration, and many correction equation variants. Coupled with the above nearly optimal methods, PRIMME demonstrates exceptional robustness and efficiency. Even without preconditioning, PRIMME has proved faster than Lanczos methods for a small number of eigenpairs of difficult problems. PRIMME's multi-layer interface allows non-expert, end-users to access the full power of these methods. Unlike traditional Jacobi-Davidson methods, where a host of parameters must be tuned, PRIMME has a sophisticated mechanism for determining appropriate defaults. Moreover, under a new DYNAMIC mode, the best method can be selected dynamically based on runtime timings and measurements. Yet, experts can still control and experiment with numerous tuning knobs. PRIMME works with any external matrix-(multi)vector and preconditioner (multi-)vector functions, including indefinite preconditioners; it can find both exterior and interior eigenpairs; it is both sequential and parallel (SPMD parallelization); and can be called from any C, C++, or Fortran program. For questions, bugs, reports please email: andreas(a)cs.wm.edu

9 23

ERROR: check loadmat case cellnest
by Nils Wagner 30 Oct '06

30 Oct '06

scipy.test(1) yields ====================================================================== ERROR: check loadmat case cell ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 84, in cc self._check_case(name, files, expected) File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 79, in _check_case self._check_level(k_label, expected, matdict[k]) File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 66, in _check_level assert_array_almost_equal(actual, expected, err_msg=label, decimal=5) File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 230, in assert_array_almost_equal header='Arrays are not almost equal') File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 200, in assert_array_compare val = comparison(x,y) File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 228, in compare return around(abs(x-y),decimal) <= 10.0**(-decimal) TypeError: unsupported operand type(s) for -: 'unicode' and 'unicode' ====================================================================== ERROR: check loadmat case cellnest ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 84, in cc self._check_case(name, files, expected) File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 79, in _check_case self._check_level(k_label, expected, matdict[k]) File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 66, in _check_level assert_array_almost_equal(actual, expected, err_msg=label, decimal=5) File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 230, in assert_array_almost_equal header='Arrays are not almost equal') File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 200, in assert_array_compare val = comparison(x,y) File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 228, in compare return around(abs(x-y),decimal) <= 10.0**(-decimal) File "/usr/lib/python2.4/site-packages/numpy/core/fromnumeric.py", line 574, in round_ return round(decimals, out) AttributeError: 'numpy.ndarray' object has no attribute 'rint' ====================================================================== ERROR: check loadmat case emptycell ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 84, in cc self._check_case(name, files, expected) File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 79, in _check_case self._check_level(k_label, expected, matdict[k]) File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 66, in _check_level assert_array_almost_equal(actual, expected, err_msg=label, decimal=5) File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 230, in assert_array_almost_equal header='Arrays are not almost equal') File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 200, in assert_array_compare val = comparison(x,y) File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 228, in compare return around(abs(x-y),decimal) <= 10.0**(-decimal) File "/usr/lib/python2.4/site-packages/numpy/core/fromnumeric.py", line 574, in round_ return round(decimals, out) AttributeError: 'numpy.float64' object has no attribute 'rint' ====================================================================== ERROR: check loadmat case stringarray ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 84, in cc self._check_case(name, files, expected) File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 79, in _check_case self._check_level(k_label, expected, matdict[k]) File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 66, in _check_level assert_array_almost_equal(actual, expected, err_msg=label, decimal=5) File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 230, in assert_array_almost_equal header='Arrays are not almost equal') File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 200, in assert_array_compare val = comparison(x,y) File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 228, in compare return around(abs(x-y),decimal) <= 10.0**(-decimal) TypeError: unsupported operand type(s) for -: 'unicode' and 'unicode' ====================================================================== ERROR: check loadmat case structarr ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 84, in cc self._check_case(name, files, expected) File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 79, in _check_case self._check_level(k_label, expected, matdict[k]) File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_mio.py", line 66, in _check_level assert_array_almost_equal(actual, expected, err_msg=label, decimal=5) File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 230, in assert_array_almost_equal header='Arrays are not almost equal') File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 200, in assert_array_compare val = comparison(x,y) File "/usr/lib/python2.4/site-packages/numpy/testing/utils.py", line 228, in compare return around(abs(x-y),decimal) <= 10.0**(-decimal) TypeError: unsupported operand type(s) for -: 'mat_struct' and 'mat_struct' ----------------------------------------------------------------------

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pyaudio, a module to make noise from numpy arrays
by David Cournapeau 27 Oct '06

27 Oct '06

Hi, I announce the first release of pyaudio, a module to make noise from numpy arrays (read, write and play audio files with numpy arrays). * WHAT FOR ?: The Goal is to give to a numpy/scipy environmenet some basic audio IO facilities (ala sound, wavread, wavwrite of matlab). With pyaudio, you should be able to read and write most common audio files from and to numpy arrays. The underlying IO operations are done using libsndfile from Erik Castro Lopo (http://www.mega-nerd.com/libsndfile/), so he is the one who did the hard work. As libsndfile has support for a vast number of audio files (including wav, aiff, but also htk, ircam, and flac, an open source lossless codec), pyaudio enables the import from and export to a fairly large number of file formats. There is also a really crude player, which uses tempfile to play audio, and which only works for linux-alsa anyway. I intend to add better support at least for linux, and for other platforms if this does not involve too much hassle. So basically, if you are lucky enough to use a recent linux system, pyaudio already gives you the equivalent of wavread, wavwrite and sound. * DOWNLOAD: http://www.ar.media.kyoto-u.ac.jp/members/david/pyaudio.tar.gz * INSTALLATION INSTRUCTIONS: Just untar the package and drop it into scipy/Lib/sandbox, and add the two following lines to scipy/Lib/sandbox/setup.py: # Package to make some noise using numpy config.add_subpackage('pyaudio') (if libsndfile.so is not in /usr/lib, a fortiori if you are a windows user, you should also change set the right location for libsndfile in pyaudio/pysndfile.py, at the line _snd.cdll.LoadLibrary('/usr/lib/libsndfile.so') ) * EXAMPLE USAGE == Reading example == # Reading from '/home/david/blop.flac' from scipy.sandbox.pyaudio import sndfile a = sndfile('/home/david/blop.flac') print a tmp = a.read_frames_float(1024) --> Prints: File : /home/david/blop.flac Sample rate : 44100 Channels : 2 Frames : 9979776 Format : 0x00170002 Sections : 1 Seekable : True Duration : 00:03:46.298 And put into tmp the 1024 first frames (a frame is the equivalent of a sample, but taking into account the number of channels: so 1024 frames gives you 2048 samples here). == Writing example == # Writing to a wavfile: from scipy.sandbox.pyaudio import sndfile import numpy as N noise = N.random.randn((44100)) a = sndfile('/home/david/blop.flac', sfm['SFM_WRITE'], sf_format['SF_FORMAT_WAV'] | sf_format['SF_FORMAT_PCM16'], 1, 44100) a.write_frames(noise, 44100) a.close() -> should gives you a lossless compressed white noise ! This is really a first release, not really tested, not much documentation, I can just say it works for me. I haven't found a good way to emulate enumerations, which libsndfile uses a lot, so I am using dictionaries generated from the library C header to get a relation enum label <=> value. If someone has a better idea, I am open to suggestions ! Cheers, David

1 0

scipy-0.5.1/numpy-1.0 x86_64 test
by Neal Becker 27 Oct '06

27 Oct '06

====================================================================== ERROR: Solve: single precision complex ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib64/python2.4/site-packages/scipy/linsolve/umfpack/tests/test_umfpack.py", line 25, in check_solve_complex_without_umfpack x = linsolve.spsolve(a, b) File "/usr/lib64/python2.4/site-packages/scipy/linsolve/linsolve.py", line 76, in spsolve return gssv(N, lastel, data, index0, index1, b, csc, permc_spec)[0] TypeError: array cannot be safely cast to required type ====================================================================== ERROR: check loadmat case cellnest ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib64/python2.4/site-packages/scipy/io/tests/test_mio.py", line 80, in cc self._check_case(name, expected) File "/usr/lib64/python2.4/site-packages/scipy/io/tests/test_mio.py", line 75, in _check_case self._check_level(k_label, expected, matdict[k]) File "/usr/lib64/python2.4/site-packages/scipy/io/tests/test_mio.py", line 33, in _check_level self._check_level(level_label, ev, actual[i]) File "/usr/lib64/python2.4/site-packages/scipy/io/tests/test_mio.py", line 33, in _check_level self._check_level(level_label, ev, actual[i]) File "/usr/lib64/python2.4/site-packages/scipy/io/tests/test_mio.py", line 33, in _check_level self._check_level(level_label, ev, actual[i]) File "/usr/lib64/python2.4/site-packages/scipy/io/tests/test_mio.py", line 30, in _check_level assert len(expected) == len(actual), "Different list lengths at %s" % label TypeError: len() of unsized object ====================================================================== FAIL: check_rvs (scipy.stats.tests.test_distributions.test_randint) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib64/python2.4/site-packages/scipy/stats/tests/test_distributions.py", line 84, in check_rvs assert isinstance(val, numpy.ScalarType),`type(val)` AssertionError: <type 'numpy.ndarray'> ---------------------------------------------------------------------- Ran 1569 tests in 5.746s FAILED (failures=1, errors=2) <unittest.TextTestRunner object at 0x2aaab917e650> >>>

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