Mailman 3 December 2009 - NumPy-Discussion

[Ann] EuroScipy 2010
by Gael Varoquaux 13 Dec '09

13 Dec '09

========================== Announcing EuroScipy 2010 ========================== --------------------------------------------------- The 3rd European meeting on Python in Science --------------------------------------------------- **Paris, Ecole Normale Supérieure, July 8-11 2010** We are happy to announce the 3rd EuroScipy meeting, in Paris, July 2010. The EuroSciPy meeting is a cross-disciplinary gathering focused on the use and development of the Python language in scientific research. This event strives to bring together both users and developers of scientific tools, as well as academic research and state of the art industry. Important dates ================== ====================================== =================================== **Registration opens** Sunday March 29 **Paper submission deadline** Sunday May 9 **Program announced** Sunday May 22 **Tutorials tracks** Thursday July 8 - Friday July 9 **Conference track** Saturday July 10 - Sunday July 11 ====================================== =================================== Tutorial ========= There will be two tutorial tracks at the conference, an introductory one, to bring up to speed with the Python language as a scientific tool, and an advanced track, during which experts of the field will lecture on specific advanced topics such as advanced use of numpy, scientific visualization, software engineering... Main conference topics ======================== We will be soliciting talks on the follow topics: - Presentations of scientific tools and libraries using the Python language, including but not limited to: - Vector and array manipulation - Parallel computing - Scientific visualization - Scientific data flow and persistence - Algorithms implemented or exposed in Python - Web applications and portals for science and engineering - Reports on the use of Python in scientific achievements or ongoing projects. - General-purpose Python tools that can be of special interest to the scientific community. Keynote Speaker: Hans Petter Langtangen ========================================== We are excited to welcome Hans Petter Langtangen as our keynote speaker. - Director of scientific computing and bio-medical research at Simula labs, Oslo - Author of the famous book Python scripting for computational science http://www.springer.com/math/cse/book/978-3-540-73915-9 -- Gaël Varoquaux, conference co-chair Nicolas Chauvat, conference co-chair Program committee ................. Romain Brette (ENS Paris, DEC) Mike Müller (Python Academy) Christophe Pradal (CIRAD/INRIA, DigiPlantes team) Pierre Raybault (CEA, DAM) Jarrod Millman (UC Berkeley, Helen Wills NeuroScience institute)

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Question on timeseries, for financial application
by THOMAS BROWNE 13 Dec '09

13 Dec '09

Hello all, Quite new to numpy / timeseries module, please forgive the elementary question. I wish to do quite to do a bunch of multivariate analysis on 1000 different financial markets series, each holding about 1800 data points (5 years of daily data). What's the best way to put this into a TimeSeries object? Should I use a structured data type (in which case I can reference each series by name), or should I put it into one big numpy array object (in which case I guess I'll have to keep track of the series name in an internal structure)? What are the advantages and disadvantages of each? Ideally I'd have liked the ease and simplicity of being able to reference each series by name, while maintaining the fast speed and clean structure of one big numpy array. Any way of getting both? Once I have a multivariate TimeSeries, how do I add another series to it? Thanks for the help. Thomas.

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A discrepancy between NumPy documentation and recomendations for beginers
by iason 13 Dec '09

13 Dec '09

Here http://www.scipy.org/NumPy_for_Matlab_Users a recommendation to use scipy.integrate.ode(...) with parameters "method='bdf', order=15" instead of the ode15s function (from Matlab) is given. But from the documentation for the scipy.integrate.ode(...) one can find out that the accuracy order ("order") of the BDF method ("method='bdf'") is no greater than 5. What is the correct value of the highest order of the BDF method? I think the answer for this question is necessary for practical use of scipy.integrate.ode(...). I tried to migrate my numerical research from Matlab to NumPy and first tried scipy.integrate.ode(...) function because it supports single step calculation. I used the default parameters listed in this guide http://www.scipy.org/NumPy_for_Matlab_Users and found the calculation process extremely slow. It was unusable because it selected too small integration step and I could complete only few percents of my numerical task (a stiff ODE integration) in an admissible time (tens of minutes). Than I switched my program to use the simpler scipy.integrate.odeint instead. The scipy.integrate.odeint function gave me an excellent solution in several seconds. But it does not support single step integrations and thus is not a good replacement for the ode15s function (Matlab). What is the difference between scipy.integrate.ode and scipy.integrate.odeint? How to select correct parameters for the scipy.integrate.ode function? After all it seems to me that there might be a bug in scipy.integrate.ode function implementation... -- View this message in context: http://old.nabble.com/A-discrepancy-between-NumPy-documentation-and-recomen… Sent from the Numpy-discussion mailing list archive at Nabble.com.

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ma feature request (log2)
by Robin 13 Dec '09

13 Dec '09

Hi, Could we have a ma aware numpy.ma.log2 please, similar to np.ma.log and np.ma.log10? I think it should be as simple as the patch below but perhaps I've missed something: Thanks, Robin --- core.py.orig 2009-12-13 15:14:14.000000000 +0000 +++ core.py 2009-12-13 15:14:53.000000000 +0000 @@ -66,7 +66,7 @@ 'identity', 'ids', 'indices', 'inner', 'innerproduct', 'isMA', 'isMaskedArray', 'is_mask', 'is_masked', 'isarray', 'left_shift', 'less', 'less_equal', 'load', 'loads', 'log', 'log10', - 'logical_and', 'logical_not', 'logical_or', 'logical_xor', + 'log2', 'logical_and', 'logical_not', 'logical_or', 'logical_xor', 'make_mask', 'make_mask_descr', 'make_mask_none', 'mask_or', 'masked', 'masked_array', 'masked_equal', 'masked_greater', 'masked_greater_equal', 'masked_inside', 'masked_invalid', @@ -1124,6 +1124,8 @@ _DomainGreater(0.0)) log10 = _MaskedUnaryOperation(umath.log10, 1.0, _DomainGreater(0.0)) +log2 = _MaskedUnaryOperation(umath.log2, 1.0, + _DomainGreater(0.0)) tan = _MaskedUnaryOperation(umath.tan, 0.0, _DomainTan(1e-35)) arcsin = _MaskedUnaryOperation(umath.arcsin, 0.0,

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nan_to_num and bool arrays
by Nicolas Rougier 12 Dec '09

12 Dec '09

Hello, Using both numpy 1.3.0 and 1.4.0rc1 I got the following exception using nan_to_num on a bool array, is that the expected behavior ? >>> import numpy >>> Z = numpy.zeros((3,3),dtype=bool) >>> numpy.nan_to_num(Z) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/lib/python2.6/dist-packages/numpy/lib/type_check.py", line 374, in nan_to_num maxf, minf = _getmaxmin(y.dtype.type) File "/usr/lib/python2.6/dist-packages/numpy/lib/type_check.py", line 307, in _getmaxmin f = getlimits.finfo(t) File "/usr/lib/python2.6/dist-packages/numpy/core/getlimits.py", line 103, in __new__ raise ValueError, "data type %r not inexact" % (dtype) ValueError: data type <type 'numpy.bool_'> not inexact Nicolas

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non-standard standard deviation
by Dr. Phillip M. Feldman 12 Dec '09

12 Dec '09

All of the statistical packages that I am currently using and have used in the past (Matlab, Minitab, R, S-plus) calculate standard deviation using the sqrt(1/(n-1)) normalization, which gives a result that is unbiased when sampling from a normally-distributed population. NumPy uses the sqrt(1/n) normalization. I'm currently using the following code to calculate standard deviations, but would much prefer if this could be fixed in NumPy itself: def mystd(x=numpy.array([]), axis=None): """This function calculates the standard deviation of the input using the definition of standard deviation that gives an unbiased result for samples from a normally-distributed population.""" -- View this message in context: http://old.nabble.com/non-standard-standard-deviation-tp26566808p26566808.h… Sent from the Numpy-discussion mailing list archive at Nabble.com.

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December Webinar: SciPy India with Travis Oliphant
by Amenity Applewhite 11 Dec '09

11 Dec '09

Next Friday Enthought will be hosting our monthly Scientific Computing with Python Webinar: Summary of SciPy India Friday December 18 1pm CST/ 7pm UTC Register at GoToMeeting Enthought President Travis Oliphant is currently in Kerala, India as the keynote speaker at SciPy India 2009. Due to a training engagement, Travis missed SciPy for the first time this summer, so he’s excited for this additional opportunity to meet and collaborate with the scientific Python community. Speakers at the event include Jarrod Millman, David Cournapeau, Christopher Burns, Prabhu Ramachandran, and Asokan Pichai — a great group. We’re looking forward to hearing Travis’ review of the proceedings. Hope to see you there! Enthought Media

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Re: [Numpy-discussion] [Nipy-devel] Impossibility to build nipy on recent numpy?
by Gael Varoquaux 11 Dec '09

11 Dec '09

On Thu, Dec 10, 2009 at 05:29:07PM +0530, David Cournapeau wrote: > On Thu, Dec 10, 2009 at 5:13 PM, Gael Varoquaux > <gael.varoquaux(a)normalesup.org> wrote: > > Does anybody have a suggestion on how to cleanly solve the problem? I > > fear I won't be the only one to have it. > The clean solution is to tell the deb package maintainer to fix the > packaging, and not to put numpy in /usr/include (at least, it should > be in /usr/include/numpy-version, like python). OK, so your point is that numpy headers should be retrieved via numpy.distutils only, right? We can probably make this argument, but it is going to make the life of those who use other build chains hard. Gaël

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Test reports of numpy 1.4.0.dev built with Visual Studio
by Christoph Gohlke 11 Dec '09

11 Dec '09

Hello, I built Windows binaries of numpy-1.4.0rc2.dev7996 for Python 2.4, 2.5, and 2.6 (32 and 64-bit) using Microsoft Visual Studio 2003 and 2008. Looks good: the 'setup.py bdist_wininst' builds were all successful. The output of the 'python.exe -c "import numpy;numpy.test()"' runs are attached. The Python 2.6 VS2008 builds pass all tests (except know failures). The Python 2.4 and 2.5 VS2003 builds fail 8 tests, mostly due to NaN/Inf/0 and formatting mismatches. I also compiled and test-run some Python extension packages (matplotlib, PIL, PyMOL, etc) against these numpy builds and did not notice any failures due to the numpy upgrade. Test platform: VS2008 9.0.30729.1 SP; VS2003 7.1.3088; Windows 7 Pro 64-bit; Core2Quad CPU; No external ATLAS/MKL/BLAS/LAPACK/FFTW libraries used; Python versions are noted in the attachments. Best, Christoph Gohlke Laboratory for Fluorescence Dynamics University of California, Irvine http://www.lfd.uci.edu/~gohlke/

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Fixed-point arithemetic...any solution yet?
by Ruben Salvador 11 Dec '09

11 Dec '09

Hello everybody. I've seen this question arise sometimes on the list, but don't know if something has "happened" yet or not. I mean, any solution feasible to use more or less right out of the box? I'm just a hardware engineer, so it would be difficult for me to create my own class for this, since my knowledge of python/numpy is very limited, and, just don't have the time/knowledge to be more than a simple user of the language, not a developer. I have just come across this: http://www.dilloneng.com/documents/downloads/demodel/ but haven't used it yet. I'll give it a try and see how it works and come back to the list to report somehow. But, is there any "official" plans for this within the numpy developers? Is there any code around that may be used? I just need to test my code with fixed point arithmetic (I'm modelling hardware....) Thanks for the good work to all the Python/Numpy developers (and all the projects related, matplotlib and so on....) and for the possiblity of freeing from matlab!!! I'm determined to do research with as many free software design tools as possible....though this fixed-point arithmetic issue is still a chain! Regards!

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