Mailman 3 March 2010 - NumPy-Discussion

Renamed numpy.lib.io ?
by Pierre GM 26 Mar '10

26 Mar '10

All, I'm surprised by the renaming of numpy.lib.io to numpy.lib.npyio. Was it really necessary ? Is it to take effect with the incoming numpy 2.0 release ? BTW, when is it scheduled ?

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should ndarray implement __round__ for py3k?
by Darren Dale 25 Mar '10

25 Mar '10

A simple test in python 3: >>> import numpy as np >>> round(np.arange(10)) Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: type numpy.ndarray doesn't define __round__ method Here is some additional context: http://bugs.python.org/issue7261 Darren

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partial autocorrelation function
by Mark Bakker 25 Mar '10

25 Mar '10

Anybody know of a partial autocorrelation function in numpy? Maybe in scipy? Thanks, Mark ps. I know, not too difficult, but if it is around I'd be happy to use it.

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draft release guide
by Ralf Gommers 25 Mar '10

25 Mar '10

Hi all, At http://github.com/rgommers/NumPy-release-guide you can find a summary of how to set up your system to build numpy binaries on OS X. I still have to add info on scipy (that's turning out to be fairly painful) but for numpy it is pretty complete. Any feedback is appreciated! Cheers, Ralf

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Hiring: Software Developer, Scientific Applications
by Amenity Applewhite 25 Mar '10

25 Mar '10

Enthought is hiring a Software Developer. See the description below, or on our website: http://www.enthought.com/company/sd-scientific-app.php Best, Amenity -- Amenity Applewhite Enthought, Inc. Scientific Computing Solutions www.enthought.com Software Developer The Software Developer at Enthought, Inc. participates in the development of scientific and technical applications involving GUIs, 2- D and 3-D graphics, workflow and pipeline architecture, and numerical algorithms. The position also involves some interaction with clients. Some travel may be required. We are interested both in experienced applicants as well as in recent graduates. Applicants should have a BS, MS, or PhD degree with a strong background in science and mathematics, as well as real experience developing quality software, either commercial or open source. More experienced applicants should also have demonstrated project management skills and the ability to lead a team of strong developers with highly technical backgrounds. Applicants will be measured against the following qualifications: • (Required) Bachelor's Degree in Computer Science or other scientific or engineering field with preferably an M.S. or Ph.D. degree. • (Required) Minimum 2 years of technical lead or development experience with 4 or more years preferred. • Ability to understand a problem domain and then conceive of and implement an intuitive user interface geared toward the scientist or engineer user. • Discipline, pride, and professionalism to write readable, documented, and unit-tested code that serves as an example to others who later study your work. • Strong work ethic and commitment to satisfying the customer. • Experience with Python, and a strong understanding of how to apply its capabilities to develop GUI frameworks, work flow frameworks, and elegant scientific applications. • Strong understanding of statistics, optimization, image processing, signal processing, or other technical area. • Experience with the following: • GUI frameworks such as NetBeans or Eclipse • wxPython, Qt • Low-level 2-D graphics APIs such as Quartz or GDI+ • 3-D graphics, preferably using VTK • Developing or working with plotting APIs • Experience using (and interest in contributing to) SciPy • numeric algorithms Enthought offers competitive salaries and the opportunity to work on varied and interesting technical projects. We are located in Austin, TX, consistently rated as one of the best places to live in the US. Benefits include health, dental, vision and a 401k plan. If you are interested in applying, submit a resume to jobs(a)enthought.com. Code samples and links to previous work are encouraged but not required.

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What does float64 mean on a 32-bit machine?
by reckoner 24 Mar '10

24 Mar '10

How can I have a float64 dtype on a 32-bit machine? For example: In [90]: x = array([1/3],dtype=float32) In [91]: x Out[91]: array([ 0.33333334], dtype=float32) In [92]: x = array([1/3],dtype=float64) In [93]: x Out[93]: array([ 0.33333333]) Obviously, the float32 and float64 representations of 1/3 are different, but what is the meaning of float64 on a 32-bit machine? Shouldn't a 32-bit machine only be able represent float32? Thanks!

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dtype='|S8' -- what does vertical bar mean?
by Reckoner 23 Mar '10

23 Mar '10

Hi, I've been looking through the documentation and occasionally there is a dtype='|S8' reference or something with a "|" in it. I don't know what the "|" this notation means. I can't find it in the documentation. This should be easy. Little help? thanks in advance.

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Column-Specific Conditions and Column-Specific Substitution Values
by Cristiano Fini 23 Mar '10

23 Mar '10

Hi Everyone, a beginner's question on how to perform some data substitution efficiently. I have a panel dataset, or in other words x individuals observed over a certain time span. For each column or individual, I need to substitute a certain value anytime a certain condition is satisfied. Both the condition and the value to be substituted into the panel dataset are individual specific. I can tackle the fact that the condition is individual specific but I cannot find a way to tackle the fact that the value to be substituted is individual specific without using a for – lop. Frankly, considering the size of the dataset the use of a for loop is perfectly acceptable in terms of the time needed to complete task but still it would be nice to learn a way to do this (a task I implement often) in a more efficient way. Thanks in advance Cristiano import numpy as np from copy import deepcopy Data = np.array([[0,4,0], [2,5,7], [2,5,6]]) EditedData = deepcopy(Data) Condition = np.array([0, 5, 6]) # individual-specific condition SubstituteData = np.array([1, 10,100]) # The logic here # if the value of any obssrvation for the 1st individual is 0, substitute 1, # the 2nd individual is 5, substitute 10 # the 3rd individual is 6, substitute 100 # This wouldn't a problem if SubstituteData was not individual specific Data # eg EditedData[Data==Condition] = 555 # As SubstituteData is individual specifc, I need to use a for loop for i in range(np.shape(EditedData)[1]): TempData = EditedData[:, i] # I introduce TempData to increase readability TempData[TempData == Condition[i]] = SubstituteData[i] EditedData[:, i] = TempData print EditedData

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integer division rule?
by Alan G Isaac 22 Mar '10

22 Mar '10

Francesc Altet once provided an example that for integer division, numpy uses the C99 rule: round towards 0. This is different than Python's rule for integer division: round towards negative infinity. But I cannot reproduce his example. (NumPy 1.3.) Did this behavior change at some point? Thanks, Alan Isaac

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[OT] Starving CPUs article featured in IEEE's ComputingNow portal
by Francesc Alted 22 Mar '10

22 Mar '10

Hi, Konrad Hinsen has just told me that my article "Why Modern CPUs Are Starving and What Can Be Done About It", which has just released on the March/April issue of "Computing in Science and Engineering", also made into this month's free-access selection on IEEE's ComputingNow portal: http://www.computer.org/portal/web/computingnow http://www.computer.org/portal/web/computingnow/0310/whatsnew/cise On it, I discuss one of my favourite subjects, memory access, and why it is important for nowadays computing. There are also recommendations for people wanting to squeeze the maximum of performance out of their computers. And, although I tried to be as language-agnostic as I could, there can be seen some Python references here and there :-). Well, sorry about this semi-OT but I could not resist :-) -- Francesc Alted

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NumPy-Discussion March 2010