Mailman 3 March 2010 - NumPy-Discussion

SciPy 2010 Tutorials: brainstorming and call for proposals
by Brian Granger 27 Mar '10

27 Mar '10

Greetings everyone, This year, there will be two days of tutorials (June 28th and 29th) before the main SciPy 2010 conference. Each of the two tutorial tracks (intro, advanced) will have a 3-4 hour morning and afternoon session both days, for a total of 4 intro sessions and 4 advanced sessions. The main tutorial web page for SciPy 2010 is here: http://conference.scipy.org/scipy2010/tutorials.html We are currently in the process of planning the tutorial sessions. You can help us in two ways: Brainstorm/vote on potential tutorial topics ============================================ To help us plan the tutorials, we have setup a web site that allow everyone in the community to brainstorm and vote on tutorial ideas/topics. The website for brainstorming/voting is here: http://conference.scipy.org/scipy2010/tutorialsUV.html The tutorial committee will use this information to help select the tutorials. Please jump in and let us know what tutorial topics you would like to see. Tutorial proposal submissions ============================= We are now accepting tutorial proposals from individuals or teams that would like to present a tutorial. Tutorials should be focused on covering a well defined topic in a hands on manner. We want to see tutorial attendees coding! We are pleased to offer tutorial presenters stipends this year for the first time: * 1 Session: $1,000 (half day) * 2 Sessions: $1,500 (full day) Optionally, part of this stipend can be applied to the presenter's registration costs. To submit a tutorial proposal please submit the following materials to 2010tutorials(a)scipy.org by April 15: * A short bio of the presenter or team members. * Which track the tutorial would be in (intro or advanced). * A short description and/or outline of the tutorial content. * A list of Python packages that attendees will need to have installed to follow along.

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Renamed numpy.lib.io ?
by Pierre GM 27 Mar '10

27 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 26 Mar '10

26 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 25 Mar '10

25 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 24 Mar '10

24 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 23 Mar '10

23 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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