Mailman 3 December 2009 - NumPy-Discussion

fsum
by Alan G Isaac 16 Dec '09

16 Dec '09

Does NumPy have an equivalent to Python's math.fsum? Thanks, Alan Isaac

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Failure building scipy.special.lambertw
by Chris 16 Dec '09

16 Dec '09

Building a current checkout of scipy on OSX 10.6 fails when trying to compile scipy.special.lambertw, giving the message: Warning: No configuration returned, assuming unavailable. The full failure is here: http://img.skitch.com/20091216-d4b8ueqh27g4fqwebu3e3wgfkq.jpg

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no ordinary Bessel functions?
by Dr. Phillip M. Feldman 15 Dec '09

15 Dec '09

When I issue the command np.lookfor('bessel') I get the following: Search results for 'bessel' --------------------------- numpy.i0 Modified Bessel function of the first kind, order 0. numpy.kaiser Return the Kaiser window. numpy.random.vonmises Draw samples from a von Mises distribution. I assume that there is an ordinary (unmodified) Bessel function in NumPy, but have not been able to figure out how to access it. Also, I need to operate sometimes on scalars, and sometimes on arrays. For operations on scalars, are the NumPy Bessel functions significantly slower than the SciPy Bessel functions? -- View this message in context: http://old.nabble.com/no-ordinary-Bessel-functions--tp26789343p26789343.html Sent from the Numpy-discussion mailing list archive at Nabble.com.

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small doc error in numpy.random.randn
by Nadav Horesh 15 Dec '09

15 Dec '09

The 2nd line of the doc string randn([d1, ..., dn]) should be randn(d1, ..., dn) Nadav

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doctest improvements patch (and possible regressions)
by Paul Ivanov 15 Dec '09

15 Dec '09

Hi Numpy-devs, I'm a long time listener, first time caller. I grabbed 1.4.0rc1 and was happy that all the tests passed. But then I tried: >>> import numpy as np >>> np.test(doctests=True) ... Ran 1696 tests in 22.027s FAILED (failures=113, errors=24) I looked at some of the failures, and they looked like trivial typos. So I said to myself: "Self, wouldn't it be cool if all the doctests worked?" Well, I didn't quite get there spelunking and snorkeling in the source code a few evenings during the past week, but I got close. With the attached patch (which patches the 1.4.0rc1 tarball), I now get: >>> import numpy as np >>> np.test(doctests=True) ... Ran 1696 tests in 20.937s FAILED (failures=33, errors=25) I marked up suspicious differences with XXX, since I don't know if they're significant. In particular: - shortening a defchararray by strip does not change it's dtype to a shorter one (apparently it used to?) - the docstring for seterr says that np.seterr() should reset all errors to defaults, but clearly doesn't do that - there's a regression in recfunctions which may be related to #1299 and may have been fixed - recfunctions.find_duplicates ignoremask flag has no effect. There are a few other things, but they're minor (e.g. I added a note about how missing values are filled with usemask=False in recfunctions.merge_arrays). I think the only code I added was to testing/noseclasses.py. There, if a test fails, I give a few more chances to pass by normalizing the endianness of both desired and actual output, as well as default int size for 32 and 64 bit machines. This is done just using replace() on the strings. Everything else is docstring stuff, so I was hoping to sneak this into 1.4.0, since it would make it that much more polished. Does that sound crazy? best, Paul Ivanov

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Problem with set_fill_value for masked structured array
by Thomas Robitaille 15 Dec '09

15 Dec '09

Hi, The following code doesn't seem to work: import numpy.ma as ma t = ma.array(zip([1,2,3],[4,5,6]),dtype=[('a',int),('b',int)]) print repr(t['a']) t['a'].set_fill_value(10) print repr(t['a']) As the output is masked_array(data = [1 2 3], mask = [False False False], fill_value = 999999) masked_array(data = [1 2 3], mask = [False False False], fill_value = 999999) (and no exception is raised) Am I doing something wrong? Thanks in advance for any help, Thomas

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ANN: job opening at Tradelink
by John Hunter 14 Dec '09

14 Dec '09

We are looking to hire a quantitative researcher to help research and develop trading ideas, and to develop and support infrastructure to put these trading strategies into production. We are looking for someone who is bright and curious with a quantitative background and a strong interest in writing good code and building systems that work. Experience with probability, statistics and time series is required, and experience working with real world data is a definite plus. We do not require a financial background, but are looking for someone with an enthusiasm to dive into this industry and learn a lot. We do most of our data modeling and production software in python and R. We have a lot of ideas to test and hopefully put into production, and you'll be working with a fast paced and friendly small team of traders, programmers and quantitative researchers. Applying: Please submit a resume and cover letter to qsjobs(a)trdlnk.com. In your cover letter, please address how your background, experience and skills will fit into the position described above. We are looking for a full-time, on-site candidate only. About Us: TradeLink Holdings LLC is a diversified alternative investment, trading and software firm. Headquartered in Chicago, TradeLink Holdings LLC includes a number of closely related entities. Since its organization in 1979, TradeLink has been actively engaged in the securities, futures, options, and commodities trading industries. Engaged in the option arbitrage business since 1983, TradeLink has a floor trading and/or electronic trading interface in commodity options, financial futures and options, and currency futures and options at all major U.S. exchanges. TradeLink is involved in various market-making programs in many different exchanges around the world, including over-the-counter derivatives markets. http://www.tradelinkllc.com

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Does Numpy support CGI-scripting?`
by yogesh karpate 14 Dec '09

14 Dec '09

Does Numpy Support CGI scripting? DO scipy and matplotlib also support? Regards ~ymk

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structured array from ordinary array
by Ernest Adrogué 14 Dec '09

14 Dec '09

Hi, How does one generate a structured array from a normal array? I use the 'view' method, but this way I get a superfluous dimension that I do not want. Example: In [619]: a = np.array([[1,2,3],[1,2,3],[1,2,4]],int) In [620]: struct = np.dtype([('a',int),('b',int),('c',int)]) In [621]: a.view(struct) Out[621]: array([[(1, 2, 3)], [(1, 2, 3)], [(1, 2, 4)]], dtype=[('a', '<i8'), ('b', '<i8'), ('c', '<i8')]) In [622]: a.view(struct)['a'] Out[622]: array([[1], [1], [1]]) In [623]: a.view(struct)['a'].shape Out[623]: (3, 1) I'd like the last array to have shape (3,). What am I doing wrong?? Ernest

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indices of values contained in a list
by Ernest Adrogué 14 Dec '09

14 Dec '09

Hi, Suppose I have a flat array, and I want to know the indices corresponding to values contained in a list of arbitrary lenght. Intuitively I would have done: a = np.array([1,2,3,4]) np.nonzero(a in (0,2,4)) However the "in" operator doesn't work element-wise, instead it compares the whole array with each member of the list. I have found that this does the trick: b = (0,2,4) reduce(np.logical_or, [a == i for i in b]) then pass the result to np.nonzero to get the indices, but, is there a numpy function that can handle this situation? Cheers. Ernest

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