NumPy-Discussion January 2013

numpy-discussion@python.org

80 participants
67 discussions

phase unwrapping (1d)
by Neal Becker 17 Jan '13

17 Jan '13

np.unwrap was too slow, so I rolled by own (in c++). I wanted to be able to handle the case of unwrap (arg (x1) + arg (x2)) Here, phase can change by more than 2pi. I came up with the following algorithm, any thoughts? In the following, y is normally set to pi. o points to output i points to input nint1 finds nearest integer value_t prev_o = init; for (; i != e; ++i, ++o) { *o = cnt * 2 * y + *i; value_t delta = *o - prev_o; if (delta / y > 1 or delta / y < -1) { int i = nint1<int> (delta / (2*y)); *o -= 2*y*i; cnt -= i; } prev_o = *o; }

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numpydoc for python 3?
by Jaakko Luttinen 16 Jan '13

16 Jan '13

Hi! I'm trying to use numpydoc (Sphinx extension) for my project written in Python 3.2. However, installing numpydoc gives errors shown at http://pastebin.com/MPED6v9G and although it says "Successfully installed numpydoc", trying to import numpydoc raises errors.. Could this be fixed or am I doing something wrong? Thanks! Jaakko

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find points unique within some epsilon
by Neal Becker 16 Jan '13

16 Jan '13

Any suggestion how to take a 2d complex array and find the set of points that are unique within some tolerance? (My preferred metric here would be Euclidean distance)

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Travis failures with no errors
by Ondřej Čertík 16 Jan '13

16 Jan '13

Hi, I found these recent weird "failures" in Travis, but I can't find any problem with the log and all tests pass. Any ideas what is going on? https://travis-ci.org/numpy/numpy/jobs/3570123 https://travis-ci.org/numpy/numpy/jobs/3539549 https://travis-ci.org/numpy/numpy/jobs/3369629 Ondrej

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argsort
by Mads Ipsen 16 Jan '13

16 Jan '13

Hi, I simply can't understand this. I'm trying to use argsort to produce indices that can be used to sort an array: from numpy import * indices = array([[4,3],[1,12],[23,7],[11,6],[8,9]]) args = argsort(indices, axis=0) print indices[args] gives: [[[ 1 12] [ 4 3]] [[ 4 3] [11 6]] [[ 8 9] [23 7]] [[11 6] [ 8 9]] [[23 7] [ 1 12]]] I thought this should produce a sorted version of the indices array. Any help is appreciated. Best regards, Mads -- +-----------------------------------------------------+ | Mads Ipsen | +----------------------+------------------------------+ | Gåsebæksvej 7, 4. tv | | | DK-2500 Valby | phone: +45-29716388 | | Denmark | email: mads.ipsen(a)gmail.com | +----------------------+------------------------------+

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algorithm for faster median calculation ?
by Jerome Caron 15 Jan '13

15 Jan '13

Dear all, I am new to the Numpy-discussion list. I would like to follow up some possibly useful information about calculating median. The message below was posted today on the AstroPy mailing list. Kind regards Jerome Caron #---------------------------------------- I think the calculation of median values in Numpy is not optimal. I don't know if there are other libraries that do better? On my machine I get these results: >>> data = numpy.random.rand(5000,5000) >>> t0=time.time();print numpy.ma.median(data);print time.time()-t0 0.499845739822 15.1949999332 >>> t0=time.time();print numpy.median(data);print time.time()-t0 0.499845739822 4.32100009918 >>> t0=time.time();print aspylib.astro.get_median(data);print time.time()-t0 [ 0.49984574] 0.90499997139 >>> The median calculation in Aspylib is using C code from Nicolas Devillard (can be found here: http://ndevilla.free.fr/median/index.html) interfaced with ctypes. It could be easily re-used for other, more official packages. I think the code also finds quantiles efficiently. See: http://www.aspylib.com/

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dtype "reduction"
by Nicolas Rougier 15 Jan '13

15 Jan '13

Hi all, I'm looking for a way to "reduce" dtype1 into dtype2 (when it is possible of course). Is there some easy way to do that by any chance ? dtype1 = np.dtype( [ ('vertex', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('normal', [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]), ('color', [('r', 'f4'), ('g', 'f4'), ('b', 'f4'), ('a', 'f4')]) ] ) dtype2 = np.dtype( [ ('vertex', 'f4', 3), ('normal', 'f4', 3), ('color', 'f4', 4)] ) Nicolas

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1.8 release
by Charles R Harris 14 Jan '13

14 Jan '13

Now that 1.7 is nearing release, it's time to look forward to the 1.8 release. I'd like us to get back to the twice yearly schedule that we tried to maintain through the 1.3 - 1.6 releases, so I propose a June release as a goal. Call it the Spring Cleaning release. As to content, I'd like to see the following. Removal of Python 2.4-2.5 support. Removal of SCons support. The index work consolidated. Initial stab at removing the need for 2to3. See Pauli's PR for scipy. Miscellaneous enhancements and fixes. Thoughts? Chuck

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Insights / lessons learned from NumPy design
by Mike Anderson 14 Jan '13

14 Jan '13

Hello all, In the Clojure community there has been some discussion about creating a common matrix maths library / API. Currently there are a few different fledgeling matrix libraries in Clojure, so it seemed like a worthwhile effort to unify them and have a common base on which to build on. NumPy has been something of an inspiration for this, so I though I'd ask here to see what lessons have been learned. We're thinking of a matrix library with roughly the following design (subject to change!) - Support for multi-dimensional matrices (but with fast paths for 1D vectors and 2D matrices as the common cases) - Immutability by default, i.e. matrix operations are pure functions that create new matrices. There could be a "backdoor" option to mutate matrices, but that would be unidiomatic in Clojure - Support for 64-bit double precision floats only (this is the standard float type in Clojure) - Ability to support multiple different back-end matrix implementations (JBLAS, Colt, EJML, Vectorz, javax.vecmath etc.) - A full range of matrix operations. Operations would be delegated to back end implementations where they are supported, otherwise generic implementations could be used. Any thoughts on this topic based on the NumPy experience? In particular would be very interesting to know: - Features in NumPy which proved to be redundant / not worth the effort - Features that you wish had been designed in at the start - Design decisions that turned out to be a particularly big mistake / success Would love to hear your insights, any ideas+advice greatly appreciated! Mike.

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How many build systems do we need?
by Charles R Harris 13 Jan '13

13 Jan '13

Hi All, In the continuing proposal for cleanups, note that we currently support three (3!) build systems, distutils, scons, and bento. That's a bit much to maintain when contemplating changes, and scons and bento both have external dependencies. Can we dispense with any of these? Thoughts? Chuck

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NumPy-Discussion January 2013