Mailman 3 April 2007 - NumPy-Discussion

Re: [Numpy-discussion] [Python-3000] ABC PEP isinstance issue Was: PEP 31XX: A Type Hierarchy for Numbers (and other algebraic entities)
by Jean-Paul Calderone 26 Apr '07

26 Apr '07

On Wed, 25 Apr 2007 18:10:23 -0400, Jim Jewett <jimjjewett(a)gmail.com> wrote: >The current ABC proposal is to use isinstance as the test; Jeffrey >Yaskin's numbers PEP highlighted the weakness there with a concrete >example. > >If you need to an abstraction less powerful than an existing ABC, >you're out of luck; you can't just assert that the existing class is >already sufficient, nor can you expect everyone else to use multiple >annotations. I'm sure everyone is already aware of the behavior of the classImplements and directlyProvides functions available in zope.interface, which exactly satisfy this use-case in the interface world. Jean-Paul

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Re: [Numpy-discussion] [Python-3000] ABC PEP isinstance issue Was: PEP 31XX: A Type Hierarchy for Numbers (and other algebraic entities)
by Phillip J. Eby 26 Apr '07

26 Apr '07

At 06:10 PM 4/25/2007 -0400, Jim Jewett wrote: >I suspect Phillip will say that we really need to make the ABCs >generic functions... Nope; they're *already* generic functions: hash(), len(), iter(), various operator.* functions, and I believe we're adding next(). I've got nothing to add to that list. :)

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Python issue of Computing in Science and Engineering available
by Andrew Straw 26 Apr '07

26 Apr '07

The May/June issue of Computing in Science and Engineering http://computer.org/cise: is out and has a Python theme. Many folks we know and love from the community and mailing lists contribute to the issue. Read articles by Paul Dubois and Travis Oliphant for free online.

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Oddity with numpy.int64 integer division
by Christian Marquardt 26 Apr '07

26 Apr '07

Dear all, this is odd: >>> import numpy as np >>> fact = 28250000L * 86400L >>> nn = np.array([-20905000L]) >>> nn array([-20905000], dtype=int64) >>> nn[0] // fact 0 But: >>> long(nn[0]) // fact -1L Is this a bug in numpy, or in python's implementation of longs? I would think both should give the same, really... (Python 2.5, numpy 1.0.3dev3725, Linux, Intel compilers...) Many thanks for any ideas / advice, Christian

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numpy migration
by Duncan Smith 26 Apr '07

26 Apr '07

Hello, Since moving to numpy I've had a few problems with my existing code. It basically revolves around the numpy scalar types. e.g. ------------------------------------------------ >>> import Numeric as N >>> a = N.array([[0,1],[2,3]]) >>> a array([[0, 1], [2, 3]]) >>> i = a[0,0] >>> 1/i Traceback (most recent call last): File "<pyshell#30>", line 1, in -toplevel- 1/i ZeroDivisionError: integer division or modulo by zero >>> b = a * 1.5 >>> b array([[ 0. , 1.5], [ 3. , 4.5]]) >>> N.floor(b) array([[ 0., 1.], [ 3., 4.]]) >>> ================================ RESTART ================================ >>> import numpy as N >>> a = N.array([[0,1],[2,3]]) >>> a array([[0, 1], [2, 3]]) >>> i = a[0,0] >>> 1/i 0 >>> b = a * 1.5 >>> b array([[ 0. , 1.5], [ 3. , 4.5]]) >>> N.floor(b) array([[ 0., 1.], [ 3., 4.]]) >>> a = N.array([[0,1],[2,3]], dtype='O') >>> a array([[0, 1], [2, 3]], dtype=object) >>> i = a[0,0] >>> 1/i Traceback (most recent call last): File "<pyshell#45>", line 1, in -toplevel- 1/i ZeroDivisionError: integer division or modulo by zero >>> b = a * 1.5 >>> b array([[0.0, 1.5], [3.0, 4.5]], dtype=object) >>> N.floor(b) Traceback (most recent call last): File "<pyshell#48>", line 1, in -toplevel- N.floor(b) AttributeError: 'float' object has no attribute 'floor' >>> ---------------------------------------------- An additional problem involves classes that have e.g. __rmul__ methods defined and are sufficiently similar to numpy arrays that my classes' __rmul__ methods are not invoked when using numpy scalars. Using the 'O' dtype gives me Python types that raise zero division errors appropriately (for my code) and the desired calls to e.g. __rmul__ methods, but reduced functionality in other repects. I might (I hope) be missing something obvious; but it seems like, to be safe, I'm going to have to do a lot of explicit conversions to Python types (or abandon catching zero division errors, and documenting some of my classes to highlight that whether scalar * a equals a * scalar depends on whether a.__rmul__ is called, which depends on the type of scalar). I suppose I might get round both issues by subclassing existing numpy dtypes. Any ideas? Cheers. TIA. Duncan

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using the functions nonzero and where
by Dennis Cooke 25 Apr '07

25 Apr '07

I'm an ex-Matlab user trying to come up to speed with python and numpy. I'm confused on how to use the Numpy functions nonzero() and where(). In matlab, the equivalent function (find(a>0) ) would return an array, whereas in numpy, where() or nonzero() will return a single element tuple. For example: In [35]: x = [ 0, 0, 0, 99, 0, 1, 5] # and I wish to file the index of the first non-zero element of x - which is 3. In [36]: from numpy import nonzero In [37]: i=nonzero(x) In [38]: i Out[38]: (array([3, 5, 6]),) In [39]: i[0] Out[39]: array([3, 5, 6]) so nonzero() has output a tuple with a single element = the string 'array([3, 5, 6])' How do I convert this string (or the original tuple) into an array where the first element = 3 (the index of the first non-zero element of my original array x).

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Bus Error with string in ndarray with named fields
by Per B. Sederberg 25 Apr '07

25 Apr '07

Hi Folks: I'm getting a very strange bus error in the recent versions of numpy (almost current svn). Here's how you can (hopefully) replicate it: On my MacBook: Python 2.4.3 (#1, Apr 7 2006, 10:54:33) [GCC 4.0.1 (Apple Computer, Inc. build 5250)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import numpy as N >>> N.version.version '1.0.2.dev3569' >>> fields = [('x',(N.str,40))] >>> dat = N.zeros(1,fields) >>> dat Bus error On my linux cluster: Python 2.4.3 (#1, May 8 2006, 11:36:25) [GCC 3.2.3 20030502 (Red Hat Linux 3.2.3-49)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import numpy as N >>> N.version.version '1.0.2.dev3567' >>> fields = [('x',(N.str,40))] >>> dat = N.zeros(1,fields) >>> dat Segmentation fault But it works on my linux desktop with an older numpy: Python 2.4.4c1 (#2, Oct 11 2006, 20:00:03) [GCC 4.1.2 20060928 (prerelease) (Ubuntu 4.1.1-13ubuntu5)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import numpy as N >>> N.version.version '1.0rc1' >>> fields = [('x',(N.str,40))] >>> dat = N.zeros(1,fields) >>> dat array([('',)], dtype=[('x', '|S40')]) >>> Does anyone have any clue as to how to deal with this? Is there a better way to create an ndarray with named fields (recarray)? My real code has a number of fields of various types, but I was able to narrow it down to anytime I use a string type. For example, this works on the machines where using a string fails: >>> import numpy as N >>> fields = [('x',N.bool),('y',N.int32)] >>> dat = N.zeros(1,fields) >>> dat array([(False, 0)], dtype=[('x', '|b1'), ('y', '<i4')]) >>> Thanks for any help, Per

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Getting subarray
by Tommy Grav 25 Apr '07

25 Apr '07

I have two arrays: a = numpy.array([0,1,2,3,4,5,6,7,8,9]) b = numpy.array([0,0,1,1,2,2,0,1,2,3]) I would like to get the part of a that corresponds to where b is equal to i. For example: i = 0 => ([0,1,6]) i = 1 => ([2,3,7]) Cheers Tommy

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ScipyTest Warning?
by mark 24 Apr '07

24 Apr '07

Hello All - I have a piece of code that works fine for me, but a friend tries to run it and gets this warning. He claims to have updated his Python (2.4), Scipy and numpy. A Does anybody know what import triggers this Warning? I didn't think I imported ScipyTest. Thanks, Mark Warning (from warnings module): File "C:\Python24\lib\site-packages\numpy\testing\numpytest.py", line 63 DeprecationWarning) DeprecationWarning: ScipyTest is now called NumpyTest; please update your code

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uint64 typecasting with scalars broken (?)
by Christian Marquardt 24 Apr '07

24 Apr '07

Hello, The following is what I expected... >>> y = 1234 >>> x = array([1], dtype = "uint64") >>> print x + y, (x + y).dtype.type [1235] <type 'numpy.uint64'> but is this the way it should be? (numpy 1.0.2, Linux, Intel comilers) >>> print x[0] + y, type(x[0] + y) 1235.0 <type 'numpy.float64'> Thanks, Christian.

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