Mailman 3 December 2010 - NumPy-Discussion

MultiIter version of PyArray_IterAllButAxis ?
by John Salvatier 01 Dec '10

01 Dec '10

Hello, I am writing a UFunc creation utility, and I would like to know: is there a way to mimic the behavior ofPyArray_IterAllButAxis for multiple arrays at a time? I would like to be able to write UFuncs that take an axis argument and also take multiple array arguments, for example I want to be able to create a moving average with a weighting that changes according to another array. Best Regards, John

2 2

printoption to allow hexified floats?
by Ken Basye 01 Dec '10

01 Dec '10

Hi Numpy folks, When working with floats, I prefer to have exact string representations in doctests and other reference-based testing; I find it helps a lot to avoid chasing cross-platform differences that are really about the string conversion rather than about numerical differences. Since Python 2.6, the, the hex() method on floats has been available and it gives an exact representation. Is there any way to have Numpy arrays of floats printed using this representation? If not, would there be interest in adding that? On a somewhat related note, is there a table someplace which shows which versions of Python are supported in each release of Numpy? I found an FAQ that mentioned 2.4 and 2.5, but since it didn't mention 2.6 or 2.7 (much less 3.1), I assume it's out of date. This relates to the above since it would be harder to support a new hex printoption for Pythons before 2.6. Thanks, Ken B.

3 2

A faster median (Wirth's method)
by Sturla Molden 01 Dec '10

01 Dec '10

We recently has a discussion regarding an optimization of NumPy's median to average O(n) complexity. After some searching, I found out there is a selection algorithm competitive in speed with Hoare's quick select. It has the advantage of being a lot simpler to implement. In plain Python: import numpy as np def wirthselect(array, k): """ Niklaus Wirth's selection algortithm """ a = np.ascontiguousarray(array) if (a is array): a = a.copy() l = 0 m = a.shape[0] - 1 while l < m: x = a[k] i = l j = m while 1: while a[i] < x: i += 1 while x < a[j]: j -= 1 if i <= j: tmp = a[i] a[i] = a[j] a[j] = tmp i += 1 j -= 1 if i > j: break if j < k: l = i if k < i: m = j return a Now, the median can be obtained in average O(n) time as: def median(x): """ median in average O(n) time """ n = x.shape[0] k = n >> 1 s = wirthselect(x, k) if n & 1: return s[k] else: return 0.5*(s[k]+s[:k].max()) The beauty of this is that Wirth select is extremely easy to migrate to Cython: import numpy ctypedef numpy.double_t T # or whatever def wirthselect(numpy.ndarray[T, ndim=1] array, int k): cdef int i, j, l, m cdef T x, tmp cdef T *a _array = np.ascontiguousarray(array) if (_array is array): _array = _array.copy() a = <T *> _array.data l = 0 m = <int> a.shape[0] - 1 with nogil: while l < m: x = a[k] i = l j = m while 1: while a[i] < x: i += 1 while x < a[j]: j -= 1 if i <= j: tmp = a[i] a[i] = a[j] a[j] = tmp i += 1 j -= 1 if i > j: break if j < k: l = i if k < i: m = j return _array For example, we could have a small script that generates withselect for all NumPy dtypes (T as template), and use a dict as jump table. Chad, you can continue to write quick select using NumPy's C quick sort in numpy/core/src/_sortmodule.c.src. When you are done, it might be about 10% faster than this. :-) Reference: http://ndevilla.free.fr/median/median.pdf Best regards, Sturla Molden

12 28

Re: [Numpy-discussion] Python versions for NumPy 1.5
by Charles R Harris 01 Dec '10

01 Dec '10

On Wed, Dec 1, 2010 at 7:26 AM, John Hornstein <John.Hornstein(a)nrl.navy.mil>wrote: > Does NumPy 1.5 work with Python 2.7 or Python 3.x? > > > Yes, both. NumPy 1.5.1 fixes some small bugs and that is what you should use. Chuck

1 0

Python versions for NumPy 1.5
by John Hornstein 01 Dec '10

01 Dec '10

Does NumPy 1.5 work with Python 2.7 or Python 3.x?

1 0

numpy speed question
by Jean-Luc Menut 01 Dec '10

01 Dec '10

Hello all, I have a little question about the speed of numpy vs IDL 7.0. I did a very simple little check by computing just a cosine in a loop. I was quite surprised to see an order of magnitude of difference between numpy and IDL, I would have thought that for such a basic function, the speed would be approximatively the same. I suppose that some of the difference may come from the default data type of 64bits in numpy and 32 bits in IDL. Is there a way to change the numpy default data type (without recompiling) ? And I'm not an expert at all, maybe there is a better explanation, like a better use of the several CPU core by IDL ? I'm working with windows 7 64 bits on a core i7. any hint is welcome. Thanks. Here the IDL code : Julian1 = SYSTIME( /JULIAN , /UTC ) for j=0,9999 do begin for i=0,999 do begin a=cos(2*!pi*i/100.) endfor endfor Julian2 = SYSTIME( /JULIAN , /UTC ) print, (Julian2-Julian1)*86400.0 print,cpt end result: % Compiled module: $MAIN$. 2.9999837 The python code: from numpy import * from time import time time1 = time() for j in range(10000): for i in range(1000): a=cos(2*pi*i/100.) time2 = time() print time2-time1 result: In [2]: run python_test_speed.py 24.1809999943

9 11