Mailman 3 May 2008 - NumPy-Discussion

FW: HELP: PROBLEMS WITH VPYTHON, NUMPY AND PY2EXE PART 2
by andres felipe sierra echeverry 02 May '08

02 May '08

Does anyone could help me generate an executable program that functions for the bounce.py with py2exe? Thank you bounce.py: from visual import * from numpy import * ball = sphere(pos=(0,4,0), color=color.red) example = zeros((10,10),dtype=float) print (example) setup.py: from distutils.core import setup import py2exe opts = { 'py2exe': {'packages':['numarray'] } } setup(windows=[{"script" : "bounce.py"}], options=opts) _________________________________________________________________ Discover the new Windows Vista http://search.msn.com/results.aspx?q=windows+vista&mkt=en-US&form=QBRE

1 0

types as functions convert 1 elm arrays to scalars
by Hoyt Koepke 01 May '08

01 May '08

Hello, I have a quick question that I'm hoping will improve my numpy understanding. I noticed some behavior when using float64 to convert a matrix type that I didn't expect: In [35]: b1 = array([1.0]) In [36]: float64(b1) Out[36]: 1.0 In [37]: b2 = array([1.0, 2.0]) In [38]: float64(b2) Out[38]: array([ 1., 2.]) I didn't expect calling float64 would convert b1 to a scalar. Seems like an inconsistency. I assume this is intentional, as someone would have noticed it a long time ago if not, so could someone explain the reasoning behind it? (or point me to a source that will help?) Thanks! --Hoyt -- +++++++++++++++++++++++++++++++++++ Hoyt Koepke UBC Department of Computer Science http://www.cs.ubc.ca/~hoytak/ hoytak(a)gmail.com +++++++++++++++++++++++++++++++++++

2 4

recarray fun
by Christopher Barker 01 May '08

01 May '08

Hi folks, Someone on the wxPython list posted a nifty recarray example that I don't quite understand. The idea is to have an array for an RGBA image: rgbarec = numpy.dtype({'r':(numpy.uint8,0), 'g':(numpy.uint8,1), 'b':(numpy.uint8,2), 'a':(numpy.uint8,3)}) A = numpy.zeros(shape, dtype=(numpy.uint32, rgbarec) ) what I don't understand is having BOTH numpy.uint32 and rgbrec as the dtype. How does that work? Actually, with a bit of testing, it's pretty cool -- if you index like: A[i,j] ==> uint32 A['r'][i,j] ==> uint8 pretty cool really. it seems that numpy is treating it as both a recarray and a regular uint32 array, which makes some sense, but I'm still confused by the semantics. also: >>> A array([[4278190080, ... [4278190080, 4278190080, 4278190080, 4278190080, 4278190080]], dtype=uint32) but: >>> A.dtype dtype(('>u4', [('r', '|u1'), ('g', '|u1'), ('b', '|u1'), ('a', '|u1')])) so what is the dtype? Also, I see advantages and disadvantages to either way. If you do: A = numpy.zeros(shape, dtype=(numpy.uint32, rgbarec) ) then you can do: A[i,j]['r'] to get the red value of a pixel A[i,j] = (red, green, blue, alpha) to set a pixel A[:,:] = (red, green, blue, alpha) to make the whole image one color With the "dual dtype" approach: A = numpy.zeros(shape, dtype=(numpy.uint32, rgbarec) ) You need to set the pixels separately: A['r'][i,j] = red B['r'][i,j] = green ... or construct uint32 values: C = numpy.uint32(red) C += numpy.uint32(green) << 8 C += numpy.uint32(blue) << 16 Which is really ugly! (and I may not even have it right!). Is there a better way? One more idea -- is there a way to have two arrays, each with a different dtype, that point to the same data? maybe: RGBImage = numpy.zeros(shape, dtype=rgbarec ) IntImage = RGBImage.view() IntImage.dtype = numpy.uint32 That way you could work with it either way, whichever was easier in the context. So -- what are folks' thoughts about how best to deal with images as numpy arrays? (I'll put it in a Wiki page if I get some good comments) -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker(a)noaa.gov

6 15

ndarray subclassing
by ctw 30 Apr '08

30 Apr '08

Hi! I ran into some strange (at least to me) issues with sublasses of ndarray. The following minimal class definition illustrates the problem: ==================================================== import numpy as np class TestArray(np.ndarray): def __new__(cls, data, info=None, dtype=None, copy=False): subarr = np.array(data, dtype=dtype, copy=copy) subarr = subarr.view(cls) return subarr def __array_finalize__(self,obj): print "self: ",self.shape print "obj: ",obj.shape ===================================================== When I run this code interactively with IPython and then generate TestArray instances, __array_finalize__ seems to get called when printing out arrays with more than 1 dimension and self.shape seems to drop a dimension. Everything works fine if the array has just 1 dimension: In [3]: x = TestArray(np.arange(5)) self: (5,) obj: (5,) In [4]: x Out[4]: TestArray([0, 1, 2, 3, 4]) This is all expected behavior. However things change when the array is 2-D: In [5]: x = TestArray(np.zeros((2,3))) self: (2, 3) obj: (2, 3) In [6]: x Out[6]: self: (3,) obj: (2, 3) self: (3,) obj: (2, 3) TestArray([[ 0., 0., 0.], [ 0., 0., 0.]]) Now when printing out the array, __array_finalize__ seems to get called twice and each time self seems to only refer to one row of the array. Can anybody explain what is going on and why? This behavior seems to lead to problems when the __array_finalize__ method performs checks on the shape of the array. In the matrix class this seems to be circumvented with a special _getitem flag that bypasses the shape checks in __array_finalize__ and an analogous solution works for my class, too. However, I'm still puzzled by this behavior and am hoping that somebody here can shed some light on it. Thanks! CTW

2 2

numpy release
by Ondrej Certik 30 Apr '08

30 Apr '08

Hi Jarrod, any news with the 1.0.5? If you have same prerelease, I'd like to test it. Debian has just moved from python2.4 to python2.5 yesterday, so I'd like to test numpy in advance, I am sure there will be some issues to fix. Ondrej

22 152