Mailman 3 May 2007 - NumPy-Discussion

numpy array sharing between processes?
by Ray Schumacher 12 May '07

12 May '07

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Re: [Numpy-discussion] numpy array sharing between processes?
by Ray Schumacher 12 May '07

12 May '07

1 0

problems with calculating numpy.float64
by michael.stoelzle＠email.de 12 May '07

12 May '07

Hello out there, i try to run this Python-code snippet after I have imported: import numpy as Numeric import numpy as numpy Numeric.Int = Numeric.int32 Numeric.Float = Numeric.float64 Code: if m < maxN and n < maxN and self.activeWide[m+1, n+1]: try: deltaX = x[m+1] - x[m] except TypeError: print '#' * 70 print 'type x', type(x) type_a, type_b = map(type, (x[m + 1], x[m])) print 'type_a, type_b', type_a, type_b, type_a is type_b print '#' * 70 raise My result for this part looks like this: ---------------------------------------- <type 'numpy.ndarray'> <type 'numpy.float64'> <type 'numpy.float64'> True ---------------------------------------- Inappropriate argument type. unsupported operand type(s) for -: 'numpy.float64' and 'numpy.float64' line 161 in setIDs in file F:\Unstrut_2007\arc_egmo\gw.py line 71 in __init__ in file F:\Unstrut_2007\arc_egmo\mf2000\mf2000exe.py line 380 in makeInactiveFringeMap in file F:\Unstrut_2007\arc_egmo\mf2000\mf2000exe.py This means that Python cant execute the line: deltaX = x[m+1] - x[m] You can see the "Types" of the values between the --- --- two lines above. I reproduce this problem in my Python 2.4 interpreter on my Windows XP Prof. OS and there are no problems in calculating with these Types, everything is OK. Thanks for your help Greetz from Berlin, Germany Michael

2 1

Getting started wiki page
by Gael Varoquaux 12 May '07

12 May '07

Hi all, I would very much link the Getting Started wiki page ( http://scipy.org/Getting_Started ) to the front page. But I am not sure it is of good enough quality so far. Could people please have a look and make comments, or edit the page. Cheers, Gaël

6 6

interrupted svn updates
by George Nurser 11 May '07

11 May '07

I'm trying to update numpy from svn. My first try was very slow, but eventially produced 72 updated files; gave message at end: svn: REPORT request failed on '/svn/numpy/!svn/vcc/default' svn: REPORT of '/svn/numpy/!svn/vcc/default': Could not read response body: connection was closed by server. (http://svn.scipy.org) 2nd try produced 4 more & then died with above error 3rd & 4th just dies with above error. Any ideas -- is thw problem here or there? Many thanks, George Nurser.

3 6

numpy version of Interactive Data Analysis tutorial available
by Perry Greenfield 11 May '07

11 May '07

I have updated the "Using Python for Interactive Data Analysis" tutorial to use numpy instead of numarray (finally!). There are further improvements I would like to make in its organization and formatting (in the process including suggestions others have made to that end), but I'd rather get this version out, which I believe addresses all the content changes needed to make it useful for numpy, without delaying it any further. The tutorial, as well as other supporting material and information, can be obtained from: http://www.scipy.org/wikis/topical_software/Tutorial I'm sure errors remain; please let me know of any you find. Perry

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Re: [Numpy-discussion] subclassing ndarray and recarray
by Bernhard Voigt 11 May '07

11 May '07

Sorry for the spam, but I didn't modify the subject of the previous mail. Here's the message again but now with the right subject: Dear Pierre, I've attached the arraydict implementation file. You can run it and take a look at the following example on your own: In [25]: run arraydict.py creates an arraydict with 10 elements in the current scope. Keys are the index numbers of items In [26]: a Out[26]: arraydict([(-0.51430764775177518, 0.17962503931139237), (-1.4037792804089142, 0.37263515556827359), (1.9048324627948983, 1.4155903391279885), (0.077070370958404841 , -1.4284963747790793), (0.20177037521016888, 0.25023158062312373), (0.88821059412119174, 0.29415143595187959), (0.46224769848661729, -0.80670670514715426), (-0.079049832245684654, - 2.5738917233959899), (-0.562854982548048, 2.0708323443154897), (-2.4176013660591691, 0.36401660943002978)], dtype=[('x', '<f8'), ('y', '<f8')]) In [27]: a.keys() Out[27]: set([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) Now select items from a, based on a cut criteria on some field (this could also be from another arraydict object) In [28]: foo = a[a.x>0] In [29]: foo Out[29]: arraydict([(1.9048324627948983, 1.4155903391279885), (0.077070370958404841, -1.4284963747790793), (0.20177037521016888, 0.25023158062312373), (0.88821059412119174, 0.29415143595187959 ), (0.46224769848661729, -0.80670670514715426)], dtype=[('x', '<f8'), ('y', '<f8')]) In [30]: foo.keys() Out[30]: set([2, 3, 4, 5, 6]) This works, because I modified the __getitem__ method and deal with keys of arraydict type as given back by calling a.x>0. A new arraydict is created with the selected items and keys. Here's the stuff I couldn't figure out how to deal with, making selections on slices, lists etc... In [31]: bar = a[1:6:2] The selection is correct, because it's passed to ndarray.__getitem__ In [32]: bar Out[32]: arraydict([(-1.4037792804089142, 0.37263515556827359), (0.077070370958404841, -1.4284963747790793), (0.88821059412119174, 0.29415143595187959)], dtype=[('x', '<f8'), ('y', '<f8')]) In [33]: bar.keys() Out[33]: set([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) However, the keys are not restricted :-( since bar is not a new arraydict. If I could get the indexes in the __array_finalize__ method it would be easy to set the mapping of the bar instance. Another solution could be to overwrite also the handling of slices, lists etc. in the __getitem__ method. Thanks for your help! Bernhard On May 9, 10:29 pm, Pierre GM <pgmdevl...(a)gmail.com> wrote: > On Wednesday 09 May 2007 08:54:37 Bernhard Voigt wrote: > > > I'm trying to subclass ndarray or recarray to build a record array that has > > a dictionary with a mapping of keys to array indexes and vice versa. > > Bernhard, > Could you send me the rest of your code ? I'd like to test a couple of things > before committing a proper answer. > _______________________________________________ > Numpy-discussion mailing list > Numpy-discuss...@scipy.orghttp://projects.scipy.org/mailman/listinfo/numpy-discussion

2 2

problems with calculating numpy.float64
by michael.stoelzle＠email.de 11 May '07

11 May '07

Hello out there, i try to run this Python-code snippet after I have imported: import numpy as Numeric import numpy as numpy Numeric.Int = Numeric.int32 Numeric.Float = Numeric.float64 Code: if m < maxN and n < maxN and self.activeWide[m+1, n+1]: try: deltaX = x[m+1] - x[m] except TypeError: print '#' * 70 print 'type x', type(x) type_a, type_b = map(type, (x[m + 1], x[m])) print 'type_a, type_b', type_a, type_b, type_a is type_b print '#' * 70 raise My result for this part looks like this: ---------------------------------------- <type 'numpy.ndarray'> <type 'numpy.float64'> <type 'numpy.float64'> True ---------------------------------------- Inappropriate argument type. unsupported operand type(s) for -: 'numpy.float64' and 'numpy.float64' line 161 in setIDs in file F:\Unstrut_2007\arc_egmo\gw.py line 71 in __init__ in file F:\Unstrut_2007\arc_egmo\mf2000\mf2000exe.py line 380 in makeInactiveFringeMap in file F:\Unstrut_2007\arc_egmo\mf2000\mf2000exe.py This means that Python cant execute the line: deltaX = x[m+1] - x[m] You can see the "Types" of the values between the --- --- two lines above. I reproduce this problem in my Python 2.4 interpreter on my Windows XP Prof. OS and there are no problems in calculating with these Types, everything is OK. Thanks for your help Greetz from Berlin, Germany Michael

1 0

Re: [Numpy-discussion] 6 new messages in 4 topics - abridged
by Bernhard Voigt 10 May '07

10 May '07

Dear Pierre, I've attached the arraydict implementation file. You can run it and take a look at the following example on your own: In [25]: run arraydict.py creates an arraydict with 10 elements in the current scope. Keys are the index numbers of items In [26]: a Out[26]: arraydict([(-0.51430764775177518, 0.17962503931139237), (-1.4037792804089142, 0.37263515556827359), (1.9048324627948983, 1.4155903391279885), (0.077070370958404841, -1.4284963747790793), (0.20177037521016888, 0.25023158062312373), (0.88821059412119174, 0.29415143595187959), (0.46224769848661729, -0.80670670514715426), (-0.079049832245684654, -2.5738917233959899), (-0.562854982548048, 2.0708323443154897), (-2.4176013660591691, 0.36401660943002978)], dtype=[('x', '<f8'), ('y', '<f8')]) In [27]: a.keys() Out[27]: set([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) Now select items from a, based on a cut criteria on some field (this could also be from another arraydict object) In [28]: foo = a[a.x>0] In [29]: foo Out[29]: arraydict([(1.9048324627948983, 1.4155903391279885), (0.077070370958404841, -1.4284963747790793), (0.20177037521016888, 0.25023158062312373), (0.88821059412119174, 0.29415143595187959), (0.46224769848661729, -0.80670670514715426)], dtype=[('x', '<f8'), ('y', '<f8')]) In [30]: foo.keys() Out[30]: set([2, 3, 4, 5, 6]) This works, because I modified the __getitem__ method and deal with keys of arraydict type as given back by calling a.x>0. A new arraydict is created with the selected items and keys. Here's the stuff I couldn't figure out how to deal with, making selections on slices, lists etc... In [31]: bar = a[1:6:2] The selection is correct, because it's passed to ndarray.__getitem__ In [32]: bar Out[32]: arraydict([(-1.4037792804089142, 0.37263515556827359), (0.077070370958404841, -1.4284963747790793), (0.88821059412119174, 0.29415143595187959)], dtype=[('x', '<f8'), ('y', '<f8')]) In [33]: bar.keys() Out[33]: set([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) However, the keys are not restricted :-( since bar is not a new arraydict. If I could get the indexes in the __array_finalize__ method it would be easy to set the mapping of the bar instance. Another solution could be to overwrite also the handling of slices, lists etc. in the __getitem__ method. Thanks for your help! Bernhard On May 9, 10:29 pm, Pierre GM <pgmdevl...(a)gmail.com> wrote: > On Wednesday 09 May 2007 08:54:37 Bernhard Voigt wrote: > > > I'm trying to subclass ndarray or recarray to build a record array that has > > a dictionary with a mapping of keys to array indexes and vice versa. > > Bernhard, > Could you send me the rest of your code ? I'd like to test a couple of things > before committing a proper answer. > _______________________________________________ > Numpy-discussion mailing list > Numpy-discuss...@scipy .orghttp://projects.scipy.org/mailman/listinfo/numpy-discussion

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scipy for centos4.4?
by Neal Becker 09 May '07

09 May '07

Anyone know where to find usable rpms from scipy on centos4.4?

3 2