Mailman 3 February 2008 - NumPy-Discussion

import issue with new Python
by Dinesh B Vadhia 14 Feb '08

14 Feb '08

I upgraded to Python 2.5.2c1 today, and got the following error for: > import numpy > import scipy Traceback (most recent call last): File "C:\ ... .py", line 19, in <module> import scipy ImportError: No module named scipy I'm using Numpy 1.0.4 and Scipy 0.6. Any ideas? Dinesh

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unexpected downcast
by Alan G Isaac 14 Feb '08

14 Feb '08

On Tue, 12 Feb 2008, dmitrey apparently wrote: > from numpy import * > a = array((1.0, 2.0), float128) > b=asfarray(a) > type(a[0]) > #<type 'numpy.float128'> > type(b[0]) > #<type 'numpy.float64'> > __version__ > '1.0.5.dev4767' Dmitrey noted an unexpected down cast (above). Is there a reason for it? Or should there be a ticket? Thank you, Alan Isaac

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sort method raises unexpected error with axis=None
by Matthew Brett 14 Feb '08

14 Feb '08

Hi, I just noticed this: >From the sort method docstring: axis : integer Axis to be sorted along. None indicates that the flattened array should be used. Default is -1. In [40]: import numpy as N In [41]: a = N.arange(10) In [42]: N.sort(a, None) Out[42]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) In [43]: a.sort(None) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /home/mb312/<ipython console> in <module>() TypeError: an integer is required Perhaps the sort method is calling the c code directly, and this is not checking for axis=None? Matthew

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New bug with "setup,py develop"
by Robert Kern 13 Feb '08

13 Feb '08

I've just updated the SVN trunk to get the latest numscons merge. Something broke the support I put in for the setuptools "develop" command. In order to make sure that setuptools' "develop" works with numpy.distutils' "build_src", we override the "develop" command to reinitialize the "build_src" command to add the --inplace option. This used to work as of r4772, but now any Fortran Extensions have the generated sources added twice. This causes links to fail since the same symbol shows up twice. David, any ideas? -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco

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Median advice
by Matthew Brett 13 Feb '08

13 Feb '08

Hi, I'm just doing the median implementation. This is just to pull together a couple of threads, about what the call signature, behavior should be. We've already established that we're going to leave the default behavior of taking the median over the first axis, even though it differs from that of max, min, mean etc - for compatibility - and change to axis=None (flatten) behavior around v1.1 to harmonize with the others. Suggestion 1: def median(a, axis=0, out=None) (same signature as max, min etc) Problem - there is no memory saving implementation in median at the moment for the out= argument. It's possible to imagine one, but it would be a little annoying to code. Should we put this in the signature, behavior without memory-saving in order to make space for a memory saving implementation in the future? Suggestion 2: def median(a, axis=0, scratch_input=False) Here, according to a suggestion by Anne A - we could allow the routine to use the input matrix a as scratch space to calculate the median, saving memory if the user does not need to preserve the values of a. a would be in an undefined state on return. Any votes, thoughts?. Matthew

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asfarray() drops precision (float128->float64) - is it correct?
by dmitrey 12 Feb '08

12 Feb '08

As for me, it yields lots of inconveniences (lots of my code should be rewritten, since I didn't know it before): from numpy import * a = array((1.0, 2.0), float128) b=asfarray(a) type(a[0]) #<type 'numpy.float128'> type(b[0]) #<type 'numpy.float64'> __version__ '1.0.5.dev4767' Shouldn't it be changed? (I.e. let's left 128). As for me I use asfarray() very often since I don't know does user provide arrays as numpy ndarray or matrix or Python list/tuple. D.

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Re: [Numpy-discussion] Numpy-discussion Digest, Vol 17, Issue 20
by matthew yeomans 12 Feb '08

12 Feb '08

> > matthew yeomans wrote: > > Thanks I been trying to compile a code that uses random,pylab and > > numpy with py2exe > > the code of setup.py(compiles mycode.py into mycode.exe) follows > > > > #Start here > > from distutils.core import setup > > import py2exe > > import pylab > > import numpy > > import glob > > import scipy > > import random > > import os > > > > setup( console=['mycode.py'],options={'py2exe': > > {"skip_archive":1,'packages':['matplotlib','pytz']',}},data_files=[ > matplotlib.get_py2exe_datafiles()]) > > > > #End here > > > > It works well for codes that uses pylab only. But It i add more > > modules i get trouble > > > > Is there any good books on how to use py2exe? > > Matthew: > > I've only used py2exe with a couple of imports (numpy and os). You're > importing an awful lot. Slimming this down to only import the things > you really need from each module may help, e.g. > > from numpy import foo > from random import bar > etc. > > # Steve I tried so, I reduced everything example from pylab import plot from pylab import axis .... and so on I think I noticed is that when i call from numpy import array it confuses array from numpy to array module. If there a way how to tell py2exe that it requires array from numpy and not module array? thanks for all the help. Matthew Yeomans

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[ANN] Release of the second PyTables video
by Ivan Vilata i Balaguer 11 Feb '08

11 Feb '08

====================================== Release of the second PyTables video ====================================== Carabos [1]_ is happy to announce the second of a series of videos dedicated to introducing the main features of PyTables to the public in a visual and easy to grasp manner: http://www.carabos.com/videos/pytables-2-tables PyTables [2]_ is a Free/Open Source package designed to handle massive amounts of data in a simple, but highly efficient way, using the HDF5 file format and NumPy data containers. .. [1] http://www.carabos.com/ .. [2] http://www.pytables.org/ Our second video explains how to work with tables, PyTables' main data container. It shows how to: * describe the structure of a table * create a table * iterate over a table * access tables by blocks * handle big tables * query a table The video is only 15 minutes long, so you can watch it while you enjoy a nice cup of coffee. If you are used to SQL databases, you may also be interested in the introduction to tables at http://www.pytables.org/moin/HintsForSQLUsers You can also see more on table queries in the latest video about ViTables (our PyTables GUI) at http://www.carabos.com/videos/vitables-2-queries More videos about PyTables will be published in the near future, so stay tuned on www.pytables.org for further announcements. We would like to hear your opinion on the video so we can do it better the next time. We are also open to suggestions for the topics of future videos. You can contact us at pytables(a)carabos.com. Best regards, :: Ivan Vilata i Balaguer >qo< http://www.carabos.com/ Cárabos Coop. V. V V Enjoy Data ""

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Setting contents of buffer for array object
by Matthew Brett 11 Feb '08

11 Feb '08

Hi, I am sorry if I have missed something obvious, but is there any way in python of doing this: import numpy as np a = np.arange(10) b = np.arange(10)+1 a.data = b.data # raises error, but I hope you see what I mean ? Thanks a lot for any pointers. Matthew

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Re: [Numpy-discussion] Setting contents of buffer for array object
by Damian R. Eads 11 Feb '08

11 Feb '08

Matthew Brett wrote: >>> import numpy as np >>> a = np.arange(10) >>> b = np.arange(10)+1 >>> a.data = b.data # raises error, but I hope you see what I mean >>> >>> ? >> Not really, no. Can you describe your use case in more detail? > > Yes - I am just writing the new median implementation. To allow > future optimization, I would like to have the same signature as > mean(): > > def median(a, axis=0, dtype=None, out=None) > > (axis=0 to change to axis=None default at some point). > > To do this, I need to copy the results of the median calculation in > the routine into the array object given by 'out' - when passed. My understanding of numerical routines that accept an "out" parameter is that this is a convention for in-place algorithms. When None is passed in the out parameter, it's the caller's way of indicating that in-place is not needed, and a new array is allocated to store the result; otherwise, the result is stored in the 'out' array. Either way, the result is returned. One can break from this convention by allocating more memory than provided by the out array but that's a performance issue that may or may not be unavoidable. Remember that A[:] = <expr> sets the value of the elements in A to the values of array elements in the expression expr, and this copying is done in-place. To copy an array C, and make the copy contiguous, use the .copy() method on C. Assigning the .data buffers is not something I have seen before in non-constructor (or npn=pseudo-constructor like from_buffer) code. I think it might even be dangerous if you don't do it right. If one does not properly recalculate the strides of A, slicing operations on A may not behave as expected. If this is library code, reassigning the .data buffer can confuse the user, since it messes up array view semantics. Suppose I'm an ignorant user and I write the following code: A=numpy.random.rand(10,20) dummy_input=numpy.random.rand(10,20) B=A.T C=B[0::-1,:] then I use a library function foo (suppose foo accepts an input array inp and an output array out, and assigns out.data to something else) foo(in=dummy_input, out=B) Now, A and B point to two different .data buffers, B's base points to A, and C's base points to B but A and C share the same .data buffer. As a user, I may expect B and C to be a view of A (certainly B isn't), and C to be a view of B (which is verified by checking 'C.base is B') but changing C's values changes A's but not B's. That's confusing. Also, suppose B's new data buffer has less elements than its original data buffer. I may be clever and set B's size and strides attributes accordingly but changing C's values might cause the manipulation of undefined memory. Damian

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NumPy-Discussion February 2008