Mailman 3 December 2011 - NumPy-Discussion

strange conversion integer to float
by Alex van Houten 17 Dec '11

17 Dec '11

Try this: $ python Python 2.7.1 (r271:86832, Apr 12 2011, 16:15:16) [GCC 4.6.0 20110331 (Red Hat 4.6.0-2)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import numpy as np >>> np.__version__ '1.5.1' >>> time=[] >>> time.append(20091231) >>> time_array=np.array(time,'f') >>> time_array array([ 20091232.], dtype=float32) 20091231--->20091232 Why? Note: >>> float(20091231) 20091231.0 Thanks, Alex.

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Problem installing NumPy with Python 3.2.2/MacOS X 10.7.2
by McNicol, Adam 17 Dec '11

17 Dec '11

Hi There, I am very new to numpy and have really only started investigating it as one of my students needs some functionality from matplotlib. I have managed to install everything under Windows for work in class but I use a Mac at home and have been struggling all night to get it to build and install. I should mention that I am using Python 3.2.2 both in school and at home and it isn't an option to use Python 2.7 as all of the rest of my class is taught in Python 3. I also have the most recent version of Xcode installed. I have installed the correct build of gcc-4.2 with Fortran (gcc-4.2 (Apple build 5666.3) with GNU Fortran 4.2.4 for Mac OS X 10.7 (Lion)) from http://r.research.att.com/tools/ I then followed the install instructions but the build fails with the following message: File "numpy/core/setup.py", line 271, in check_types "Cannot compile 'Python.h'. Perhaps you need to "\ SystemError: Cannot compile 'Python.h'. Perhaps you need to install python-dev|python-devel. I have got no idea what to do with this error message. Any help would be much appreciated. Kind Regards, Adam.

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Array min from argmin along an axis?
by Ken Basye 17 Dec '11

17 Dec '11

Hi folks, I need an efficient way to get both the min and argmin of a 2-d array along one axis. It seemed to me that the way to do this was to get the argmin and then use it to index into the array to get the min, but I can't figure out how to do it. Here's my toy example: >>> x = np.arange(25).reshape((5,5)) >>> x array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19], [20, 21, 22, 23, 24]]) >>> y = np.abs(x - x.T) >>> y array([[ 0, 4, 8, 12, 16], [ 4, 0, 4, 8, 12], [ 8, 4, 0, 4, 8], [12, 8, 4, 0, 4], [16, 12, 8, 4, 0]]) >>> np.argmin(y, axis=0) array([0, 1, 2, 3, 4]) >>> np.min(y, axis=0) array([0, 0, 0, 0, 0]) Here it seems like there should be a simple way to get the same array that min() returns using the argmin result, which won't need to 'search' in the array. Thanks very much, Ken

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Ann: OpenOpt and FuncDesigner 0.37
by Dmitrey 15 Dec '11

15 Dec '11

Hi all, I'm glad to inform you about new release 0.37 (2011-Dec-15) of our free software: > OpenOpt (numerical optimization): > IPOPT initialization time gap (time till first iteration) for FuncDesigner models has been decreased Some improvements and bugfixes for interalg, especially for "search all SNLE solutions" mode (Systems of Non Linear Equations) Eigenvalue problems (EIG) (in both OpenOpt and FuncDesigner) Equality constraints for GLP (global) solver de Some changes for goldenSection ftol stop criterion GUI func "manage" - now button "Enough" works in Python3, but "Run/Pause" not yet (probably something with threading and it will be fixed in Python instead) > FuncDesigner: Major sparse Automatic differentiation improvements for badly-vectorized or unvectorized problems with lots of constraints (except of box bounds); some problems now work many times or orders faster (of course not faster than vectorized problems with insufficient number of variable arrays). It is recommended to retest your large-scale problems with useSparse = 'auto' | True| False > Two new methods for splines to check their quality: plot and residual Solving ODE dy/dt = f(t) with specifiable accuracy by interalg Speedup for solving 1-dimensional IP by interalg > SpaceFuncs and DerApproximator: > Some code cleanup > > You may trace OpenOpt development information in our recently created entries in Twitter and Facebook, see http://openopt.org for details. > > See also: FuturePlans, this release announcement in OpenOpt forum > > Regards, D. >

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scipy installation problem
by Alex Ter-Sarkissov 15 Dec '11

15 Dec '11

OK thanks guys, reinstalling the interpreter did the trick. I'm quite sure I did it before though, without any effect. More interestingly, I have two interpreters running, one for 2.6 and the other the auto. So the latter one still tells me the module isn't found, the former works just fine. Mystery?

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scipy installation problem
by Alex Ter-Sarkissov 15 Dec '11

15 Dec '11

yeah, I've already removed it, still doesn't work. I'm running Python 2.6 and SciPy version I'm trying to install is scipy-0.10.0-py2.6-python.org-macosx10.3., pydev version is 2.2.4 I've had no trouble running numpy or Tkinter for example. Also none of the other modules I'm using have been added to PythonPath The only problem I've had when installing the interpreter is the error message that sdlib was either not found or without .py files. I didn't add these files yet, but everything seems to be runnning smoothly except this issue with scipy.

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scipy installation problem
by Alex Ter-Sarkissov 15 Dec '11

15 Dec '11

I'm using Eclipse (PyDev) on MacOS. I downloaded scipy010, installed it and added path to .mpkg file to PYTHONPATH and scipy to forced built-in. Nothing worked, I keep getting 'module scipy not found'. I then removed the link to the .mpkg and still nothing works. Strange enough, numpy works just fine. What should I do?

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scipy installation problem
by Alex Ter-Sarkissov 15 Dec '11

15 Dec '11

yes, that's exactly what i did. I'm using Python2.6 both in PyDev and Scipy.

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Re: [Numpy-discussion] Fast Reading of ASCII files
by Chris Barker 15 Dec '11

15 Dec '11

NOTE: Let's keep this on the list. On Tue, Dec 13, 2011 at 9:19 AM, denis <denis-bz-gg(a)t-online.de> wrote: > Chris, > unified, consistent save / load is a nice goal > > 1) header lines with date, pwd etc.: "where'd this come from ?" > > # (5, 5) svm.py bz/py/ml/svm 2011-12-13 Dec 11:56 -- automatic > # 80.6 % correct -- user info > 245 39 4 5 26 > ... > I'm not sure I understand what you are expecting here: What would be automatic? if itparses a datetime on the header, what would it do with it? But anyway, this seems to me: - very application specific -- this is for the users code to write - not what we are talking about at this point anyway -- I think this discussion is about a lower-level, does-the-simple-things-fast reader -- that may or may not be able to form the basis of a higher-level fuller featured reader. > 2) read any CSVs: comma or blank-delimited, with/without column names, > a la loadcsv() below > yup -- though the column name reading would be part of a higher-level reader as far as I'm concerned. > 3) sparse or masked arrays ? > > sparse probably not, that seem pretty domain dependent to me -- though hopefully one could build such a thing on top of the lower level reader. Masked support would be good -- once we're convinced what the future of masked arrays are in numpy. I was thinking that the masked array issue would really be a higher-level feature -- it certainly could be if you need to mask "special value" stype files (i.e. 9999), but we may have to build it into the lower level reader for cases where the mask is specified by non-numerical values -- i.e. there are some met files that use "MM" or some other text, so you can't put it into a numerical array first. > > Longterm wishes: beyond the scope of one file <-> one array > but essential for larger projects: > 1) dicts / dotdicts: > Dotdict( A=anysizearray, N=scalar ... ) <-> a directory of little > files > is easy, better than np.savez > (Haven't used hdf5, I believe Matlabv7 does.) > > 2) workflows: has anyone there used visTrails ? > outside of the spec of this thread... > > Anyway it seems to me (old grey cynic) that Numpy/scipy developers > prefer to code first, spec and doc later. Too pessimistic ? > > Well, I think many of us believe in a more agile style approach -- incremental development. But really, as an open source project, it's really about scratching an itch -- so there is usually a spec in mind for the itch at hand. In this case, however, that has been a weakness -- clearly a number of us hav written small solutions to our particular problem at hand, but no we haven't arrived at a more general purpose solution yet. So a bit of spec-ing ahead of time may be called for. On that: I"ve been thinking from teh botom-up -- imaging what I need for the simple case, and how it might apply to more complex cases -- but maybe we should think about this another way: What we're talking about here is really about core software engineering -- optimization. It's easy to write a pure-python simple file parser, and reasonable to write a complex one (genfromtxt) -- the issue is performance -- we need some more C (or Cython) code to really speed it up, but none of us wants to write the complex case code in C. So: genfromtxt is really nice for many of the complex cases. So perhaps another approach is to look at genfromtxt, and see what high performance lower-level functionality we could develop that could make it fast -- then we are done. This actually mirrors exactly what we all usually recommend for python development in general -- write it in Python, then, if it's really not fast enough, write the bottle-neck in C. So where are the bottle necks in genfromtxt? Are there self-contained portions that could be re-written in C/Cython? -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

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numpy.mean problems
by ferreirafm 14 Dec '11

14 Dec '11

Hi everyone, I'm quite new to numpy and python either. Could someone, please, tell me what I'm doing wrong? Here goes my peace of code: def stats(filename): """Utilility to perform some basic statistics on columns.""" tab = get_textab(filename) stat_list = [ ] for row in sort_tab(tab): if row['length'] >= 15: stat_list.append(row) stat_array = np.array(stat_list) print type(sort_tab(tab)) print type(stat_array) #print stat_array.mean(axis=0) print np.mean(stat_array, axis=0) Which results in: <type 'numpy.ndarray'> <type 'numpy.ndarray'> Traceback (most recent call last): File "/home/ferreirafm/bin/cross.py", line 213, in <module> main() File "/home/ferreirafm/bin/cross.py", line 204, in main stats(filename) File "/home/ferreirafm/bin/cross.py", line 146, in stats print np.mean(stat_array, axis=0) File "/usr/lib64/python2.7/site-packages/numpy/core/fromnumeric.py", line 2374, in mean return mean(axis, dtype, out) TypeError: unsupported operand type(s) for +: 'numpy.void' and 'numpy.void' -- View this message in context: http://old.nabble.com/numpy.mean-problems-tp32945124p32945124.html Sent from the Numpy-discussion mailing list archive at Nabble.com.

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