Mailman 3 December 2011 - NumPy-Discussion

dtype related deprecations
by Ralf Gommers 05 Feb '12

05 Feb '12

Hi, I'm having some trouble cleaning up tests to deal with these two deprecations: DeprecationWarning: Setting NumPy dtype names is deprecated, the dtype will become immutable in a future version DeprecationWarning: DType strings 'O4' and 'O8' are deprecated because they are platform specific. Use 'O' instead They seem fairly invasive, judged by the test noise in both numpy and scipy. Record arrays rely on setting dtype names. There are tests for picking and the buffer protocol that generate warnings for O4/O8. Can anyone comment on the necessity of these deprecations and how to deal with them? Ralf

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GSOC
by Charles R Harris 05 Feb '12

05 Feb '12

Hi All, I thought I'd raise this topic just to get some ideas out there. At the moment I see two areas that I'd like to see addressed. 1. Documentation editor. This would involve looking at the generated documentation and it's organization/coverage as well such things as style and maybe reviewing stuff on the documentation site. This would be more technical writing than coding. 2. Test coverage. There are a lot of areas of numpy that are not well tested as well as some tests that are still doc tests and should probably be updated. This is a substantial amount of work and would require some familiarity with numpy as well as a willingness to ping developers for clarification of some topics. Thoughts? Chuck

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installing matplotlib in MacOs 10.6.8.
by Alex Ter-Sarkissov 24 Jan '12

24 Jan '12

hi everyone, I run python 2.7.2. in Eclipse (recently upgraded from 2.6). I have a problem with installing matplotlib (I found the version for python 2.7. MacOs 10.3, no later versions). If I run python in terminal using arch -i386 python, and then from matplotlib.pylab import * and similar stuff, everything works fine. If I run python in eclipse or just without arch -i386, I can import matplotlib as from matplotlib import * but actually nothing gets imported. If I do it in the same way as above, I get the message no matching architecture in universal wrapper which means there's conflict of versions or something like that. I tried reinstalling the interpreter and adding matplotlib to forced built-ins, but nothing helped. For some reason I didn't have this problem with numpy and tkinter. Any suggestions are appreciated.

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Upgrade to 1.6.x: frompyfunc() ufunc casting issue
by Aditya Sethi 20 Jan '12

20 Jan '12

Hi all, I am facing an issue upgrading numpy from 1.5.1 to 1.6.1. In numPy 1.6, the casting behaviour for ufunc has changed and has become stricter. Can someone advise how to implement the below simple example which worked in 1.5.1 but fails in 1.6.1? >>> import numpy as np >>> def add(a,b): ... return (a+b) >>> uadd = np.frompyfunc(add,2,1) >>> uadd <ufunc 'add (vectorized)'> >>> uadd.accumulate([1,2,3]) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: could not find a matching type for add (vectorized).accumulate, requested type has type code 'l' Thanks, Aditya

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Ufuncs and flexible types, CAPI
by Val Kalatsky 10 Jan '12

10 Jan '12

Hi folks, First post, may not follow the standards, please bear with me. Need to define a ufunc that takes care of various type. Fixed - no problem, userdef - no problem, flexible - problem. It appears that the standard ufunc loop does not provide means to deliver the size of variable size items. Questions and suggestions: 1) Please no laughing: I have to code for NumPy 1.3.0. Perhaps this issue has been resolved, then the discussion becomes moot. If so please direct me to the right link. 2) A reasonable approach here would be to use callbacks and to give the user (read programmer) a chance to intervene at least twice: OnInit and OnFail (OnFinish may not be unreasonable as well). OnInit: before starting the type resolution the user is given a chance to do something (e.g. check for that pesky type and take control then return a flag indicating a stop) before the resolution starts OnFail: the resolution took place and did not succeed, the user is given a chance to fix it. In most of the case these callbacks are NULLs. I could patch numpy with a generic method that does it, but it's a shame not to use the good ufunc machine. Thanks for tips and suggestions. Val Kalatsky

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find location of maximum values
by questions anon 10 Jan '12

10 Jan '12

I have a netcdf file that contains hourly temperature data for a whole month. I would like to find the maximum temperature within that file and also the corresponding Latitude and Longitude and Time and then plot this. Below is the code I have so far. I think everything is working except for identifying the corresponding latitude and longitude. The latitude and longitude always seems to be in the same area (from file to file) and they are not where the maximum values are occurring. Any feedback will be greatly appreciated. from netCDF4 import Dataset import matplotlib.pyplot as plt import numpy as N from mpl_toolkits.basemap import Basemap from netcdftime import utime from datetime import datetime from numpy import ma as MA import os TSFCall=[] for (path, dirs, files) in os.walk(MainFolder): for dir in dirs: print dir path=path+'/' for ncfile in files: if ncfile[-3:]=='.nc': print "dealing with ncfiles:", path+ncfile ncfile=os.path.join(path,ncfile) ncfile=Dataset(ncfile, 'r+', 'NETCDF4') TSFC=ncfile.variables['T_SFC'][:] TIME=ncfile.variables['time'][:] LAT=ncfile.variables['latitude'][:] LON=ncfile.variables['longitude'][:] ncfile.close() TSFCall.append(TSFC) big_array=N.ma.concatenate(TSFCall) tmax=MA.max(TSFC) print tmax t=TIME[tmax] indexTIME=N.where(TIME==t) TSFCmax=TSFC[tmax] print t, indexTIME print LAT[tmax], LON[tmax], TSFCmax cdftime=utime('seconds since 1970-01-01 00:00:00') ncfiletime=cdftime.num2date(t) print ncfiletime timestr=str(ncfiletime) d = datetime.strptime(timestr, '%Y-%m-%d %H:%M:%S') date_string = d.strftime('%Y%m%d_%H%M') map = Basemap(projection='merc',llcrnrlat=-40,urcrnrlat=-33,llcrnrlon=139.0,urcrnrlon=151.0,lat_ts=0,resolution='i') x,y=map(*N.meshgrid(LON,LAT)) map.tissot(LON[tmax], LAT[tmax], 0.15, 100, facecolor='none', edgecolor='black', linewidth=2) map.readshapefile(shapefile1, 'DSE_REGIONS') map.drawcoastlines(linewidth=0.5) map.drawstates() plt.title('test'+' %s'%date_string) ticks=[-5,0,5,10,15,20,25,30,35,40,45,50] CS = map.contourf(x,y,TSFCmax, 15, cmap=plt.cm.jet) l,b,w,h =0.1,0.1,0.8,0.8 cax = plt.axes([l+w+0.025, b, 0.025, h], ) cbar=plt.colorbar(CS, cax=cax, drawedges=True) plt.show() plt.close()

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ANN: Numexpr 2.0 released
by Francesc Alted 08 Jan '12

08 Jan '12

======================== Announcing Numexpr 2.0 ======================== Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python. It wears multi-threaded capabilities, as well as support for Intel's VML library, which allows for squeezing the last drop of performance out of your multi-core processors. What's new ========== This version comes with support for the new iterator in NumPy (introduced in NumPy 1.6), allowing for improved performance in practically all the scenarios (the exception being very small arrays), and most specially for operations implying broadcasting, fortran-ordered or non-native byte orderings. The carefully crafted mix of the new NumPy iterator and direct access to data buffers turned out to be so powerful and flexible, that the internal virtual machine has been completely revamped around this combination. The drawback is that you will need NumPy >= 1.6 to run numexpr 2.0. However, NumPy 1.6 has been released more than 6 months ago now, so we think this is a good time for taking advantage of it. Many thanks to Mark Wiebe for such an important contribution! For some benchmarks on the new virtual machine, see: http://code.google.com/p/numexpr/wiki/NewVM Also, Gaëtan de Menten contributed important bug fixes, code cleanup as well as speed enhancements. Francesc Alted contributed some fixes, and added compatibility code with existing applications (PyTables) too. In case you want to know more in detail what has changed in this version, see: http://code.google.com/p/numexpr/wiki/ReleaseNotes or have a look at RELEASE_NOTES.txt in the tarball. Where I can find Numexpr? ========================= The project is hosted at Google code in: http://code.google.com/p/numexpr/ You can get the packages from PyPI as well: http://pypi.python.org/pypi/numexpr Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy! -- Francesc Alted

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numpy 1.7.0 release?
by Ralf Gommers 07 Jan '12

07 Jan '12

Hi all, It's been a little over 6 months since the release of 1.6.0 and the NA debate has quieted down, so I'd like to ask your opinion on the timing of 1.7.0. It looks to me like we have a healthy amount of bug fixes and small improvements, plus three larger chucks of work: - datetime - NA - Bento support My impression is that both datetime and NA are releasable, but should be labeled "tech preview" or something similar, because they may still see significant changes. Please correct me if I'm wrong. There's still some maintenance work to do and pull requests to merge, but a beta release by Christmas should be feasible. What do you all think? Cheers, Ralf

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PyInt and Numpy's int64 conversion
by xantares 09 06 Jan '12

06 Jan '12

Hi, I'm using Numpy from the C python api side while tweaking my SWIG interface to work with numpy array types. I want to convert a numpy array of integers (whose elements are numpy's 'int64') The problem is that it this int64 type is not compatible with the standard python integer type: I cannot use PyInt_Check, and PyInt_AsUnsignedLongMask to check and convert from int64: basically PyInt_Check returns false. I checked the numpy config header and npy_int64 does have a size of 8o, which should be the same as int on my x86_64. What is the correct way to do that ? I checked for a Int64_Check function and didn't find any in numpy headers. Regards, x.

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Mersenne Twister: Python vs. NumPy
by Alan G Isaac 30 Dec '11

30 Dec '11

If I seed NumPy's random number generator, I get the expected sequence. If I use the same seed for Python's random number generator, I get a different sequence. 1. Why does the Python sequence differ from others? 2. Can I somehow put both MT's in a common state? Thank you, Alan Isaac

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