Mailman 3 January 2008 - NumPy-Discussion

data type specifications
by Gary Pajer 23 Jan '08

23 Jan '08

Occasionally I find myself poking into docstrings and googling randomly trying to find the proper way to specify a data type, or trying to remind myself just what a "float" is. I can never find the info easily. Preferable: Is there a docstring somewhere that lists the data types and acceptable ways to specify them? Less Preferable: Is there a web page? Least Preferable: I have The Book, but it's usually not accessible when I need it for this kind of on-the-fly question. thanks, gary

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How to build on Solaris 10 (x86) using sunperf?
by Peter Ward 23 Jan '08

23 Jan '08

Hi, I have been trying to build numpy on an opteron based Solaris 10 machine, with poor results. After skimming over the discussion archives I noticed a branch for numpy.sunperf in the svn repository. Unfortunately this is no longer available, so I was curious as to the best approach to building numpy on my problem platform. Thanks, Pete

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location of ma in maskedarray branch
by Matt Knox 23 Jan '08

23 Jan '08

I noticed that the new masked array module resides in numpy/ma in the maskedarray branch as opposed to numpy/core/ma like it does in the current trunk. Was this intentional? Code that explicitly imports ma from the core subfolder will break from this change (like the __init__.py script for the ma subfolder in matplotlib for example). - Matt

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Looking to access C array in numpy.
by Clarke, Trevor 22 Jan '08

22 Jan '08

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I'm calling some python code from a C++ app via an intermediary library (i.e. I can't directly create Python C objects like Buffers). I'm passing a void* (cast to a long) to the python method and I'd like to use numpy to access that memory as an array. I'll know what the C data type is and the minimum length of the data. It's a continuous array but is multiple dimensions (either 2 dimensional or 3 dimensional BIP). I was able to find a may to create an array accessor over a python buffer but I'm not sure how to back an array with a void* (as a long) or create a buffer object for that memory inside python. Could someone point me in the right direction? - ------------------------------ Trevor R.H. Clarke tclarke(a)ball.com Ball Aerospace & Technologies Corp GPG key available on random.sks.keyserver.penguin.de -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.6 (MingW32) iD8DBQFHlkvo+xUTKUxH/LkRAuzCAJ4ojQRgMgQj6vbhnSU3pxfiilIRYACePqiR hZyD05z1CgJuOtzw7pdJ9TQ= =H+HS -----END PGP SIGNATURE----- This message and any enclosures are intended only for the addressee. Please notify the sender by email if you are not the intended recipient. If you are not the intended recipient, you may not use, copy, disclose, or distribute this message or its contents or enclosures to any other person and any such actions may be unlawful. Ball reserves the right to monitor and review all messages and enclosures sent to or from this email address.

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SciPy/NumPy Doc-day on Friday Jan 25th
by Travis E. Oliphant 22 Jan '08

22 Jan '08

It's time for another doc-day for SciPy/NumPy. We will convene on IRC during the day on Friday. I will try and spend some time in the afternoon/evening on Friday night (Central Standard Time), but will be logged on to IRC during the rest of the day. Come join us at irc.freenode.net (channel scipy). We may update the list of priorities which is still located on the SciPy Trac Wiki: http://projects.scipy.org/scipy/scipy/wiki/DocDays I look forward to "irc-eeing" as many of you as can participate on Friday-Saturday (depending on your timezone). -Travis O.

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Optimizing speed for large-array inter-element algorithms (specifically, color space conversion)
by theodore test 22 Jan '08

22 Jan '08

Hello all, I'm scratching my head over how to make this image color space conversion from "RGB" to "HSV" quicker. It requires input from all three bands of a given pixel at each pixel, and thus can't be easily flattened. I've already implemented psyco and removed the extra step of calling colorsys's rgb_to_hsv function by adapting the code into my primary for loop. Right now it takes around 9 seconds for a single 1600x1200 RGB image, a conversion that I've seen implemented more or less instantly in, say, ImageJ. What can I do to make this conversion more efficient? Thank you in advance, Theodore Test ------------ #Necessary imports and hand-waving at psyco usage. import numpy from numpy import * from scipy.misc import pilutil import psyco psyco.full() # Read image file, cram it into a normalized array with one row per pixel # with each column holding the given pixel's R,G, or B band-value. s = pilutil.imread('A_Biggish_Image.tif') r,c,d = s.shape l = r*c t = t.reshape(l,3) t = t/255.0 # Cycle through all of the pixels, converting them to HSV space and putting them # back into the array. This is the big time-waster. # # The conversion is adapted directly from colorsys.rgb_to_hsv to reduce call time for x in range(0,l): tr = float(t[x,0]) tg = float(t[x,1]) tb = float(t[x,2]) maxc = max(tr, tg, tb) minc = min(tr, tg, tb) v = maxc if minc == maxc: t[x,0],t[x,1],t[x,2] = 0.0, 0.0, v else: s = (maxc-minc) / maxc rc = (maxc-tr) / (maxc-minc) gc = (maxc-tg) / (maxc-minc) bc = (maxc-tb) / (maxc-minc) if tr == maxc: h = bc-gc elif tg == maxc: h = 2.0+rc-bc else: h = 4.0+gc-rc h = (h/6.0) % 1.0 t[x,0],t[x,1],t[x,2] = h, s, v # Renormalize shape and contents to image-file standards. t = t*255.0 t = t.astype(uint8) t = t.reshape(r,c,d) finaloutputarray = t --------

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compiling c extension
by Danny Chan 22 Jan '08

22 Jan '08

Hi! I am trying to compile a c extension that uses numpy arrays on windows. I can compile the extension file, but once I get to the linking stage I get unresolved references to PyArray_API and import_array. Can anyone help me with the correct linker flags? Thanks, Danny running build running build_py file libaid.py (for module libaid) not found file libaid.py (for module libaid) not found running build_ext building '_libaid' extension writing build\temp.win32-2.5\Release\.\python\_libaid.def x:\eda\mingw\5.1.3\bin\gcc.exe -mno-cygwin -shared -s build\temp.win32-2.5\Release\.\python\libaid_wrap.o build\temp.win32-2.5\Release\.\python\_libaid.def -L../../build/win32/bin -Lx:\eda\python\windows\libs -Lx:\eda\python\windows\PCBuild -laid -lpython25 -lmsvcr71 -o build\lib.win32-2.5\_libaid.pyd build\temp.win32-2.5\Release\.\python\libaid_wrap.o:libaid_wrap.c:(.text+0x2602): undefined reference to `PyArray_API' build\temp.win32-2.5\Release\.\python\libaid_wrap.o:libaid_wrap.c:(.text+0x2635): undefined reference to `PyArray_API' build\temp.win32-2.5\Release\.\python\libaid_wrap.o:libaid_wrap.c:(.text+0x264f): undefined reference to `PyArray_API' build\temp.win32-2.5\Release\.\python\libaid_wrap.o:libaid_wrap.c:(.text+0x2709): undefined reference to `PyArray_API' build\temp.win32-2.5\Release\.\python\libaid_wrap.o:libaid_wrap.c:(.text+0x2752): undefined reference to `PyArray_API' build\temp.win32-2.5\Release\.\python\libaid_wrap.o:libaid_wrap.c:(.text+0x2784): more undefined references to `PyArray_API' follow build\temp.win32-2.5\Release\.\python\libaid_wrap.o:libaid_wrap.c:(.text+0xc216): undefined reference to `import_array' collect2: ld returned 1 exit status error: command 'gcc' failed with exit status 1 --------------------------------- Beginnen Sie den Tag mit den neuesten Nachrichten. Machen Sie Yahoo! zu Ihrer Startseite!

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Docstring page, out of date?
by Matthew Brett 22 Jan '08

22 Jan '08

Hi, Search for the docstring standard, I hit this: http://www.scipy.org/DocstringStandard but I think the current thinking is this: http://projects.scipy.org/scipy/numpy/wiki/CodingStyleGuidelines Is that correct? Does the first page apply to matplotlib in some way? Should we change the first page to match the second now? Matthew

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maskedarray bug?
by Eric Firing 22 Jan '08

22 Jan '08

Pierre, The attached script shows how one can make a masked array with different dimensions for the mask than for the data. I presume this is a bug. It is triggered by the present code in the matplotlib quiver function. Eric

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def of var of complex
by Neal Becker 21 Jan '08

21 Jan '08

I noticed that if I generate complex rv i.i.d. with var=1, that numpy says: var (<real part>) -> (close to 1.0) var (<imag part>) -> (close to 1.0) but var (complex array) -> (close to complex 0) Is that not a strange definition?

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