Mailman 3 February 2011 - NumPy-Discussion

Vectorize or rewrite function to work with array inputs?
by DParker＠chromalloy.com Feb. 1, 2011

Feb. 1, 2011

I have several functions like the example below that I would like to make compatible with array inputs. The problem is the conditional statements give a ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all(). I can use numpy.vectorize, but if possible I'd prefer to rewrite the function. Does anyone have any advice the best way to modify the code to accept array inputs? Thanks in advance for any assistance. NAN = float('nan') def air_gamma(t, far=0.0): """ Specific heat ratio (gamma) of Air/JP8 t - static temperature, Rankine [far] - fuel air ratio [- defaults to 0.0 (dry air)] air_gamma - specific heat ratio """ if far < 0.: return NAN elif far < 0.005: if t < 379. or t > 4731.: return NAN else: air_gamma = -3.472487e-22 * t ** 6. + 6.218811e-18 * t ** 5. - 4.428098e-14 * t ** 4. + 1.569889e-10 * t ** 3. - 0.0000002753524 * t ** 2. + 0.0001684666 * t + 1.368652 elif far < 0.069: if t < 699. or t > 4731.: return NAN else: a6 = 4.114808e-20 * far ** 3. - 1.644588e-20 * far ** 2. + 3.103507e-21 * far - 3.391308e-22 a5 = -6.819015e-16 * far ** 3. + 2.773945e-16 * far ** 2. - 5.469399e-17 * far + 6.058125e-18 a4 = 4.684637e-12 * far ** 3. - 1.887227e-12 * far ** 2. + 3.865306e-13 * far - 4.302534e-14 a3 = -0.00000001700602 * far ** 3. + 0.000000006593809 * far ** 2. - 0.000000001392629 * far + 1.520583e-10 a2 = 0.00003431136 * far ** 3. - 0.00001248285 * far ** 2. + 0.000002688007 * far - 0.0000002651616 a1 = -0.03792449 * far ** 3. + 0.01261025 * far ** 2. - 0.002676877 * far + 0.0001580424 a0 = 13.65379 * far ** 3. - 3.311225 * far ** 2. + 0.3573201 * far + 1.372714 air_gamma = a6 * t ** 6. + a5 * t ** 5. + a4 * t ** 4. + a3 * t ** 3. + a2 * t ** 2. + a1 * t + a0 elif far >= 0.069: return NAN else: return NAN return air_gamma David Parker Chromalloy - TDAG

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numpy.var fails on memmap
by Bartosz Telenczuk Feb. 1, 2011

Feb. 1, 2011

Hi, numpy.var on a memory mapped array returns a 0-d memmap object instead of a scalar (like with numpy arrays): >>> import numpy as np >>> fp = np.memmap('test.mm', dtype='float32', mode='w+', shape=(10,)) >>> fp[:] = np.random.rand(10) >>> fp_var= np.var(fp) >>> print type(fp_var) <class 'numpy.core.memmap.memmap'> >>> print fp_var.ndim 0 Moreover, the obtained object is missing 'filename' attribute, so that it can not be reused in further calculations: >>> fp_std = np.sqrt(fp_var) ... AttributeError: 'memmap' object has no attribute 'filename' I can produce this bug in numpy 1.5.1 with python2.6 (from macports). Cheers, Bartosz Institute for Theoretical Biology Humboldt University of Berlin Germany http://neuroscience.telenczuk.pl

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SWIG examples from the Cookbook
by K. B. Feb. 1, 2011

Feb. 1, 2011

Hi, I asked a question on this mailing list few days ago but nobody could help me. So I wonder if it is more appropriate to post this question on the Scipy-Dev mailing list or to fill a bug repport. Thank you ! KB ----------------- original post : Hi, I'd like to ask for your help regarding the use of SWIG with numpy. ** problem description ** While I can compile successfully the examples provided in ./numpy/doc/swig I can't compile the first example provided in the Cookbook. http://www.scipy.org/Cookbook/SWIG_NumPy_examples "A simple ARGOUT_ARRAY1 example" I have this error: ezrange_wrap.c: In function ‘SWIG_AsVal_long’: ezrange_wrap.c:3227: error: initializer element is not constant ** hypothesis and ugly workaround ** Comparing the examples from ./numpy/doc/swig and from the Cookbook, I noticed the main difference lies in the fact the first are in C++ while the latter in plain C. I "converted" the example from the Coobook to look like a c++ project. Essentially I renamed the .c file with a .cxx extension and I modified setup.py accordingly. This time it compiles successfully and the module is usable in python. ** questions ** 1/ What should I do to compile the Cookbook example with a C compiler? 2/ If it appears swig is now only compatible with the C++ compiler, do you have a practical workaround to propose? (without renaming the C files with a .cxx extension) ? ** configuration ** debian testing i686 swig version 1.3.40 python-numpy version 1.5.1 (I also tried 1.4.1) python version 2.6

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Re: [Numpy-discussion] create a numpy array of images
by Asmi Shah Feb. 1, 2011

Feb. 1, 2011

Hi Zach and Sturla, Well I am a "she" :)) Thanks for your inputs.. I am using 32 bit python as have so many libraries integrated with it.. and moreover, i plan to put this volume rendered on a web page or distribute the exe in the end, so want to be minimal for the memory requirements on the clients' systems.. the physical memory should not be a problem as I have 8GB RAM.. specially when the images are RGB then it gets into trouble as it adds the 4th dimension already in my case.. - asmi ---------- Forwarded message ---------- From: Sturla Molden <sturla(a)molden.no> To: Discussion of Numerical Python <numpy-discussion(a)scipy.org> Date: Tue, 1 Feb 2011 14:49:39 +0100 Subject: Re: [Numpy-discussion] create a numpy array of images Den 1. feb. 2011 kl. 11.20 skrev "totonixsame(a)gmail.com" < totonixsame(a)gmail.com>: I have one more question: how to avoid the limitation of memoryerror in > >> >> numpy. as I have like 200 images to stack in the numpy array of say >> 1024x1344 resolution.. have any idea apart from downsampling? >> > > Take a look at numpy.memmap or h5py [1]. > > memmap will not help unless he uses 64 bit Python, in which case he can just buy more RAM if he has too little. I suspect he is running out of virtual memory, not physical, for which 64 bit is the easiest solution. It is not possible to compensate for lack of virtual memory (typically 2GB limit with 32 bit) by memory mapping file into the already exhausted memory space. Using a database like h5py will help too, unless he tries to extract them all at once. Sturla

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current numpy in python 2.5 ?
by Mark Sienkiewicz Feb. 1, 2011

Feb. 1, 2011

Hi, I looked on the web site, but I couldn't find a list of python versions that numpy expects to work on. Is the trunk still expected to work in Python 2.5 ? I'm asking because it doesn't, but I don't want to file a bug report if it is not expected to. The problem shows up when I run numpy.test(): File "/usr/stsci/pyssgdev/2.5.4/numpy/testing/utils.py", line 619 except ValueError as e: ^ SyntaxError: invalid syntax Mark S.

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Strange behaviour of numpy.asarray() in corner case
by Friedrich Romstedt Feb. 1, 2011

Feb. 1, 2011

Python 2.6.6 (r266:84374, Aug 31 2010, 11:00:51) [GCC 4.0.1 (Apple Inc. build 5493)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import numpy >>> numpy.__version__ '1.5.1' >>> class X: ... pass ... >>> numpy.asarray([X(), numpy.asarray([1, 1])]).shape (2,) >>> numpy.asarray([numpy.asarray([1, 1]), X()]).shape () >>> I would expect (2,) in the second case too. The following works ok: >>> numpy.asarray([1, X()]).shape (2,) >>> numpy.asarray([X(), 1]).shape (2,) Friedrich

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