Mailman 3 September 2013 - NumPy-Discussion

strange behaviour with numpy arrays
by antlarac Sept. 11, 2013

Sept. 11, 2013

Hi, I have a numpy array, and I want to create another variable equal to it, to back it up for later calculations, because the first array will change. But after the first array changes, the second automatically changes to the same value. An example of what happens: import numpy as np a=np.zeros((4,4)) b=a print(b) a[3,3]=3 print(' ') print(b) gives the result: [[ 0. 0. 0. 0.] [ 0. 0. 0. 0.] [ 0. 0. 0. 0.] [ 0. 0. 0. 0.]] [[ 0. 0. 0. 0.] [ 0. 0. 0. 0.] [ 0. 0. 0. 0.] [ 0. 0. 0. 3.]] As you can see, when the value of a changes, the value of b automatically changes, even when this is not asked. Is there a way of avoiding this? This problem does not happen with normal python variables. Thank you for your time. -- View this message in context: http://numpy-discussion.10968.n7.nabble.com/strange-behaviour-with-numpy-ar… Sent from the Numpy-discussion mailing list archive at Nabble.com.

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my code (python version)
by Neal Becker Sept. 10, 2013

Sept. 10, 2013

Here's code I use for basic 2d histogram

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2-D Histogram
by David Reed Sept. 10, 2013

Sept. 10, 2013

Hi there, Is there a faster way to perform a 2D Histogram from a 2D matrix than what I have below: def spatial_histogram(frame, n_bins): shape = frame.shape h_len = shape[0]/n_bins w_len = shape[1]/n_bins h_ind = range(0, shape[0], h_len) w_ind = range(0, shape[1], w_len) max_val = 255*h_len*w_len out = np.empty((n_bins, n_bins), np.uint8) for ii in range(n_bins): for jj in range(n_bins): out[ii, jj] = np.sum(frame[h_ind[ii]:h_ind[ii]+h_len, w_ind[jj]:w_ind[jj]+w_len])/max_val*255 return out Should I try implementing this in Cython, or is there something I can do in Numpy? Thanks!

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PEP8
by Charles R Harris Sept. 9, 2013

Sept. 9, 2013

Hi All, I've been doing some PEP8 work using autopep8. One problem that has turned up is that the default behavior of autopep8 is version dependent. I'd like to put a script in numpy tools that runs autopep8 with some features disabled, namely 1. E226 -- puts spaces around arithmetic operators (+, -, *, /, **). 2. E241 -- allows only single spaces after ',' The first leaves expression formatting in the hands of the coder and avoids things like "2 ** 3". The second allows array entries to be vertically aligned, which can be useful in clarifying the values used in tests. A few other things that might need decisions: 1. [:,:, 2] or [:, :, 2] 2. Blank line before first function after class Foo(): The advantage of having a fixed script for PEP8 compliance is that once the code is brought into compliance it will stay there and we can recommend running the script before pushing. That will not only keep the code looking neat and regular, but will also strip trailing whitespace. Of course, the autopep8 folks might make further changes in the defaults that will cause trouble in the future, but I hope that will be a minor nuisance that we can deal with. There are options for autopep8<https://pypi.python.org/pypi/autopep8>and pep8 <https://pep8.readthedocs.org/en/latest/intro.html#error-codes> in case folks have other suggestions for features to add or disable. Thoughts? Chuck

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Bug (?) converting list to array
by Chad Kidder Sept. 9, 2013

Sept. 9, 2013

I'm trying to enter a 2-D array and np.array() is returning a 1-D array of lists. I'm using Python (x,y) on Windows 7 with numpy 1.7.1. Here's the code that is giving me issues. >>> f1 = [[15.207, 15.266, 15.181, 15.189, 15.215, 15.198], [-45, -57, -62, -70, -72, -73.5, -77]] >>> f1a = np.array(f1) >>> f1a array([[15.207, 15.266, 15.181, 15.189, 15.215, 15.198], [-45, -57, -62, -70, -72, -73.5, -77]], dtype=object) What am I missing?

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Funny business with 'is' operator?
by James Bergstra Sept. 9, 2013

Sept. 9, 2013

Hi, could someone help me understand why this assertion fails? def test_is(self): a = np.empty(1) b = np.empty(1) if a.data is not b.data: assert id(a.data) != id(b.data) # <-- fail I'm trying to write an alternate may_share_memory function. Thanks, - James

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Re: [Numpy-discussion] NumPy-Discussion Digest, Vol 84, Issue 18
by Graeme B. Bell Sept. 9, 2013

Sept. 9, 2013

> > This is good stuff, but I can't help thinking that if I needed to do an > any/all test on a number of arrays with common and/or combos -- > I'd probably write a Cython function to do it. Hi Chris, I agree, if you want the best performance and have no constraints on implementation, then using something like cython might be the way to do it. However, not everyone has the option of using cython. I'm using a GIS program which accepts a single numpy expression and performs it across every cell. What I'm doing has to a) be compatible with the standard release b) should 'just work' for all users, not just me and preferably c) shouldn't be complicated to implement/test. I don't know if GDAL and SWIG will work out of the box with a hybrid numpy/Cython approach. From what I've read, I believe it probably won't be practical for most users, particularly on Windows. Whereas python based or core numpy improvements are drop-in upgrades to performance. I suppose this leads to a more general point that using cython instead of improving numpy performance during projects, is locally good, but globally bad, rather like the situation with idiomatic workarounds for performance as opposed to fixing the implementation. Graeme.

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Removal of beta and rc files from Sourceforge
by Charles R Harris Sept. 8, 2013

Sept. 8, 2013

Hi All, Currently the beta and rc files for numpy versions >= 1.6.1 are still up on sourceforge. I think at this point they are at best clutter and propose that they be removed. Thoughts? Chuck

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(no subject)
by Carl Canuck Sept. 7, 2013

Sept. 7, 2013

Hello, I'm new to numpy, and I'm a stuck on my first real project with it. I am trying to take the rfft of a numpy array, like this: >>> my_rfft = numpy.fft.rfft(my_numpy_array) and replace the amplitudes that can be obtained with: >>> my_amplitudes = numpy.abs(my_rfft) with amplitudes from an arbitrary numpy array's rFFT, which is to then be converted back using numpy.fft.irfft . Alternately, some future plans will involve having to modify individual array element amplitudes directly based on other parameters. I would think that modifying and re-synthesizing signals using FFT is a fairly common use-case, but my attempts at Googling example code have been fruitless. I'm not sure if my rudimentary knowledge of FFT is failing me, or if I'm just not understanding how numpy represents and exposes the data, but I would really appreciate any help I can get :) /carl/

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Really cruel draft of vbench setup for NumPy (.add.reduce benchmarks since 2011)
by Yaroslav Halchenko Sept. 7, 2013

Sept. 7, 2013

On Wed, 01 May 2013, Sebastian Berg wrote: > > btw -- is there something like panda's vbench for numpy? i.e. where > > it would be possible to track/visualize such performance > > improvements/hits? > Sorry if it seemed harsh, but only skimmed mails and it seemed a bit > like the an obvious piece was missing... There are no benchmark tests I > am aware of. You can try: > a = np.random.random((1000, 1000)) > and then time a.sum(1) and a.sum(0), on 1.7. the fast axis (1), is only > slightly faster then the sum over the slow axis. On earlier numpy > versions you will probably see something like half the speed for the > slow axis (only got ancient or 1.7 numpy right now, so reluctant to give > exact timings). FWIW -- just as a cruel first attempt look at http://www.onerussian.com/tmp/numpy-vbench-20130506/vb_vb_reduce.html why float16 case is so special? I have pushed this really coarse setup (based on some elderly copy of pandas' vbench) to https://github.com/yarikoptic/numpy-vbench if you care to tune it up/extend and then I could fire it up again on that box (which doesn't do anything else ATM AFAIK). Since majority of time is spent actually building it (did it with ccache though) it would be neat if you come up with more of benchmarks to run which you might think could be interesting/important. -- Yaroslav O. Halchenko, Ph.D. http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org Senior Research Associate, Psychological and Brain Sciences Dept. Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755 Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419 WWW: http://www.linkedin.com/in/yarik

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