Mailman 3 November 2012 - NumPy-Discussion

NumFOCUS has received 501(c)3 status
by Travis Oliphant 06 Nov '12

06 Nov '12

Hello all, I'm really happy to report that NumFOCUS has received it's 501(c)3 status from the IRS. You can now make tax-deductible donations to NumFOCUS for the support of NumPy. We will put a NumPy-specific button on the home-page of NumPy soon so you can specifically direct your funds. But, for now you can go to http://numfocus.org/donate and be confident that your funds will support: 1) Continuous integration 2) The John Hunter Technical fellowships (which are awards made to students and post-docs and their mentors who will contribute substantially to a supported project during a 3-18 month period). 3) Equipment grants 4) Development sprints 5) Student travel to conferences 6) Project specific grants For example, most of Ondrej's time to work on the release of NumPy 1.7.0 has been paid for by donations to NumFOCUS from Continuum Analytics. NumFOCUS is also seeking nominations for 5 new board members (to bring the total to 9). If you would like to nominate someone please subscribe to numfocus(a)googlegroups.com (by sending an email to numfocus+subscribe(a)googlegroups.com) and then send your nomination. Alternatively, you can email me or one of the other directors directly. Best, -Travis

1 0

ticket 2228: Scientific package seeing ABI change in 1.6.x
by Ralf Gommers 05 Nov '12

05 Nov '12

Hi, Ticket 2228 says ABI was broken in 1.6.x. Specifically, NPY_CHAR in the NPY_TYPES enum seems to be have been moved. Can anyone comment on why the 3 datetime related values were inserted instead of appended? Ralf P.S. that ticket has escaped the Github move.

4 6

yet another trivial question
by Neal Becker 02 Nov '12

02 Nov '12

I'm trying to convert some matlab code. I see this: b(1)=[]; AFAICT, this removes the first element of the array, shifting the others. What is the preferred numpy equivalent? I'm not sure if b[:] = b[1:] is safe or not

2 1

Re: [Numpy-discussion] yet another trivial question
by Joe Kington 02 Nov '12

02 Nov '12

On Fri, Nov 2, 2012 at 9:18 AM, Neal Becker <ndbecker2(a)gmail.com> wrote: > I'm trying to convert some matlab code. I see this: > > b(1)=[]; > > AFAICT, this removes the first element of the array, shifting the others. > > What is the preferred numpy equivalent? > > I'm not sure if > > b[:] = b[1:] > Unless I'm missing something, don't you just want: b = b[1:] > > is safe or not > It's not exactly the same as the matlab equivalent, as matlab will always make a copy, and this will be a view of the same array. For example, if you do something like this: import numpy as np a = np.arange(10) b = a[1:] b[3] = 1000 print a print b You'll see that modifying "b" in-place will modify "a" as well, as "b" is just a view into "a". This wouldn't be the case in matlab (if I remember correctly, anyway...). > _________________ > NumPy-Discussion mailing list > NumPy-Discussion(a)scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion

2 1

Blaze
by Damon McDougall 01 Nov '12

01 Nov '12

I just saw this on a news site I read pretty often and thought this community would be interested. It certainly looks useful! https://speakerdeck.com/sdiehl/blaze-next-generation-numpy Sorry if it's been posted already. Best wishes, Damon -- Damon McDougall http://www.damon-is-a-geek.com B2.39 Mathematics Institute University of Warwick Coventry West Midlands CV4 7AL United Kingdom

3 2

Simple question about scatter plot graph
by klo uo 01 Nov '12

01 Nov '12

I guess this is simple for many on this list I have 20 samples with ~420 points each, and scatter plot (lag_plot) for all samples is attached 16x16 grid pattern is easily visible, but I can't make the meaning Thanks [image: Inline image 1]

4 6

using numpy.argmax to index into another array
by Moroney, Catherine M (388D) 01 Nov '12

01 Nov '12

Hello Everybody, I have the following problem that I would be interested in finding an easy/elegant solution to. I've got it working, but my solution is exceedingly clunky and I'm sure that there must be a better way. Here is the (boiled-down) problem: I have 2 different 3-d array, shaped (4,4,4), "a" and "b" I can find the max values and location of those max values in "a" in the 0-th dimension using max and argmax resulting in a 4x4 matrix. So far, very easy. I then want to find the values in "b" that correspond to the maximum values in a. This is where I got stuck. Below find the sample code I used (pretty clumsy stuff ...) Can somebody show a better (less clumsy) way of finding bmax in the code excerpt below? >>> a = numpy.random.randint(0, high=15, size=(4,4,4)) >>> b = numpy.random.randint(0, high=15, size=(4,4,4)) >>> amax = a.argmax(axis=0) >>> idx2 = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3] >>> idx1 = [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3] >>> idx_all = (amax.flatten(), numpy.array(idx1), numpy.array(idx2)) >>> bmax = b[idx_all].reshape(4,4) >>> a array([[[ 7, 1, 7, 10], [11, 5, 9, 0], [13, 1, 13, 14], [ 4, 13, 4, 7]], [[ 2, 11, 4, 5], [ 6, 12, 14, 9], [ 0, 4, 5, 14], [13, 5, 3, 4]], [[ 1, 4, 5, 4], [ 7, 13, 1, 11], [ 9, 2, 10, 4], [ 3, 4, 10, 13]], [[14, 6, 7, 1], [ 8, 0, 8, 2], [ 6, 8, 7, 12], [ 4, 9, 0, 12]]]) >>> idx array([[3, 1, 0, 0], [0, 2, 1, 2], [0, 3, 0, 0], [1, 0, 2, 2]]) >>> b array([[[ 4, 2, 3, 2], [ 9, 6, 1, 4], [ 1, 10, 13, 11], [10, 8, 1, 10]], [[ 3, 13, 7, 7], [ 4, 7, 9, 7], [ 3, 13, 10, 10], [13, 8, 14, 6]], [[14, 10, 13, 6], [13, 2, 12, 12], [ 9, 6, 3, 8], [ 0, 7, 8, 11]], [[12, 10, 3, 0], [11, 7, 0, 4], [ 9, 7, 11, 12], [ 7, 12, 1, 1]]]) >>> bmax = b[idx_all].reshape(4,4) >>> bmax array([[12, 13, 3, 2], [ 9, 2, 9, 12], [ 1, 7, 13, 11], [13, 8, 8, 11]]) Catherine

3 3

numpy.savez(_compressed) in loop
by Radek Machulka 01 Nov '12

01 Nov '12

Hi, is there a way how to save more arrays into single npy (npz if possible) file in loop? Something like: fw = open('foo.bar', 'wb') while foo: arr = np.array(bar) np.savez_compressed(fw, arr) fw.close() Or some workaround maybe? I go through hundreds of thousands arrays and can not keep them in memory. Yes, I can save each array into single file, but I would better have them all in single one. Thanks Radek

5 4

NumPy-Discussion November 2012