Mailman 3 February 2010 - NumPy-Discussion

Check out my photos on Facebook
by Aswad Gurjar Feb. 7, 2010

Feb. 7, 2010

Hi Numpy-discussion, I set up a Facebook profile where I can post my pictures, videos and events and I want to add you as a friend so you can see it. First, you need to join Facebook! Once you join, you can also create your own profile. Thanks, Aswad To sign up for Facebook, follow the link below: http://www.facebook.com/p.php?i=100000738053270&k=Z6E3Y5PX452D4EDJPG33QPYPP… Already have an account? Add this email address to your account http://www.facebook.com/n/?merge_accounts.php&e=numpy-discussion@scipy.org&… was invited to join Facebook by Aswad Gurjar. If you do not wish to receive this type of email from Facebook in the future, please click on the link below to unsubscribe. http://www.facebook.com/o.php?k=e68275&u=1530048334&mid=1d87694G5b32af4eG0G8 Facebook's offices are located at 1601 S. California Ave., Palo Alto, CA 94304.

1 0

numpy release process, adding a compatibility test step.
by René Dudfield Feb. 7, 2010

Feb. 7, 2010

Hi, may I suggest an addition to the release process... 'Tests against popular libraries that rely on numpy at the RC stage. Test at least these libraries pass their numpy related tests: matplotlib, scipy, pygame, (insert others here?). The release manage should ask the mailing list for people to test the RC against these libraries to make sure they work ok.' - This will catch problems like the ABI one with the recent release. - It is an explicit request for testing, with concrete tasks that people can do. Rather than a more abstract request for testing. - not much extra work for the release manager. testing work can be distributed to anyone who can try an app, or library with the binaries supplied in the RC stage of a release. - binary installer users can test as well, not just people who build from source. - tests libraries the numpy developers may not be using all the time themselves. On another project, adding this simple step helped prevent a number of bugs which affected other programs and libraries using the project. Before this was added we had a couple of releases which broke a few popular libraries/apps with very simple regressions or bugs. cu,

2 1

numpy 10x slower than native Python arrays for simple operations?
by Joseph Turian Feb. 7, 2010

Feb. 7, 2010

I have done some profiling, and the results are completely counterintuitive. For simple array access operations, numpy and array.array are 10x slower than native Python arrays. I am using numpy 1.3.0, the standard Ubuntu 9.03 package. Why am I getting such slow access speeds? Note that for "array access", I am doing operations of the form: a[i] += 1 Profiles: [0] * 20000000 Access: 2.3M / sec Initialization: 0.8s numpy.zeros(shape=(20000000,), dtype=numpy.int32) Access: 160K/sec Initialization: 0.2s array.array('L', [0] * 20000000) Access: 175K/sec Initialization: 2.0s array.array('L', (0 for i in range(20000000))) Access: 175K/sec, presumably, based upon the profile for the other array.array Initialization: 6.7s Any idea why my numpy array access is so slow? Thanks, Joseph

3 2

Installed NumPy and MatPlotLib in the Wrong Order. How uninstall MPL?
by Wayne Watson Feb. 6, 2010

Feb. 6, 2010

See Subject. I'm working in IDLE in Win7. It seems to me MPL gets stuck in site-packages under C:\Python25. Maybe this is as simple as deleting the entry? Well, yes there's a MPL folder under site-packages and an info MPL file of 540 bytes. There are also pylab.py, pyc,and py0 files under site. What to do next? -- My life in two words. "Interrupted Projects." -- WTW (quote originator)

5 7

error fromnumeric: 254 (repeat) return repeat(repeats, axis)
by Thomas Evangelidis Feb. 6, 2010

Feb. 6, 2010

Dear programmers, I'm not familiar with numpy therefore I need a little help to debug code which was not written by me. The lines which generate the error are the following: index = N.concatenate( (index, [len_i]) ) > delta = index[1:] - index[:-1] > return N.repeat( mask, delta.astype( N.int32 ) ) > and this is the error message I get: PDBModel: 1619 (extendMask) return N.repeat( mask, delta.astype( N.int32 ) ) > functions: 19 (repeat) return np.repeat(a, repeats, axis) > fromnumeric: 254 (repeat) return repeat(repeats, axis) > below I provide you with the values of each variables in these 3 lines of code: index = [ 0 9 20 29 37 44 55 66 76 88 96 103 114 > 123 131 > 138 147 155 167 176 184 192 198 209 216 224 233 242 246 > 255 > 262 274 279 290 297 301 310 316 320 326 330 339 345 352 > 360 > 368 377 385 393 402 413 421 433 441 448 455 464 468 476 > 484 > 492 499 507 515 521 531 539 547 556 564 572 580 588 597 > 611 > 625 636 642 651 659 663 670 677 683 692 700 707 715 723 > 734 > 740 748 754 762 771 779 787 795 804 816 822 830 842 848 > 856 > 865 873 881 890 895 903 912 920 932 944 953 962 970 977 > 985 > 993 1000 1012 1021 1029 1038 1046 1057 1063 1071 1079 1085 1093 1099 > 1107 > 1114 1120 1128 1137 1145 1153 1162 1170 1179 1188 1197 1209 1218 1225 > 1233 > 1242 1250 1256 1264 1271 1278 1286 1293 1299 1308 1317 1324 1332 1340 > 1350 > 1358 1369 1376 1382 1388 1396 1403 1411 1420 1432 1440 1447 1455 1462 > 1466 > 1472 1480 1485 1491 1500 1508 1514 1518 1522 1531 1540 1549 1560 1568 > 1574 > 1582 1587 1598 1603 1611 1619 1630 1638 1645 1654 1662 1670 1678 1686 > 1694 > 1703 1708 1715 1721 1727 1735 1743 1751 1760 1766 1775 1787 1795 1802 > 1811 > 1820 1827 1835 1843 1851 1859 1868 1872 1880 1889 1897 1908 1916 1923 > 1931 > 1939 1947 1952 1962 1973 1981 1987 1994 2002 2013 2025 2030 2038 2045 > 2053 > 2064 2071 2079 2085 2093 2104 2113 2124 2130 2138 2146 2154 2162 2170 > 2178 > 2186 2194 2202 2210 2218 2226 2234 2242 2250 2258 2266 2274 2282 > 2290] > > [len_i] = [2300] > > (index, [len_i]) = (array([ 0, 9, 20, 29, 37, 44, 55, > 66, 76, 88, 96, > 103, 114, 123, 131, 138, 147, 155, 167, 176, 184, > 192, > 198, 209, 216, 224, 233, 242, 246, 255, 262, 274, > 279, > 290, 297, 301, 310, 316, 320, 326, 330, 339, 345, > 352, > 360, 368, 377, 385, 393, 402, 413, 421, 433, 441, > 448, > 455, 464, 468, 476, 484, 492, 499, 507, 515, 521, > 531, > 539, 547, 556, 564, 572, 580, 588, 597, 611, 625, > 636, > 642, 651, 659, 663, 670, 677, 683, 692, 700, 707, > 715, > 723, 734, 740, 748, 754, 762, 771, 779, 787, 795, > 804, > 816, 822, 830, 842, 848, 856, 865, 873, 881, 890, > 895, > 903, 912, 920, 932, 944, 953, 962, 970, 977, 985, > 993, > 1000, 1012, 1021, 1029, 1038, 1046, 1057, 1063, 1071, 1079, > 1085, > 1093, 1099, 1107, 1114, 1120, 1128, 1137, 1145, 1153, 1162, > 1170, > 1179, 1188, 1197, 1209, 1218, 1225, 1233, 1242, 1250, 1256, > 1264, > 1271, 1278, 1286, 1293, 1299, 1308, 1317, 1324, 1332, 1340, > 1350, > 1358, 1369, 1376, 1382, 1388, 1396, 1403, 1411, 1420, 1432, > 1440, > 1447, 1455, 1462, 1466, 1472, 1480, 1485, 1491, 1500, 1508, > 1514, > 1518, 1522, 1531, 1540, 1549, 1560, 1568, 1574, 1582, 1587, > 1598, > 1603, 1611, 1619, 1630, 1638, 1645, 1654, 1662, 1670, 1678, > 1686, > 1694, 1703, 1708, 1715, 1721, 1727, 1735, 1743, 1751, 1760, > 1766, > 1775, 1787, 1795, 1802, 1811, 1820, 1827, 1835, 1843, 1851, > 1859, > 1868, 1872, 1880, 1889, 1897, 1908, 1916, 1923, 1931, 1939, > 1947, > 1952, 1962, 1973, 1981, 1987, 1994, 2002, 2013, 2025, 2030, > 2038, > 2045, 2053, 2064, 2071, 2079, 2085, 2093, 2104, 2113, 2124, > 2130, > 2138, 2146, 2154, 2162, 2170, 2178, 2186, 2194, 2202, 2210, > 2218, > 2226, 2234, 2242, 2250, 2258, 2266, 2274, 2282, 2290]), [2300]) > > index[1:] = [ 9 20 29 37 44 55 66 76 88 96 103 114 > 123 131 138 > 147 155 167 176 184 192 198 209 216 224 233 242 246 255 > 262 > 274 279 290 297 301 310 316 320 326 330 339 345 352 360 > 368 > 377 385 393 402 413 421 433 441 448 455 464 468 476 484 > 492 > 499 507 515 521 531 539 547 556 564 572 580 588 597 611 > 625 > 636 642 651 659 663 670 677 683 692 700 707 715 723 734 > 740 > 748 754 762 771 779 787 795 804 816 822 830 842 848 856 > 865 > 873 881 890 895 903 912 920 932 944 953 962 970 977 985 > 993 > 1000 1012 1021 1029 1038 1046 1057 1063 1071 1079 1085 1093 1099 1107 > 1114 > 1120 1128 1137 1145 1153 1162 1170 1179 1188 1197 1209 1218 1225 1233 > 1242 > 1250 1256 1264 1271 1278 1286 1293 1299 1308 1317 1324 1332 1340 1350 > 1358 > 1369 1376 1382 1388 1396 1403 1411 1420 1432 1440 1447 1455 1462 1466 > 1472 > 1480 1485 1491 1500 1508 1514 1518 1522 1531 1540 1549 1560 1568 1574 > 1582 > 1587 1598 1603 1611 1619 1630 1638 1645 1654 1662 1670 1678 1686 1694 > 1703 > 1708 1715 1721 1727 1735 1743 1751 1760 1766 1775 1787 1795 1802 1811 > 1820 > 1827 1835 1843 1851 1859 1868 1872 1880 1889 1897 1908 1916 1923 1931 > 1939 > 1947 1952 1962 1973 1981 1987 1994 2002 2013 2025 2030 2038 2045 2053 > 2064 > 2071 2079 2085 2093 2104 2113 2124 2130 2138 2146 2154 2162 2170 2178 > 2186 > 2194 2202 2210 2218 2226 2234 2242 2250 2258 2266 2274 2282 2290 > 2300] > > index[:-1] = [ 0 9 20 29 37 44 55 66 76 88 96 103 > 114 123 131 > 138 147 155 167 176 184 192 198 209 216 224 233 242 246 > 255 > 262 274 279 290 297 301 310 316 320 326 330 339 345 352 > 360 > 368 377 385 393 402 413 421 433 441 448 455 464 468 476 > 484 > 492 499 507 515 521 531 539 547 556 564 572 580 588 597 > 611 > 625 636 642 651 659 663 670 677 683 692 700 707 715 723 > 734 > 740 748 754 762 771 779 787 795 804 816 822 830 842 848 > 856 > 865 873 881 890 895 903 912 920 932 944 953 962 970 977 > 985 > 993 1000 1012 1021 1029 1038 1046 1057 1063 1071 1079 1085 1093 1099 > 1107 > 1114 1120 1128 1137 1145 1153 1162 1170 1179 1188 1197 1209 1218 1225 > 1233 > 1242 1250 1256 1264 1271 1278 1286 1293 1299 1308 1317 1324 1332 1340 > 1350 > 1358 1369 1376 1382 1388 1396 1403 1411 1420 1432 1440 1447 1455 1462 > 1466 > 1472 1480 1485 1491 1500 1508 1514 1518 1522 1531 1540 1549 1560 1568 > 1574 > 1582 1587 1598 1603 1611 1619 1630 1638 1645 1654 1662 1670 1678 1686 > 1694 > 1703 1708 1715 1721 1727 1735 1743 1751 1760 1766 1775 1787 1795 1802 > 1811 > 1820 1827 1835 1843 1851 1859 1868 1872 1880 1889 1897 1908 1916 1923 > 1931 > 1939 1947 1952 1962 1973 1981 1987 1994 2002 2013 2025 2030 2038 2045 > 2053 > 2064 2071 2079 2085 2093 2104 2113 2124 2130 2138 2146 2154 2162 2170 > 2178 > 2186 2194 2202 2210 2218 2226 2234 2242 2250 2258 2266 2274 2282 > 2290] > mask = [ True True True True True True True True True True > True True > True True True True True True True True True True True > True > True True True True True True True True True True True > True > True True True True True True True True True True True > True > True True True True True True True True True True True > True > True False True True True True True True True True True > True > True True True True True True True True True True True > True > True True True True True True True True True True True > True > True True True True True True True False False False False > False > False False False False False False False False False False False > False > False False False False False False False False False False False > False > False False False False False False False False False False False > False > False False True True True True True True True True True > True > True True True True True True True True True True True > True > True True True True True True True True True True True > True > True True True True True True True True True True True > True > True True True True True True True True True True True True > True True True True True True True True True True True True > True True True True True True True True True True True True > True True True False False True True True True True True True > True True True True True True True True True True True True > True True True True True True True True True True True True > True True True True True True True True True True True True > True True True True True True True True True] > > N.int32 = <type 'numpy.int32'> > > delta.astype(N.int32) = [ 9 11 9 8 7 11 11 10 12 8 7 11 9 8 7 9 > 8 12 9 8 8 6 11 7 8 > 9 9 4 9 7 12 5 11 7 4 9 6 4 6 4 9 6 7 8 8 9 8 8 9 11 > 8 12 8 7 7 9 4 8 8 8 7 8 8 6 10 8 8 9 8 8 8 8 9 14 14 > 11 6 9 8 4 7 7 6 9 8 7 8 8 11 6 8 6 8 9 8 8 8 9 12 6 > 8 12 6 8 9 8 8 9 5 8 9 8 12 12 9 9 8 7 8 8 7 12 9 8 9 > 8 11 6 8 8 6 8 6 8 7 6 8 9 8 8 9 8 9 9 9 12 9 7 8 9 > 8 6 8 7 7 8 7 6 9 9 7 8 8 10 8 11 7 6 6 8 7 8 9 12 8 > 7 8 7 4 6 8 5 6 9 8 6 4 4 9 9 9 11 8 6 8 5 11 5 8 8 > 11 8 7 9 8 8 8 8 8 9 5 7 6 6 8 8 8 9 6 9 12 8 7 9 9 > 7 8 8 8 8 9 4 8 9 8 11 8 7 8 8 8 5 10 11 8 6 7 8 11 12 > 5 8 7 8 11 7 8 6 8 11 9 11 6 8 8 8 8 8 8 8 8 8 8 8 8 > 8 8 8 8 8 8 8 8 10] > Do you have any idea what's wrong? Any advice will be greatly appreciated. Tom

2 1

Porting numpy to python 3.x, status update
by Rohit Garg Feb. 6, 2010

Feb. 6, 2010

Hi all, This says that planning for migration to python 3 has begun. http://blog.jarrodmillman.com/2009/11/numpy-14-coming-soon.html It has been a month since 1.4 was released. Is there a status page somewhere where one can checkup on progress for the same? Is Python 3.x support planned for 1.5? This year, next year? Regards, -- Rohit Garg http://rpg-314.blogspot.com/ Senior Undergraduate Department of Physics Indian Institute of Technology Bombay

2 1

Conversion of matlab import containing objects into 3d array
by Angus McMorland Feb. 6, 2010

Feb. 6, 2010

Hi all, I'm trying to import data from a matlab file using scipy.io.loadmat. One of the variables in the file imports as an array of shape (51,) of dtype object, with each element being an array of shape (23,100) of dtype float. How do I convert this array into a single array of dtype float with shape (51,23,100)? objarr.astype(float), which I thought might work (from [1]), gives me the error "ValueError: setting an array element with a sequence.". [1] http://aspn.activestate.com/ASPN/Mail/Message/numpy-discussion/2998408 Many thanks for any help, Angus. -- AJC McMorland Post-doctoral research fellow Neurobiology, University of Pittsburgh

3 2

How long does it take to create an array?
by Keith Goodman Feb. 5, 2010

Feb. 5, 2010

Why is the second method of converting a list of tuples to an array so much faster? >> x = range(500) >> x = [(z,) for z in x] # <-- e.g. output of a sql database >> x[:5] [(0,), (1,), (2,), (3,), (4,)] >> >> timeit np.array(x).reshape(-1) # <-- slow 1000 loops, best of 3: 832 us per loop >> timeit np.array([z[0] for z in x]) 10000 loops, best of 3: 106 us per loop # <-- fast Is it a fixed overhead advantage? Doesn't seems so: >> x = range(50000) >> x = [[z] for z in x] >> timeit np.array(x).reshape(-1) 10 loops, best of 3: 83 ms per loop >> timeit np.array([z[0] for z in x]) 100 loops, best of 3: 9.81 ms per loop So it is probably faster to make a 1d array and reshape it: >> timeit np.array([[1,2], [3,4], [5,6]]) 100000 loops, best of 3: 11.8 us per loop >> timeit np.array([1,2,3,4,5,6]).reshape(-1,2) 100000 loops, best of 3: 6.62 us per loop Yep.

2 1

u in [u+1]
by Neal Becker Feb. 5, 2010

Feb. 5, 2010

I'm having some trouble here. I have a list of numpy arrays. I want to know if an array 'u' is in the list. As an example, u = np.arange(10) : u not in [u+1] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) /home/nbecker/raysat/test/<ipython console> in <module>() ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() What would be the way to do this?

3 2

Audio signal capture and processing
by Gerardo Gutierrez Feb. 5, 2010

Feb. 5, 2010

Hello. I'm working with audio signals with wavelet analisys, and I want to know if someone has work with some audio capture (with the mic and through a file) library so that I can get the time-series... Also I need to play the transformed signal. Thanks. * *"Solo existen 10 tipos de personas en el mundo... las que saben binario y las que no" _-`````-, ,- '- . .' .- - | | - -. `. /.' / `. \ :/ : _... ..._ `` : :: : /._ .`:'_.._\. || : :: `._ ./ ,` : \ . _.'' . `:. / | -. \-. \\_ / \:._ _/ .' .@) \@) ` `\ ,.' _/,--' .- .\,-.`--`. ,'/'' (( \ ` ) /'/' \ `-' ( '/'' `._,-----' ''/' .,---' ''/' ;: ''/'' ''/ ''/''/'' '/'/' `;

4 4