Mailman 3 August 2009 - NumPy-Discussion

why does b[:-0] not work, and is there an elegant solution?
by Mark Bakker 20 Aug '09

20 Aug '09

Hello list, I compute the index of the last term in an array that I need and call the index n. I can then call the array b as b[:-n] If I need all terms in the array, the logical syntax would be: b[:-0] but that doesn't work. Any reason why that has not been implemented? Any elegant workaround? Thanks, Mark

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Recipe: extract a sub-array using given shape, centered on given position
by Nicolas Rougier 19 Aug '09

19 Aug '09

Hi, I've coded a function that allows to extract a contiguous array from another one using a given shape and centered on a given position. I did not find an equivalent within numpy so I hope I did not miss it. The only interest of the function is to guarantee that the resulting sub-array will have the required shape. If some values are out of bounds, result array is padded with a fill value. Hope it can be useful to someone. Nicolas Code: ----- import numpy def extract(Z, shape, position, fill=numpy.NaN): """ Extract a sub-array from Z using given shape and centered on position. If some part of the sub-array is out of Z bounds, result is padded with fill value. **Parameters** `Z` : array_like Input array. `shape` : tuple Shape of the output array `position` : tuple Position within Z `fill` : scalar Fill value **Returns** `out` : array_like Z slice with given shape and center **Examples** >>> Z = numpy.arange(0,16).reshape((4,4)) >>> extract(Z, shape=(3,3), position=(0,0)) [[ NaN NaN NaN] [ NaN 0. 1.] [ NaN 4. 5.]] Schema: +-----------+ | 0 0 0 | = extract (Z, shape=(3,3), position=(0,0)) | +---------------+ | 0 | 0 1 | 2 3 | = Z | | | | | 0 | 4 5 | 6 7 | +---|-------+ | | 8 9 10 11 | | | | 12 13 14 15 | +---------------+ >>> Z = numpy.arange(0,16).reshape((4,4)) >>> extract(Z, shape=(3,3), position=(3,3)) [[ 10. 11. NaN] [ 14. 15. NaN] [ NaN NaN NaN]] Schema: +---------------+ | 0 1 2 3 | = Z | | | 4 5 6 7 | | +-----------+ | 8 9 |10 11 | 0 | = extract (Z, shape=(3,3), position=(3,3)) | | | | | 12 13 |14 15 | 0 | +---------------+ | | 0 0 0 | +-----------+ """ # assert(len(position) == len(Z.shape)) # if len(shape) < len(Z.shape): # shape = shape + Z.shape[len(Z.shape)-len(shape):] R = numpy.ones(shape, dtype=Z.dtype)*fill P = numpy.array(list(position)).astype(int) Rs = numpy.array(list(R.shape)).astype(int) Zs = numpy.array(list(Z.shape)).astype(int) R_start = numpy.zeros((len(shape),)).astype(int) R_stop = numpy.array(list(shape)).astype(int) Z_start = (P-Rs//2) Z_stop = (P+Rs//2)+Rs%2 R_start = (R_start - numpy.minimum(Z_start,0)).tolist() Z_start = (numpy.maximum(Z_start,0)).tolist() R_stop = (R_stop - numpy.maximum(Z_stop-Zs,0)).tolist() Z_stop = (numpy.minimum(Z_stop,Zs)).tolist() r = [slice(start,stop) for start,stop in zip(R_start,R_stop)] z = [slice(start,stop) for start,stop in zip(Z_start,Z_stop)] R[r] = Z[z] return R Z = numpy.arange(0,16).reshape((4,4)) print Z print print extract(Z, shape=(3,3), position=(0,0)) print print extract(Z, shape=(3,3), position=(3,3))

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Re: [Numpy-discussion] why does b[:-0] not work, and is there an elegant solution?
by Mark Bakker 19 Aug '09

19 Aug '09

The winner so far: x[: -n or None] works fine when n = 0, relatively elegant, even pretty slick I think. And I expect it to be quick. Thanks for all the replys, Mark

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Strange crash in numpy.linalg.lstsq.
by Jonathan Taylor 18 Aug '09

18 Aug '09

Hi, I am getting a strange crash in numpy.linalg.lstsq. I have put the code that causes the crash along with two data files on my website at: http://www.cs.toronto.edu/~jtaylor/crash/ I would be interested to know if this bug can be duplicated and/or if anyone has any suggestions as to why: import numpy as np A = np.load('A.npy') b = np.load('b.npy') rc = np.linalg.lstsq(A,b) produces: *** glibc detected *** /usr/bin/python: free(): invalid next size (normal): 0x091793c0 *** ======= Backtrace: ========= /lib/tls/i686/cmov/libc.so.6[0xb7dc7a85] /lib/tls/i686/cmov/libc.so.6(cfree+0x90)[0xb7dcb4f0] /u/jtaylor/lib/python2.5/site-packages/numpy/core/multiarray.so[0xb795403e] /usr/bin/python[0x811247a] /usr/bin/python(PyEval_EvalCodeEx+0x323)[0x80cae33] /usr/bin/python(PyEval_EvalFrameEx+0x565e)[0x80c93fe] /usr/bin/python(PyEval_EvalCodeEx+0x6e7)[0x80cb1f7] /usr/bin/python(PyEval_EvalCode+0x57)[0x80cb347] /usr/bin/python(PyRun_FileExFlags+0xf8)[0x80ea818] /usr/bin/python[0x80c1f5a] /usr/bin/python(PyObject_Call+0x27)[0x805cb97] /usr/bin/python(PyEval_EvalFrameEx+0x4064)[0x80c7e04] /usr/bin/python(PyEval_EvalCodeEx+0x6e7)[0x80cb1f7] /usr/bin/python[0x8113696] /usr/bin/python(PyObject_Call+0x27)[0x805cb97] /usr/bin/python(PyEval_EvalFrameEx+0x4064)[0x80c7e04] /usr/bin/python(PyEval_EvalCodeEx+0x6e7)[0x80cb1f7] /usr/bin/python(PyEval_EvalFrameEx+0x565e)[0x80c93fe] /usr/bin/python(PyEval_EvalCodeEx+0x6e7)[0x80cb1f7] /usr/bin/python[0x8113696] /usr/bin/python(PyObject_Call+0x27)[0x805cb97] /usr/bin/python[0x8062bfb] /usr/bin/python(PyObject_Call+0x27)[0x805cb97] /usr/bin/python(PyEval_EvalFrameEx+0x3d07)[0x80c7aa7] /usr/bin/python(PyEval_EvalCodeEx+0x6e7)[0x80cb1f7] /usr/bin/python(PyEval_EvalFrameEx+0x565e)[0x80c93fe] /usr/bin/python(PyEval_EvalFrameEx+0x5945)[0x80c96e5] /usr/bin/python(PyEval_EvalCodeEx+0x6e7)[0x80cb1f7] /usr/bin/python(PyEval_EvalFrameEx+0x6d09)[0x80caaa9] /usr/bin/python(PyEval_EvalCodeEx+0x6e7)[0x80cb1f7] /usr/bin/python(PyEval_EvalFrameEx+0x565e)[0x80c93fe] /usr/bin/python(PyEval_EvalCodeEx+0x6e7)[0x80cb1f7] /usr/bin/python(PyEval_EvalFrameEx+0x565e)[0x80c93fe] /usr/bin/python(PyEval_EvalFrameEx+0x5945)[0x80c96e5] /usr/bin/python(PyEval_EvalCodeEx+0x6e7)[0x80cb1f7] /usr/bin/python(PyEval_EvalFrameEx+0x565e)[0x80c93fe] /usr/bin/python(PyEval_EvalCodeEx+0x6e7)[0x80cb1f7] /usr/bin/python(PyEval_EvalFrameEx+0x565e)[0x80c93fe] /usr/bin/python(PyEval_EvalCodeEx+0x6e7)[0x80cb1f7] /usr/bin/python(PyEval_EvalFrameEx+0x565e)[0x80c93fe] /usr/bin/python(PyEval_EvalCodeEx+0x6e7)[0x80cb1f7] /usr/bin/python(PyEval_EvalCode+0x57)[0x80cb347] /usr/bin/python(PyRun_FileExFlags+0xf8)[0x80ea818] /usr/bin/python(PyRun_SimpleFileExFlags+0x199)[0x80eaab9] /usr/bin/python(Py_Main+0xa35)[0x8059335] /usr/bin/python(main+0x22)[0x80587f2] /lib/tls/i686/cmov/libc.so.6(__libc_start_main+0xe0)[0xb7d72450] /usr/bin/python[0x8058761] ======= Memory map: ======== 08048000-08140000 r-xp 00000000 08:06 83501 /usr/bin/python2.5 08140000-08165000 rw-p 000f7000 08:06 83501 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/h/44/jtaylor/build/matplotlib/lib/matplotlib/backends/_tkagg.so b5726000-b5727000 ---p b5726000 00:00 0 b5727000-b5f27000 rwxp b5727000 00:00 0 b5f27000-b5f3e000 r-xp 00000000 08:06 85532 /usr/lib/libxcb.so.1.0.0 b5f3e000-b5f3f000 rw-p 00016000 08:06 85532 /usr/lib/libxcb.so.1.0.0 b5f3f000-b5f53000 r-xp 00000000 08:06 1187870 /lib/tls/i686/cmov/ libnsl-2.7.so b5f53000-b5f55000 rw-p 00013000 08:06 1187870 /lib/tls/i686/cmov/ libnsl-2.7.so b5f55000-b5f57000 rw-p b5f55000 00:00 0 b5f57000-b603b000 r-xp 00000000 08:06 85536 /usr/lib/libX11.so.6.2.0 b603b000-b603e000 rw-p 000e4000 08:06 85536 /usr/lib/libX11.so.6.2.0 b603e000-b60e7000 r-xp 00000000 08:06 85098 /usr/lib/libtcl8.4.so.0 b60e7000-b60f1000 rw-p 000a8000 08:06 85098 /usr/lib/libtcl8.4.so.0 b60f1000-b60f2000 rw-p b60f1000 00:00 0 b60f2000-b61c4000 r-xp 00000000 08:06 85102 /usr/lib/libtk8.4.so.0 b61c4000-b61cf000 rw-p 000d2000 08:06 85102 /usr/lib/libtk8.4.so.0 b61cf000-b61d0000 rw-p b61cf000 00:00 0 b61d0000-b62a8000 r-xp 00000000 08:06 85103 /usr/lib/libBLT.2.4.so.8.4 b62a8000-b62b9000 rw-p 000d8000 08:06 85103 /usr/lib/libBLT.2.4.so.8.4 b62b9000-b62ba000 rw-p b62b9000 00:00 0 b62ba000-b62dc000 r-xp 00000000 08:06 180469 /usr/lib/libpng12.so.0.15.0 b62dc000-b62dd000 rw-p 00022000 08:06 180469 /usr/lib/libpng12.so.0.15.0 b62f5000-b62f6000 rw-p b62f5000 00:00 0 b62f6000-b631d000 r-xp 00000000 00:1a 553544 /h/44/jtaylor/build/matplotlib/lib/matplotlib/_png.so b631d000-b631e000 rw-p 00027000 00:1a 553544 /h/44/jtaylor/build/matplotlib/lib/matplotlib/_png.so b631e000-b6367000 r-xp 00000000 00:1a 553543 /h/44/jtaylor/build/matplotlib/lib/matplotlib/_image.so b6367000-b6369000 rw-p 00049000 00:1a 553543 /h/44/jtaylor/build/matplotlib/lib/matplotlib/_image.so b6369000-b63d3000 r-xp 00000000 08:06 83795 /usr/lib/libfreetype.so.6.3.16 b63d3000-b63d6000 rw-p 0006a000 08:06 83795 /usr/lib/libfreetype.so.6.3.16 b63d6000-b6424000 r-xp 00000000 00:1a 553535 /h/44/jtaylor/build/matplotlib/lib/matplotlib/ft2font.so 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r-xp 00000000 08:06 92437 /usr/lib/python2.5/lib-dynload/_csv.so b6f7f000-b6f81000 rw-p 00004000 08:06 92437 /usr/lib/python2.5/lib-dynload/_csv.so b6f81000-b6f84000 r-xp 00000000 08:06 92427 /usr/lib/python2.5/lib-dynload/_locale.so b6f84000-b6f85000 rw-p 00003000 08:06 92427 /usr/lib/python2.5/lib-dynload/_locale.so b6f85000-b6f8e000 r-xp 00000000 00:1a 533113 /h/44/jtaylor/lib/python2.5/site-packages/numpy/fft/fftpack_lite.so b6f8e000-b6f8f000 rw-p 00008000 00:1a 533113 /h/44/jtaylor/lib/python2.5/site-packages/numpy/fft/fftpack_lite.so b6f8f000-b6fad000 r-xp 00000000 00:1a 533028 /h/44/jtaylor/lib/python2.5/site-packages/numpy/core/scalarmath.so b6fad000-b6fae000 rw-p 0001e000 00:1a 533028 /h/44/jtaylor/lib/python2.Aborted

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how can i optimize this function further more?
by Victor Noagbodji 18 Aug '09

18 Aug '09

hello, i'm fairly new to numpy. i need help with a snow effect done with pygame. the entire code is below. the performance drops in the snowfall function. the original c code and a demo can be found here : http://sol.gfxile.net/gp/ch04.html as you can see. the original c code went the pixel by pixel way, but i couldn't do that with pygame. i'm asking here because i think there might be a way numpy could help. the idea of the snowfall function is to plot white pixel one line down (while avoiding the green ground, thus the test array1-array2 == white) thanks a lot in advance for your help. ps. sorry for the ugly code. this is the result of several optimizations, plus help from the pygame mailing list. import numpy import cProfile from math import sin, cos from random import randint from sys import exit, stdout import pygame import pygame.event as event import pygame.display as display from pygame.locals import * from pygame import Surface from pygame.time import Clock, get_ticks from pygame.surfarray import pixels2d, blit_array, make_surface WIDTH = 640 HEIGHT = 480 RESOLUTION = (WIDTH, HEIGHT) def init(screen_array, green=int(0x007f00)): lowest_level = 0 for i in xrange(WIDTH): sins = (sin((i + 3247) * 0.02) * 0.3 + sin((i + 2347) * 0.04) * 0.1 + sin((i + 4378) * 0.01) * 0.6) p = int(sins * 100 + 380) lowest_level = max(p, lowest_level) for j in range(p, HEIGHT): screen_array[i, j] = green return lowest_level def newsnow(screen_array, white=int(0xffffff), density=1): new_indices = numpy.array([randint(1, WIDTH-2) for i in xrange(density)]) screen_array[new_indices, 0] = white def snowfall(screen_array, white=int(0xffffff), fallrng=xrange(HEIGHT-2, -1, -1)): screen_array = numpy.transpose(screen_array) snow_in_next_layer = numpy.zeros(WIDTH, dtype=int) for j in fallrng: array1 = screen_array[j] array2 = screen_array[j+1] indices_where_snow_moved_down = numpy.where(array1-array2 == white)[0] snow_in_next_layer.fill(0) snow_in_next_layer[indices_where_snow_moved_down] = white screen_array[j ] = array1 - snow_in_next_layer screen_array[j+1] = array2 + snow_in_next_layer screen_array = numpy.transpose(screen_array) def main(): pygame.init() screen_surf = display.set_mode(RESOLUTION) screen_rect = screen_surf.get_rect() screen_array = pixels2d(screen_surf) lowest_level = init(screen_array) display.update(screen_rect) fallrng = xrange(lowest_level-2, -1, -1) white = int(0xffffff) black = 0 c = Clock() while True: newsnow(screen_array, density=8) snowfall(screen_array) display.update(screen_rect) for e in event.get(): type = e.type if type == QUIT: exit() elif type == KEYUP and e.key == K_ESCAPE: return c.tick() stdout.write('fps: ~%s\r' % round(c.get_fps())) stdout.flush() if __name__ == '__main__': cProfile.runctx('main()', globals(), {'main': main}, 'last_gp3_stats') -- paul victor noagbodji

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indexing problem
by Ernest Adrogué 18 Aug '09

18 Aug '09

Hi, Suppose I have a 3-dimansional array, where one dimension is time. I'm not particularly interested in selecting specific moments in time, so most of the time I won't be indexing this dimension. Intuitively, one would make time the third dimension, but if you do that you have to specifiy the time in every index, which is annoying, because then all indices must start with an empty slice, e.g., a[:,1] a[:,:,3] a[:,0,1] etc. On the other hand, the arrays resulting from indexing have all elements sorted by time, which is a good thing. Then, if I change it and make time the first dimension, it's handy because I can omit time in indices, BUT then the sub-arrays produced by indexing are not sorted by time! Is it possible to change the way numpy traverses the array, so that it moves "less" on the first dimension (instead of the default, which is to move less on the last dimension), so that I get arrays sorted by time when time is not on the last dimension? Thanks. Ernest

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unique rows of array
by Maria Liukis 18 Aug '09

18 Aug '09

Hello everybody, While re-implementing some Matlab code in Python, I've run into a problem of finding a NumPy function analogous to the Matlab's "unique (array, 'rows')" to get unique rows of an array. Searching the web, I've found a similar discussion from couple of years ago with an example: ############## A SNIPPET FROM THE DISCUSSION [Numpy-discussion] Finding unique rows in an array [Was: Finding a row match within a numpy array] A Tuesday 21 August 2007, Mark.Miller escrigué: > A slightly related question on this topic... > > Is there a good loopless way to identify all of the unique rows in an > array? Something like numpy.unique() is ideal, but capable of > extracting unique subarrays along an axis. You can always do a view of the rows as strings and then use unique(). Here is an example: In [1]: import numpy In [2]: a=numpy.arange(12).reshape(4,3) In [3]: a[2]=(3,4,5) In [4]: a Out[4]: array([[ 0, 1, 2], [ 3, 4, 5], [ 3, 4, 5], [ 9, 10, 11]]) now, create the view and select the unique rows: In [5]: b=numpy.unique(a.view('S%d'%a.itemsize*a.shape[0])).view('i4') and finally restore the shape: In [6]: b.reshape((len(b)/a.shape[1], a.shape[1])) Out[6]: array([[ 0, 1, 2], [ 3, 4, 5], [ 9, 10, 11]]) If you want to find unique columns instead of rows, do a tranpose first on the initial array. ################END OF DISCUSSION Provided example works only because array elements are row-sorted. Changing tested array to (in my case, it's 'c'): >>> c array([[ 0, 1, 2], [ 3, 4, 5], [ 3, 4, 5], [ 9, 10, 11]]) >>> c[0] = (11, 10, 0) >>> c array([[11, 10, 0], [ 3, 4, 5], [ 3, 4, 5], [ 9, 10, 11]]) >>> b = np.unique(c.view('S%s' %c.itemsize*c.shape[0])) >>> b array(['', '\x03', '\x04', '\x05', '\t', '\n', '\x0b'], dtype='|S4') >>> b.view('i4') array([ 0, 3, 4, 5, 9, 10, 11]) >>> b.reshape((len(b)/c.shape[1], c.shape[1])).view('i4') Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: total size of new array must be unchanged >>> Since len(b) = 7. Suggested approach would work if the whole row would be converted to a single string, I guess. But from what I could gather, numpy.array.view() only changes display element-wise. Before I start re-inventing the wheel, I was just wondering if using existing numpy functionality one could find unique rows in an array. Many thanks in advance! Masha -------------------- liukis(a)usc.edu

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add axis to results of reduction (mean, min, ...)
by josef.pktd＠gmail.com 18 Aug '09

18 Aug '09

What's the best way of getting back the correct shape to be able to broadcast, mean, min,.. to the original array, that works for arbitrary dimension and axis? I thought I have seen some helper functions, but I don't find them anymore? Josef >>> a array([[1, 2, 3, 3, 0], [2, 2, 3, 2, 1]]) >>> a-a.max(0) array([[-1, 0, 0, 0, -1], [ 0, 0, 0, -1, 0]]) >>> a-a.max(1) Traceback (most recent call last): File "<pyshell#135>", line 1, in <module> a-a.max(1) ValueError: shape mismatch: objects cannot be broadcast to a single shape >>> a-a.max(1)[:,None] array([[-2, -1, 0, 0, -3], [-1, -1, 0, -1, -2]])

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How to compile numpy without ATLAS support?
by Jonathan Taylor 18 Aug '09

18 Aug '09

I am wondering how I might be able to compile numpy without ATLAS on a ubuntu machine that has an atlas deb installed. It seems that the numpy build routine automatically detects it. Thanks for any help, Jonathan.

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Indexing empty array with empty boolean array causes "IndexError: invalid index exception"
by Maria Liukis 18 Aug '09

18 Aug '09

Hello everybody, I'm using following versions of Scipy and Numpy packages: >>> scipy.__version__ '0.7.1' >>> np.__version__ '1.3.0' My code uses boolean array to filter 2-dimensional array which sometimes happens to be an empty array. It seems like I have to take special care when dimension I'm filtering is zero, otherwise I'm getting an "IndexError: invalid index" exception: >>> import numpy as np >>> a = np.zeros((2,10)) >>> a array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]) >>> filter_array = np.zeros(2,) >>> filter_array array([False, False], dtype=bool) >>> a[filter_array,:] array([], shape=(0, 10), dtype=float64) >>>>>> Now if filtered dimension is zero: >>> a = np.ones((0,10)) >>> a array([], shape=(0, 10), dtype=float64) >>> filter_array = np.zeros((0,), dtype=bool) >>> filter_array array([], dtype=bool) >>> filter_array.shape (0,) >>> a.shape (0, 10) >>> a[filter_array,:] Traceback (most recent call last): File "<stdin>", line 1, in <module> IndexError: invalid index >>> Would somebody know if it's an expected behavior, a package bug or am I doing something wrong? Thanks in advance, Masha -------------------- liukis(a)usc.edu

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