Re: [Numpy-discussion] Iterate over all 1-dim views
On Sun, Oct 07, 2007 at 06:52:11AM -0400, Neal Becker wrote: Suppose I have a function F(), which is defined for 1-dim arguments. If the user passes an n1 dim array, I want to apply F to each 1-dim view. For example, for a 2-d array, apply F to each row and return a 2-d result. For a 3-d array, select each 2-d subarray and see above. Return 3-d result. Any suggestions on how to code something like this in numpy? Not the most efficient way, but easy to read and understand: import numpy as N def func(a): return a.shape z = N.zeros((2,2,2,2)) print N.array([func(sub) for sub in z]) Regards Stéfan ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] dot product not behaving as expected
Hi, I have a problem using numpy.dot, see below: In [151]: m=5 In [152]: n=5 In [153]: x=(m*ones((1,5)))**arange(0,n) In [154]: y=test.order_length[::-1] In [155]: x Out[155]: array([[ 1.,5., 25., 125., 625.]]) In [156]: y Out[156]: array([[ 1024.], [ 1280.], [ 640.], [ 160.], [ 20.]]) In [157]: numpy.dot(x,y) Out[157]: array([[ 64.]]) In [158]: sum(x*y.T) Out[158]: 55924.0 Am I missing something? Thanks, Robin ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] dot product not behaving as expected
Robin wrote: Hi, I have a problem using numpy.dot, see below: In [151]: m=5 In [152]: n=5 In [153]: x=(m*ones((1,5)))**arange(0,n) In [154]: y=test.order_length[::-1] In [155]: x Out[155]: array([[ 1.,5., 25., 125., 625.]]) In [156]: y Out[156]: array([[ 1024.], [ 1280.], [ 640.], [ 160.], [ 20.]]) In [157]: numpy.dot(x,y) Out[157]: array([[ 64.]]) In [158]: sum(x* y.T) Out[158]: 55924.0 Am I missing something? In [1]: from numpy import * In [2]: x = array([[ 1.,5., 25., 125., 625.]]) In [3]: y = array([[ 1024.], ...:[ 1280.], ...:[ 640.], ...:[ 160.], ...:[ 20.]]) In [4]: dot(x, y) Out[4]: array([[ 55924.]]) In [5]: sum(x * y.T) Out[5]: 55924.0 It works for me with a recent SVN numpy on OS X. What version of numpy are you using? What platform are you on? Did you build with ATLAS or other optimized linear algebra library? -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] dot product not behaving as expected
It works for me with a recent SVN numpy on OS X. What version of numpy are you using? What platform are you on? Did you build with ATLAS or other optimized linear algebra library? I am on Ubuntu 7.04, gcc 4.1.2 In [181]: numpy.__version__ Out[181]: '1.0.4.dev4155' Built with ATLAS 3.7.37, Lapack 3.1.1 It also occurs for me on Windows XP SP2 with 1.0.4dev4154 built with cygwin gcc 3.4.4 and atlas 3.7.37 and lapack 3.1.1. Actually I found that for me it also doesn't occur if I type them in like that. The problem comes when y is reverse with the [::-1] index. Is this the correct way to reverse a vector? Anyway try this: In [36]: x=array([[1.,5.,25.,125.,625.]]) In [37]: y=array([[20.],[160.],[640.],[1280.],[1024.]]) In [38]: y Out[38]: array([[ 20.], [ 160.], [ 640.], [ 1280.], [ 1024.]]) In [39]: y2=y[::-1] In [40]: y2 Out[40]: array([[ 1024.], [ 1280.], [ 640.], [ 160.], [ 20.]]) In [41]: dot(x,y) Out[41]: array([[ 816820.]]) In [42]: dot(x,y2) Out[42]: array([[ 64.]]) Thanks, Robin ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] dot product not behaving as expected
Robin wrote: It works for me with a recent SVN numpy on OS X. What version of numpy are you using? What platform are you on? Did you build with ATLAS or other optimized linear algebra library? I am on Ubuntu 7.04, gcc 4.1.2 In [181]: numpy.__version__ Out[181]: '1.0.4.dev4155' Built with ATLAS 3.7.37, Lapack 3.1.1 It also occurs for me on Windows XP SP2 with 1.0.4dev4154 built with cygwin gcc 3.4.4 and atlas 3.7.37 and lapack 3.1.1. Actually I found that for me it also doesn't occur if I type them in like that. The problem comes when y is reverse with the [::-1] index. Is this the correct way to reverse a vector? Aha. Yes, that is the correct way to do it. However, it appears that there is a bug in how dot() is interpreting the non-contiguous array. In the meantime, you can make a contiguous array with array(y2). -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Convert array type
Gary Ruben wrote: Try using astype. This works: values = array(wavearray.split()).astype(float) Why not use numpy.fromstring? fromstring(string, dtype=float, count=-1, sep='') Return a new 1d array initialized from the raw binary data in string. If count is positive, the new array will have count elements, otherwise its size is determined by the size of string. If sep is not empty then the string is interpreted in ASCII mode and converted to the desired number type using sep as the separator between elements (extra whitespace is ignored). Ryan -- Ryan May Graduate Research Assistant School of Meteorology University of Oklahoma ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy/Windows shared arrays between processes?
Is anyone sharing arrays between processes on Windows? I tried compiling the posh sources (once, so far) with the new MS toolkit and failed... What other solutions are in use? Have a second process create an array view from an address would suffice for this particular purpose. I could pass the address as a parameter of the second process's argv. I've also tried things like pb=pythonapi.PyBuffer_FromReadWriteMemory(9508824, 9*sizeof(c_int)) N.frombuffer(pb, N.int32) which fails since pb is and int. What are my options? Ray Schumacher ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Convert array type
On 08/10/2007, Ryan May [EMAIL PROTECTED] wrote: Why not use numpy.fromstring? because that results in the array being filled with gibberish values = numpy.fromstring(wavearray, dtype=float, count=-1, sep='') print values gives: [ 1.39804329e-076 1.30354290e-076 1.18295070e-076 ..., 5.45168074e-067 2.11101912e-052 6.58519056e-260] where using values = array(wavearray.split()).astype(float) print values results in the correct [ 0.e+00 0.e+00 0.e+00 ..., 4.22233200e-23 3.86799900e-23 3.48452000e-23] Cheers Adam ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Convert array type
Adam Mercer wrote: On 08/10/2007, Ryan May [EMAIL PROTECTED] wrote: Why not use numpy.fromstring? because that results in the array being filled with gibberish values = numpy.fromstring(wavearray, dtype=float, count=-1, sep='') Use sep=' '. As the docstring says, if sep is empty, then the string is interpreted as binary data. If it is not empty, then the string is interpreted as ASCII. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Convert array type
On 10/8/07, Adam Mercer [EMAIL PROTECTED] wrote: On 08/10/2007, Ryan May [EMAIL PROTECTED] wrote: Why not use numpy.fromstring? because that results in the array being filled with gibberish values = numpy.fromstring(wavearray, dtype=float, count=-1, sep='') print values You need to use values = numpy.fromstring(wavearray, dtype=float, count=-1, sep=' ') Note that sep is a blank (whitespace). This will convert ascii strings. Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] beginner question: rank-1 arrays
On Mon, Oct 08, 2007 at 11:00:39PM +0100, Robin wrote: Coming from matlab and being use to 0:10 for row or (0:10)' for column this seems a bit messy. Is there a better way of constructing row/column 2d arrays from a slice type range? r_[0:10] and c_[0:10]. Does that suit you ? The first one is indeed only 1D, but I don't see the problem with that. If you really want 2D you can use c_[0:10].T . Gaël ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] beginner question: rank-1 arrays
On Mon, Oct 08, 2007 at 11:00:39PM +0100, Robin wrote: Hi, I am trying to implement a project in scipy. I think I am getting somewhere finally. However in my code (I am converting from MATLAB) it is important to maintain 2d arrays, and keep the difference between row and column vectors. After working through some initial problems I think I am getting more of a picture of how things work in numpy. However I am finding my code littered with things like: np.array(np.r_[0:nterms],ndmin=2)(for a row vector) or np.array(np.r_[0:nterms],ndmin=2).T (for a column vector) You can use N.arange(10).reshape(-1,1) or N.c_[:10,] But if you use this sort of thing often, just write your own factory method: def col(n): return N.arange(n).reshape(-1,1) Cheers Stéfan ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] beginner question: rank-1 arrays
On Mon, Oct 08, 2007 at 11:12:07PM +0100, Robin wrote: On 10/8/07, Gael Varoquaux [EMAIL PROTECTED] wrote: r_[0:10] and c_[0:10]. Does that suit you ? The first one is indeed only 1D, but I don't see the problem with that. If you really want 2D you can use c_[0:10].T . Thanks, but not really :) Firstly - for me I don' see any difference between the two, they both give a numpy rank-1 array which doesn't have a row/column characteristic. x.shape for both of the above is (10,) I need (10,1) or (1,10) for my code. Damn it. Shame on me. I meant c_[0:10,]. If you really need a shape of (1,10) (I have never had such a need) you can use c_[0:10,].T. HTH, Gaël ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] beginner question: rank-1 arrays
On Mon, 8 Oct 2007, Robin apparently wrote: However in my code (I am converting from MATLAB) it is important to maintain 2d arrays, and keep the difference between row and column vectors. How about using matrices? help(numpy.mat) hth, Alan Isaac ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] beginner question: rank-1 arrays
On 10/8/07, Gael Varoquaux [EMAIL PROTECTED] wrote: Damn it. Shame on me. I meant c_[0:10,]. If you really need a shape of (1,10) (I have never had such a need) you can use c_[0:10,].T. Thanks! - the trick with the , is just the sort of thing I was looking for - I knew there must be an easy way... Cheers Robin ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Convert array type
On 08/10/2007, Robert Kern [EMAIL PROTECTED] wrote: Use sep=' '. As the docstring says, if sep is empty, then the string is interpreted as binary data. If it is not empty, then the string is interpreted as ASCII. Thanks, got it the wrong way round. That works now. Cheers Adam ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
