Mailman 3 June 2012 - NumPy-Discussion

Numpy + SWIG
by Gideon Simpson June 4, 2012

June 4, 2012

There are two types of swig problems that I was hoping to get some help with. First, suppose I have some C function void f(double *x, int nx, double *y, int ny); where we input one array, and we output another array, both of which should be the same size. I have used in my .i file: %apply(double *IN_ARRAY1, int DIM1){(double *x, int nx)} %apply(double *ARGOUT_ARRAY1, int DIM1){(double *y, int ny)} and this produces a workable function. However, it expects, as the functions second argument, the length of the array x. Now, it's easy enough to call: module.f(x, x.shape[0]) but is there a way to automatically get it to use the length of the array? The second problem I have is for a function of the fomr void g(double *x, int nx, double *y, int ny, double *z, int nz); which evaluates some function g at all (x,y) pairs. The the thing is that nx and ny need not be the same size, but nz should be nx * ny. I'd like to wrap this too, and ideally it would also automatically handle the array lengths, but I'd be happy to have anything right now. I'm also quite comfortable with the idea of packing z as a column array and reshaping it as necessary. -gideon

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Debian/Ubuntu patch help (was: ANN: NumPy 1.6.2 release candidate 1)
by Julian Taylor June 4, 2012

June 4, 2012

> Hi, if there's anyone wants to have a look at the above issue this > week, >that would be great. > If there's a patch by this weekend I can create a second RC, so we can > still have the final release before the end of this month (needed for > Debian freeze). Otherwise a second RC won't be needed. bugfixes are still allowed during the debian freeze, so that should not be an issue for the release timing. I don't see the issue with the gcc --print-multiarch patch besides maybe some cleanup. --print-multiarch is a debian specific gcc patch, but multiarch is debian specific for now. It doesn't work in 11.04, but who cares, that will be end of life in 5 month anyway. Besides x11 almost nothing is multiarched in 11.04 anyway and that can still be covered by the currently existing method. gcc should be available for pretty much anything requiring numpy.distutils anyway so that should be not be an issue. On systems without --print-multiarch or gcc you just ignore the failing, there will be no repercussions as there will also not be any multiarched libraries. the only potential issue I see is that upstream gcc adds a --print-multiarch that does something completely different and harmful for distutils, but I don't consider that very likely. Hardcoding a bunch of paths defeats the whole purpose of multiarch. It will just to break in future (e.g. when the x32 abi comes to debian/ubuntu) and will make cross compiling harder (though I guess numpy distutils may not be built for that anyway)

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How to remove any row or column of a numpy matrix whose sum is 3?
by bob tnur June 4, 2012

June 4, 2012

Hello every body. I am new to python. How to remove any row or column of a numpy matrix whose sum is 3. To obtain and save new matrix P with (sum(anyrow)!=3 and sum(anycolumn)!=3 elements. I tried like this: P = M[np.logical_not( (M[n,:].sum()==3) & (M[:,n].sum()==3))] or P = M[np.logical_not( (np.sum(M[n,:])==3) & (np.sum(M[:,n])==3))] M is the nxn numpy matrix. But I got indexerror. So can anyone correct this or any other elegant way of doing this? Thanks for your help

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How to remove any row or column of a numpy matrix whose sum is 3?
by bob tnur June 4, 2012

June 4, 2012

Hello every body. I am new to python. How to remove any row or column of a numpy matrix whose sum is 3. To obtain and save new matrix P with (sum(anyrow)!=3 and sum(anycolumn)!=3 elements. I tried like this: P = M[np.logical_not( (M[n,:].sum()==3) & (M[:,n].sum()==3))] or P = M[np.logical_not( (np.sum(M[n,:])==3) & (np.sum(M[:,n])==3))] M is the nxn numpy matrix. But I got indexerror. So can anyone correct this or any other elegant way of doing this? Thanks for your help

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fast access and normalizing of ndarray slices
by Wolfgang Kerzendorf June 4, 2012

June 4, 2012

Dear all, I have an ndarray which consists of many arrays stacked behind each other (only conceptually, in truth it's a normal 1d float64 array). I have a second array which tells me the start of the individual data sets in the 1d float64 array and another one which tells me the length. Example: data_array = (conceptually) [[1,2], [1,2,3,4], [1,2,3]] = in reality [1,2,1,2,3,4,1,2,3, dtype=float64] start_pointer = [0, 2, 6] length_data = [2, 4, 3] I now want to normalize each of the individual data sets. I wrote a simple for loop over the start_pointer and length data grabbed the data and normalized it and wrote it back to the big array. That's slow. Is there an elegant numpy way to do that? Do I have to go the cython way? Cheers Wolfgang

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NumPy release manager post
by Ralf Gommers June 3, 2012

June 3, 2012

Hi all, You probably remember that I said that after numpy 1.7.0 was out I wanted to step down as release manager for NumPy and focus more on SciPy. That was 4.5 months ago, and now that 1.7.0 keeps being postponed I'm actually planning to not wait for it. I have found that it's not possible for me to handle overlapping NumPy and SciPy release schedules well, therefore even if the NA stuff is sorted out soon I don't think I could do a 1.7.0 release within the next 6-8 weeks. A possible 1.6.3 release I could still do without much trouble though. Travis has already volunteered to manage the next release - thanks for that Travis. If anyone else would like to pitch in, I'm sure that's very welcome too. It doesn't require that you already know everything there is to know about NumPy, just a willingness to dig in. Feel free to ask me either on- or off-list for more details. Cheers, Ralf P.S. I'll certainly not completely stop working on numpy (docs, testing, distutils, ...)

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Error when multiplying large sparse matrices
by Jeremy Lecoeur June 1, 2012

June 1, 2012

Hi, I have been using the sparse matrix tools for a while to do all sort of things and, using the same code that was working just fine, I now encounter a problem when trying . I do have very large sparse matrices and when i multiplying them the number of non zeros exceed the max value of an intc, which cause indptr to hold negative values. Hence in the multiplication function of csr, whenc reating the resulting matrix, i get an error as it is not possible to have a negative value for a matrix size. Am I missing something that would allow me to do that computation ? Here is the code I am using: def main(): inCondMatFile = sys.argv[1] inNodeSize = sys.argv[2] outProfileFile = sys.argv[3] outNodeDistFile = sys.argv[4] outNodeDensityFile = sys.argv[5] Acsr = scipy.io.mmread(inCondMatFile).tocsr().sorted_indices() A = Acsr.tocoo() n = A.shape[0] nnz = A.nnz rows=numpy.zeros(3*nnz+n, dtype=numpy.int32) cols=numpy.zeros(3*nnz+n, dtype=numpy.int32) data=numpy.zeros(3*nnz+n, dtype=numpy.float64) #first n rows of constraint mat is A - I rows[0:nnz] = A.row cols[0:nnz] = A.col data[0:nnz] = A.data rows[nnz:nnz+n] = numpy.arange(n) cols[nnz:nnz+n] = numpy.arange(n) data[nnz:nnz+n] = -numpy.ones(n) #rows n to n+nnz are #A_{i,j} d_{j} - A_{j,i} d_{i} == 0 rows[nnz+n:] = numpy.append(numpy.arange(n,n+nnz),numpy.arange(n,n+nnz)) cols[nnz+n:] = numpy.append(A.col,A.row) data[nnz+n:] = numpy.append(A.data,-Acsr[A.col,A.row]) tmpC = scipy.sparse.coo_matrix( (data, (rows,cols) ) ) Ptmp = (tmpC.transpose().tocsr() * tmpC.tocsr()).tocoo() And it fails for that last multiplication (i did not include the rest of the code) because of an nnz way too big for an intc. Jeremy The information contained in this e-mail message is intended only for the personal and confidential use of the recipient(s) named above. If the reader of this message is not the intended recipient or an agent responsible for delivering it to the intended recipient, you are hereby notified that you have received this document in error and that any review, dissemination, distribution, or copying of this message is strictly prohibited. If you have received this communication in error, please notify us immediately by e-mail, and delete the original message.

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