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For the most general form of binning I use a convolution (by a 2D mask) followed by a subsmapling. For example for a 3x3 binning: mask = ones((3,3)) binned = convolve2d(data,mask,'same')[1::3,1::3] Nadav. -----Original Message----- From: Russell E. Owen [mailto:owen@astro.washington.edu] Sent: Sat 28-Aug-04 03:34 To: numpy-discussion@lists.sourceforge.net Cc: Subject: [Numpy-discussion] rebin Any suggestions on an efficient means to bin a 2-d array? REBIN is the IDL function I'm trying to mimic. Binning allows one to combine sets of pixels from one array to form a new array that is smaller by a given factor along each dimension. To nxm bin a 2-dimensional array, one averages (or sums or ?) each nxm block of entries from the input image to form the corresponding entry of the output image. For example, to 2x2 bin a two-dimensional image, one would: average (0,0), (0,1), (1,0), (1,1) to form (0,0) average (0,2), (0,3), (1,2), (1,3) to form (0,1) ... In case it helps, in my immediate case I'm binning a boolean array (a mask) and thus can live with almost any kind of combination. -- Russell ------------------------------------------------------- This SF.Net email is sponsored by BEA Weblogic Workshop FREE Java Enterprise J2EE developer tools! Get your free copy of BEA WebLogic Workshop 8.1 today. http://ads.osdn.com/?ad_id=5047&alloc_id=10808&op=click _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
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Nadav Horesh