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2026-09-17 22:59:07 +09:00 · 2026-08-07 15:56:42 +09:00
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+% BINNING_2D - bin data along first two axis, 
+%  x = binning_2D(x, binning, centered)
+%
+%  Inputs: 
+%   **x - original array, upsampling will be performed only along the first two axis, array size has to be dividable by binning size 
+%   **binning - scalar or (2,1) array,  positive integer binning factor 
+%  *Optional*:
+%   **centered - default false, shift the binning by binning/2 offset 
+%
+%   Outputs:
+%   ++x - binned array 
+
+%*-----------------------------------------------------------------------*
+%|                                                                       |
+%|  Except where otherwise noted, this work is licensed under a          |
+%|  Creative Commons Attribution-NonCommercial-ShareAlike 4.0            |
+%|  International (CC BY-NC-SA 4.0) license.                             |
+%|                                                                       |
+%|  Copyright (c) 2017 by Paul Scherrer Institute (http://www.psi.ch)    |
+%|                                                                       |
+%|       Author: CXS group, PSI                                          |
+%*-----------------------------------------------------------------------*
+% You may use this code with the following provisions:
+%
+% If the code is fully or partially redistributed, or rewritten in another
+%   computing language this notice should be included in the redistribution.
+%
+% If this code, or subfunctions or parts of it, is used for research in a 
+%   publication or if it is fully or partially rewritten for another 
+%   computing language the authors and institution should be acknowledged 
+%   in written form in the publication: “Data processing was carried out 
+%   using the “cSAXS matlab package” developed by the CXS group,
+%   Paul Scherrer Institut, Switzerland.” 
+%   Variations on the latter text can be incorporated upon discussion with 
+%   the CXS group if needed to more specifically reflect the use of the package 
+%   for the published work.
+%
+% A publication that focuses on describing features, or parameters, that
+%    are already existing in the code should be first discussed with the
+%    authors.
+%   
+% This code and subroutines are part of a continuous development, they 
+%    are provided “as they are” without guarantees or liability on part
+%    of PSI or the authors. It is the user responsibility to ensure its 
+%    proper use and the correctness of the results.
+
+
+
+
+function x = binning_2D(x, binning, centered)
+    
+    if all(binning <= 1); return ; end 
+
+    if nargin < 3 
+        centered = false; 
+    end
+    Npix = [size(x,1), size(x,2), size(x,3)]; 
+    if isscalar(binning); binning = repmat(binning, 1,2); end
+    binning = reshape(binning,1,[]);
+    
+    if all(Npix(1:2) >= binning(:))
+        % faster but less general version 
+        if centered
+            % it will be slower due to memory copy 
+            x = x(ceil(binning(1)/2):end-ceil(binning(1)/2)-1, ceil(binning(2)/2):end-ceil(binning(2)/2)-1,:); 
+            Npix(1:2) = Npix(1:2) - binning; 
+        end
+        if any(~math.isint(Npix(1:2)./binning))
+           % is the array cannot be easily split for binning, crop it 
+           % it will be slower due to memory copy 
+           Npix(1:2) = floor(Npix(1:2)./binning) .* binning;
+           x = x(1:Npix(1),1:Npix(2),:); 
+        end
+        x = reshape(x,binning(1), Npix(1)/binning(1), binning(2), Npix(2)/binning(2), Npix(3)); 
+        x = squeeze(sum(sum(x,1),3)); 
+        x = x / prod(binning(1:2)); 
+        if centered
+           x = padarray(x, [1,1], 'replicate', 'post');  % account for the removed pixels to keep the size
+        end
+    else
+        x = convn(single(x), ones(binning, 'single'), 'same'); 
+        norm = binning.^2; 
+        ind = {ceil(binning(1)/2):binning(1):Npix(1), ceil(binning(2)/2):binning(2):Npix(2)};
+        % avoid issues with void dimensions 
+        for i = find(Npix == 1)
+            ind{i} = ':'; 
+            norm = norm / binning(i);  %% avoid summing up by convolution
+        end
+        x = x(ind{:},:) / norm; 
+    end
+end