Generalized, version of histc (histogram count), allows weighted values. Creates a histogram h of the values in I, with edges as specified. h will have length nbins, where nbins=length(edges)-1. Each value in I has associated weight given by weightmask, which should have the same dimensions as I. h(q) contains the weighted count of values v in I such that edges(q) <= v < edges(q+1). h(nbins) additionally contains the weighted count of values in I such that v==edges(nbins+1) -- which is different then how histc treates the boundary condition. Finally, h is normalized so that sum(h(:))==1. It usually makes sense to specify edges explicitly, especially if different histograms are going to be compared. In general, edges must have monotonically non-decreasing values. Also, if the exact bounds are unknown then it is convenient to set the first element in edges to -inf and the last to inf. If h = histc_1D( I, nbins, ...), edges are automatically generated and have bins equally spaced between min(I) and max(I). That is edges is generated via: 'edges = linspace( minI-eps, maxI+eps, nbins+1 )'. See histc for more information. INPUTS I - numeric array [treated as a vector] edges - either nbins+1 length vector of quantization bounds, or scalar nbins weightmask - [optional] size(I) numeric array of weights OUTPUTS h - histogram (vector of size 1xnbins) EXAMPLE G = filter_gauss_nD([1000 1000],[],[],1); h1 = histc_1D( G, 25 ); figure(1); bar(h1); h2 = histc_1D( G, 25, G ); figure(2); bar(h2); DATESTAMP 29-Sep-2005 2:00pm See also HISTC, ASSIGN2BINS