Jan 09, 2017 · You can use StatsBase for this julia> using StatsBase julia> result = fit(Histogram, randn(1000)) StatsBase.Histogram{Int64,1,Tuple{FloatRange{Float64}}} edges: -4.0:1.0:4.0 weights: [2,24,131,361,342,129,10,1] closed: right Replace the randn(1000) in the code above with the vector your are working with. You can access the properties of the result using result.edges and result.weights. errorDocCallbackpolar ApplicationsMATLABR2014aapptoolboxmatlabgraph2dpolarm 60 from MATH 3607 at Ohio State University Jun 13, 2016 · For functions that use them, the colormap is applied evenly over the entire range of the plotted data. This means that it is possible to create a colormap where particular colors correspond to particular values in the plotted data, but again this requires knowing something about the plotted values, how they are plotted, and then adjusting/creating a colormap to suit this. histc counts the number of items that fall into bins whose edges you specify. histc (or equivalent) are the fundamental routines behind creating histograms -- histograms require counting the number of items that fall into each bin and then using a bar plot on the result.