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One approach: fit a kernel density estimator to the observed coordinates $\{ (X_i,Y_i,Z_i) \}$ and color by density: http://stackoverflow.com/questions/25286811/how-to-plot-a-3d-density-map-in-python-with-matplotlib/25354417#25354417 Incorporating the weights $w$ of each observation is a bit trickier, but there's code to do this: ...


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R packages misc3d and rgl. The function image3d in misc3d does what you need and is very parametrizable via rgl (change the transparency, the view angle, axes, etc...). I used it with fairly big arrays (~10000000 data points/plots) and it produced great 3D density plots. Both R (e.g. package biOps) and Matlab (image processing toolbox) have very powerful ...


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Trying to analyze that much data in ImageJ seems likely to end in frustration. Matlab is probably a more productive angle. Personally, I would use Python. Specifically, the numpy module. Try looking into the histogramdd function in numpy. It might be able to accomplish what you're after.



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