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I have a data set containing values for power and direction. I would like to produce a probability density estimate. The data can have multiple sources so I want to use a nonparametric method. I work in python which has a method for kernal density estimation (KDE), which I think should be suitable. However, currently the method does not allow the data to be weighted, so I can only use the directions. Also, it does not allow polar coordinates so any bins near the ends of the distribution do not include all relevant values (i.e. bins centered close to zero degrees should include points close to 360 degrees). The result is a curve that is discontinuous across zero degrees. Does anyone know where I might find an implementation for KDE (any language) that allows polar coordinates, I might write one in python but would like to try it out somewhere to make sure it is suitable to what I need first. Alternatively, if there are any better suggestions on how to estimate the distribution I would be very interested??

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# Kernal density estimate in polar coordinates.

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