On Mon, Sep 28, 2009 at 4:48 PM, <josef.p...@gmail.com> wrote: > On Mon, Sep 28, 2009 at 7:19 PM, jah <jah.mailingl...@gmail.com> wrote: > > Hi, > > > > Suppose I have a set of x,y,c data (something useful for > > matplotlib.pyplot.plot() ). Generally, this data is not rectangular at > > all. Does there exist a numpy function (or set of functions) which will > > take this data and construct the smallest two-dimensional arrays X,Y,C ( > > suitable for matplotlib.pyplot.contour() ). > > > > Essentially, I want to pass in the data and a grid step size in the x- > and > > y-directions. The function would average the c-values for all points > which > > land in any particular square. Optionally, I'd like to be able to > specify a > > value to use when there are no points in x,y which are in the square. > > > > Hope this makes sense. > > If I understand correctly numpy.histogram2d(x, y, ..., weights=c) might do > what you want. > > There was a recent thread on its usage. >
It is very close, but it normed=True, will first normalize the weights (undesirably) and then it will normalize the normalized weights by dividing by the cell area. Instead, what I want is the cell value to be the average off all the points that were placed in the cell. This seems like a common use case, so I'm guessing this functionality is present already. So if 3 points with weights [10,20,30] were placed in cell (i,j), then the cell should have value 20 (the arithmetic mean of the points placed in the cell). Here is the desired use case: I have a set of x,y,c values that I could pass into matplotlib's scatter() or hexbin(). I'd like to take this same set of points and transform them so that I can pass them into matplotlib's contour() function. Perhaps matplotlib has a function which does this.
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