[Numpy-discussion] Convert data into rectangular grid
Mon Sep 28 19:45:15 CDT 2009
On Mon, Sep 28, 2009 at 4:48 PM, <email@example.com> wrote:
> On Mon, Sep 28, 2009 at 7:19 PM, jah <firstname.lastname@example.org> 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-
> > y-directions. The function would average the c-values for all points
> > 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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