[Numpy-discussion] Convert data into rectangular grid
Tue Sep 29 23:08:20 CDT 2009
On Mon, Sep 28, 2009 at 8:45 PM, jah <firstname.lastname@example.org> wrote:
> 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
>> 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).
Would this work for you ?
>>> s = histogram2d(x,y,weights=c) # Not normalized, so you get the sum of
>>> n = histogram2d(x,y) # Now you have the number of elements in each bin
>>> mean = s/n
> 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.
> NumPy-Discussion mailing list
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