[SciPy-User] Easy Way To Find Confidence Intervals For 2D Data?
Tue Jul 28 21:33:17 CDT 2009
On Tue, Jul 28, 2009 at 20:34, Joseph Smidt<firstname.lastname@example.org> wrote:
> I have a function on a 2d grid that looks like a skewed mound. I would
> like to calculate everything in the 68% (one sigma or standard
> deviation) confidence interval, and 95% (two sigma) as seen done in
> this plot: http://lambda.gsfc.nasa.gov/product/map/current/pub_papers/threeyear/parameters/images/Med/ds_f07_PPT_M.png.
> (The first line is 68% and second line is 95% confidence intervals.)
> I would like to the set everything in the 68% confidence interval to
> 1, 95% to 2 and everything else to 0. Thanks.
It depends somewhat on what that function is. Is it a proper PDF?
Let's say it is.
I'll assume that the data has been normalized such that the sum of all
the entries is 1. If not you'll have to keep track of that
normalization constant. Let's call this array normed_pdf. What you are
going to do is make a function that takes a value x, computes the sum
normed_pdf[normed_pdf > x].sum(), and then subtracts the target value,
say 0.68. Then use a root finder from scipy.optimize to find out where
this function equals 0. Repeat for 0.95. Now just do a contour plot
using those two values as the contour lines; e.g. in matplotlib, use
the contour(Z, V) form.
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco
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