# [SciPy-User] Easy Way To Find Confidence Intervals For 2D Data?

Joseph Smidt josephsmidt@gmail....
Wed Jul 29 00:07:09 CDT 2009

```Thanks, that's what i needed!

On Tue, Jul 28, 2009 at 7:33 PM, Robert Kern<robert.kern@gmail.com> wrote:
> On Tue, Jul 28, 2009 at 20:34, Joseph Smidt<josephsmidt@gmail.com> 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.
>
> --
> Robert Kern
>
> "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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>

--
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Joseph Smidt <josephsmidt@gmail.com>

Physics and Astronomy
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