[SciPy-User] solving integration, density function

[josef.pktd at gmail.com]

On Tue, Dec 21, 2010 at 9:33 AM, Johannes Radinger <JRadinger at gmx.at> wrote:
>
> -------- Original-Nachricht --------
>> Datum: Tue, 21 Dec 2010 09:18:15 -0500
>> Von: Skipper Seabold <jsseabold at gmail.com>
>> An: SciPy Users List <scipy-user at scipy.org>
>> Betreff: Re: [SciPy-User] solving integration, density function
>
>> On Tue, Dec 21, 2010 at 7:48 AM, Johannes Radinger <JRadinger at gmx.at>
>> wrote:
>> >
>> > -------- Original-Nachricht --------
>> >> Datum: Tue, 21 Dec 2010 13:20:47 +0100
>> >> Von: Gregor Thalhammer <Gregor.Thalhammer at gmail.com>
>> >> An: SciPy Users List <scipy-user at scipy.org>
>> >> Betreff: Re: [SciPy-User] solving integration, density function
>> >
>> >>
>> >> Am 21.12.2010 um 12:06 schrieb Johannes Radinger:
>> >>
>> >> > Hello,
>> >> >
>> >> > I am really new to python and Scipy.
>> >> > I want to solve a integrated function with a python script
>> >> > and I think Scipy should do that :)
>> >> >
>> >> > My task:
>> >> >
>> >> > I do have some variables (s, m, K,) which are now absolutely set, but
>> in
>> >> future I'll get the values via another process of pyhton.
>> >> >
>> >> > s = 400
>> >> > m = 0
>> >> > K = 1
>> >> >
>> >> > And have have following function:
>> >> > (1/((s*K)*sqrt(2*pi)))*exp((-1/2*(((x-m)/s*K))^2) which is the
>> density
>> >> function of the normal distribution a symetrical curve with the mean
>> (m) of
>> >> 0.
>> >> >
>> >> > The total area under the curve is 1 (100%) which is for an
>> integration
>> >> from -inf to +inf.
>> >> > I want to know x in the case of 99%: meaning that the integral (-x to
>> >> +x) of the function is 0.99. Due to the symetry of the curve you can
>> also set
>> >> the integral from 0 to +x equal to (0.99/2):
>> >> >
>> >> > 0.99 = integral((1/((s*K)*sqrt(2*pi)))*exp((-1/2*(((x-m)/s*K))^2)),
>> -x,
>> >> x)
>> >> > resp.
>> >> > (0.99/2) =
>> integral((1/((s*K)*sqrt(2*pi)))*exp((-1/2*(((x-m)/s*K))^2)),
>> >> 0, x)
>> >> >
>> >> > How can I solve that question in Scipy/python
>> >> > so that I get x in the end. I don't know how to write
>> >> > the code...
>> >>
>> >>
>> >> --->
>> >> erf(x[, out])
>> >>
>> >>     y=erf(z) returns the error function of complex argument defined
>> as
>> >>     as 2/sqrt(pi)*integral(exp(-t**2),t=0..z)
>> >> ---
>> >>
>> >> from scipy.special import erf, erfinv
>> >> erfinv(0.99)*sqrt(2)
>> >>
>> >>
>> >> Gregor
>> >>
>> >
>> >
>> > Thank you Gregor,
>> > I only understand a part of your answer... I know that the integral of
>> the density function is a error function and I know that the argument "from
>> scipy.special import erf, erfinv" is to load the module.
>> >
>> > But how do I write the code including my orignial function so that I can
>> modify it (I have also another function I want to integrate). how do i
>> start? I want to save the whole code to a python-script I can then load e.g.
>> into ArcGIS where I want to use the value of x for further calculations.
>> >
>>
>> Are you always integrating densities?  If so, you don't want to use
>> integrals probably, but you could use scipy.stats
>>
>> erfinv(.99)*np.sqrt(2)
>> 2.5758293035489004
>>
>> from scipy import stats
>>
>> stats.norm.ppf(.995)
>> 2.5758293035489004
>
>> Skipper
>
> The second function I want to integrate is different, it is a combination of two normal distributions like:
>
> 0.99 = integrate(0.6*((1/((s1*K)*sqrt(2*pi)))*exp((-1/2*(((x-m)/s1*K))^2))+0,4*((1/((s2*K)*sqrt(2*pi)))*exp((-1/2*(((x-m)/s2*K))^2)))
>
> and here again I know s1, s2, m and K and want to get x in the case when the integral is 0.99. What do I write into the script I want create?
>
> I think it is better if I can explain it with a graph but I don't know if I can just attach pictures to the mail-list-mail.

The generic way for finding the ppf in stats distribution, is use
scipy.integrate.quad for the integration and scipy.optimize solve for
finding x

(I'm still too busy for a full answer)

Josef

>
> /j
>
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