Thanks Jose for your kind message. Indeed you are right. However, if there is a place for erf definition in the special functions of scipy, why not the Q thing? Even if not, maybe there is a place to add a note in the documentations. The trivial thing one new to scipy will do is to search for Q-function and then if not for erf or erfc. At least, this would be done by an immigrant from Matlab. I am rather new to scipy and just now learned from you the gizmo of the norm object (a really cool one, thanks a lot for your message). I must add that even in stackoverflow when searching for Q function in python I did not tackle with these sf and isf methods (maybe I'll post a question and you'll post the answer :-)). On Wed, Feb 17, 2016 at 1:56 PM, José Vinícius <josevinicius@iecom.org.br> wrote:
From the Wikipedia page, we see that the Q-function is equal to the Gaussian Survival function (scipy.stats.norm.sf) as well.
CVLattes: http://lattes.cnpq.br/3267230342393209
On Wed, Feb 17, 2016 at 4:54 AM, Eran Hof <eran.hof@gmail.com> wrote:
Dear scipy devs
If I may suggest, there is an interest in adding a new error function to the scipy special functions. The Q-function (see please, https://en.wikipedia.org/wiki/Q-function). This function is of great interest and much in use within the communication engineering and error correcting coding communities.
There is a 1:1 mapping from Q-function to erf function. However, in engineering, in contrast to statistics, most text book use the Q-function instead and this is the very first function a new to python engineer will try to look for.
Also, the implementation is so fast, as Q(x) = 0.5 - 0.5 * erf (x / sqrt(2)). So why not having this function aboard? It will be used immediately by every communication engineer that adopts the scipy instead of Matlab.
Also, inverse is easily implemented as well since Qinv(y) = sqrt(2) erfinv(1-2y).
I think this small contribution may be of great service.
Your comments are much appreciated. Thanks.
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