[SciPy-Dev] fortran docstrings

[josef.pktd at gmail.com]

playing with parsing fortran documentation in special cdflib
https://gist.github.com/josef-pkt/6426618
some fortran files don't work

example

#################
cumpoi(s, xlam, cum, ccum)
CUMulative POIsson distribution

Returns the probability of S or fewer events in a Poisson
distribution with mean XLAM.

Parameters
----------
s : array_like
    Upper limit of cumulation of the Poisson.
xlam : array_like
    Mean of the Poisson distribution.


Returns
-------
cum : array_like
    Cumulative poisson distribution.
ccum : array_like
    Compliment of Cumulative poisson distribution.


Notes
-----
Uses formula 26.4.21 of Abramowitz and Stegun, Handbook of
Mathematical Functions to reduce the cumulative Poisson to the
cumulative chi-square distribution.

#################
cumt(t, df, cum, ccum)
CUMulative T-distribution

Computes the integral from -infinity to T of the t-density.

Parameters
----------
t : array_like
    Upper limit of integration of the t-density.
df : array_like
    Degrees of freedom of the t-distribution.


Returns
-------
cum : array_like
    Cumulative t-distribution.
ccum : array_like
    Compliment of Cumulative t-distribution.


Notes
-----
Formula 26.5.27 of Abramowitz and Stegun, Handbook of Mathematical
Functions is used to reduce the t-distribution to an incomplete
beta.

#################
cumtnc(t, df, pnonc, cum, ccum)
CUMulative Non-Central T-distribution

Computes the integral from -infinity to T of the non-central
t-density.

Parameters
----------
t : array_like
    Upper limit of integration of the non-central t-density.
df : array_like
    Degrees of freedom of the non-central t-distribution.
pnonc : array_like
    Non-centrality parameter of the non-central t distibutio


Returns
-------
cum : array_like
    Cumulative t-distribution.
ccum : array_like
    Compliment of Cumulative t-distribution.


Notes
-----
Upper tail of the cumulative noncentral t using formulae from page
532 of Johnson, Kotz, Balakrishnan, Coninuous Univariate
Distributions, Vol 2, 2nd Edition. Wiley (1995) This
implementation starts the calculation at i = lambda, which is near
the largest Di. It then sums forward and backward.