SDA  7.1
Simulation of Diffusional Association
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Functions/Subroutines
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Functions/Subroutines

real *8 function fmax3 (x1, x2, x3)
 
subroutine cross (ab, a, b)
 
subroutine cross4 (ab, a, b)
 
subroutine matrix (om, rot)
 
subroutine tr (vo, vn, ex, ey, ez)
 
subroutine tr4 (vo, vn, ex, ey, ez)
 
subroutine tr_vector (vo, vn, ex, ey, ez, nat)
 
subroutine dot (ab, a, b)
 
subroutine rotate31 (c, rot, b)
 
subroutine rotate3n (c, rot, b, n)
 
subroutine rotate33 (c, rot, b)
 
real(kind=8) function determinant (mat)
 
subroutine ggnml (n, gaus)
 
subroutine ggnml4_new (n, gaus)
 
subroutine ggubs (unif)
 
subroutine meanvar (input, mean, var)
 
subroutine least_square (n, X, Y, a, b, d, r2)
 Linear least square.
The input data set is X(m), Y(m).
The number of data points is n (n must be > 2).
The returned parameters are:
a,b, coefficients of equation
Y = a + b X, and d, standard deviation of fit.
original function label array from 0 to n-1 !!
 
subroutine least_square2 (n, X, Y, a, b, d, r2)
 same function, but label array from 1 to N
 
subroutine derivative (input, output, dt, opt_factor)
 
subroutine simple_derivative (input, output, dt, opt_factor)
 kind of derivative, simplified version, gives a smoother approximation
 
subroutine norm (wa)
 

Detailed Description

group general mathematical functions

Version
{version 7.1 (2015)}

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References (see also http://mcm.h-its.org/sda7/doc/doc_sda7/references.html):

Brownian dynamics simulation of protein-protein diffusional encounter.

(1998) Methods, 14, 329-341.

Authors: M.Martinez, N.J.Bruce, J.Romanowska, D.B.Kokh, P.Mereghetti, R.R.Gabdoulline, M. Ozboyaci, S.Richter and R.C.Wade