Functions/Subroutines | |
| real *8 function | fmax3 (x1, x2, x3) |
| subroutine | cross (ab, a, b) |
| subroutine | cross4 (ab, a, b) |
| subroutine | matrix_old (om, rot) |
| subroutine | matrix_new (om, rot) |
| subroutine | matrix (om, rot, old) |
| 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) |
Copyright (c) 2009, 2010, 2015, 2016, 2019 Heidelberg Institute of Theoretical Studies (HITS, www.h-its.org) Schloss-Wolfsbrunnenweg 35 69118 Heidelberg, Germany
Please send your contact address to get information on updates and new features to "mcmsoft@h-its.org". Questions will be answered as soon as possible.
References: see also http://mcm.h-its.org/sda7/do:c/doc_sda7/references.html:
Brownian dynamics simulation of protein-protein diffusional encounter. (1998) Methods, 14, 329-341.
SDA 7: A modular and parallel implementation of the simulation of diffusional association software. Journal of computational chemistry 36.21 (2015): 1631-1645.
Authors: M.Martinez, N.J.Bruce, J.Romanowska, D.B.Kokh, P.Mereghetti, X. Yu, M. Ozboyaci, M. Reinhardt, P. Friedrich, R.R.Gabdoulline, S.Richter and R.C.Wade
Copyright (c) 2009, 2010, 2015, 2016, 2019 Heidelberg Institute of Theoretical Studies (HITS, www.h-its.org) Schloss-Wolfsbrunnenweg 35 69118 Heidelberg, Germany
Please send your contact address to get information on updates and new features to "mcmsoft@h-its.org". Questions will be answered as soon as possible.
References: see also http://mcm.h-its.org/sda7/do:c/doc_sda7/references.html:
Brownian dynamics simulation of protein-protein diffusional encounter. (1998) Methods, 14, 329-341.
SDA 7: A modular and parallel implementation of the simulation of diffusional association software. Journal of computational chemistry 36.21 (2015): 1631-1645.
Authors: M.Martinez, N.J.Bruce, J.Romanowska, D.B.Kokh, P.Mereghetti, X. Yu, M. Ozboyaci, M. Reinhardt, P. Friedrich, R.R.Gabdoulline, S.Richter and R.C.Wade
group general mathematical functions
| subroutine cross | ( | real ( kind=8 ), dimension ( 3 ), intent(out) | ab, |
| real ( kind=8 ), dimension ( 3 ), intent(in) | a, | ||
| real ( kind=8 ), dimension ( 3 ), intent(in) | b | ||
| ) |
| subroutine cross4 | ( | real ( kind=4 ), dimension ( 3 ), intent(out) | ab, |
| real ( kind=4 ), dimension ( 3 ), intent(in) | a, | ||
| real ( kind=4 ), dimension ( 3 ), intent(in) | b | ||
| ) |
| subroutine derivative | ( | real ( kind=8 ), dimension(:), intent(in) | input, |
| real ( kind=8 ), dimension(:), intent(out) | output, | ||
| real ( kind=8 ) | dt, | ||
| real ( kind=8 ), optional | opt_factor | ||
| ) |
| subroutine dot | ( | real(kind=8), intent(out) | ab, |
| real(kind=8), dimension(3), intent(in) | a, | ||
| real(kind=8), dimension(3), intent(in) | b | ||
| ) |
| subroutine ggnml | ( | n, | |
| dimension(3) | gaus | ||
| ) |
| subroutine ggnml4_new | ( | integer | n, |
| real ( kind=4 ), dimension ( n ) | gaus | ||
| ) |
| subroutine ggubs | ( | unif | ) |
| subroutine least_square | ( | integer | n, |
| real ( kind=8 ), dimension (n), intent(in) | x, | ||
| real ( kind=8 ), dimension (n), intent(in) | y, | ||
| real*8 | a, | ||
| real*8 | b, | ||
| real*8 | d, | ||
| real*8 | 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 | ( | integer, intent(in) | n, |
| real ( kind=8 ), dimension (n), intent(in) | x, | ||
| real ( kind=8 ), dimension (n), intent(in) | y, | ||
| real ( kind=8 ), intent(out) | a, | ||
| real ( kind=8 ), intent(out) | b, | ||
| real ( kind=8 ), intent(out) | d, | ||
| real ( kind=8 ), intent(out) | r2 | ||
| ) |
same function, but label array from 1 to N
| subroutine matrix | ( | real ( kind=8 ), dimension ( 3 ), intent(in) | om, |
| real ( kind=8 ), dimension ( 3,3 ), intent(out) | rot, | ||
| logical, intent(in) | old | ||
| ) |
| subroutine matrix_new | ( | real ( kind=8 ), dimension ( 3 ), intent(in) | om, |
| real ( kind=8 ), dimension ( 3,3 ), intent(out) | rot | ||
| ) |
| subroutine matrix_old | ( | real ( kind=8 ), dimension ( 3 ), intent(in) | om, |
| real ( kind=8 ), dimension ( 3,3 ), intent(out) | rot | ||
| ) |
| subroutine meanvar | ( | real(kind=8), dimension(:), intent(in) | input, |
| real(kind=8), intent(out) | mean, | ||
| real(kind=8), intent(out) | var | ||
| ) |
| subroutine norm | ( | real ( kind = 8 ), dimension ( 3 ) | wa | ) |
| subroutine rotate31 | ( | real (kind=8 ), dimension(3), intent(out) | c, |
| real( kind=8 ), dimension(3,3), intent(in) | rot, | ||
| real (kind=8 ), dimension(3), intent(in) | b | ||
| ) |
| subroutine rotate33 | ( | real (kind=8 ), dimension(3,3), intent(out) | c, |
| real( kind=8 ), dimension(3,3), intent(in) | rot, | ||
| real (kind=8 ), dimension(3,3), intent(in) | b | ||
| ) |
| subroutine rotate3n | ( | real (kind=8 ), dimension(3,n), intent(out) | c, |
| real( kind=8 ), dimension(3,3), intent(in) | rot, | ||
| real (kind=8 ), dimension(3,n), intent(in) | b, | ||
| integer | n | ||
| ) |
| subroutine simple_derivative | ( | real ( kind=8 ), dimension(:), intent(in) | input, |
| real ( kind=8 ), dimension(:), intent(out) | output, | ||
| real ( kind=8 ) | dt, | ||
| real ( kind=8 ), optional | opt_factor | ||
| ) |
kind of derivative, simplified version, gives a smoother approximation
| subroutine tr | ( | real( kind=8 ), dimension( 3 ), intent(in) | vo, |
| real ( kind=8 ), dimension ( 3 ), intent(out) | vn, | ||
| real( kind=8 ), dimension( 3 ), intent(in) | ex, | ||
| real( kind=8 ), dimension( 3 ), intent(in) | ey, | ||
| real( kind=8 ), dimension( 3 ), intent(in) | ez | ||
| ) |
| subroutine tr4 | ( | real ( kind = 4 ), dimension ( 3 ) | vo, |
| real ( kind = 4 ), dimension ( 3 ) | vn, | ||
| real ( kind = 8 ), dimension ( 3 ) | ex, | ||
| real ( kind = 8 ), dimension ( 3 ) | ey, | ||
| real ( kind = 8 ), dimension ( 3 ) | ez | ||
| ) |
| subroutine tr_vector | ( | real ( kind = 8 ), dimension ( 3,nat ), intent(in) | vo, |
| real ( kind = 8 ), dimension ( 3,nat ), intent(out) | vn, | ||
| real ( kind = 8 ), dimension ( 3 ), intent(in) | ex, | ||
| real ( kind = 8 ), dimension ( 3 ), intent(in) | ey, | ||
| real ( kind = 8 ), dimension ( 3 ), intent(in) | ez, | ||
| integer, intent(in) | nat | ||
| ) |
1.9.8
Imprint/Privacy