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, A.Muñiz-Chicharro, R.Beccaria 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 ) |
| real ( kind=8 ) function determinant | ( | real ( kind=8 ), dimension(3,3), intent(in) | mat | ) |
| subroutine dot | ( | real(kind=8), intent(out) | ab, |
| real(kind=8), dimension(3), intent(in) | a, | ||
| real(kind=8), dimension(3), intent(in) | b ) |
| real*8 function fmax3 | ( | x1, | |
| x2, | |||
| x3 ) |
| 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 ) |
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