SDA (SDA flex)  7.2 Simulation of Diffusional Association
maths.f File Reference

## Data Types

interface  meanvar

## 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 !! More...

subroutine least_square2 (n, X, Y, a, b, d, r2)
same function, but label array from 1 to N More...

subroutine derivative (input, output, dt, opt_factor)

subroutine simple_derivative (input, output, dt, opt_factor)
kind of derivative, simplified version, gives a smoother approximation More...

subroutine norm (wa)

## Detailed Description

Version
{version 7.2.3 (2019)}

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.

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

Version
{version 7.2.3 (2019)}

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.

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

## ◆ cross()

 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 )
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## ◆ cross4()

 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 )

## ◆ derivative()

 subroutine meanvar::derivative ( real ( kind=8 ) input, real ( kind=8 ) output, real ( kind=8 ) dt, real ( kind=8 ) opt_factor )
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## ◆ determinant()

 real ( kind=8 ) function determinant ( real ( kind=8 ), dimension(3,3), intent(in) mat )

## ◆ dot()

 subroutine dot ( real(kind=8), intent(out) ab, real(kind=8), dimension(3), intent(in) a, real(kind=8), dimension(3), intent(in) b )

## ◆ fmax3()

 real*8 function fmax3 ( x1, x2, x3 )

## ◆ ggnml()

 subroutine ggnml ( n, dimension(3) gaus )
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## ◆ ggnml4_new()

 subroutine ggnml4_new ( integer n, real ( kind=4 ), dimension ( n ) gaus )
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## ◆ ggubs()

 subroutine ggubs ( unif )
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## ◆ least_square()

 subroutine meanvar::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 !!

## ◆ least_square2()

 subroutine meanvar::least_square2 ( n, X, Y, a, b, d, r2 )

same function, but label array from 1 to N

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## ◆ matrix()

 subroutine matrix ( real ( kind=8 ), dimension ( 3 ), intent(in) om, real ( kind=8 ), dimension ( 3,3 ), intent(out) rot, logical, intent(in) old )
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## ◆ matrix_new()

 subroutine matrix_new ( real ( kind=8 ), dimension ( 3 ), intent(in) om, real ( kind=8 ), dimension ( 3,3 ), intent(out) rot )
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## ◆ matrix_old()

 subroutine matrix_old ( real ( kind=8 ), dimension ( 3 ), intent(in) om, real ( kind=8 ), dimension ( 3,3 ), intent(out) rot )
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## ◆ meanvar()

 subroutine meanvar ( real(kind=8) input, real(kind=8) mean, real(kind=8) var )

## ◆ norm()

 subroutine simple_derivative::norm ( real ( kind = 8 ) wa )
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## ◆ rotate31()

 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 )
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## ◆ rotate33()

 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 )
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## ◆ rotate3n()

 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 )
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## ◆ simple_derivative()

 subroutine derivative::simple_derivative ( real ( kind=8 ) input, real ( kind=8 ) output, real ( kind=8 ) dt, real ( kind=8 ) opt_factor )

kind of derivative, simplified version, gives a smoother approximation

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## ◆ tr()

 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 )
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## ◆ tr4()

 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 )

## ◆ tr_vector()

 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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