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

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 !! 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.

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

Function/Subroutine Documentation

◆ 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 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 
)
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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 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 !!

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◆ least_square2()

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

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◆ matrix()

subroutine matrix ( 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), dimension(:), intent(in)  input,
real(kind=8), intent(out)  mean,
real(kind=8), intent(out)  var 
)
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◆ norm()

subroutine norm ( real ( kind = 8 ), dimension ( 3 )  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 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

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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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