SDA (SDA flex)  7.2
Simulation of Diffusional Association
Data Types | Functions/Subroutines
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.

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


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 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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