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!! |
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!! Copyright (c) 2006 The University of Notre Dame. All Rights Reserved. |
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!! |
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!! The University of Notre Dame grants you ("Licensee") a |
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!! non-exclusive, royalty free, license to use, modify and |
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!! redistribute this software in source and binary code form, provided |
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!! that the following conditions are met: |
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!! |
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!! 1. Acknowledgement of the program authors must be made in any |
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!! publication of scientific results based in part on use of the |
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!! program. An acceptable form of acknowledgement is citation of |
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!! the article in which the program was described (Matthew |
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!! A. Meineke, Charles F. Vardeman II, Teng Lin, Christopher |
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!! J. Fennell and J. Daniel Gezelter, "OOPSE: An Object-Oriented |
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!! Parallel Simulation Engine for Molecular Dynamics," |
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!! J. Comput. Chem. 26, pp. 252-271 (2005)) |
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!! |
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!! 2. Redistributions of source code must retain the above copyright |
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!! notice, this list of conditions and the following disclaimer. |
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!! |
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!! 3. Redistributions in binary form must reproduce the above copyright |
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!! notice, this list of conditions and the following disclaimer in the |
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!! documentation and/or other materials provided with the |
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!! distribution. |
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!! |
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!! This software is provided "AS IS," without a warranty of any |
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!! kind. All express or implied conditions, representations and |
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!! warranties, including any implied warranty of merchantability, |
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!! fitness for a particular purpose or non-infringement, are hereby |
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!! excluded. The University of Notre Dame and its licensors shall not |
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!! be liable for any damages suffered by licensee as a result of |
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!! using, modifying or distributing the software or its |
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!! derivatives. In no event will the University of Notre Dame or its |
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!! licensors be liable for any lost revenue, profit or data, or for |
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!! direct, indirect, special, consequential, incidental or punitive |
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!! damages, however caused and regardless of the theory of liability, |
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!! arising out of the use of or inability to use software, even if the |
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!! University of Notre Dame has been advised of the possibility of |
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!! such damages. |
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!! |
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!! |
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!! interpolation.F90 |
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!! |
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!! Created by Charles F. Vardeman II on 03 Apr 2006. |
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!! |
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!! PURPOSE: Generic Spline interpolation routines. |
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!! |
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!! @author Charles F. Vardeman II |
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!! @version $Id: interpolation.F90,v 1.9 2006-06-06 17:43:28 gezelter Exp $ |
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|
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|
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module interpolation |
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use definitions |
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use status |
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implicit none |
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PRIVATE |
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|
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type, public :: cubicSpline |
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logical :: isUniform = .false. |
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integer :: n = 0 |
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real(kind=dp) :: dx_i |
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real (kind=dp), pointer,dimension(:) :: x => null() |
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real (kind=dp), pointer,dimension(:) :: y => null() |
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real (kind=dp), pointer,dimension(:) :: b => null() |
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real (kind=dp), pointer,dimension(:) :: c => null() |
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real (kind=dp), pointer,dimension(:) :: d => null() |
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end type cubicSpline |
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|
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public :: newSpline |
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public :: deleteSpline |
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public :: lookupSpline |
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public :: lookupUniformSpline |
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public :: lookupNonuniformSpline |
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public :: lookupUniformSpline1d |
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|
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contains |
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|
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|
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subroutine newSpline(cs, x, y, isUniform) |
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|
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implicit none |
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|
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type (cubicSpline), intent(inout) :: cs |
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real( kind = DP ), intent(in) :: x(:), y(:) |
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real( kind = DP ) :: fp1, fpn, p |
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REAL( KIND = DP), DIMENSION(size(x)-1) :: diff_y, H |
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|
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logical, intent(in) :: isUniform |
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integer :: i, alloc_error, n, k |
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|
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alloc_error = 0 |
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|
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if (cs%n .ne. 0) then |
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call handleWarning("interpolation::newSpline", & |
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"cubicSpline struct was already created") |
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call deleteSpline(cs) |
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end if |
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|
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! make sure the sizes match |
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|
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n = size(x) |
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|
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if ( size(y) .ne. size(x) ) then |
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call handleError("interpolation::newSpline", & |
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"Array size mismatch") |
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end if |
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|
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cs%n = n |
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cs%isUniform = isUniform |
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|
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allocate(cs%x(n), stat=alloc_error) |
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if(alloc_error .ne. 0) then |
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call handleError("interpolation::newSpline", & |
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"Error in allocating storage for x") |
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endif |
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|
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allocate(cs%y(n), stat=alloc_error) |
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if(alloc_error .ne. 0) then |
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call handleError("interpolation::newSpline", & |
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"Error in allocating storage for y") |
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endif |
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|
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allocate(cs%b(n), stat=alloc_error) |
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if(alloc_error .ne. 0) then |
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call handleError("interpolation::newSpline", & |
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"Error in allocating storage for b") |
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endif |
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|
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allocate(cs%c(n), stat=alloc_error) |
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if(alloc_error .ne. 0) then |
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call handleError("interpolation::newSpline", & |
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"Error in allocating storage for c") |
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endif |
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|
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allocate(cs%d(n), stat=alloc_error) |
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if(alloc_error .ne. 0) then |
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call handleError("interpolation::newSpline", & |
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"Error in allocating storage for d") |
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endif |
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|
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! make sure we are monotinically increasing in x: |
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|
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h = diff(x) |
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if (any(h <= 0)) then |
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call handleError("interpolation::newSpline", & |
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"Negative dx interval found") |
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end if |
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|
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! load x and y values into the cubicSpline structure: |
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|
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do i = 1, n |
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cs%x(i) = x(i) |
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cs%y(i) = y(i) |
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end do |
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|
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! Calculate coefficients for the tridiagonal system: store |
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! sub-diagonal in B, diagonal in D, difference quotient in C. |
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|
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cs%b(1:n-1) = h |
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diff_y = diff(y) |
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cs%c(1:n-1) = diff_y / h |
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|
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if (n == 2) then |
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! Assume the derivatives at both endpoints are zero |
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! another assumption could be made to have a linear interpolant |
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! between the two points. In that case, the b coefficients |
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! below would be diff_y(1)/h(1) and the c and d coefficients would |
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! both be zero. |
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cs%b(1) = 0.0_dp |
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cs%c(1) = -3.0_dp * (diff_y(1)/h(1))**2 |
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cs%d(1) = -2.0_dp * (diff_y(1)/h(1))**3 |
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cs%b(2) = cs%b(1) |
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cs%c(2) = 0.0_dp |
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cs%d(2) = 0.0_dp |
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cs%dx_i = 1.0_dp / h(1) |
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return |
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end if |
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|
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cs%d(1) = 2.0_dp * cs%b(1) |
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do i = 2, n-1 |
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cs%d(i) = 2.0_dp * (cs%b(i) + cs%b(i-1)) |
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end do |
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cs%d(n) = 2.0_dp * cs%b(n-1) |
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|
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! Calculate estimates for the end slopes using polynomials |
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! that interpolate the data nearest the end. |
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|
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fp1 = cs%c(1) - cs%b(1)*(cs%c(2) - cs%c(1))/(cs%b(1) + cs%b(2)) |
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if (n > 3) then |
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fp1 = fp1 + cs%b(1)*((cs%b(1) + cs%b(2))*(cs%c(3) - cs%c(2))/ & |
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(cs%b(2) + cs%b(3)) - cs%c(2) + cs%c(1))/(x(4) - x(1)) |
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end if |
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|
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fpn = cs%c(n-1) + cs%b(n-1)*(cs%c(n-1) - cs%c(n-2))/(cs%b(n-2) + cs%b(n-1)) |
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if (n > 3) then |
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fpn = fpn + cs%b(n-1)*(cs%c(n-1) - cs%c(n-2) - (cs%b(n-2) + cs%b(n-1))* & |
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(cs%c(n-2) - cs%c(n-3))/(cs%b(n-2) + cs%b(n-3)))/(x(n) - x(n-3)) |
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end if |
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|
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! Calculate the right hand side and store it in C. |
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|
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cs%c(n) = 3.0_dp * (fpn - cs%c(n-1)) |
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do i = n-1,2,-1 |
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cs%c(i) = 3.0_dp * (cs%c(i) - cs%c(i-1)) |
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end do |
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cs%c(1) = 3.0_dp * (cs%c(1) - fp1) |
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|
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! Solve the tridiagonal system. |
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|
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do k = 2, n |
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p = cs%b(k-1) / cs%d(k-1) |
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cs%d(k) = cs%d(k) - p*cs%b(k-1) |
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cs%c(k) = cs%c(k) - p*cs%c(k-1) |
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end do |
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cs%c(n) = cs%c(n) / cs%d(n) |
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do k = n-1, 1, -1 |
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cs%c(k) = (cs%c(k) - cs%b(k) * cs%c(k+1)) / cs%d(k) |
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end do |
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|
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! Calculate the coefficients defining the spline. |
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|
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cs%d(1:n-1) = diff(cs%c) / (3.0_dp * h) |
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cs%b(1:n-1) = diff_y / h - h * (cs%c(1:n-1) + h * cs%d(1:n-1)) |
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cs%b(n) = cs%b(n-1) + h(n-1) * (2.0_dp*cs%c(n-1) + h(n-1)*3.0_dp*cs%d(n-1)) |
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|
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if (isUniform) then |
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cs%dx_i = 1.0_dp / (x(2) - x(1)) |
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endif |
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|
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return |
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|
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contains |
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|
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function diff(v) |
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! Auxiliary function to compute the forward difference |
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! of data stored in a vector v. |
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|
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implicit none |
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real (kind = dp), dimension(:), intent(in) :: v |
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real (kind = dp), dimension(size(v)-1) :: diff |
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|
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integer :: n |
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|
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n = size(v) |
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diff = v(2:n) - v(1:n-1) |
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return |
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end function diff |
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|
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end subroutine newSpline |
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|
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subroutine deleteSpline(this) |
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|
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type(cubicSpline) :: this |
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|
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if(associated(this%d)) then |
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deallocate(this%d) |
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this%d => null() |
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end if |
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if(associated(this%c)) then |
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deallocate(this%c) |
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this%c => null() |
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end if |
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if(associated(this%b)) then |
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deallocate(this%b) |
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this%b => null() |
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end if |
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if(associated(this%y)) then |
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deallocate(this%y) |
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this%y => null() |
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end if |
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if(associated(this%x)) then |
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deallocate(this%x) |
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this%x => null() |
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end if |
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|
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this%n = 0 |
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|
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end subroutine deleteSpline |
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|
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subroutine lookupNonuniformSpline(cs, xval, yval) |
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|
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implicit none |
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|
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type (cubicSpline), intent(in) :: cs |
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real( kind = DP ), intent(in) :: xval |
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real( kind = DP ), intent(out) :: yval |
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real( kind = DP ) :: dx |
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integer :: i, j |
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! |
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! Find the interval J = [ cs%x(J), cs%x(J+1) ] that contains |
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! or is nearest to xval. |
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! |
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j = cs%n - 1 |
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|
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do i = 0, cs%n - 2 |
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|
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if ( xval < cs%x(i+1) ) then |
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j = i |
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exit |
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end if |
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|
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end do |
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! |
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! Evaluate the cubic polynomial. |
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! |
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dx = xval - cs%x(j) |
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yval = cs%y(j) + dx*(cs%b(j) + dx*(cs%c(j) + dx*cs%d(j))) |
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|
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return |
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end subroutine lookupNonuniformSpline |
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|
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subroutine lookupUniformSpline(cs, xval, yval) |
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|
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implicit none |
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|
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type (cubicSpline), intent(in) :: cs |
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real( kind = DP ), intent(in) :: xval |
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real( kind = DP ), intent(out) :: yval |
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real( kind = DP ) :: dx |
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integer :: i, j |
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! |
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! Find the interval J = [ cs%x(J), cs%x(J+1) ] that contains |
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! or is nearest to xval. |
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|
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j = MAX(1, MIN(cs%n-1, int((xval-cs%x(1)) * cs%dx_i) + 1)) |
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|
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dx = xval - cs%x(j) |
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yval = cs%y(j) + dx*(cs%b(j) + dx*(cs%c(j) + dx*cs%d(j))) |
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|
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return |
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end subroutine lookupUniformSpline |
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|
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subroutine lookupUniformSpline1d(cs, xval, yval, dydx) |
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|
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implicit none |
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|
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type (cubicSpline), intent(in) :: cs |
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real( kind = DP ), intent(in) :: xval |
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real( kind = DP ), intent(out) :: yval, dydx |
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real( kind = DP ) :: dx |
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integer :: i, j |
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|
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! Find the interval J = [ cs%x(J), cs%x(J+1) ] that contains |
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! or is nearest to xval. |
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|
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|
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j = MAX(1, MIN(cs%n-1, int((xval-cs%x(1)) * cs%dx_i) + 1)) |
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|
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dx = xval - cs%x(j) |
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yval = cs%y(j) + dx*(cs%b(j) + dx*(cs%c(j) + dx*cs%d(j))) |
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|
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dydx = cs%b(j) + dx*(2.0_dp * cs%c(j) + 3.0_dp * dx * cs%d(j)) |
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|
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return |
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end subroutine lookupUniformSpline1d |
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|
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subroutine lookupSpline(cs, xval, yval) |
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|
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type (cubicSpline), intent(in) :: cs |
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real( kind = DP ), intent(inout) :: xval |
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real( kind = DP ), intent(inout) :: yval |
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|
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if (cs%isUniform) then |
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call lookupUniformSpline(cs, xval, yval) |
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else |
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call lookupNonuniformSpline(cs, xval, yval) |
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endif |
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|
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return |
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end subroutine lookupSpline |
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|
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end module interpolation |
