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! Copyright 2011 Max-Planck-Institut für Eisenforschung GmbH
!
! This file is part of DAMASK,
! the Düsseldorf Advanced MAterial Simulation Kit.
!
! DAMASK is free software: you can redistribute it and/or modify
! it under the terms of the GNU General Public License as published by
! the Free Software Foundation, either version 3 of the License, or
! (at your option) any later version.
!
! DAMASK is distributed in the hope that it will be useful,
! but WITHOUT ANY WARRANTY; without even the implied warranty of
! MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
! GNU General Public License for more details.
!
! You should have received a copy of the GNU General Public License
! along with DAMASK. If not, see <http://www.gnu.org/licenses/>.
!
!##############################################################
!* $Id$
!##############################################################
MODULE math
!##############################################################
use, intrinsic :: iso_c_binding
use prec, only: pReal,pInt
use IO, only: IO_error
implicit none
real(pReal), parameter :: pi = 3.14159265358979323846264338327950288419716939937510_pReal
real(pReal), parameter :: inDeg = 180.0_pReal/pi
real(pReal), parameter :: inRad = pi/180.0_pReal
! *** 3x3 Identity ***
real(pReal), dimension(3,3), parameter :: math_I3 = &
reshape( (/ &
1.0_pReal,0.0_pReal,0.0_pReal, &
0.0_pReal,1.0_pReal,0.0_pReal, &
0.0_pReal,0.0_pReal,1.0_pReal /),(/3,3/))
! *** Mandel notation ***
integer(pInt), dimension (2,6), parameter :: mapMandel = &
reshape((/&
1_pInt,1_pInt, &
2_pInt,2_pInt, &
3_pInt,3_pInt, &
1_pInt,2_pInt, &
2_pInt,3_pInt, &
1_pInt,3_pInt &
/),(/2,6/))
real(pReal), dimension(6), parameter :: nrmMandel = &
(/1.0_pReal,1.0_pReal,1.0_pReal, 1.414213562373095_pReal, 1.414213562373095_pReal, 1.414213562373095_pReal/)
real(pReal), dimension(6), parameter :: invnrmMandel = &
(/1.0_pReal,1.0_pReal,1.0_pReal,0.7071067811865476_pReal,0.7071067811865476_pReal,0.7071067811865476_pReal/)
! *** Voigt notation ***
integer(pInt), dimension (2,6), parameter :: mapVoigt = &
reshape((/&
1_pInt,1_pInt, &
2_pInt,2_pInt, &
3_pInt,3_pInt, &
2_pInt,3_pInt, &
1_pInt,3_pInt, &
1_pInt,2_pInt &
/),(/2,6/))
real(pReal), dimension(6), parameter :: nrmVoigt = &
(/1.0_pReal,1.0_pReal,1.0_pReal, 1.0_pReal, 1.0_pReal, 1.0_pReal/)
real(pReal), dimension(6), parameter :: invnrmVoigt = &
(/1.0_pReal,1.0_pReal,1.0_pReal, 1.0_pReal, 1.0_pReal, 1.0_pReal/)
! *** Plain notation ***
integer(pInt), dimension (2,9), parameter :: mapPlain = &
reshape((/&
1_pInt,1_pInt, &
1_pInt,2_pInt, &
1_pInt,3_pInt, &
2_pInt,1_pInt, &
2_pInt,2_pInt, &
2_pInt,3_pInt, &
3_pInt,1_pInt, &
3_pInt,2_pInt, &
3_pInt,3_pInt &
/),(/2,9/))
! Symmetry operations as quaternions
! 24 for cubic, 12 for hexagonal = 36
integer(pInt), dimension(2), parameter :: math_NsymOperations = (/24_pInt,12_pInt/)
real(pReal), dimension(4,36), parameter :: math_symOperations = &
reshape((/&
1.0_pReal, 0.0_pReal, 0.0_pReal, 0.0_pReal, & ! cubic symmetry operations
0.0_pReal, 0.0_pReal, 0.7071067811865476_pReal, 0.7071067811865476_pReal, & ! 2-fold symmetry
0.0_pReal, 0.7071067811865476_pReal, 0.0_pReal, 0.7071067811865476_pReal, &
0.0_pReal, 0.7071067811865476_pReal, 0.7071067811865476_pReal, 0.0_pReal, &
0.0_pReal, 0.0_pReal, 0.7071067811865476_pReal, -0.7071067811865476_pReal, &
0.0_pReal, -0.7071067811865476_pReal, 0.0_pReal, 0.7071067811865476_pReal, &
0.0_pReal, 0.7071067811865476_pReal, -0.7071067811865476_pReal, 0.0_pReal, &
0.5_pReal, 0.5_pReal, 0.5_pReal, 0.5_pReal, & ! 3-fold symmetry
-0.5_pReal, 0.5_pReal, 0.5_pReal, 0.5_pReal, &
0.5_pReal, -0.5_pReal, 0.5_pReal, 0.5_pReal, &
-0.5_pReal, -0.5_pReal, 0.5_pReal, 0.5_pReal, &
0.5_pReal, 0.5_pReal, -0.5_pReal, 0.5_pReal, &
-0.5_pReal, 0.5_pReal, -0.5_pReal, 0.5_pReal, &
0.5_pReal, 0.5_pReal, 0.5_pReal, -0.5_pReal, &
-0.5_pReal, 0.5_pReal, 0.5_pReal, -0.5_pReal, &
0.7071067811865476_pReal, 0.7071067811865476_pReal, 0.0_pReal, 0.0_pReal, & ! 4-fold symmetry
0.0_pReal, 1.0_pReal, 0.0_pReal, 0.0_pReal, &
-0.7071067811865476_pReal, 0.7071067811865476_pReal, 0.0_pReal, 0.0_pReal, &
0.7071067811865476_pReal, 0.0_pReal, 0.7071067811865476_pReal, 0.0_pReal, &
0.0_pReal, 0.0_pReal, 1.0_pReal, 0.0_pReal, &
-0.7071067811865476_pReal, 0.0_pReal, 0.7071067811865476_pReal, 0.0_pReal, &
0.7071067811865476_pReal, 0.0_pReal, 0.0_pReal, 0.7071067811865476_pReal, &
0.0_pReal, 0.0_pReal, 0.0_pReal, 1.0_pReal, &
-0.7071067811865476_pReal, 0.0_pReal, 0.0_pReal, 0.7071067811865476_pReal, &
1.0_pReal, 0.0_pReal, 0.0_pReal, 0.0_pReal, & ! hexagonal symmetry operations
0.0_pReal, 1.0_pReal, 0.0_pReal, 0.0_pReal, & ! 2-fold symmetry
0.0_pReal, 0.0_pReal, 1.0_pReal, 0.0_pReal, &
0.0_pReal, 0.5_pReal, 0.866025403784439_pReal, 0.0_pReal, &
0.0_pReal, -0.5_pReal, 0.866025403784439_pReal, 0.0_pReal, &
0.0_pReal, 0.866025403784439_pReal, 0.5_pReal, 0.0_pReal, &
0.0_pReal, -0.866025403784439_pReal, 0.5_pReal, 0.0_pReal, &
0.866025403784439_pReal, 0.0_pReal, 0.0_pReal, 0.5_pReal, & ! 6-fold symmetry
-0.866025403784439_pReal, 0.0_pReal, 0.0_pReal, 0.5_pReal, &
0.5_pReal, 0.0_pReal, 0.0_pReal, 0.866025403784439_pReal, &
-0.5_pReal, 0.0_pReal, 0.0_pReal, 0.866025403784439_pReal, &
0.0_pReal, 0.0_pReal, 0.0_pReal, 1.0_pReal &
/),(/4,36/))
include 'fftw3.f03'
CONTAINS
!**************************************************************************
! initialization of module
!**************************************************************************
SUBROUTINE math_init ()
use prec, only: tol_math_check
use numerics, only: fixedSeed
use IO, only: IO_error
use debug, only: debug_verbosity
implicit none
integer(pInt) :: i
real(pReal), dimension(3,3) :: R,R2
real(pReal), dimension(3) :: Eulers
real(pReal), dimension(4) :: q,q2,axisangle,randTest
! the following variables are system dependend and shound NOT be pInt
integer :: randSize ! gfortran requires a variable length to compile
integer, dimension(:), allocatable :: randInit ! if recalculations of former randomness (with given seed) is necessary
! comment the first random_seed call out, set randSize to 1, and use ifort
!$OMP CRITICAL (write2out)
write(6,*)
write(6,*) '<<<+- math init -+>>>'
write(6,*) '$Id$'
write(6,*)
!$OMP END CRITICAL (write2out)
call random_seed(size=randSize)
allocate(randInit(randSize))
if (fixedSeed > 0_pInt) then
randInit(1:randSize) = int(fixedSeed) ! fixedSeed is of type pInt, randInit not
call random_seed(put=randInit)
else
call random_seed()
endif
call random_seed(get=randInit)
do i = 1_pInt, 4_pInt
call random_number(randTest(i))
enddo
!$OMP CRITICAL (write2out)
! this critical block did cause trouble at IWM
write(6,*) 'value of random seed: ', randInit(1)
write(6,*) 'size of random seed: ', randSize
write(6,'(a,4(/,26x,f16.14))') ' start of random sequence: ', randTest
write(6,*) ''
!$OMP END CRITICAL (write2out)
call random_seed(put=randInit)
call random_seed(get=randInit)
call halton_seed_set(randInit(1))
call halton_ndim_set(3_pInt)
! --- check rotation dictionary ---
! +++ q -> a -> q +++
q = math_qRnd();
axisangle = math_QuaternionToAxisAngle(q);
q2 = math_AxisAngleToQuaternion(axisangle(1:3),axisangle(4))
if ( any(abs( q-q2) > tol_math_check) .and. &
any(abs(-q-q2) > tol_math_check) ) &
call IO_error(670_pInt)
! +++ q -> R -> q +++
R = math_QuaternionToR(q);
q2 = math_RToQuaternion(R)
if ( any(abs( q-q2) > tol_math_check) .and. &
any(abs(-q-q2) > tol_math_check) ) &
call IO_error(671_pInt)
! +++ q -> euler -> q +++
Eulers = math_QuaternionToEuler(q);
q2 = math_EulerToQuaternion(Eulers)
if ( any(abs( q-q2) > tol_math_check) .and. &
any(abs(-q-q2) > tol_math_check) ) &
call IO_error(672_pInt)
! +++ R -> euler -> R +++
Eulers = math_RToEuler(R);
R2 = math_EulerToR(Eulers)
if ( any(abs( R-R2) > tol_math_check) ) &
call IO_error(673_pInt)
ENDSUBROUTINE math_init
!**************************************************************************
! Quicksort algorithm for two-dimensional integer arrays
!
! Sorting is done with respect to array(1,:)
! and keeps array(2:N,:) linked to it.
!**************************************************************************
RECURSIVE SUBROUTINE qsort(a, istart, iend)
implicit none
integer(pInt), dimension(:,:) :: a
integer(pInt) :: istart,iend,ipivot
if (istart < iend) then
ipivot = math_partition(a,istart, iend)
call qsort(a, istart, ipivot-1_pInt)
call qsort(a, ipivot+1_pInt, iend)
endif
ENDSUBROUTINE qsort
!**************************************************************************
! Partitioning required for quicksort
!**************************************************************************
integer(pInt) function math_partition(a, istart, iend)
implicit none
integer(pInt), dimension(:,:) :: a
integer(pInt) :: istart,iend,d,i,j,k,x,tmp
d = size(a,1_pInt) ! number of linked data
! set the starting and ending points, and the pivot point
i = istart
j = iend
x = a(1,istart)
do
! find the first element on the right side less than or equal to the pivot point
do j = j, istart, -1_pInt
if (a(1,j) <= x) exit
enddo
! find the first element on the left side greater than the pivot point
do i = i, iend
if (a(1,i) > x) exit
enddo
if (i < j) then ! if the indexes do not cross, exchange values
do k = 1_pInt,d
tmp = a(k,i)
a(k,i) = a(k,j)
a(k,j) = tmp
enddo
else ! if they do cross, exchange left value with pivot and return with the partition index
do k = 1_pInt,d
tmp = a(k,istart)
a(k,istart) = a(k,j)
a(k,j) = tmp
enddo
math_partition = j
return
endif
enddo
endfunction math_partition
!**************************************************************************
! range of integers starting at one
!**************************************************************************
pure function math_range(N)
implicit none
integer(pInt), intent(in) :: N
integer(pInt) :: i
integer(pInt), dimension(N) :: math_range
forall (i=1_pInt:N) math_range(i) = i
endfunction math_range
!**************************************************************************
! second rank identity tensor of specified dimension
!**************************************************************************
pure function math_identity2nd(dimen)
implicit none
integer(pInt), intent(in) :: dimen
integer(pInt) :: i
real(pReal), dimension(dimen,dimen) :: math_identity2nd
math_identity2nd = 0.0_pReal
forall (i=1_pInt:dimen) math_identity2nd(i,i) = 1.0_pReal
endfunction math_identity2nd
!**************************************************************************
! permutation tensor e_ijk used for computing cross product of two tensors
! e_ijk = 1 if even permutation of ijk
! e_ijk = -1 if odd permutation of ijk
! e_ijk = 0 otherwise
!**************************************************************************
pure function math_civita(i,j,k)
implicit none
integer(pInt), intent(in) :: i,j,k
real(pReal) math_civita
math_civita = 0.0_pReal
if (((i == 1_pInt).and.(j == 2_pInt).and.(k == 3_pInt)) .or. &
((i == 2_pInt).and.(j == 3_pInt).and.(k == 1_pInt)) .or. &
((i == 3_pInt).and.(j == 1_pInt).and.(k == 2_pInt))) math_civita = 1.0_pReal
if (((i == 1_pInt).and.(j == 3_pInt).and.(k == 2_pInt)) .or. &
((i == 2_pInt).and.(j == 1_pInt).and.(k == 3_pInt)) .or. &
((i == 3_pInt).and.(j == 2_pInt).and.(k == 1_pInt))) math_civita = -1.0_pReal
endfunction math_civita
!**************************************************************************
! kronecker delta function d_ij
! d_ij = 1 if i = j
! d_ij = 0 otherwise
!**************************************************************************
pure function math_delta(i,j)
implicit none
integer(pInt), intent (in) :: i,j
real(pReal) :: math_delta
math_delta = 0.0_pReal
if (i == j) math_delta = 1.0_pReal
endfunction math_delta
!**************************************************************************
! fourth rank identity tensor of specified dimension
!**************************************************************************
pure function math_identity4th(dimen)
implicit none
integer(pInt), intent(in) :: dimen
integer(pInt) :: i,j,k,l
real(pReal), dimension(dimen,dimen,dimen,dimen) :: math_identity4th
forall (i=1_pInt:dimen,j=1_pInt:dimen,k=1_pInt:dimen,l=1_pInt:dimen) math_identity4th(i,j,k,l) = &
0.5_pReal*(math_I3(i,k)*math_I3(j,k)+math_I3(i,l)*math_I3(j,k))
endfunction math_identity4th
!**************************************************************************
! vector product a x b
!**************************************************************************
pure function math_vectorproduct(A,B)
implicit none
real(pReal), dimension(3), intent(in) :: A,B
real(pReal), dimension(3) :: math_vectorproduct
math_vectorproduct(1) = A(2)*B(3)-A(3)*B(2)
math_vectorproduct(2) = A(3)*B(1)-A(1)*B(3)
math_vectorproduct(3) = A(1)*B(2)-A(2)*B(1)
endfunction math_vectorproduct
!**************************************************************************
! tensor product a \otimes b
!**************************************************************************
pure function math_tensorproduct(A,B)
implicit none
real(pReal), dimension(3), intent(in) :: A,B
real(pReal), dimension(3,3) :: math_tensorproduct
integer(pInt) :: i,j
forall (i=1_pInt:3_pInt,j=1_pInt:3_pInt) math_tensorproduct(i,j) = A(i)*B(j)
endfunction math_tensorproduct
!**************************************************************************
! matrix multiplication 3x3 = 1
!**************************************************************************
pure function math_mul3x3(A,B)
implicit none
integer(pInt) :: i
real(pReal), dimension(3), intent(in) :: A,B
real(pReal), dimension(3) :: C
real(pReal) :: math_mul3x3
forall (i=1_pInt:3_pInt) C(i) = A(i)*B(i)
math_mul3x3 = sum(C)
endfunction math_mul3x3
!**************************************************************************
! matrix multiplication 6x6 = 1
!**************************************************************************
pure function math_mul6x6(A,B)
implicit none
integer(pInt) :: i
real(pReal), dimension(6), intent(in) :: A,B
real(pReal), dimension(6) :: C
real(pReal) :: math_mul6x6
forall (i=1_pInt:6_pInt) C(i) = A(i)*B(i)
math_mul6x6 = sum(C)
endfunction math_mul6x6
!**************************************************************************
! matrix multiplication 33x33 = 1 (double contraction --> ij * ij)
!**************************************************************************
pure function math_mul33xx33(A,B)
implicit none
integer(pInt) :: i,j
real(pReal), dimension(3,3), intent(in) :: A,B
real(pReal), dimension(3,3) :: C
real(pReal) :: math_mul33xx33
forall (i=1_pInt:3_pInt,j=1_pInt:3_pInt) C(i,j) = A(i,j) * B(i,j)
math_mul33xx33 = sum(C)
endfunction math_mul33xx33
!**************************************************************************
! matrix multiplication 3333x33 = 33 (double contraction --> ijkl *kl = ij)
!**************************************************************************
pure function math_mul3333xx33(A,B)
implicit none
integer(pInt) :: i,j
real(pReal), dimension(3,3,3,3), intent(in) :: A
real(pReal), dimension(3,3), intent(in) :: B
real(pReal), dimension(3,3) :: math_mul3333xx33
do i = 1_pInt,3_pInt
do j = 1_pInt,3_pInt
math_mul3333xx33(i,j) = sum(A(i,j,1:3,1:3)*B(1:3,1:3))
enddo; enddo
endfunction math_mul3333xx33
!**************************************************************************
! matrix multiplication 33x33 = 3x3
!**************************************************************************
pure function math_mul33x33(A,B)
implicit none
integer(pInt) :: i,j
real(pReal), dimension(3,3), intent(in) :: A,B
real(pReal), dimension(3,3) :: math_mul33x33
forall (i=1_pInt:3_pInt,j=1_pInt:3_pInt) math_mul33x33(i,j) = &
A(i,1)*B(1,j) + A(i,2)*B(2,j) + A(i,3)*B(3,j)
endfunction math_mul33x33
!**************************************************************************
! matrix multiplication 66x66 = 6x6
!**************************************************************************
pure function math_mul66x66(A,B)
implicit none
integer(pInt) :: i,j
real(pReal), dimension(6,6), intent(in) :: A,B
real(pReal), dimension(6,6) :: math_mul66x66
forall (i=1_pInt:6_pInt,j=1_pInt:6_pInt) math_mul66x66(i,j) = &
A(i,1)*B(1,j) + A(i,2)*B(2,j) + A(i,3)*B(3,j) + &
A(i,4)*B(4,j) + A(i,5)*B(5,j) + A(i,6)*B(6,j)
endfunction math_mul66x66
!**************************************************************************
! matrix multiplication 99x99 = 9x9
!**************************************************************************
pure function math_mul99x99(A,B)
use prec, only: pReal, pInt
implicit none
integer(pInt) i,j
real(pReal), dimension(9,9), intent(in) :: A,B
real(pReal), dimension(9,9) :: math_mul99x99
forall (i=1_pInt:9_pInt,j=1_pInt:9_pInt) math_mul99x99(i,j) = &
A(i,1)*B(1,j) + A(i,2)*B(2,j) + A(i,3)*B(3,j) + &
A(i,4)*B(4,j) + A(i,5)*B(5,j) + A(i,6)*B(6,j) + &
A(i,7)*B(7,j) + A(i,8)*B(8,j) + A(i,9)*B(9,j)
endfunction math_mul99x99
!**************************************************************************
! matrix multiplication 33x3 = 3
!**************************************************************************
pure function math_mul33x3(A,B)
implicit none
integer(pInt) :: i
real(pReal), dimension(3,3), intent(in) :: A
real(pReal), dimension(3), intent(in) :: B
real(pReal), dimension(3) :: math_mul33x3
forall (i=1_pInt:3_pInt) math_mul33x3(i) = A(i,1)*B(1) + A(i,2)*B(2) + A(i,3)*B(3)
endfunction math_mul33x3
!**************************************************************************
! matrix multiplication complex(33) x real(3) = complex(3)
!**************************************************************************
pure function math_mul33x3_complex(A,B)
implicit none
integer(pInt) :: i
complex(pReal), dimension(3,3), intent(in) :: A
real(pReal), dimension(3), intent(in) :: B
complex(pReal), dimension(3) :: math_mul33x3_complex
forall (i=1_pInt:3_pInt) math_mul33x3_complex(i) = A(i,1)*B(1) + A(i,2)*B(2) + A(i,3)*B(3)
endfunction math_mul33x3_complex
!**************************************************************************
! matrix multiplication 66x6 = 6
!**************************************************************************
pure function math_mul66x6(A,B)
implicit none
integer(pInt) :: i
real(pReal), dimension(6,6), intent(in) :: A
real(pReal), dimension(6), intent(in) :: B
real(pReal), dimension(6) :: math_mul66x6
forall (i=1_pInt:6_pInt) math_mul66x6(i) = &
A(i,1)*B(1) + A(i,2)*B(2) + A(i,3)*B(3) + &
A(i,4)*B(4) + A(i,5)*B(5) + A(i,6)*B(6)
endfunction math_mul66x6
!**************************************************************************
! random quaternion
!**************************************************************************
function math_qRnd()
implicit none
real(pReal), dimension(4) :: math_qRnd
real(pReal), dimension(3) :: rnd
call halton(3,rnd)
math_qRnd(1) = cos(2.0_pReal*pi*rnd(1))*sqrt(rnd(3))
math_qRnd(2) = sin(2.0_pReal*pi*rnd(2))*sqrt(1.0_pReal-rnd(3))
math_qRnd(3) = cos(2.0_pReal*pi*rnd(2))*sqrt(1.0_pReal-rnd(3))
math_qRnd(4) = sin(2.0_pReal*pi*rnd(1))*sqrt(rnd(3))
endfunction math_qRnd
!**************************************************************************
! quaternion multiplication q1xq2 = q12
!**************************************************************************
pure function math_qMul(A,B)
implicit none
real(pReal), dimension(4), intent(in) :: A, B
real(pReal), dimension(4) :: math_qMul
math_qMul(1) = A(1)*B(1) - A(2)*B(2) - A(3)*B(3) - A(4)*B(4)
math_qMul(2) = A(1)*B(2) + A(2)*B(1) + A(3)*B(4) - A(4)*B(3)
math_qMul(3) = A(1)*B(3) - A(2)*B(4) + A(3)*B(1) + A(4)*B(2)
math_qMul(4) = A(1)*B(4) + A(2)*B(3) - A(3)*B(2) + A(4)*B(1)
endfunction math_qMul
!**************************************************************************
! quaternion dotproduct
!**************************************************************************
pure function math_qDot(A,B)
implicit none
real(pReal), dimension(4), intent(in) :: A, B
real(pReal) :: math_qDot
math_qDot = A(1)*B(1) + A(2)*B(2) + A(3)*B(3) + A(4)*B(4)
endfunction math_qDot
!**************************************************************************
! quaternion conjugation
!**************************************************************************
pure function math_qConj(Q)
implicit none
real(pReal), dimension(4), intent(in) :: Q
real(pReal), dimension(4) :: math_qConj
math_qConj(1) = Q(1)
math_qConj(2:4) = -Q(2:4)
endfunction math_qConj
!**************************************************************************
! quaternion norm
!**************************************************************************
pure function math_qNorm(Q)
implicit none
real(pReal), dimension(4), intent(in) :: Q
real(pReal) :: math_qNorm
math_qNorm = sqrt(max(0.0_pReal, Q(1)*Q(1) + Q(2)*Q(2) + Q(3)*Q(3) + Q(4)*Q(4)))
endfunction math_qNorm
!**************************************************************************
! quaternion inversion
!**************************************************************************
pure function math_qInv(Q)
implicit none
real(pReal), dimension(4), intent(in) :: Q
real(pReal), dimension(4) :: math_qInv
real(pReal) :: squareNorm
math_qInv = 0.0_pReal
squareNorm = math_qDot(Q,Q)
if (squareNorm > tiny(squareNorm)) &
math_qInv = math_qConj(Q) / squareNorm
endfunction math_qInv
!**************************************************************************
! action of a quaternion on a vector (rotate vector v with Q)
!**************************************************************************
pure function math_qRot(Q,v)
implicit none
real(pReal), dimension(4), intent(in) :: Q
real(pReal), dimension(3), intent(in) :: v
real(pReal), dimension(3) :: math_qRot
real(pReal), dimension(4,4) :: T
integer(pInt) :: i, j
do i = 1_pInt,4_pInt
do j = 1_pInt,i
T(i,j) = Q(i) * Q(j)
enddo
enddo
math_qRot(1) = -v(1)*(T(3,3)+T(4,4)) + v(2)*(T(3,2)-T(4,1)) + v(3)*(T(4,2)+T(3,1))
math_qRot(2) = v(1)*(T(3,2)+T(4,1)) - v(2)*(T(2,2)+T(4,4)) + v(3)*(T(4,3)-T(2,1))
math_qRot(3) = v(1)*(T(4,2)-T(3,1)) + v(2)*(T(4,3)+T(2,1)) - v(3)*(T(2,2)+T(3,3))
math_qRot = 2.0_pReal * math_qRot + v
endfunction math_qRot
!**************************************************************************
! transposition of a 3x3 matrix
!**************************************************************************
pure function math_transpose3x3(A)
implicit none
real(pReal),dimension(3,3),intent(in) :: A
real(pReal),dimension(3,3) :: math_transpose3x3
integer(pInt) :: i,j
forall(i=1_pInt:3_pInt, j=1_pInt:3_pInt) math_transpose3x3(i,j) = A(j,i)
endfunction math_transpose3x3
!**************************************************************************
! Cramer inversion of 3x3 matrix (function)
!**************************************************************************
pure function math_inv3x3(A)
! direct Cramer inversion of matrix A.
! returns all zeroes if not possible, i.e. if det close to zero
implicit none
real(pReal),dimension(3,3),intent(in) :: A
real(pReal) :: DetA
real(pReal),dimension(3,3) :: math_inv3x3
math_inv3x3 = 0.0_pReal
DetA = A(1,1) * (A(2,2) * A(3,3) - A(2,3) * A(3,2))&
- A(1,2) * (A(2,1) * A(3,3) - A(2,3) * A(3,1))&
+ A(1,3) * (A(2,1) * A(3,2) - A(2,2) * A(3,1))
if (abs(DetA) > tiny(abs(DetA))) then
math_inv3x3(1,1) = ( A(2,2) * A(3,3) - A(2,3) * A(3,2)) / DetA
math_inv3x3(2,1) = (-A(2,1) * A(3,3) + A(2,3) * A(3,1)) / DetA
math_inv3x3(3,1) = ( A(2,1) * A(3,2) - A(2,2) * A(3,1)) / DetA
math_inv3x3(1,2) = (-A(1,2) * A(3,3) + A(1,3) * A(3,2)) / DetA
math_inv3x3(2,2) = ( A(1,1) * A(3,3) - A(1,3) * A(3,1)) / DetA
math_inv3x3(3,2) = (-A(1,1) * A(3,2) + A(1,2) * A(3,1)) / DetA
math_inv3x3(1,3) = ( A(1,2) * A(2,3) - A(1,3) * A(2,2)) / DetA
math_inv3x3(2,3) = (-A(1,1) * A(2,3) + A(1,3) * A(2,1)) / DetA
math_inv3x3(3,3) = ( A(1,1) * A(2,2) - A(1,2) * A(2,1)) / DetA
endif
endfunction math_inv3x3
!**************************************************************************
! Cramer inversion of 3x3 matrix (subroutine)
!**************************************************************************
PURE SUBROUTINE math_invert3x3(A, InvA, DetA, error)
! Bestimmung der Determinanten und Inversen einer 3x3-Matrix
! A = Matrix A
! InvA = Inverse of A
! DetA = Determinant of A
! error = logical
implicit none
logical, intent(out) :: error
real(pReal),dimension(3,3),intent(in) :: A
real(pReal),dimension(3,3),intent(out) :: InvA
real(pReal), intent(out) :: DetA
DetA = A(1,1) * (A(2,2) * A(3,3) - A(2,3) * A(3,2))&
- A(1,2) * (A(2,1) * A(3,3) - A(2,3) * A(3,1))&
+ A(1,3) * (A(2,1) * A(3,2) - A(2,2) * A(3,1))
if (abs(DetA) <= tiny(abs(DetA))) then
error = .true.
else
InvA(1,1) = ( A(2,2) * A(3,3) - A(2,3) * A(3,2)) / DetA
InvA(2,1) = (-A(2,1) * A(3,3) + A(2,3) * A(3,1)) / DetA
InvA(3,1) = ( A(2,1) * A(3,2) - A(2,2) * A(3,1)) / DetA
InvA(1,2) = (-A(1,2) * A(3,3) + A(1,3) * A(3,2)) / DetA
InvA(2,2) = ( A(1,1) * A(3,3) - A(1,3) * A(3,1)) / DetA
InvA(3,2) = (-A(1,1) * A(3,2) + A(1,2) * A(3,1)) / DetA
InvA(1,3) = ( A(1,2) * A(2,3) - A(1,3) * A(2,2)) / DetA
InvA(2,3) = (-A(1,1) * A(2,3) + A(1,3) * A(2,1)) / DetA
InvA(3,3) = ( A(1,1) * A(2,2) - A(1,2) * A(2,1)) / DetA
error = .false.
endif
ENDSUBROUTINE math_invert3x3
!**************************************************************************
! Gauss elimination to invert matrix of arbitrary dimension
!**************************************************************************
PURE SUBROUTINE math_invert(dimen,A, InvA, AnzNegEW, error)
! Invertieren einer dimen x dimen - Matrix
! A = Matrix A
! InvA = Inverse von A
! AnzNegEW = Anzahl der negativen Eigenwerte von A
! error = logical
! = false: Inversion wurde durchgefuehrt.
! = true: Die Inversion in SymGauss wurde wegen eines verschwindenen
! Pivotelement abgebrochen.
implicit none
integer(pInt), intent(in) :: dimen
real(pReal), dimension(dimen,dimen), intent(in) :: A
real(pReal), dimension(dimen,dimen), intent(out) :: InvA
integer(pInt), intent(out) :: AnzNegEW
logical, intent(out) :: error
real(pReal) :: LogAbsDetA
real(pReal), dimension(dimen,dimen) :: B
InvA = math_identity2nd(dimen)
B = A
CALL Gauss(dimen,B,InvA,LogAbsDetA,AnzNegEW,error)
ENDSUBROUTINE math_invert
! ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
! ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
PURE SUBROUTINE Gauss (dimen,A,B,LogAbsDetA,NegHDK,error)
! Loesung eines linearen Gleichungsssystem A * X = B mit Hilfe des
! GAUSS-Algorithmus
! Zur numerischen Stabilisierung wird eine Zeilen- und Spaltenpivotsuche
! durchgefuehrt.
!
! Eingabeparameter:
! A(dimen,dimen) = Koeffizientenmatrix A
! B(dimen,dimen) = rechte Seiten B
!
! Ausgabeparameter:
! B(dimen,dimen) = Matrix der Unbekanntenvektoren X
! LogAbsDetA = 10-Logarithmus des Betrages der Determinanten von A
! NegHDK = Anzahl der negativen Hauptdiagonalkoeffizienten nach der
! Vorwaertszerlegung
! error = logical
! = false: Das Gleichungssystem wurde geloest.
! = true : Matrix A ist singulaer.
!
! A und B werden veraendert!
implicit none
logical, intent(out) :: error
integer(pInt), intent(in) :: dimen
integer(pInt), intent(out) :: NegHDK
real(pReal), intent(out) :: LogAbsDetA
real(pReal), intent(inout), dimension(dimen,dimen) :: A, B
logical :: SortX
integer(pInt) :: PivotZeile, PivotSpalte, StoreI, I, IP1, J, K, L
integer(pInt), dimension(dimen) :: XNr
real(pReal) :: AbsA, PivotWert, EpsAbs, Quote
real(pReal), dimension(dimen) :: StoreA, StoreB
error = .true.; NegHDK = 1_pInt; SortX = .false.
! Unbekanntennumerierung
DO I = 1_pInt, dimen
XNr(I) = I
ENDDO
! Genauigkeitsschranke und Bestimmung des groessten Pivotelementes
PivotWert = ABS(A(1,1))
PivotZeile = 1_pInt
PivotSpalte = 1_pInt
do I = 1_pInt, dimen; do J = 1_pInt, dimen
AbsA = ABS(A(I,J))
IF (AbsA .GT. PivotWert) THEN
PivotWert = AbsA
PivotZeile = I
PivotSpalte = J
ENDIF
enddo; enddo
IF (PivotWert .LT. 0.0000001_pReal) RETURN ! Pivotelement = 0?
EpsAbs = PivotWert * 0.1_pReal ** PRECISION(1.0_pReal)
! V O R W A E R T S T R I A N G U L A T I O N
DO I = 1_pInt, dimen - 1_pInt
! Zeilentausch?
IF (PivotZeile .NE. I) THEN
StoreA(I:dimen) = A(I,I:dimen)
A(I,I:dimen) = A(PivotZeile,I:dimen)
A(PivotZeile,I:dimen) = StoreA(I:dimen)
StoreB(1:dimen) = B(I,1:dimen)
B(I,1:dimen) = B(PivotZeile,1:dimen)
B(PivotZeile,1:dimen) = StoreB(1:dimen)
SortX = .TRUE.
ENDIF
! Spaltentausch?
IF (PivotSpalte .NE. I) THEN
StoreA(1:dimen) = A(1:dimen,I)
A(1:dimen,I) = A(1:dimen,PivotSpalte)
A(1:dimen,PivotSpalte) = StoreA(1:dimen)
StoreI = XNr(I)
XNr(I) = XNr(PivotSpalte)
XNr(PivotSpalte) = StoreI
SortX = .TRUE.
ENDIF
! Triangulation
DO J = I + 1_pInt, dimen
Quote = A(J,I) / A(I,I)
DO K = I + 1_pInt, dimen
A(J,K) = A(J,K) - Quote * A(I,K)
ENDDO
DO K = 1_pInt, dimen
B(J,K) = B(J,K) - Quote * B(I,K)
ENDDO
ENDDO
! Bestimmung des groessten Pivotelementes
IP1 = I + 1_pInt
PivotWert = ABS(A(IP1,IP1))
PivotZeile = IP1
PivotSpalte = IP1
DO J = IP1, dimen
DO K = IP1, dimen
AbsA = ABS(A(J,K))
IF (AbsA .GT. PivotWert) THEN
PivotWert = AbsA
PivotZeile = J
PivotSpalte = K
ENDIF
ENDDO
ENDDO
IF (PivotWert .LT. EpsAbs) RETURN ! Pivotelement = 0?
ENDDO
! R U E C K W A E R T S A U F L O E S U N G
DO I = dimen, 1_pInt, -1_pInt
DO L = 1_pInt, dimen
DO J = I + 1_pInt, dimen
B(I,L) = B(I,L) - A(I,J) * B(J,L)
ENDDO
B(I,L) = B(I,L) / A(I,I)
ENDDO
ENDDO
! Sortieren der Unbekanntenvektoren?
IF (SortX) THEN
DO L = 1_pInt, dimen
StoreA(1:dimen) = B(1:dimen,L)
DO I = 1_pInt, dimen
J = XNr(I)
B(J,L) = StoreA(I)
ENDDO
ENDDO
ENDIF
! Determinante
LogAbsDetA = 0.0_pReal
NegHDK = 0_pInt
DO I = 1_pInt, dimen
IF (A(I,I) .LT. 0.0_pReal) NegHDK = NegHDK + 1_pInt
AbsA = ABS(A(I,I))
LogAbsDetA = LogAbsDetA + LOG10(AbsA)
ENDDO
error = .false.
ENDSUBROUTINE Gauss
!********************************************************************
! symmetrize a 3x3 matrix
!********************************************************************
function math_symmetric3x3(m)
implicit none
real(pReal), dimension(3,3) :: math_symmetric3x3
real(pReal), dimension(3,3), intent(in) :: m
integer(pInt) :: i,j
forall (i=1_pInt:3_pInt,j=1_pInt:3_pInt) math_symmetric3x3(i,j) = 0.5_pReal * (m(i,j) + m(j,i))
endfunction math_symmetric3x3
!********************************************************************
! symmetrize a 6x6 matrix
!********************************************************************
pure function math_symmetric6x6(m)
implicit none
integer(pInt) :: i,j
real(pReal), dimension(6,6), intent(in) :: m
real(pReal), dimension(6,6) :: math_symmetric6x6
forall (i=1_pInt:6_pInt,j=1_pInt:6_pInt) math_symmetric6x6(i,j) = 0.5_pReal * (m(i,j) + m(j,i))
endfunction math_symmetric6x6
!********************************************************************
! equivalent scalar quantity of a full strain tensor
!********************************************************************
pure function math_equivStrain33(m)
implicit none
real(pReal), dimension(3,3), intent(in) :: m
real(pReal) :: math_equivStrain33,e11,e22,e33,s12,s23,s31
e11 = (2.0_pReal*m(1,1)-m(2,2)-m(3,3))/3.0_pReal
e22 = (2.0_pReal*m(2,2)-m(3,3)-m(1,1))/3.0_pReal
e33 = (2.0_pReal*m(3,3)-m(1,1)-m(2,2))/3.0_pReal
s12 = 2.0_pReal*m(1,2)
s23 = 2.0_pReal*m(2,3)
s31 = 2.0_pReal*m(3,1)
math_equivStrain33 = 2.0_pReal*(1.50_pReal*(e11**2.0_pReal+e22**2.0_pReal+e33**2.0_pReal) + &
0.75_pReal*(s12**2.0_pReal+s23**2.0_pReal+s31**2.0_pReal))**(0.5_pReal)/3.0_pReal
endfunction math_equivStrain33
!********************************************************************
subroutine math_equivStrain33_field(res,tensor,vm)
!********************************************************************
!calculate von Mises equivalent of tensor field
!
implicit none
! input variables
integer(pInt), intent(in), dimension(3) :: res
real(pReal), intent(in), dimension(res(1),res(2),res(3),3,3) :: tensor
! output variables
real(pReal), intent(out), dimension(res(1),res(2),res(3)) :: vm
! other variables
integer(pInt) :: i, j, k
real(pReal), dimension(3,3) :: deviator, delta = 0.0_pReal
real(pReal) :: J_2
delta(1,1) = 1.0_pReal
delta(2,2) = 1.0_pReal
delta(3,3) = 1.0_pReal
do k = 1_pInt, res(3); do j = 1_pInt, res(2); do i = 1_pInt, res(1)
deviator = tensor(i,j,k,1:3,1:3) - 1.0_pReal/3.0_pReal*tensor(i,j,k,1,1)*tensor(i,j,k,2,2)*tensor(i,j,k,3,3)*delta
J_2 = deviator(1,1)*deviator(2,2)&
+ deviator(2,2)*deviator(3,3)&
+ deviator(1,1)*deviator(3,3)&
- (deviator(1,2))**2.0_pReal&
- (deviator(2,3))**2.0_pReal&
- (deviator(1,3))**2.0_pReal
vm(i,j,k) = sqrt(3.0_pReal*J_2)
enddo; enddo; enddo
end subroutine math_equivStrain33_field
!********************************************************************
! determinant of a 3x3 matrix
!********************************************************************
pure function math_det3x3(m)
implicit none
real(pReal), dimension(3,3), intent(in) :: m
real(pReal) :: math_det3x3
math_det3x3 = m(1,1)*(m(2,2)*m(3,3)-m(2,3)*m(3,2)) &
-m(1,2)*(m(2,1)*m(3,3)-m(2,3)*m(3,1)) &
+m(1,3)*(m(2,1)*m(3,2)-m(2,2)*m(3,1))
endfunction math_det3x3
!********************************************************************
! norm of a 3x3 matrix
!********************************************************************
pure function math_norm33(m)
implicit none
real(pReal), dimension(3,3), intent(in) :: m
real(pReal) :: math_norm33
math_norm33 = sqrt(sum(m**2.0_pReal))
endfunction
!********************************************************************
! euclidic norm of a 3x1 vector
!********************************************************************
pure function math_norm3(v)
implicit none
real(pReal), dimension(3), intent(in) :: v
real(pReal) :: math_norm3
math_norm3 = sqrt(v(1)*v(1) + v(2)*v(2) + v(3)*v(3))
endfunction math_norm3
!********************************************************************
! convert 3x3 matrix into vector 9x1
!********************************************************************
pure function math_Plain33to9(m33)
implicit none
real(pReal), dimension(3,3), intent(in) :: m33
real(pReal), dimension(9) :: math_Plain33to9
integer(pInt) :: i
forall (i=1_pInt:9_pInt) math_Plain33to9(i) = m33(mapPlain(1,i),mapPlain(2,i))
endfunction math_Plain33to9
!********************************************************************
! convert Plain 9x1 back to 3x3 matrix
!********************************************************************
pure function math_Plain9to33(v9)
implicit none
real(pReal), dimension(9), intent(in) :: v9
real(pReal), dimension(3,3) :: math_Plain9to33
integer(pInt) :: i
forall (i=1_pInt:9_pInt) math_Plain9to33(mapPlain(1,i),mapPlain(2,i)) = v9(i)
endfunction math_Plain9to33
!********************************************************************
! convert symmetric 3x3 matrix into Mandel vector 6x1
!********************************************************************
pure function math_Mandel33to6(m33)
implicit none
real(pReal), dimension(3,3), intent(in) :: m33
real(pReal), dimension(6) :: math_Mandel33to6
integer(pInt) :: i
forall (i=1_pInt:6_pInt) math_Mandel33to6(i) = nrmMandel(i)*m33(mapMandel(1,i),mapMandel(2,i))
endfunction math_Mandel33to6
!********************************************************************
! convert Mandel 6x1 back to symmetric 3x3 matrix
!********************************************************************
pure function math_Mandel6to33(v6)
implicit none
real(pReal), dimension(6), intent(in) :: v6
real(pReal), dimension(3,3) :: math_Mandel6to33
integer(pInt) :: i
forall (i=1_pInt:6_pInt)
math_Mandel6to33(mapMandel(1,i),mapMandel(2,i)) = invnrmMandel(i)*v6(i)
math_Mandel6to33(mapMandel(2,i),mapMandel(1,i)) = invnrmMandel(i)*v6(i)
end forall
endfunction math_Mandel6to33
!********************************************************************
! convert 3x3x3x3 tensor into plain matrix 9x9
!********************************************************************
pure function math_Plain3333to99(m3333)
implicit none
real(pReal), dimension(3,3,3,3), intent(in) :: m3333
real(pReal), dimension(9,9) :: math_Plain3333to99
integer(pInt) :: i,j
forall (i=1_pInt:9_pInt,j=1_pInt:9_pInt) math_Plain3333to99(i,j) = &
m3333(mapPlain(1,i),mapPlain(2,i),mapPlain(1,j),mapPlain(2,j))
endfunction math_Plain3333to99
!********************************************************************
! plain matrix 9x9 into 3x3x3x3 tensor
!********************************************************************
pure function math_Plain99to3333(m99)
implicit none
real(pReal), dimension(9,9), intent(in) :: m99
real(pReal), dimension(3,3,3,3) :: math_Plain99to3333
integer(pInt) :: i,j
forall (i=1_pInt:9_pInt,j=1_pInt:9_pInt) math_Plain99to3333(mapPlain(1,i),mapPlain(2,i),&
mapPlain(1,j),mapPlain(2,j)) = m99(i,j)
endfunction math_Plain99to3333
!********************************************************************
! convert Mandel matrix 6x6 into Plain matrix 6x6
!********************************************************************
pure function math_Mandel66toPlain66(m66)
implicit none
real(pReal), dimension(6,6), intent(in) :: m66
real(pReal), dimension(6,6) :: math_Mandel66toPlain66
integer(pInt) :: i,j
forall (i=1_pInt:6_pInt,j=1_pInt:6_pInt) &
math_Mandel66toPlain66(i,j) = invnrmMandel(i) * invnrmMandel(j) * m66(i,j)
return
endfunction
!********************************************************************
! convert Plain matrix 6x6 into Mandel matrix 6x6
!********************************************************************
pure function math_Plain66toMandel66(m66)
implicit none
real(pReal), dimension(6,6), intent(in) :: m66
real(pReal), dimension(6,6) :: math_Plain66toMandel66
integer(pInt) i,j
forall (i=1_pInt:6_pInt,j=1_pInt:6_pInt) &
math_Plain66toMandel66(i,j) = nrmMandel(i) * nrmMandel(j) * m66(i,j)
return
endfunction
!********************************************************************
! convert symmetric 3x3x3x3 tensor into Mandel matrix 6x6
!********************************************************************
pure function math_Mandel3333to66(m3333)
implicit none
real(pReal), dimension(3,3,3,3), intent(in) :: m3333
real(pReal), dimension(6,6) :: math_Mandel3333to66
integer(pInt) :: i,j
forall (i=1_pInt:6_pInt,j=1_pInt:6_pInt) math_Mandel3333to66(i,j) = &
nrmMandel(i)*nrmMandel(j)*m3333(mapMandel(1,i),mapMandel(2,i),mapMandel(1,j),mapMandel(2,j))
endfunction math_Mandel3333to66
!********************************************************************
! convert Mandel matrix 6x6 back to symmetric 3x3x3x3 tensor
!********************************************************************
pure function math_Mandel66to3333(m66)
implicit none
real(pReal), dimension(6,6), intent(in) :: m66
real(pReal), dimension(3,3,3,3) :: math_Mandel66to3333
integer(pInt) :: i,j
forall (i=1_pInt:6_pInt,j=1_pInt:6_pInt)
math_Mandel66to3333(mapMandel(1,i),mapMandel(2,i),mapMandel(1,j),mapMandel(2,j)) = invnrmMandel(i)*invnrmMandel(j)*m66(i,j)
math_Mandel66to3333(mapMandel(2,i),mapMandel(1,i),mapMandel(1,j),mapMandel(2,j)) = invnrmMandel(i)*invnrmMandel(j)*m66(i,j)
math_Mandel66to3333(mapMandel(1,i),mapMandel(2,i),mapMandel(2,j),mapMandel(1,j)) = invnrmMandel(i)*invnrmMandel(j)*m66(i,j)
math_Mandel66to3333(mapMandel(2,i),mapMandel(1,i),mapMandel(2,j),mapMandel(1,j)) = invnrmMandel(i)*invnrmMandel(j)*m66(i,j)
end forall
endfunction math_Mandel66to3333
!********************************************************************
! convert Voigt matrix 6x6 back to symmetric 3x3x3x3 tensor
!********************************************************************
pure function math_Voigt66to3333(m66)
implicit none
real(pReal), dimension(6,6), intent(in) :: m66
real(pReal), dimension(3,3,3,3) :: math_Voigt66to3333
integer(pInt) :: i,j
forall (i=1_pInt:6_pInt,j=1_pInt:6_pInt)
math_Voigt66to3333(mapVoigt(1,i),mapVoigt(2,i),mapVoigt(1,j),mapVoigt(2,j)) = invnrmVoigt(i)*invnrmVoigt(j)*m66(i,j)
math_Voigt66to3333(mapVoigt(2,i),mapVoigt(1,i),mapVoigt(1,j),mapVoigt(2,j)) = invnrmVoigt(i)*invnrmVoigt(j)*m66(i,j)
math_Voigt66to3333(mapVoigt(1,i),mapVoigt(2,i),mapVoigt(2,j),mapVoigt(1,j)) = invnrmVoigt(i)*invnrmVoigt(j)*m66(i,j)
math_Voigt66to3333(mapVoigt(2,i),mapVoigt(1,i),mapVoigt(2,j),mapVoigt(1,j)) = invnrmVoigt(i)*invnrmVoigt(j)*m66(i,j)
end forall
endfunction math_Voigt66to3333
!********************************************************************
! Euler angles (in radians) from rotation matrix
!********************************************************************
pure function math_RtoEuler(R)
implicit none
real(pReal), dimension (3,3), intent(in) :: R
real(pReal), dimension(3) :: math_RtoEuler
real(pReal) :: sqhkl, squvw, sqhk, val
sqhkl=sqrt(R(1,3)*R(1,3)+R(2,3)*R(2,3)+R(3,3)*R(3,3))
squvw=sqrt(R(1,1)*R(1,1)+R(2,1)*R(2,1)+R(3,1)*R(3,1))
sqhk=sqrt(R(1,3)*R(1,3)+R(2,3)*R(2,3))
! calculate PHI
val=R(3,3)/sqhkl
if(val > 1.0_pReal) val = 1.0_pReal
if(val < -1.0_pReal) val = -1.0_pReal
math_RtoEuler(2) = acos(val)
if(math_RtoEuler(2) < 1.0e-8_pReal) then
! calculate phi2
math_RtoEuler(3) = 0.0_pReal
! calculate phi1
val=R(1,1)/squvw
if(val > 1.0_pReal) val = 1.0_pReal
if(val < -1.0_pReal) val = -1.0_pReal
math_RtoEuler(1) = acos(val)
if(R(2,1) > 0.0_pReal) math_RtoEuler(1) = 2.0_pReal*pi-math_RtoEuler(1)
else
! calculate phi2
val=R(2,3)/sqhk
if(val > 1.0_pReal) val = 1.0_pReal
if(val < -1.0_pReal) val = -1.0_pReal
math_RtoEuler(3) = acos(val)
if(R(1,3) < 0.0) math_RtoEuler(3) = 2.0_pReal*pi-math_RtoEuler(3)
! calculate phi1
val=-R(3,2)/sin(math_RtoEuler(2))
if(val > 1.0_pReal) val = 1.0_pReal
if(val < -1.0_pReal) val = -1.0_pReal
math_RtoEuler(1) = acos(val)
if(R(3,1) < 0.0) math_RtoEuler(1) = 2.0_pReal*pi-math_RtoEuler(1)
end if
endfunction math_RtoEuler
!********************************************************************
! quaternion (w+ix+jy+kz) from orientation matrix
!********************************************************************
! math adopted from http://code.google.com/p/mtex/source/browse/trunk/geometry/geometry_tools/mat2quat.m
pure function math_RtoQuaternion(R)
implicit none
real(pReal), dimension (3,3), intent(in) :: R
real(pReal), dimension(4) :: absQ, math_RtoQuaternion
real(pReal) :: max_absQ
integer(pInt), dimension(1) :: largest
absQ(1) = 1.0_pReal+R(1,1)+R(2,2)+R(3,3)
absQ(2) = 1.0_pReal+R(1,1)-R(2,2)-R(3,3)
absQ(3) = 1.0_pReal-R(1,1)+R(2,2)-R(3,3)
absQ(4) = 1.0_pReal-R(1,1)-R(2,2)+R(3,3)
math_RtoQuaternion = 0.0_pReal
largest = maxloc(absQ)
max_absQ=0.5_pReal * sqrt(absQ(largest(1)))
select case(largest(1))
case (1_pInt)
!1----------------------------------
math_RtoQuaternion(2) = R(2,3)-R(3,2)
math_RtoQuaternion(3) = R(3,1)-R(1,3)
math_RtoQuaternion(4) = R(1,2)-R(2,1)
case (2_pInt)
math_RtoQuaternion(1) = R(2,3)-R(3,2)
!2----------------------------------
math_RtoQuaternion(3) = R(1,2)+R(2,1)
math_RtoQuaternion(4) = R(3,1)+R(1,3)
case (3_pInt)
math_RtoQuaternion(1) = R(3,1)-R(1,3)
math_RtoQuaternion(2) = R(1,2)+R(2,1)
!3----------------------------------
math_RtoQuaternion(4) = R(2,3)+R(3,2)
case (4_pInt)
math_RtoQuaternion (1) = R(1,2)-R(2,1)
math_RtoQuaternion (2) = R(3,1)+R(1,3)
math_RtoQuaternion (3) = R(3,2)+R(2,3)
!4----------------------------------
end select
math_RtoQuaternion = math_RtoQuaternion*0.25_pReal/max_absQ
math_RtoQuaternion(largest(1)) = max_absQ
endfunction math_RtoQuaternion
!****************************************************************
! rotation matrix from Euler angles (in radians)
!****************************************************************
pure function math_EulerToR(Euler)
implicit none
real(pReal), dimension(3), intent(in) :: Euler
real(pReal), dimension(3,3) :: math_EulerToR
real(pReal) c1, c, c2, s1, s, s2
C1 = cos(Euler(1))
C = cos(Euler(2))
C2 = cos(Euler(3))
S1 = sin(Euler(1))
S = sin(Euler(2))
S2 = sin(Euler(3))
math_EulerToR(1,1)=C1*C2-S1*S2*C
math_EulerToR(1,2)=S1*C2+C1*S2*C
math_EulerToR(1,3)=S2*S
math_EulerToR(2,1)=-C1*S2-S1*C2*C
math_EulerToR(2,2)=-S1*S2+C1*C2*C
math_EulerToR(2,3)=C2*S
math_EulerToR(3,1)=S1*S
math_EulerToR(3,2)=-C1*S
math_EulerToR(3,3)=C
endfunction math_EulerToR
!********************************************************************
! quaternion (w+ix+jy+kz) from 3-1-3 Euler angles (in radians)
!********************************************************************
pure function math_EulerToQuaternion(eulerangles)
implicit none
real(pReal), dimension(3), intent(in) :: eulerangles
real(pReal), dimension(4) :: math_EulerToQuaternion
real(pReal), dimension(3) :: halfangles
real(pReal) :: c, s
halfangles = 0.5_pReal * eulerangles
c = cos(halfangles(2))
s = sin(halfangles(2))
math_EulerToQuaternion(1) = cos(halfangles(1)+halfangles(3)) * c
math_EulerToQuaternion(2) = cos(halfangles(1)-halfangles(3)) * s
math_EulerToQuaternion(3) = sin(halfangles(1)-halfangles(3)) * s
math_EulerToQuaternion(4) = sin(halfangles(1)+halfangles(3)) * c
endfunction math_EulerToQuaternion
!****************************************************************
! rotation matrix from axis and angle (in radians)
!****************************************************************
pure function math_AxisAngleToR(axis,omega)
implicit none
real(pReal), dimension(3), intent(in) :: axis
real(pReal), intent(in) :: omega
real(pReal), dimension(3) :: axisNrm
real(pReal), dimension(3,3) :: math_AxisAngleToR
real(pReal) :: norm,s,c,c1
integer(pInt) :: i
norm = sqrt(math_mul3x3(axis,axis))
if (norm > 1.0e-8_pReal) then ! non-zero rotation
forall (i=1_pInt:3_pInt) axisNrm(i) = axis(i)/norm ! normalize axis to be sure
s = sin(omega)
c = cos(omega)
c1 = 1.0_pReal - c
! formula for active rotation taken from http://mathworld.wolfram.com/RodriguesRotationFormula.html
! below is transposed form to get passive rotation
math_AxisAngleToR(1,1) = c + c1*axisNrm(1)**2.0_pReal
math_AxisAngleToR(2,1) = -s*axisNrm(3) + c1*axisNrm(1)*axisNrm(2)
math_AxisAngleToR(3,1) = s*axisNrm(2) + c1*axisNrm(1)*axisNrm(3)
math_AxisAngleToR(1,2) = s*axisNrm(3) + c1*axisNrm(2)*axisNrm(1)
math_AxisAngleToR(2,2) = c + c1*axisNrm(2)**2.0_pReal
math_AxisAngleToR(3,2) = -s*axisNrm(1) + c1*axisNrm(2)*axisNrm(3)
math_AxisAngleToR(1,3) = -s*axisNrm(2) + c1*axisNrm(3)*axisNrm(1)
math_AxisAngleToR(2,3) = s*axisNrm(1) + c1*axisNrm(3)*axisNrm(2)
math_AxisAngleToR(3,3) = c + c1*axisNrm(3)**2.0_pReal
else
math_AxisAngleToR = math_I3
endif
endfunction math_AxisAngleToR
!****************************************************************
! quaternion (w+ix+jy+kz) from axis and angle (in radians)
!****************************************************************
pure function math_AxisAngleToQuaternion(axis,omega)
implicit none
real(pReal), dimension(3), intent(in) :: axis
real(pReal), intent(in) :: omega
real(pReal), dimension(3) :: axisNrm
real(pReal), dimension(4) :: math_AxisAngleToQuaternion
real(pReal) :: s,c,norm
integer(pInt) :: i
norm = sqrt(math_mul3x3(axis,axis))
if (norm > 1.0e-8_pReal) then ! non-zero rotation
forall (i=1_pInt:3_pInt) axisNrm(i) = axis(i)/norm ! normalize axis to be sure
! formula taken from http://en.wikipedia.org/wiki/Rotation_representation_%28mathematics%29#Rodrigues_parameters
s = sin(omega/2.0_pReal)
c = cos(omega/2.0_pReal)
math_AxisAngleToQuaternion(1) = c
math_AxisAngleToQuaternion(2:4) = s * axisNrm(1:3)
else
math_AxisAngleToQuaternion = (/1.0_pReal,0.0_pReal,0.0_pReal,0.0_pReal/) ! no rotation
endif
endfunction math_AxisAngleToQuaternion
!********************************************************************
! orientation matrix from quaternion (w+ix+jy+kz)
!********************************************************************
pure function math_QuaternionToR(Q)
implicit none
real(pReal), dimension(4), intent(in) :: Q
real(pReal), dimension(3,3) :: math_QuaternionToR, T,S
integer(pInt) :: i, j
forall (i = 1_pInt:3_pInt, j = 1_pInt:3_pInt) &
T(i,j) = Q(i+1_pInt) * Q(j+1_pInt)
S = reshape( (/0.0_pReal, Q(4), -Q(3), &
-Q(4),0.0_pReal, +Q(2), &
Q(3), -Q(2),0.0_pReal/),(/3,3/)) ! notation is transposed!
math_QuaternionToR = (2.0_pReal * Q(1)*Q(1) - 1.0_pReal) * math_I3 + &
2.0_pReal * T - &
2.0_pReal * Q(1) * S
endfunction math_QuaternionToR
!********************************************************************
! 3-1-3 Euler angles (in radians) from quaternion (w+ix+jy+kz)
!********************************************************************
pure function math_QuaternionToEuler(Q)
implicit none
real(pReal), dimension(4), intent(in) :: Q
real(pReal), dimension(3) :: math_QuaternionToEuler
real(pReal) :: acos_arg
math_QuaternionToEuler(2) = acos(1.0_pReal-2.0_pReal*(Q(2)*Q(2)+Q(3)*Q(3)))
if (abs(math_QuaternionToEuler(2)) < 1.0e-3_pReal) then
acos_arg=Q(1)
if(acos_arg > 1.0_pReal)acos_arg = 1.0_pReal
if(acos_arg < -1.0_pReal)acos_arg = -1.0_pReal
math_QuaternionToEuler(1) = 2.0_pReal*acos(acos_arg)
math_QuaternionToEuler(3) = 0.0_pReal
else
math_QuaternionToEuler(1) = atan2(Q(1)*Q(3)+Q(2)*Q(4), Q(1)*Q(2)-Q(3)*Q(4))
if (math_QuaternionToEuler(1) < 0.0_pReal) &
math_QuaternionToEuler(1) = math_QuaternionToEuler(1) + 2.0_pReal * pi
math_QuaternionToEuler(3) = atan2(-Q(1)*Q(3)+Q(2)*Q(4), Q(1)*Q(2)+Q(3)*Q(4))
if (math_QuaternionToEuler(3) < 0.0_pReal) &
math_QuaternionToEuler(3) = math_QuaternionToEuler(3) + 2.0_pReal * pi
endif
if (math_QuaternionToEuler(2) < 0.0_pReal) &
math_QuaternionToEuler(2) = math_QuaternionToEuler(2) + pi
endfunction math_QuaternionToEuler
!********************************************************************
! axis-angle (x, y, z, ang in radians) from quaternion (w+ix+jy+kz)
!********************************************************************
pure function math_QuaternionToAxisAngle(Q)
implicit none
real(pReal), dimension(4), intent(in) :: Q
real(pReal) :: halfAngle, sinHalfAngle
real(pReal), dimension(4) :: math_QuaternionToAxisAngle
halfAngle = acos(max(-1.0_pReal, min(1.0_pReal, Q(1)))) ! limit to [-1,1] --> 0 to 180 deg
sinHalfAngle = sin(halfAngle)
if (sinHalfAngle <= 1.0e-4_pReal) then ! very small rotation angle?
math_QuaternionToAxisAngle = 0.0_pReal
else
math_QuaternionToAxisAngle(1:3) = Q(2:4)/sinHalfAngle
math_QuaternionToAxisAngle(4) = halfAngle*2.0_pReal
endif
endfunction math_QuaternionToAxisAngle
!********************************************************************
! Rodrigues vector (x, y, z) from unit quaternion (w+ix+jy+kz)
!********************************************************************
pure function math_QuaternionToRodrig(Q)
use prec, only: DAMASK_NaN
implicit none
real(pReal), dimension(4), intent(in) :: Q
real(pReal), dimension(3) :: math_QuaternionToRodrig
if (Q(1) /= 0.0_pReal) then ! unless rotation by 180 deg
math_QuaternionToRodrig = Q(2:4)/Q(1)
else
math_QuaternionToRodrig = DAMASK_NaN ! NaN since Rodrig is unbound for 180 deg...
endif
endfunction math_QuaternionToRodrig
!**************************************************************************
! misorientation angle between two sets of Euler angles
!**************************************************************************
pure function math_EulerMisorientation(EulerA,EulerB)
implicit none
real(pReal), dimension(3), intent(in) :: EulerA,EulerB
real(pReal), dimension(3,3) :: r
real(pReal) :: math_EulerMisorientation, tr
r = math_mul33x33(math_EulerToR(EulerB),transpose(math_EulerToR(EulerA)))
tr = (r(1,1)+r(2,2)+r(3,3)-1.0_pReal)*0.4999999_pReal
math_EulerMisorientation = abs(0.5_pReal*pi-asin(tr))
endfunction math_EulerMisorientation
!**************************************************************************
! figures whether unit quat falls into stereographic standard triangle
!**************************************************************************
pure function math_QuaternionInSST(Q, symmetryType)
implicit none
!*** input variables
real(pReal), dimension(4), intent(in) :: Q ! orientation
integer(pInt), intent(in) :: symmetryType ! Type of crystal symmetry; 1:cubic, 2:hexagonal
!*** output variables
logical :: math_QuaternionInSST
!*** local variables
real(pReal), dimension(3) :: Rodrig ! Rodrigues vector of Q
Rodrig = math_QuaternionToRodrig(Q)
select case (symmetryType)
case (1_pInt)
math_QuaternionInSST = Rodrig(1) > Rodrig(2) .and. &
Rodrig(2) > Rodrig(3) .and. &
Rodrig(3) > 0.0_pReal
case (2_pInt)
math_QuaternionInSST = Rodrig(1) > sqrt(3.0_pReal)*Rodrig(2) .and. &
Rodrig(2) > 0.0_pReal .and. &
Rodrig(3) > 0.0_pReal
case default
math_QuaternionInSST = .true.
end select
endfunction math_QuaternionInSST
!**************************************************************************
! calculates the disorientation for 2 unit quaternions
!**************************************************************************
function math_QuaternionDisorientation(Q1, Q2, symmetryType)
use IO, only: IO_error
implicit none
!*** input variables
real(pReal), dimension(4), intent(in) :: Q1, & ! 1st orientation
Q2 ! 2nd orientation
integer(pInt), intent(in) :: symmetryType ! Type of crystal symmetry; 1:cubic, 2:hexagonal
!*** output variables
real(pReal), dimension(4) :: math_QuaternionDisorientation ! disorientation
!*** local variables
real(pReal), dimension(4) :: dQ,dQsymA,mis
integer(pInt) :: i,j,k,s
dQ = math_qMul(math_qConj(Q1),Q2)
math_QuaternionDisorientation = dQ
select case (symmetryType)
case (0_pInt)
if (math_QuaternionDisorientation(1) < 0.0_pReal) &
math_QuaternionDisorientation = -math_QuaternionDisorientation ! keep omega within 0 to 180 deg
case (1_pInt,2_pInt)
s = sum(math_NsymOperations(1:symmetryType-1_pInt))
do i = 1_pInt,2_pInt
dQ = math_qConj(dQ) ! switch order of "from -- to"
do j = 1_pInt,math_NsymOperations(symmetryType) ! run through first crystal's symmetries
dQsymA = math_qMul(math_symOperations(1:4,s+j),dQ) ! apply sym
do k = 1_pInt,math_NsymOperations(symmetryType) ! run through 2nd crystal's symmetries
mis = math_qMul(dQsymA,math_symOperations(1:4,s+k)) ! apply sym
if (mis(1) < 0.0_pReal) & ! want positive angle
mis = -mis
if (mis(1)-math_QuaternionDisorientation(1) > -1e-8_pReal .and. &
math_QuaternionInSST(mis,symmetryType)) &
math_QuaternionDisorientation = mis ! found better one
enddo; enddo; enddo
case default
call IO_error(550_pInt,symmetryType) ! complain about unknown symmetry
end select
endfunction math_QuaternionDisorientation
!********************************************************************
! draw a random sample from Euler space
!********************************************************************
function math_sampleRandomOri()
implicit none
real(pReal), dimension(3) :: math_sampleRandomOri, rnd
call halton(3_pInt,rnd)
math_sampleRandomOri(1) = rnd(1)*2.0_pReal*pi
math_sampleRandomOri(2) = acos(2.0_pReal*rnd(2)-1.0_pReal)
math_sampleRandomOri(3) = rnd(3)*2.0_pReal*pi
endfunction math_sampleRandomOri
!********************************************************************
! draw a random sample from Gauss component
! with noise (in radians) half-width
!********************************************************************
function math_sampleGaussOri(center,noise)
implicit none
real(pReal), dimension(3) :: math_sampleGaussOri, center, disturb
real(pReal), dimension(3), parameter :: origin = (/0.0_pReal,0.0_pReal,0.0_pReal/)
real(pReal), dimension(5) :: rnd
real(pReal) :: noise,scatter,cosScatter
integer(pInt) i
if (noise==0.0_pReal) then
math_sampleGaussOri = center
return
endif
! Helming uses different distribution with Bessel functions
! therefore the gauss scatter width has to be scaled differently
scatter = 0.95_pReal * noise
cosScatter = cos(scatter)
do
call halton(5_pInt,rnd)
forall (i=1_pInt:3_pInt) rnd(i) = 2.0_pReal*rnd(i)-1.0_pReal ! expand 1:3 to range [-1,+1]
disturb(1) = scatter * rnd(1) ! phi1
disturb(2) = sign(1.0_pReal,rnd(2))*acos(cosScatter+(1.0_pReal-cosScatter)*rnd(4)) ! Phi
disturb(3) = scatter * rnd(2) ! phi2
if (rnd(5) <= exp(-1.0_pReal*(math_EulerMisorientation(origin,disturb)/scatter)**2_pReal)) exit
enddo
math_sampleGaussOri = math_RtoEuler(math_mul33x33(math_EulerToR(disturb),math_EulerToR(center)))
endfunction math_sampleGaussOri
!********************************************************************
! draw a random sample from Fiber component
! with noise (in radians)
!********************************************************************
function math_sampleFiberOri(alpha,beta,noise)
implicit none
real(pReal), dimension(3) :: math_sampleFiberOri, fiberInC,fiberInS,axis
real(pReal), dimension(2) :: alpha,beta, rnd
real(pReal), dimension(3,3) :: oRot,fRot,pRot
real(pReal) :: noise, scatter, cos2Scatter, angle
integer(pInt), dimension(2,3), parameter :: rotMap = reshape((/2_pInt,3_pInt,&
3_pInt,1_pInt,&
1_pInt,2_pInt/),(/2,3/))
integer(pInt) :: i
! Helming uses different distribution with Bessel functions
! therefore the gauss scatter width has to be scaled differently
scatter = 0.95_pReal * noise
cos2Scatter = cos(2.0_pReal*scatter)
! fiber axis in crystal coordinate system
fiberInC(1)=sin(alpha(1))*cos(alpha(2))
fiberInC(2)=sin(alpha(1))*sin(alpha(2))
fiberInC(3)=cos(alpha(1))
! fiber axis in sample coordinate system
fiberInS(1)=sin(beta(1))*cos(beta(2))
fiberInS(2)=sin(beta(1))*sin(beta(2))
fiberInS(3)=cos(beta(1))
! ---# rotation matrix from sample to crystal system #---
angle = -acos(dot_product(fiberInC,fiberInS))
if(angle /= 0.0_pReal) then
! rotation axis between sample and crystal system (cross product)
forall(i=1:3) axis(i) = fiberInC(rotMap(1,i))*fiberInS(rotMap(2,i))-fiberInC(rotMap(2,i))*fiberInS(rotMap(1,i))
oRot = math_AxisAngleToR(math_vectorproduct(fiberInC,fiberInS),angle)
else
oRot = math_I3
end if
! ---# rotation matrix about fiber axis (random angle) #---
call halton(1_pInt,rnd)
fRot = math_AxisAngleToR(fiberInS,rnd(1)*2.0_pReal*pi)
! ---# rotation about random axis perpend to fiber #---
! random axis pependicular to fiber axis
call halton(2_pInt,axis)
if (fiberInS(3) /= 0.0_pReal) then
axis(3)=-(axis(1)*fiberInS(1)+axis(2)*fiberInS(2))/fiberInS(3)
else if(fiberInS(2) /= 0.0_pReal) then
axis(3)=axis(2)
axis(2)=-(axis(1)*fiberInS(1)+axis(3)*fiberInS(3))/fiberInS(2)
else if(fiberInS(1) /= 0.0_pReal) then
axis(3)=axis(1)
axis(1)=-(axis(2)*fiberInS(2)+axis(3)*fiberInS(3))/fiberInS(1)
end if
! scattered rotation angle
do
call halton(2_pInt,rnd)
angle = acos(cos2Scatter+(1.0_pReal-cos2Scatter)*rnd(1))
if (rnd(2) <= exp(-1.0_pReal*(angle/scatter)**2.0_pReal)) exit
enddo
call halton(1_pInt,rnd)
if (rnd(1) <= 0.5) angle = -angle
pRot = math_AxisAngleToR(axis,angle)
! ---# apply the three rotations #---
math_sampleFiberOri = math_RtoEuler(math_mul33x33(pRot,math_mul33x33(fRot,oRot)))
endfunction math_sampleFiberOri
!********************************************************************
! symmetric Euler angles for given symmetry string
! 'triclinic' or '', 'monoclinic', 'orthotropic'
!********************************************************************
pure function math_symmetricEulers(sym,Euler)
implicit none
integer(pInt), intent(in) :: sym
real(pReal), dimension(3), intent(in) :: Euler
real(pReal), dimension(3,3) :: math_symmetricEulers
integer(pInt) :: i,j
math_symmetricEulers(1,1) = pi+Euler(1)
math_symmetricEulers(2,1) = Euler(2)
math_symmetricEulers(3,1) = Euler(3)
math_symmetricEulers(1,2) = pi-Euler(1)
math_symmetricEulers(2,2) = pi-Euler(2)
math_symmetricEulers(3,2) = pi+Euler(3)
math_symmetricEulers(1,3) = 2.0_pReal*pi-Euler(1)
math_symmetricEulers(2,3) = pi-Euler(2)
math_symmetricEulers(3,3) = pi+Euler(3)
forall (i=1_pInt:3_pInt,j=1_pInt:3_pInt) math_symmetricEulers(j,i) = modulo(math_symmetricEulers(j,i),2.0_pReal*pi)
select case (sym)
case (4_pInt) ! all done
case (2_pInt) ! return only first
math_symmetricEulers(1:3,2:3) = 0.0_pReal
case default ! return blank
math_symmetricEulers = 0.0_pReal
end select
endfunction math_symmetricEulers
!********************************************************************
! draw a random sample from Gauss variable
!********************************************************************
function math_sampleGaussVar(meanvalue, stddev, width)
implicit none
!*** input variables
real(pReal), intent(in) :: meanvalue, & ! meanvalue of gauss distribution
stddev ! standard deviation of gauss distribution
real(pReal), intent(in), optional :: width ! width of considered values as multiples of standard deviation
!*** output variables
real(pReal) :: math_sampleGaussVar
!*** local variables
real(pReal), dimension(2) :: rnd ! random numbers
real(pReal) :: scatter, & ! normalized scatter around meanvalue
myWidth
if (stddev == 0.0_pReal) then
math_sampleGaussVar = meanvalue
return
endif
if (present(width)) then
myWidth = width
else
myWidth = 3.0_pReal ! use +-3*sigma as default value for scatter
endif
do
call halton(2_pInt, rnd)
scatter = myWidth * (2.0_pReal * rnd(1) - 1.0_pReal)
if (rnd(2) <= exp(-0.5_pReal * scatter ** 2.0_pReal)) & ! test if scattered value is drawn
exit
enddo
math_sampleGaussVar = scatter * stddev
endfunction math_sampleGaussVar
!****************************************************************
subroutine math_spectralDecompositionSym3x3(M,values,vectors,error)
!****************************************************************
implicit none
real(pReal), dimension(3,3), intent(in) :: M
real(pReal), dimension(3), intent(out) :: values
real(pReal), dimension(3,3), intent(out) :: vectors
logical, intent(out) :: error
integer(pInt) :: info
real(pReal), dimension((64+2)*3) :: work ! block size of 64 taken from http://www.netlib.org/lapack/double/dsyev.f
vectors = M ! copy matrix to input (doubles as output) array
call DSYEV('V','U',3,vectors,3,values,work,(64+2)*3,info)
error = (info == 0_pInt)
return
end subroutine
!****************************************************************
pure subroutine math_pDecomposition(FE,U,R,error)
!-----FE = R.U
!****************************************************************
implicit none
real(pReal), intent(in), dimension(3,3) :: FE
real(pReal), intent(out), dimension(3,3) :: R, U
logical, intent(out) :: error
real(pReal), dimension(3,3) :: CE, EB1, EB2, EB3, UI
real(pReal) :: EW1, EW2, EW3, det
error = .false.
ce = math_mul33x33(math_transpose3x3(FE),FE)
CALL math_spectral1(CE,EW1,EW2,EW3,EB1,EB2,EB3)
U=sqrt(EW1)*EB1+sqrt(EW2)*EB2+sqrt(EW3)*EB3
call math_invert3x3(U,UI,det,error)
if (.not. error) R = math_mul33x33(FE,UI)
ENDSUBROUTINE math_pDecomposition
!**********************************************************************
pure subroutine math_spectral1(M,EW1,EW2,EW3,EB1,EB2,EB3)
!**** EIGENWERTE UND EIGENWERTBASIS DER SYMMETRISCHEN 3X3 MATRIX M
implicit none
real(pReal), dimension(3,3), intent(in) :: M
real(pReal), dimension(3,3), intent(out) :: EB1, EB2, EB3
real(pReal), intent(out) :: EW1,EW2,EW3
real(pReal) HI1M, HI2M, HI3M, R, S, T, P, Q, RHO, PHI, Y1, Y2, Y3, D1, D2, D3
real(pReal), parameter :: TOL=1.e-14_pReal
real(pReal), dimension(3,3) :: M1, M2, M3
real(pReal) C1,C2,C3,arg
CALL math_hi(M,HI1M,HI2M,HI3M)
R=-HI1M
S= HI2M
T=-HI3M
P=S-R**2.0_pReal/3.0_pReal
Q=2.0_pReal/27.0_pReal*R**3.0_pReal-R*S/3.0_pReal+T
EB1=0.0_pReal
EB2=0.0_pReal
EB3=0.0_pReal
IF((ABS(P).LT.TOL).AND.(ABS(Q).LT.TOL))THEN
! DREI GLEICHE EIGENWERTE
EW1=HI1M/3.0_pReal
EW2=EW1
EW3=EW1
! this is not really correct, but this way U is calculated
! correctly in PDECOMPOSITION (correct is EB?=I)
EB1(1,1)=1.0_pReal
EB2(2,2)=1.0_pReal
EB3(3,3)=1.0_pReal
ELSE
RHO=sqrt(-3.0_pReal*P**3.0_pReal)/9.0_pReal
arg=-Q/RHO/2.0_pReal
if(arg.GT.1.0_pReal) arg=1.0_pReal
if(arg.LT.-1.0_pReal) arg=-1.0_pReal
PHI=acos(arg)
Y1=2.0_pReal*RHO**(1.0_pReal/3.0_pReal)*cos(PHI/3.0_pReal)
Y2=2.0_pReal*RHO**(1.0_pReal/3.0_pReal)*cos(PHI/3.0_pReal+2.0_pReal/3.0_pReal*PI)
Y3=2.0_pReal*RHO**(1.0_pReal/3.0_pReal)*cos(PHI/3.0_pReal+4.0_pReal/3.0_pReal*PI)
EW1=Y1-R/3.0_pReal
EW2=Y2-R/3.0_pReal
EW3=Y3-R/3.0_pReal
C1=ABS(EW1-EW2)
C2=ABS(EW2-EW3)
C3=ABS(EW3-EW1)
IF(C1.LT.TOL) THEN
! EW1 is equal to EW2
D3=1.0_pReal/(EW3-EW1)/(EW3-EW2)
M1=M-EW1*math_I3
M2=M-EW2*math_I3
EB3=math_mul33x33(M1,M2)*D3
EB1=math_I3-EB3
! both EB2 and EW2 are set to zero so that they do not
! contribute to U in PDECOMPOSITION
EW2=0.0_pReal
ELSE IF(C2.LT.TOL) THEN
! EW2 is equal to EW3
D1=1.0_pReal/(EW1-EW2)/(EW1-EW3)
M2=M-math_I3*EW2
M3=M-math_I3*EW3
EB1=math_mul33x33(M2,M3)*D1
EB2=math_I3-EB1
! both EB3 and EW3 are set to zero so that they do not
! contribute to U in PDECOMPOSITION
EW3=0.0_pReal
ELSE IF(C3.LT.TOL) THEN
! EW1 is equal to EW3
D2=1.0_pReal/(EW2-EW1)/(EW2-EW3)
M1=M-math_I3*EW1
M3=M-math_I3*EW3
EB2=math_mul33x33(M1,M3)*D2
EB1=math_I3-EB2
! both EB3 and EW3 are set to zero so that they do not
! contribute to U in PDECOMPOSITION
EW3=0.0_pReal
ELSE
! all three eigenvectors are different
D1=1.0_pReal/(EW1-EW2)/(EW1-EW3)
D2=1.0_pReal/(EW2-EW1)/(EW2-EW3)
D3=1.0_pReal/(EW3-EW1)/(EW3-EW2)
M1=M-EW1*math_I3
M2=M-EW2*math_I3
M3=M-EW3*math_I3
EB1=math_mul33x33(M2,M3)*D1
EB2=math_mul33x33(M1,M3)*D2
EB3=math_mul33x33(M1,M2)*D3
END IF
END IF
ENDSUBROUTINE math_spectral1
!**********************************************************************
function math_eigenvalues3x3(M)
!**** Eigenvalues of symmetric 3X3 matrix M
implicit none
real(pReal), intent(in), dimension(3,3) :: M
real(pReal), dimension(3,3) :: EB1 = 0.0_pReal, EB2 = 0.0_pReal, EB3 = 0.0_pReal
real(pReal), dimension(3) :: math_eigenvalues3x3
real(pReal) :: HI1M, HI2M, HI3M, R, S, T, P, Q, RHO, PHI, Y1, Y2, Y3, arg
real(pReal), parameter :: TOL=1.e-14_pReal
CALL math_hi(M,HI1M,HI2M,HI3M)
R=-HI1M
S= HI2M
T=-HI3M
P=S-R**2.0_pReal/3.0_pReal
Q=2.0_pReal/27.0_pReal*R**3.0_pReal-R*S/3.0_pReal+T
if((abs(P) < TOL) .and. (abs(Q) < TOL)) THEN
! three equivalent eigenvalues
math_eigenvalues3x3(1) = HI1M/3.0_pReal
math_eigenvalues3x3(2)=math_eigenvalues3x3(1)
math_eigenvalues3x3(3)=math_eigenvalues3x3(1)
! this is not really correct, but this way U is calculated
! correctly in PDECOMPOSITION (correct is EB?=I)
EB1(1,1)=1.0_pReal
EB2(2,2)=1.0_pReal
EB3(3,3)=1.0_pReal
else
RHO=sqrt(-3.0_pReal*P**3.0_pReal)/9.0_pReal
arg=-Q/RHO/2.0_pReal
if(arg.GT.1.0_pReal) arg=1.0_pReal
if(arg.LT.-1.0_pReal) arg=-1.0_pReal
PHI=acos(arg)
Y1=2*RHO**(1.0_pReal/3.0_pReal)*cos(PHI/3.0_pReal)
Y2=2*RHO**(1.0_pReal/3.0_pReal)*cos(PHI/3.0_pReal+2.0_pReal/3.0_pReal*PI)
Y3=2*RHO**(1.0_pReal/3.0_pReal)*cos(PHI/3.0_pReal+4.0_pReal/3.0_pReal*PI)
math_eigenvalues3x3(1) = Y1-R/3.0_pReal
math_eigenvalues3x3(2) = Y2-R/3.0_pReal
math_eigenvalues3x3(3) = Y3-R/3.0_pReal
endif
endfunction math_eigenvalues3x3
!**********************************************************************
!**** HAUPTINVARIANTEN HI1M, HI2M, HI3M DER 3X3 MATRIX M
PURE SUBROUTINE math_hi(M,HI1M,HI2M,HI3M)
implicit none
real(pReal), intent(in) :: M(3,3)
real(pReal), intent(out) :: HI1M, HI2M, HI3M
HI1M=M(1,1)+M(2,2)+M(3,3)
HI2M=HI1M**2.0_pReal/2.0_pReal- (M(1,1)**2.0_pReal+M(2,2)**2.0_pReal+M(3,3)**2.0_pReal)&
/2.0_pReal-M(1,2)*M(2,1)-M(1,3)*M(3,1)-M(2,3)*M(3,2)
HI3M=math_det3x3(M)
! QUESTION: is 3rd equiv det(M) ?? if yes, use function math_det !agreed on YES
ENDSUBROUTINE math_hi
!*******************************************************************************
! GET_SEED returns a seed for the random number generator.
!
! The seed depends on the current time, and ought to be (slightly)
! different every millisecond. Once the seed is obtained, a random
! number generator should be called a few times to further process
! the seed.
!
! Parameters:
! Output, integer SEED, a pseudorandom seed value.
!
! Modified: 27 June 2000
! Author: John Burkardt
!
! Modified: 29 April 2005
! Author: Franz Roters
!
SUBROUTINE get_seed(seed)
implicit none
integer(pInt) :: seed
real(pReal) :: temp = 0.0_pReal
character(len = 10) :: time
character(len = 8) :: today
integer(pInt) :: values(8)
character(len = 5) :: zone
call date_and_time (today, time, zone, values)
temp = temp + real(values(2)- 1_pInt, pReal) / 11.0_pReal
temp = temp + real(values(3)- 1_pInt, pReal) / 30.0_pReal
temp = temp + real(values(5), pReal) / 23.0_pReal
temp = temp + real(values(6), pReal) / 59.0_pReal
temp = temp + real(values(7), pReal) / 59.0_pReal
temp = temp + real(values(8), pReal) / 999.0_pReal
temp = temp / 6.0_pReal
if (temp <= 0.0_pReal) then
temp = 1.0_pReal / 3.0_pReal
else if (1.0_pReal <= temp) then
temp = 2.0_pReal / 3.0_pReal
end if
seed = int(real(huge(1_pInt),pReal)*temp, pInt)
!
! Never use a seed of 0 or maximum integer.
!
if (seed == 0_pInt) then
seed = 1_pInt
end if
if (seed == huge(1_pInt)) then
seed = seed -1_pInt
end if
ENDSUBROUTINE get_seed
!*******************************************************************************
! HALTON computes the next element in the Halton sequence.
!
! Parameters:
! Input, integer NDIM, the dimension of the element.
! Output, real R(NDIM), the next element of the current Halton sequence.
!
! Modified: 09 March 2003
! Author: John Burkardt
!
! Modified: 29 April 2005
! Author: Franz Roters
!
subroutine halton(ndim, r)
implicit none
integer(pInt), intent(in) :: ndim
real(pReal), intent(out), dimension(ndim) :: r
integer(pInt), dimension(ndim) :: base
integer(pInt) :: seed
integer(pInt), dimension(1) :: value_halton
call halton_memory ('GET', 'SEED', 1_pInt, value_halton)
seed = value_halton(1)
call halton_memory ('GET', 'BASE', ndim, base)
call i_to_halton (seed, base, ndim, r)
value_halton(1) = 1_pInt
call halton_memory ('INC', 'SEED', 1_pInt, value_halton)
ENDSUBROUTINE halton
!*******************************************************************************
! HALTON_MEMORY sets or returns quantities associated with the Halton sequence.
!
! Parameters:
! Input, character (len = *) action_halton, the desired action.
! 'GET' means get the value of a particular quantity.
! 'SET' means set the value of a particular quantity.
! 'INC' means increment the value of a particular quantity.
! (Only the SEED can be incremented.)
!
! Input, character (len = *) name_halton, the name of the quantity.
! 'BASE' means the Halton base or bases.
! 'NDIM' means the spatial dimension.
! 'SEED' means the current Halton seed.
!
! Input/output, integer NDIM, the dimension of the quantity.
! If action_halton is 'SET' and action_halton is 'BASE', then NDIM is input, and
! is the number of entries in value_halton to be put into BASE.
!
! Input/output, integer value_halton(NDIM), contains a value.
! If action_halton is 'SET', then on input, value_halton contains values to be assigned
! to the internal variable.
! If action_halton is 'GET', then on output, value_halton contains the values of
! the specified internal variable.
! If action_halton is 'INC', then on input, value_halton contains the increment to
! be added to the specified internal variable.
!
! Modified: 09 March 2003
! Author: John Burkardt
!
! Modified: 29 April 2005
! Author: Franz Roters
subroutine halton_memory (action_halton, name_halton, ndim, value_halton)
implicit none
character(len = *), intent(in) :: action_halton, name_halton
integer(pInt), dimension(*), intent(inout) :: value_halton
integer(pInt), allocatable, save, dimension(:) :: base
logical, save :: first_call = .true.
integer(pInt), intent(in) :: ndim
integer(pInt):: i
integer(pInt), save :: ndim_save = 0_pInt, seed = 1_pInt
if (first_call) then
ndim_save = 1_pInt
allocate(base(ndim_save))
base(1) = 2_pInt
first_call = .false.
endif
!
! Set
!
if(action_halton(1:1) == 'S' .or. action_halton(1:1) == 's') then
if(name_halton(1:1) == 'B' .or. name_halton(1:1) == 'b') then
if(ndim_save /= ndim) then
deallocate(base)
ndim_save = ndim
allocate(base(ndim_save))
endif
base(1:ndim) = value_halton(1:ndim)
elseif(name_halton(1:1) == 'N' .or. name_halton(1:1) == 'n') then
if(ndim_save /= value_halton(1)) then
deallocate(base)
ndim_save = value_halton(1)
allocate(base(ndim_save))
do i = 1_pInt, ndim_save
base(i) = prime (i)
enddo
else
ndim_save = value_halton(1)
endif
elseif(name_halton(1:1) == 'S' .or. name_halton(1:1) == 's') then
seed = value_halton(1)
endif
!
! Get
!
elseif(action_halton(1:1) == 'G' .or. action_halton(1:1) == 'g') then
if(name_halton(1:1) == 'B' .or. name_halton(1:1) == 'b') then
if(ndim /= ndim_save) then
deallocate(base)
ndim_save = ndim
allocate(base(ndim_save))
do i = 1_pInt, ndim_save
base(i) = prime(i)
enddo
endif
value_halton(1:ndim_save) = base(1:ndim_save)
elseif(name_halton(1:1) == 'N' .or. name_halton(1:1) == 'n') then
value_halton(1) = ndim_save
elseif(name_halton(1:1) == 'S' .or. name_halton(1:1) == 's') then
value_halton(1) = seed
endif
!
! Increment
!
elseif(action_halton(1:1) == 'I' .or. action_halton(1:1) == 'i') then
if(name_halton(1:1) == 'S' .or. name_halton(1:1) == 's') then
seed = seed + value_halton(1)
end if
endif
ENDSUBROUTINE halton_memory
!*******************************************************************************
! HALTON_NDIM_SET sets the dimension for a Halton sequence.
!
! Parameters:
! Input, integer NDIM, the dimension of the Halton vectors.
!
! Modified: 26 February 2001
! Author: John Burkardt
!
! Modified: 29 April 2005
! Author: Franz Roters
!
subroutine halton_ndim_set (ndim)
implicit none
integer(pInt), intent(in) :: ndim
integer(pInt) :: value_halton(1)
value_halton(1) = ndim
call halton_memory ('SET', 'NDIM', 1_pInt, value_halton)
ENDSUBROUTINE halton_ndim_set
!*******************************************************************************
! HALTON_SEED_SET sets the "seed" for the Halton sequence.
!
! Calling HALTON repeatedly returns the elements of the
! Halton sequence in order, starting with element number 1.
! An internal counter, called SEED, keeps track of the next element
! to return. Each time the routine is called, the SEED-th element
! is computed, and then SEED is incremented by 1.
!
! To restart the Halton sequence, it is only necessary to reset
! SEED to 1. It might also be desirable to reset SEED to some other value.
! This routine allows the user to specify any value of SEED.
!
! The default value of SEED is 1, which restarts the Halton sequence.
!
! Parameters:
! Input, integer SEED, the seed for the Halton sequence.
!
! Modified: 26 February 2001
! Author: John Burkardt
!
! Modified: 29 April 2005
! Author: Franz Roters
!
subroutine halton_seed_set (seed)
implicit none
integer(pInt), parameter :: ndim = 1_pInt
integer(pInt), intent(in) :: seed
integer(pInt) :: value_halton(ndim)
value_halton(1) = seed
call halton_memory ('SET', 'SEED', ndim, value_halton)
ENDSUBROUTINE halton_seed_set
!*******************************************************************************
! I_TO_HALTON computes an element of a Halton sequence.
!
! Reference:
! J H Halton: On the efficiency of certain quasi-random sequences of points
! in evaluating multi-dimensional integrals, Numerische Mathematik, Volume 2, pages 84-90, 1960.
!
! Parameters:
! Input, integer SEED, the index of the desired element.
! Only the absolute value of SEED is considered. SEED = 0 is allowed,
! and returns R = 0.
!
! Input, integer BASE(NDIM), the Halton bases, which should be
! distinct prime numbers. This routine only checks that each base
! is greater than 1.
!
! Input, integer NDIM, the dimension of the sequence.
!
! Output, real R(NDIM), the SEED-th element of the Halton sequence
! for the given bases.
!
! Modified: 26 February 2001
! Author: John Burkardt
!
! Modified: 29 April 2005
! Author: Franz RotersA
subroutine i_to_halton (seed, base, ndim, r)
implicit none
integer(pInt), intent(in) :: ndim
integer(pInt), intent(in), dimension(ndim) :: base
real(pReal), dimension(ndim) :: base_inv
integer(pInt), dimension(ndim) :: digit
integer(pInt) :: i
real(pReal), dimension(ndim), intent(out) ::r
integer(pInt) :: seed
integer(pInt), dimension(ndim) :: seed2
seed2(1:ndim) = abs(seed)
r(1:ndim) = 0.0_pReal
if (any (base(1:ndim) <= 1_pInt)) then
!$OMP CRITICAL (write2out)
write (*, '(a)') ' '
write (*, '(a)') 'I_TO_HALTON - Fatal error!'
write (*, '(a)') ' An input base BASE is <= 1!'
do i = 1, ndim
write (*, '(i6,i6)') i, base(i)
enddo
call flush(6)
!$OMP END CRITICAL (write2out)
stop
end if
base_inv(1:ndim) = 1.0_pReal / real (base(1:ndim), pReal)
do while ( any ( seed2(1:ndim) /= 0_pInt) )
digit(1:ndim) = mod ( seed2(1:ndim), base(1:ndim))
r(1:ndim) = r(1:ndim) + real ( digit(1:ndim), pReal) * base_inv(1:ndim)
base_inv(1:ndim) = base_inv(1:ndim) / real ( base(1:ndim), pReal)
seed2(1:ndim) = seed2(1:ndim) / base(1:ndim)
enddo
ENDSUBROUTINE i_to_halton
!*******************************************************************************
! PRIME returns any of the first PRIME_MAX prime numbers.
!
! Note:
! PRIME_MAX is 1500, and the largest prime stored is 12553.
! Reference:
! Milton Abramowitz and Irene Stegun: Handbook of Mathematical Functions,
! US Department of Commerce, 1964, pages 870-873.
!
! Daniel Zwillinger: CRC Standard Mathematical Tables and Formulae,
! 30th Edition, CRC Press, 1996, pages 95-98.
!
! Parameters:
! Input, integer N, the index of the desired prime number.
! N = -1 returns PRIME_MAX, the index of the largest prime available.
! N = 0 is legal, returning PRIME = 1.
! It should generally be true that 0 <= N <= PRIME_MAX.
!
! Output, integer PRIME, the N-th prime. If N is out of range, PRIME
! is returned as 0.
!
! Modified: 21 June 2002
! Author: John Burkardt
!
! Modified: 29 April 2005
! Author: Franz Roters
!
function prime(n)
implicit none
integer(pInt), parameter :: prime_max = 1500
integer(pInt), save :: icall = 0_pInt
integer(pInt), intent(in) :: n
integer(pInt), save, dimension(prime_max) :: npvec
integer(pInt) prime
if (icall == 0_pInt) then
icall = 1_pInt
npvec(1:100) = (/&
2_pInt, 3_pInt, 5_pInt, 7_pInt, 11_pInt, 13_pInt, 17_pInt, 19_pInt, 23_pInt, 29_pInt, &
31_pInt, 37_pInt, 41_pInt, 43_pInt, 47_pInt, 53_pInt, 59_pInt, 61_pInt, 67_pInt, 71_pInt, &
73_pInt, 79_pInt, 83_pInt, 89_pInt, 97_pInt, 101_pInt, 103_pInt, 107_pInt, 109_pInt, 113_pInt, &
127_pInt, 131_pInt, 137_pInt, 139_pInt, 149_pInt, 151_pInt, 157_pInt, 163_pInt, 167_pInt, 173_pInt, &
179_pInt, 181_pInt, 191_pInt, 193_pInt, 197_pInt, 199_pInt, 211_pInt, 223_pInt, 227_pInt, 229_pInt, &
233_pInt, 239_pInt, 241_pInt, 251_pInt, 257_pInt, 263_pInt, 269_pInt, 271_pInt, 277_pInt, 281_pInt, &
283_pInt, 293_pInt, 307_pInt, 311_pInt, 313_pInt, 317_pInt, 331_pInt, 337_pInt, 347_pInt, 349_pInt, &
353_pInt, 359_pInt, 367_pInt, 373_pInt, 379_pInt, 383_pInt, 389_pInt, 397_pInt, 401_pInt, 409_pInt, &
419_pInt, 421_pInt, 431_pInt, 433_pInt, 439_pInt, 443_pInt, 449_pInt, 457_pInt, 461_pInt, 463_pInt, &
467_pInt, 479_pInt, 487_pInt, 491_pInt, 499_pInt, 503_pInt, 509_pInt, 521_pInt, 523_pInt, 541_pInt/)
npvec(101:200) = (/ &
547_pInt, 557_pInt, 563_pInt, 569_pInt, 571_pInt, 577_pInt, 587_pInt, 593_pInt, 599_pInt, 601_pInt, &
607_pInt, 613_pInt, 617_pInt, 619_pInt, 631_pInt, 641_pInt, 643_pInt, 647_pInt, 653_pInt, 659_pInt, &
661_pInt, 673_pInt, 677_pInt, 683_pInt, 691_pInt, 701_pInt, 709_pInt, 719_pInt, 727_pInt, 733_pInt, &
739_pInt, 743_pInt, 751_pInt, 757_pInt, 761_pInt, 769_pInt, 773_pInt, 787_pInt, 797_pInt, 809_pInt, &
811_pInt, 821_pInt, 823_pInt, 827_pInt, 829_pInt, 839_pInt, 853_pInt, 857_pInt, 859_pInt, 863_pInt, &
877_pInt, 881_pInt, 883_pInt, 887_pInt, 907_pInt, 911_pInt, 919_pInt, 929_pInt, 937_pInt, 941_pInt, &
947_pInt, 953_pInt, 967_pInt, 971_pInt, 977_pInt, 983_pInt, 991_pInt, 997_pInt, 1009_pInt, 1013_pInt, &
1019_pInt, 1021_pInt, 1031_pInt, 1033_pInt, 1039_pInt, 1049_pInt, 1051_pInt, 1061_pInt, 1063_pInt, 1069_pInt, &
1087_pInt, 1091_pInt, 1093_pInt, 1097_pInt, 1103_pInt, 1109_pInt, 1117_pInt, 1123_pInt, 1129_pInt, 1151_pInt, &
1153_pInt, 1163_pInt, 1171_pInt, 1181_pInt, 1187_pInt, 1193_pInt, 1201_pInt, 1213_pInt, 1217_pInt, 1223_pInt/)
npvec(201:300) = (/ &
1229_pInt, 1231_pInt, 1237_pInt, 1249_pInt, 1259_pInt, 1277_pInt, 1279_pInt, 1283_pInt, 1289_pInt, 1291_pInt, &
1297_pInt, 1301_pInt, 1303_pInt, 1307_pInt, 1319_pInt, 1321_pInt, 1327_pInt, 1361_pInt, 1367_pInt, 1373_pInt, &
1381_pInt, 1399_pInt, 1409_pInt, 1423_pInt, 1427_pInt, 1429_pInt, 1433_pInt, 1439_pInt, 1447_pInt, 1451_pInt, &
1453_pInt, 1459_pInt, 1471_pInt, 1481_pInt, 1483_pInt, 1487_pInt, 1489_pInt, 1493_pInt, 1499_pInt, 1511_pInt, &
1523_pInt, 1531_pInt, 1543_pInt, 1549_pInt, 1553_pInt, 1559_pInt, 1567_pInt, 1571_pInt, 1579_pInt, 1583_pInt, &
1597_pInt, 1601_pInt, 1607_pInt, 1609_pInt, 1613_pInt, 1619_pInt, 1621_pInt, 1627_pInt, 1637_pInt, 1657_pInt, &
1663_pInt, 1667_pInt, 1669_pInt, 1693_pInt, 1697_pInt, 1699_pInt, 1709_pInt, 1721_pInt, 1723_pInt, 1733_pInt, &
1741_pInt, 1747_pInt, 1753_pInt, 1759_pInt, 1777_pInt, 1783_pInt, 1787_pInt, 1789_pInt, 1801_pInt, 1811_pInt, &
1823_pInt, 1831_pInt, 1847_pInt, 1861_pInt, 1867_pInt, 1871_pInt, 1873_pInt, 1877_pInt, 1879_pInt, 1889_pInt, &
1901_pInt, 1907_pInt, 1913_pInt, 1931_pInt, 1933_pInt, 1949_pInt, 1951_pInt, 1973_pInt, 1979_pInt, 1987_pInt/)
npvec(301:400) = (/ &
1993_pInt, 1997_pInt, 1999_pInt, 2003_pInt, 2011_pInt, 2017_pInt, 2027_pInt, 2029_pInt, 2039_pInt, 2053_pInt, &
2063_pInt, 2069_pInt, 2081_pInt, 2083_pInt, 2087_pInt, 2089_pInt, 2099_pInt, 2111_pInt, 2113_pInt, 2129_pInt, &
2131_pInt, 2137_pInt, 2141_pInt, 2143_pInt, 2153_pInt, 2161_pInt, 2179_pInt, 2203_pInt, 2207_pInt, 2213_pInt, &
2221_pInt, 2237_pInt, 2239_pInt, 2243_pInt, 2251_pInt, 2267_pInt, 2269_pInt, 2273_pInt, 2281_pInt, 2287_pInt, &
2293_pInt, 2297_pInt, 2309_pInt, 2311_pInt, 2333_pInt, 2339_pInt, 2341_pInt, 2347_pInt, 2351_pInt, 2357_pInt, &
2371_pInt, 2377_pInt, 2381_pInt, 2383_pInt, 2389_pInt, 2393_pInt, 2399_pInt, 2411_pInt, 2417_pInt, 2423_pInt, &
2437_pInt, 2441_pInt, 2447_pInt, 2459_pInt, 2467_pInt, 2473_pInt, 2477_pInt, 2503_pInt, 2521_pInt, 2531_pInt, &
2539_pInt, 2543_pInt, 2549_pInt, 2551_pInt, 2557_pInt, 2579_pInt, 2591_pInt, 2593_pInt, 2609_pInt, 2617_pInt, &
2621_pInt, 2633_pInt, 2647_pInt, 2657_pInt, 2659_pInt, 2663_pInt, 2671_pInt, 2677_pInt, 2683_pInt, 2687_pInt, &
2689_pInt, 2693_pInt, 2699_pInt, 2707_pInt, 2711_pInt, 2713_pInt, 2719_pInt, 2729_pInt, 2731_pInt, 2741_pInt/)
npvec(401:500) = (/ &
2749_pInt, 2753_pInt, 2767_pInt, 2777_pInt, 2789_pInt, 2791_pInt, 2797_pInt, 2801_pInt, 2803_pInt, 2819_pInt, &
2833_pInt, 2837_pInt, 2843_pInt, 2851_pInt, 2857_pInt, 2861_pInt, 2879_pInt, 2887_pInt, 2897_pInt, 2903_pInt, &
2909_pInt, 2917_pInt, 2927_pInt, 2939_pInt, 2953_pInt, 2957_pInt, 2963_pInt, 2969_pInt, 2971_pInt, 2999_pInt, &
3001_pInt, 3011_pInt, 3019_pInt, 3023_pInt, 3037_pInt, 3041_pInt, 3049_pInt, 3061_pInt, 3067_pInt, 3079_pInt, &
3083_pInt, 3089_pInt, 3109_pInt, 3119_pInt, 3121_pInt, 3137_pInt, 3163_pInt, 3167_pInt, 3169_pInt, 3181_pInt, &
3187_pInt, 3191_pInt, 3203_pInt, 3209_pInt, 3217_pInt, 3221_pInt, 3229_pInt, 3251_pInt, 3253_pInt, 3257_pInt, &
3259_pInt, 3271_pInt, 3299_pInt, 3301_pInt, 3307_pInt, 3313_pInt, 3319_pInt, 3323_pInt, 3329_pInt, 3331_pInt, &
3343_pInt, 3347_pInt, 3359_pInt, 3361_pInt, 3371_pInt, 3373_pInt, 3389_pInt, 3391_pInt, 3407_pInt, 3413_pInt, &
3433_pInt, 3449_pInt, 3457_pInt, 3461_pInt, 3463_pInt, 3467_pInt, 3469_pInt, 3491_pInt, 3499_pInt, 3511_pInt, &
3517_pInt, 3527_pInt, 3529_pInt, 3533_pInt, 3539_pInt, 3541_pInt, 3547_pInt, 3557_pInt, 3559_pInt, 3571_pInt/)
npvec(501:600) = (/ &
3581_pInt, 3583_pInt, 3593_pInt, 3607_pInt, 3613_pInt, 3617_pInt, 3623_pInt, 3631_pInt, 3637_pInt, 3643_pInt, &
3659_pInt, 3671_pInt, 3673_pInt, 3677_pInt, 3691_pInt, 3697_pInt, 3701_pInt, 3709_pInt, 3719_pInt, 3727_pInt, &
3733_pInt, 3739_pInt, 3761_pInt, 3767_pInt, 3769_pInt, 3779_pInt, 3793_pInt, 3797_pInt, 3803_pInt, 3821_pInt, &
3823_pInt, 3833_pInt, 3847_pInt, 3851_pInt, 3853_pInt, 3863_pInt, 3877_pInt, 3881_pInt, 3889_pInt, 3907_pInt, &
3911_pInt, 3917_pInt, 3919_pInt, 3923_pInt, 3929_pInt, 3931_pInt, 3943_pInt, 3947_pInt, 3967_pInt, 3989_pInt, &
4001_pInt, 4003_pInt, 4007_pInt, 4013_pInt, 4019_pInt, 4021_pInt, 4027_pInt, 4049_pInt, 4051_pInt, 4057_pInt, &
4073_pInt, 4079_pInt, 4091_pInt, 4093_pInt, 4099_pInt, 4111_pInt, 4127_pInt, 4129_pInt, 4133_pInt, 4139_pInt, &
4153_pInt, 4157_pInt, 4159_pInt, 4177_pInt, 4201_pInt, 4211_pInt, 4217_pInt, 4219_pInt, 4229_pInt, 4231_pInt, &
4241_pInt, 4243_pInt, 4253_pInt, 4259_pInt, 4261_pInt, 4271_pInt, 4273_pInt, 4283_pInt, 4289_pInt, 4297_pInt, &
4327_pInt, 4337_pInt, 4339_pInt, 4349_pInt, 4357_pInt, 4363_pInt, 4373_pInt, 4391_pInt, 4397_pInt, 4409_pInt/)
npvec(601:700) = (/ &
4421_pInt, 4423_pInt, 4441_pInt, 4447_pInt, 4451_pInt, 4457_pInt, 4463_pInt, 4481_pInt, 4483_pInt, 4493_pInt, &
4507_pInt, 4513_pInt, 4517_pInt, 4519_pInt, 4523_pInt, 4547_pInt, 4549_pInt, 4561_pInt, 4567_pInt, 4583_pInt, &
4591_pInt, 4597_pInt, 4603_pInt, 4621_pInt, 4637_pInt, 4639_pInt, 4643_pInt, 4649_pInt, 4651_pInt, 4657_pInt, &
4663_pInt, 4673_pInt, 4679_pInt, 4691_pInt, 4703_pInt, 4721_pInt, 4723_pInt, 4729_pInt, 4733_pInt, 4751_pInt, &
4759_pInt, 4783_pInt, 4787_pInt, 4789_pInt, 4793_pInt, 4799_pInt, 4801_pInt, 4813_pInt, 4817_pInt, 4831_pInt, &
4861_pInt, 4871_pInt, 4877_pInt, 4889_pInt, 4903_pInt, 4909_pInt, 4919_pInt, 4931_pInt, 4933_pInt, 4937_pInt, &
4943_pInt, 4951_pInt, 4957_pInt, 4967_pInt, 4969_pInt, 4973_pInt, 4987_pInt, 4993_pInt, 4999_pInt, 5003_pInt, &
5009_pInt, 5011_pInt, 5021_pInt, 5023_pInt, 5039_pInt, 5051_pInt, 5059_pInt, 5077_pInt, 5081_pInt, 5087_pInt, &
5099_pInt, 5101_pInt, 5107_pInt, 5113_pInt, 5119_pInt, 5147_pInt, 5153_pInt, 5167_pInt, 5171_pInt, 5179_pInt, &
5189_pInt, 5197_pInt, 5209_pInt, 5227_pInt, 5231_pInt, 5233_pInt, 5237_pInt, 5261_pInt, 5273_pInt, 5279_pInt/)
npvec(701:800) = (/ &
5281_pInt, 5297_pInt, 5303_pInt, 5309_pInt, 5323_pInt, 5333_pInt, 5347_pInt, 5351_pInt, 5381_pInt, 5387_pInt, &
5393_pInt, 5399_pInt, 5407_pInt, 5413_pInt, 5417_pInt, 5419_pInt, 5431_pInt, 5437_pInt, 5441_pInt, 5443_pInt, &
5449_pInt, 5471_pInt, 5477_pInt, 5479_pInt, 5483_pInt, 5501_pInt, 5503_pInt, 5507_pInt, 5519_pInt, 5521_pInt, &
5527_pInt, 5531_pInt, 5557_pInt, 5563_pInt, 5569_pInt, 5573_pInt, 5581_pInt, 5591_pInt, 5623_pInt, 5639_pInt, &
5641_pInt, 5647_pInt, 5651_pInt, 5653_pInt, 5657_pInt, 5659_pInt, 5669_pInt, 5683_pInt, 5689_pInt, 5693_pInt, &
5701_pInt, 5711_pInt, 5717_pInt, 5737_pInt, 5741_pInt, 5743_pInt, 5749_pInt, 5779_pInt, 5783_pInt, 5791_pInt, &
5801_pInt, 5807_pInt, 5813_pInt, 5821_pInt, 5827_pInt, 5839_pInt, 5843_pInt, 5849_pInt, 5851_pInt, 5857_pInt, &
5861_pInt, 5867_pInt, 5869_pInt, 5879_pInt, 5881_pInt, 5897_pInt, 5903_pInt, 5923_pInt, 5927_pInt, 5939_pInt, &
5953_pInt, 5981_pInt, 5987_pInt, 6007_pInt, 6011_pInt, 6029_pInt, 6037_pInt, 6043_pInt, 6047_pInt, 6053_pInt, &
6067_pInt, 6073_pInt, 6079_pInt, 6089_pInt, 6091_pInt, 6101_pInt, 6113_pInt, 6121_pInt, 6131_pInt, 6133_pInt/)
npvec(801:900) = (/ &
6143_pInt, 6151_pInt, 6163_pInt, 6173_pInt, 6197_pInt, 6199_pInt, 6203_pInt, 6211_pInt, 6217_pInt, 6221_pInt, &
6229_pInt, 6247_pInt, 6257_pInt, 6263_pInt, 6269_pInt, 6271_pInt, 6277_pInt, 6287_pInt, 6299_pInt, 6301_pInt, &
6311_pInt, 6317_pInt, 6323_pInt, 6329_pInt, 6337_pInt, 6343_pInt, 6353_pInt, 6359_pInt, 6361_pInt, 6367_pInt, &
6373_pInt, 6379_pInt, 6389_pInt, 6397_pInt, 6421_pInt, 6427_pInt, 6449_pInt, 6451_pInt, 6469_pInt, 6473_pInt, &
6481_pInt, 6491_pInt, 6521_pInt, 6529_pInt, 6547_pInt, 6551_pInt, 6553_pInt, 6563_pInt, 6569_pInt, 6571_pInt, &
6577_pInt, 6581_pInt, 6599_pInt, 6607_pInt, 6619_pInt, 6637_pInt, 6653_pInt, 6659_pInt, 6661_pInt, 6673_pInt, &
6679_pInt, 6689_pInt, 6691_pInt, 6701_pInt, 6703_pInt, 6709_pInt, 6719_pInt, 6733_pInt, 6737_pInt, 6761_pInt, &
6763_pInt, 6779_pInt, 6781_pInt, 6791_pInt, 6793_pInt, 6803_pInt, 6823_pInt, 6827_pInt, 6829_pInt, 6833_pInt, &
6841_pInt, 6857_pInt, 6863_pInt, 6869_pInt, 6871_pInt, 6883_pInt, 6899_pInt, 6907_pInt, 6911_pInt, 6917_pInt, &
6947_pInt, 6949_pInt, 6959_pInt, 6961_pInt, 6967_pInt, 6971_pInt, 6977_pInt, 6983_pInt, 6991_pInt, 6997_pInt/)
npvec(901:1000) = (/ &
7001_pInt, 7013_pInt, 7019_pInt, 7027_pInt, 7039_pInt, 7043_pInt, 7057_pInt, 7069_pInt, 7079_pInt, 7103_pInt, &
7109_pInt, 7121_pInt, 7127_pInt, 7129_pInt, 7151_pInt, 7159_pInt, 7177_pInt, 7187_pInt, 7193_pInt, 7207_pInt, &
7211_pInt, 7213_pInt, 7219_pInt, 7229_pInt, 7237_pInt, 7243_pInt, 7247_pInt, 7253_pInt, 7283_pInt, 7297_pInt, &
7307_pInt, 7309_pInt, 7321_pInt, 7331_pInt, 7333_pInt, 7349_pInt, 7351_pInt, 7369_pInt, 7393_pInt, 7411_pInt, &
7417_pInt, 7433_pInt, 7451_pInt, 7457_pInt, 7459_pInt, 7477_pInt, 7481_pInt, 7487_pInt, 7489_pInt, 7499_pInt, &
7507_pInt, 7517_pInt, 7523_pInt, 7529_pInt, 7537_pInt, 7541_pInt, 7547_pInt, 7549_pInt, 7559_pInt, 7561_pInt, &
7573_pInt, 7577_pInt, 7583_pInt, 7589_pInt, 7591_pInt, 7603_pInt, 7607_pInt, 7621_pInt, 7639_pInt, 7643_pInt, &
7649_pInt, 7669_pInt, 7673_pInt, 7681_pInt, 7687_pInt, 7691_pInt, 7699_pInt, 7703_pInt, 7717_pInt, 7723_pInt, &
7727_pInt, 7741_pInt, 7753_pInt, 7757_pInt, 7759_pInt, 7789_pInt, 7793_pInt, 7817_pInt, 7823_pInt, 7829_pInt, &
7841_pInt, 7853_pInt, 7867_pInt, 7873_pInt, 7877_pInt, 7879_pInt, 7883_pInt, 7901_pInt, 7907_pInt, 7919_pInt/)
npvec(1001:1100) = (/ &
7927_pInt, 7933_pInt, 7937_pInt, 7949_pInt, 7951_pInt, 7963_pInt, 7993_pInt, 8009_pInt, 8011_pInt, 8017_pInt, &
8039_pInt, 8053_pInt, 8059_pInt, 8069_pInt, 8081_pInt, 8087_pInt, 8089_pInt, 8093_pInt, 8101_pInt, 8111_pInt, &
8117_pInt, 8123_pInt, 8147_pInt, 8161_pInt, 8167_pInt, 8171_pInt, 8179_pInt, 8191_pInt, 8209_pInt, 8219_pInt, &
8221_pInt, 8231_pInt, 8233_pInt, 8237_pInt, 8243_pInt, 8263_pInt, 8269_pInt, 8273_pInt, 8287_pInt, 8291_pInt, &
8293_pInt, 8297_pInt, 8311_pInt, 8317_pInt, 8329_pInt, 8353_pInt, 8363_pInt, 8369_pInt, 8377_pInt, 8387_pInt, &
8389_pInt, 8419_pInt, 8423_pInt, 8429_pInt, 8431_pInt, 8443_pInt, 8447_pInt, 8461_pInt, 8467_pInt, 8501_pInt, &
8513_pInt, 8521_pInt, 8527_pInt, 8537_pInt, 8539_pInt, 8543_pInt, 8563_pInt, 8573_pInt, 8581_pInt, 8597_pInt, &
8599_pInt, 8609_pInt, 8623_pInt, 8627_pInt, 8629_pInt, 8641_pInt, 8647_pInt, 8663_pInt, 8669_pInt, 8677_pInt, &
8681_pInt, 8689_pInt, 8693_pInt, 8699_pInt, 8707_pInt, 8713_pInt, 8719_pInt, 8731_pInt, 8737_pInt, 8741_pInt, &
8747_pInt, 8753_pInt, 8761_pInt, 8779_pInt, 8783_pInt, 8803_pInt, 8807_pInt, 8819_pInt, 8821_pInt, 8831_pInt/)
npvec(1101:1200) = (/ &
8837_pInt, 8839_pInt, 8849_pInt, 8861_pInt, 8863_pInt, 8867_pInt, 8887_pInt, 8893_pInt, 8923_pInt, 8929_pInt, &
8933_pInt, 8941_pInt, 8951_pInt, 8963_pInt, 8969_pInt, 8971_pInt, 8999_pInt, 9001_pInt, 9007_pInt, 9011_pInt, &
9013_pInt, 9029_pInt, 9041_pInt, 9043_pInt, 9049_pInt, 9059_pInt, 9067_pInt, 9091_pInt, 9103_pInt, 9109_pInt, &
9127_pInt, 9133_pInt, 9137_pInt, 9151_pInt, 9157_pInt, 9161_pInt, 9173_pInt, 9181_pInt, 9187_pInt, 9199_pInt, &
9203_pInt, 9209_pInt, 9221_pInt, 9227_pInt, 9239_pInt, 9241_pInt, 9257_pInt, 9277_pInt, 9281_pInt, 9283_pInt, &
9293_pInt, 9311_pInt, 9319_pInt, 9323_pInt, 9337_pInt, 9341_pInt, 9343_pInt, 9349_pInt, 9371_pInt, 9377_pInt, &
9391_pInt, 9397_pInt, 9403_pInt, 9413_pInt, 9419_pInt, 9421_pInt, 9431_pInt, 9433_pInt, 9437_pInt, 9439_pInt, &
9461_pInt, 9463_pInt, 9467_pInt, 9473_pInt, 9479_pInt, 9491_pInt, 9497_pInt, 9511_pInt, 9521_pInt, 9533_pInt, &
9539_pInt, 9547_pInt, 9551_pInt, 9587_pInt, 9601_pInt, 9613_pInt, 9619_pInt, 9623_pInt, 9629_pInt, 9631_pInt, &
9643_pInt, 9649_pInt, 9661_pInt, 9677_pInt, 9679_pInt, 9689_pInt, 9697_pInt, 9719_pInt, 9721_pInt, 9733_pInt/)
npvec(1201:1300) = (/ &
9739_pInt, 9743_pInt, 9749_pInt, 9767_pInt, 9769_pInt, 9781_pInt, 9787_pInt, 9791_pInt, 9803_pInt, 9811_pInt, &
9817_pInt, 9829_pInt, 9833_pInt, 9839_pInt, 9851_pInt, 9857_pInt, 9859_pInt, 9871_pInt, 9883_pInt, 9887_pInt, &
9901_pInt, 9907_pInt, 9923_pInt, 9929_pInt, 9931_pInt, 9941_pInt, 9949_pInt, 9967_pInt, 9973_pInt,10007_pInt, &
10009_pInt,10037_pInt,10039_pInt,10061_pInt,10067_pInt,10069_pInt,10079_pInt,10091_pInt,10093_pInt,10099_pInt, &
10103_pInt,10111_pInt,10133_pInt,10139_pInt,10141_pInt,10151_pInt,10159_pInt,10163_pInt,10169_pInt,10177_pInt, &
10181_pInt,10193_pInt,10211_pInt,10223_pInt,10243_pInt,10247_pInt,10253_pInt,10259_pInt,10267_pInt,10271_pInt, &
10273_pInt,10289_pInt,10301_pInt,10303_pInt,10313_pInt,10321_pInt,10331_pInt,10333_pInt,10337_pInt,10343_pInt, &
10357_pInt,10369_pInt,10391_pInt,10399_pInt,10427_pInt,10429_pInt,10433_pInt,10453_pInt,10457_pInt,10459_pInt, &
10463_pInt,10477_pInt,10487_pInt,10499_pInt,10501_pInt,10513_pInt,10529_pInt,10531_pInt,10559_pInt,10567_pInt, &
10589_pInt,10597_pInt,10601_pInt,10607_pInt,10613_pInt,10627_pInt,10631_pInt,10639_pInt,10651_pInt,10657_pInt/)
npvec(1301:1400) = (/ &
10663_pInt,10667_pInt,10687_pInt,10691_pInt,10709_pInt,10711_pInt,10723_pInt,10729_pInt,10733_pInt,10739_pInt, &
10753_pInt,10771_pInt,10781_pInt,10789_pInt,10799_pInt,10831_pInt,10837_pInt,10847_pInt,10853_pInt,10859_pInt, &
10861_pInt,10867_pInt,10883_pInt,10889_pInt,10891_pInt,10903_pInt,10909_pInt,19037_pInt,10939_pInt,10949_pInt, &
10957_pInt,10973_pInt,10979_pInt,10987_pInt,10993_pInt,11003_pInt,11027_pInt,11047_pInt,11057_pInt,11059_pInt, &
11069_pInt,11071_pInt,11083_pInt,11087_pInt,11093_pInt,11113_pInt,11117_pInt,11119_pInt,11131_pInt,11149_pInt, &
11159_pInt,11161_pInt,11171_pInt,11173_pInt,11177_pInt,11197_pInt,11213_pInt,11239_pInt,11243_pInt,11251_pInt, &
11257_pInt,11261_pInt,11273_pInt,11279_pInt,11287_pInt,11299_pInt,11311_pInt,11317_pInt,11321_pInt,11329_pInt, &
11351_pInt,11353_pInt,11369_pInt,11383_pInt,11393_pInt,11399_pInt,11411_pInt,11423_pInt,11437_pInt,11443_pInt, &
11447_pInt,11467_pInt,11471_pInt,11483_pInt,11489_pInt,11491_pInt,11497_pInt,11503_pInt,11519_pInt,11527_pInt, &
11549_pInt,11551_pInt,11579_pInt,11587_pInt,11593_pInt,11597_pInt,11617_pInt,11621_pInt,11633_pInt,11657_pInt/)
npvec(1401:1500) = (/ &
11677_pInt,11681_pInt,11689_pInt,11699_pInt,11701_pInt,11717_pInt,11719_pInt,11731_pInt,11743_pInt,11777_pInt, &
11779_pInt,11783_pInt,11789_pInt,11801_pInt,11807_pInt,11813_pInt,11821_pInt,11827_pInt,11831_pInt,11833_pInt, &
11839_pInt,11863_pInt,11867_pInt,11887_pInt,11897_pInt,11903_pInt,11909_pInt,11923_pInt,11927_pInt,11933_pInt, &
11939_pInt,11941_pInt,11953_pInt,11959_pInt,11969_pInt,11971_pInt,11981_pInt,11987_pInt,12007_pInt,12011_pInt, &
12037_pInt,12041_pInt,12043_pInt,12049_pInt,12071_pInt,12073_pInt,12097_pInt,12101_pInt,12107_pInt,12109_pInt, &
12113_pInt,12119_pInt,12143_pInt,12149_pInt,12157_pInt,12161_pInt,12163_pInt,12197_pInt,12203_pInt,12211_pInt, &
12227_pInt,12239_pInt,12241_pInt,12251_pInt,12253_pInt,12263_pInt,12269_pInt,12277_pInt,12281_pInt,12289_pInt, &
12301_pInt,12323_pInt,12329_pInt,12343_pInt,12347_pInt,12373_pInt,12377_pInt,12379_pInt,12391_pInt,12401_pInt, &
12409_pInt,12413_pInt,12421_pInt,12433_pInt,12437_pInt,12451_pInt,12457_pInt,12473_pInt,12479_pInt,12487_pInt, &
12491_pInt,12497_pInt,12503_pInt,12511_pInt,12517_pInt,12527_pInt,12539_pInt,12541_pInt,12547_pInt,12553_pInt/)
endif
if(n == -1_pInt) then
prime = prime_max
else if (n == 0_pInt) then
prime = 1_pInt
else if (n <= prime_max) then
prime = npvec(n)
else ! why not use io_error here?
prime = 0_pInt
!$OMP CRITICAL (write2out)
write (6, '(a)') ' '
write (6, '(a)') 'PRIME - Fatal error!'
write (6, '(a,i6)') ' Illegal prime index N = ', n
write (6, '(a,i6)') ' N must be between 0 and PRIME_MAX = ', prime_max
call flush(6)
!$OMP END CRITICAL (write2out)
stop
end if
endfunction prime
!**************************************************************************
! volume of tetrahedron given by four vertices
!**************************************************************************
pure function math_volTetrahedron(v1,v2,v3,v4)
implicit none
real(pReal) math_volTetrahedron
real(pReal), dimension (3), intent(in) :: v1,v2,v3,v4
real(pReal), dimension (3,3) :: m
m(1:3,1) = v1-v2
m(1:3,2) = v2-v3
m(1:3,3) = v3-v4
math_volTetrahedron = math_det3x3(m)/6.0_pReal
endfunction math_volTetrahedron
!**************************************************************************
! rotate 3x3 tensor forward
!**************************************************************************
pure function math_rotate_forward3x3(tensor,rot_tensor)
implicit none
real(pReal), dimension(3,3) :: math_rotate_forward3x3
real(pReal), dimension(3,3), intent(in) :: tensor, rot_tensor
math_rotate_forward3x3 = math_mul33x33(rot_tensor,&
math_mul33x33(tensor,math_transpose3x3(rot_tensor)))
endfunction math_rotate_forward3x3
!**************************************************************************
! rotate 3x3 tensor backward
!**************************************************************************
pure function math_rotate_backward3x3(tensor,rot_tensor)
implicit none
real(pReal), dimension(3,3) :: math_rotate_backward3x3
real(pReal), dimension(3,3), intent(in) :: tensor, rot_tensor
math_rotate_backward3x3 = math_mul33x33(math_transpose3x3(rot_tensor),&
math_mul33x33(tensor,rot_tensor))
endfunction math_rotate_backward3x3
!**************************************************************************
! rotate 3x3x3x3 tensor
! C'_ijkl=g_im*g_jn*g_ko*g_lp*C_mnop
!**************************************************************************
pure function math_rotate_forward3x3x3x3(tensor,rot_tensor)
implicit none
real(pReal), dimension(3,3,3,3) :: math_rotate_forward3x3x3x3
real(pReal), dimension(3,3), intent(in) :: rot_tensor
real(pReal), dimension(3,3,3,3), intent(in) :: tensor
integer(pInt) :: i,j,k,l,m,n,o,p
math_rotate_forward3x3x3x3= 0.0_pReal
do i = 1_pInt,3_pInt; do j = 1_pInt,3_pInt; do k = 1_pInt,3_pInt; do l = 1_pInt,3_pInt
do m = 1_pInt,3_pInt; do n = 1_pInt,3_pInt; do o = 1_pInt,3_pInt; do p = 1_pInt,3_pInt
math_rotate_forward3x3x3x3(i,j,k,l) = tensor(i,j,k,l)+rot_tensor(m,i)*rot_tensor(n,j)*&
rot_tensor(o,k)*rot_tensor(p,l)*tensor(m,n,o,p)
enddo; enddo; enddo; enddo; enddo; enddo; enddo; enddo
endfunction math_rotate_forward3x3x3x3
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
! Functions below are taken from the old postprocessingMath.f90
! mostly they are used in combination with f2py to build fortran
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
! put the next two funtions into mesh?
function mesh_location(idx,resolution)
! small helper functions for indexing
! CAREFULL, index and location runs from 0 to N-1 (python style)
integer(pInt), intent(in) :: idx
integer(pInt), intent(in), dimension(3) :: resolution
integer(pInt), dimension(3) :: mesh_location
mesh_location = (/modulo(idx/ resolution(3) / resolution(2),resolution(1)), &
modulo(idx/ resolution(3), resolution(2)), &
modulo(idx, resolution(3))/)
end function mesh_location
function mesh_index(location,resolution)
! small helper functions for indexing
! CAREFULL, index and location runs from 0 to N-1 (python style)
integer(pInt), intent(in), dimension(3) :: resolution, location
integer(pInt) :: mesh_index
mesh_index = modulo(location(3), resolution(3)) +&
(modulo(location(2), resolution(2)))*resolution(3) +&
(modulo(location(1), resolution(1)))*resolution(3)*resolution(2)
end function mesh_index
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
subroutine volume_compare(res,geomdim,defgrad,nodes,volume_mismatch)
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
! Routine to calculate the mismatch between volume of reconstructed (compatible
! cube and determinant of defgrad at the FP
use debug, only: debug_verbosity
implicit none
! input variables
integer(pInt), intent(in), dimension(3) :: res
real(pReal), intent(in), dimension(3) :: geomdim
real(pReal), intent(in), dimension(res(1), res(2), res(3), 3,3) :: defgrad
real(pReal), intent(in), dimension(res(1)+1_pInt,res(2)+1_pInt,res(3)+1_pInt,3) :: nodes
! output variables
real(pReal), intent(out), dimension(res(1), res(2), res(3)) :: volume_mismatch
! other variables
real(pReal), dimension(8,3) :: coords
integer(pInt) i,j,k
real(pReal) vol_initial
if (debug_verbosity) then
print*, 'Calculating volume mismatch'
print '(a,/,e12.5,e12.5,e12.5)', ' Dimension:', geomdim
print '(a,/,i5,i5,i5)', ' Resolution:', res
endif
vol_initial = geomdim(1)*geomdim(2)*geomdim(3)/(real(res(1)*res(2)*res(3), pReal))
do k = 1_pInt,res(3)
do j = 1_pInt,res(2)
do i = 1_pInt,res(1)
coords(1,1:3) = nodes(i, j, k ,1:3)
coords(2,1:3) = nodes(i+1_pInt,j, k ,1:3)
coords(3,1:3) = nodes(i+1_pInt,j+1_pInt,k ,1:3)
coords(4,1:3) = nodes(i, j+1_pInt,k ,1:3)
coords(5,1:3) = nodes(i, j, k+1_pInt,1:3)
coords(6,1:3) = nodes(i+1_pInt,j, k+1_pInt,1:3)
coords(7,1:3) = nodes(i+1_pInt,j+1_pInt,k+1_pInt,1:3)
coords(8,1:3) = nodes(i, j+1_pInt,k+1_pInt,1:3)
volume_mismatch(i,j,k) = abs(math_volTetrahedron(coords(7,1:3),coords(1,1:3),coords(8,1:3),coords(4,1:3))) &
+ abs(math_volTetrahedron(coords(7,1:3),coords(1,1:3),coords(8,1:3),coords(5,1:3))) &
+ abs(math_volTetrahedron(coords(7,1:3),coords(1,1:3),coords(3,1:3),coords(4,1:3))) &
+ abs(math_volTetrahedron(coords(7,1:3),coords(1,1:3),coords(3,1:3),coords(2,1:3))) &
+ abs(math_volTetrahedron(coords(7,1:3),coords(5,1:3),coords(2,1:3),coords(6,1:3))) &
+ abs(math_volTetrahedron(coords(7,1:3),coords(5,1:3),coords(2,1:3),coords(1,1:3)))
volume_mismatch(i,j,k) = volume_mismatch(i,j,k)/math_det3x3(defgrad(i,j,k,1:3,1:3))
enddo; enddo; enddo
volume_mismatch = volume_mismatch/vol_initial
end subroutine volume_compare
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
subroutine shape_compare(res,geomdim,defgrad,nodes,centroids,shape_mismatch)
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
! Routine to calculate the mismatch between the vectors from the central point to
! the corners of reconstructed (combatible) volume element and the vectors calculated by deforming
! the initial volume element with the current deformation gradient
use debug, only: debug_verbosity
implicit none
! input variables
integer(pInt), intent(in), dimension(3) :: res
real(pReal), intent(in), dimension(3) :: geomdim
real(pReal), intent(in), dimension(res(1), res(2), res(3), 3,3) :: defgrad
real(pReal), intent(in), dimension(res(1)+1_pInt,res(2)+1_pInt,res(3)+1_pInt,3) :: nodes
real(pReal), intent(in), dimension(res(1), res(2), res(3), 3) :: centroids
! output variables
real(pReal), intent(out), dimension(res(1), res(2), res(3)) :: shape_mismatch
! other variables
real(pReal), dimension(8,3) :: coords_initial
integer(pInt) i,j,k
if (debug_verbosity) then
print*, 'Calculating shape mismatch'
print '(a,/,e12.5,e12.5,e12.5)', ' Dimension:', geomdim
print '(a,/,i5,i5,i5)', ' Resolution:', res
endif
coords_initial(1,1:3) = (/-geomdim(1)/2.0_pReal/real(res(1),pReal),&
-geomdim(2)/2.0_pReal/real(res(2),pReal),&
-geomdim(3)/2.0_pReal/real(res(3),pReal)/)
coords_initial(2,1:3) = (/+geomdim(1)/2.0_pReal/real(res(1),pReal),&
-geomdim(2)/2.0_pReal/real(res(2),pReal),&
-geomdim(3)/2.0_pReal/real(res(3),pReal)/)
coords_initial(3,1:3) = (/+geomdim(1)/2.0_pReal/real(res(1),pReal),&
+geomdim(2)/2.0_pReal/real(res(2),pReal),&
-geomdim(3)/2.0_pReal/real(res(3),pReal)/)
coords_initial(4,1:3) = (/-geomdim(1)/2.0_pReal/real(res(1),pReal),&
+geomdim(2)/2.0_pReal/real(res(2),pReal),&
-geomdim(3)/2.0_pReal/real(res(3),pReal)/)
coords_initial(5,1:3) = (/-geomdim(1)/2.0_pReal/real(res(1),pReal),&
-geomdim(2)/2.0_pReal/real(res(2),pReal),&
+geomdim(3)/2.0_pReal/real(res(3),pReal)/)
coords_initial(6,1:3) = (/+geomdim(1)/2.0_pReal/real(res(1),pReal),&
-geomdim(2)/2.0_pReal/real(res(2),pReal),&
+geomdim(3)/2.0_pReal/real(res(3),pReal)/)
coords_initial(7,1:3) = (/+geomdim(1)/2.0_pReal/real(res(1),pReal),&
+geomdim(2)/2.0_pReal/real(res(2),pReal),&
+geomdim(3)/2.0_pReal/real(res(3),pReal)/)
coords_initial(8,1:3) = (/-geomdim(1)/2.0_pReal/real(res(1),pReal),&
+geomdim(2)/2.0_pReal/real(res(2),pReal),&
+geomdim(3)/2.0_pReal/real(res(3),pReal)/)
do i=1_pInt,8_pInt
enddo
do k = 1_pInt,res(3)
do j = 1_pInt,res(2)
do i = 1_pInt,res(1)
shape_mismatch(i,j,k) = &
sqrt(sum((nodes(i, j, k, 1:3) - centroids(i,j,k,1:3)&
- matmul(defgrad(i,j,k,1:3,1:3), coords_initial(1,1:3)))**2.0_pReal))&
+ sqrt(sum((nodes(i+1_pInt,j, k, 1:3) - centroids(i,j,k,1:3)&
- matmul(defgrad(i,j,k,1:3,1:3), coords_initial(2,1:3)))**2.0_pReal))&
+ sqrt(sum((nodes(i+1_pInt,j+1_pInt,k, 1:3) - centroids(i,j,k,1:3)&
- matmul(defgrad(i,j,k,1:3,1:3), coords_initial(3,1:3)))**2.0_pReal))&
+ sqrt(sum((nodes(i, j+1_pInt,k, 1:3) - centroids(i,j,k,1:3)&
- matmul(defgrad(i,j,k,1:3,1:3), coords_initial(4,1:3)))**2.0_pReal))&
+ sqrt(sum((nodes(i, j, k+1_pInt,1:3) - centroids(i,j,k,1:3)&
- matmul(defgrad(i,j,k,1:3,1:3), coords_initial(5,1:3)))**2.0_pReal))&
+ sqrt(sum((nodes(i+1_pInt,j, k+1_pInt,1:3) - centroids(i,j,k,1:3)&
- matmul(defgrad(i,j,k,1:3,1:3), coords_initial(6,1:3)))**2.0_pReal))&
+ sqrt(sum((nodes(i+1_pInt,j+1_pInt,k+1_pInt,1:3) - centroids(i,j,k,1:3)&
- matmul(defgrad(i,j,k,1:3,1:3), coords_initial(7,1:3)))**2.0_pReal))&
+ sqrt(sum((nodes(i, j+1_pInt,k+1_pInt,1:3) - centroids(i,j,k,1:3)&
- matmul(defgrad(i,j,k,1:3,1:3), coords_initial(8,1:3)))**2.0_pReal))
enddo; enddo; enddo
end subroutine shape_compare
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
subroutine mesh_regular_grid(res,geomdim,defgrad_av,centroids,nodes)
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
! Routine to build mesh of (distoreted) cubes for given coordinates (= center of the cubes)
!
use debug, only: debug_verbosity
implicit none
! input variables
integer(pInt), intent(in), dimension(3) :: res
real(pReal), intent(in), dimension(3) :: geomdim
real(pReal), intent(in), dimension(3,3) :: defgrad_av
real(pReal), intent(in), dimension(res(1), res(2), res(3), 3) :: centroids
! output variables
real(pReal),intent(out), dimension(res(1)+1_pInt,res(2)+1_pInt,res(3)+1_pInt,3) :: nodes
! variables with dimension depending on input
real(pReal), dimension(res(1)+2_pInt,res(2)+2_pInt,res(3)+2_pInt,3) :: wrappedCentroids
! other variables
integer(pInt) :: i,j,k,n
integer(pInt), dimension(3), parameter :: diag = 1_pInt
integer(pInt), dimension(3) :: shift = 0_pInt, lookup = 0_pInt, me = 0_pInt
integer(pInt), dimension(3,8) :: neighbor = reshape((/ &
0_pInt, 0_pInt, 0_pInt, &
1_pInt, 0_pInt, 0_pInt, &
1_pInt, 1_pInt, 0_pInt, &
0_pInt, 1_pInt, 0_pInt, &
0_pInt, 0_pInt, 1_pInt, &
1_pInt, 0_pInt, 1_pInt, &
1_pInt, 1_pInt, 1_pInt, &
0_pInt, 1_pInt, 1_pInt &
/), &
(/3,8/))
if (debug_verbosity) then
print*, 'Meshing cubes around centroids'
print '(a,/,e12.5,e12.5,e12.5)', ' Dimension:', geomdim
print '(a,/,i5,i5,i5)', ' Resolution:', res
endif
nodes = 0.0_pReal
wrappedCentroids = 0.0_pReal
wrappedCentroids(2_pInt:res(1)+1_pInt,2_pInt:res(2)+1_pInt,2_pInt:res(3)+1_pInt,1:3) = centroids
do k = 0_pInt,res(3)+1_pInt
do j = 0_pInt,res(2)+1_pInt
do i = 0_pInt,res(1)+1_pInt
if (k==0_pInt .or. k==res(3)+1_pInt .or. & ! z skin
j==0_pInt .or. j==res(2)+1_pInt .or. & ! y skin
i==0_pInt .or. i==res(1)+1_pInt ) then ! x skin
me = (/i,j,k/) ! me on skin
shift = sign(abs(res+diag-2_pInt*me)/(res+diag),res+diag-2_pInt*me)
lookup = me-diag+shift*res
wrappedCentroids(i+1_pInt,j+1_pInt,k+1_pInt,1:3) = &
centroids(lookup(1)+1_pInt,lookup(2)+1_pInt,lookup(3)+1_pInt,1:3) - &
matmul(defgrad_av, shift*geomdim)
endif
enddo; enddo; enddo
do k = 0_pInt,res(3)
do j = 0_pInt,res(2)
do i = 0_pInt,res(1)
do n = 1_pInt,8_pInt
nodes(i+1_pInt,j+1_pInt,k+1_pInt,1:3) = &
nodes(i+1_pInt,j+1_pInt,k+1_pInt,1:3) + wrappedCentroids(i+1_pInt+neighbor(1_pInt,n), &
j+1_pInt+neighbor(2,n), &
k+1_pInt+neighbor(3,n),1:3)
enddo; enddo; enddo; enddo
nodes = nodes/8.0_pReal
end subroutine mesh_regular_grid
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
subroutine deformed_linear(res,geomdim,defgrad_av,defgrad,coord_avgCorner)
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
! Routine to calculate coordinates in current configuration for given defgrad
! using linear interpolation (blurres out high frequency defomation)
!
use debug, only: debug_verbosity
implicit none
! input variables
integer(pInt), intent(in), dimension(3) :: res
real(pReal), intent(in), dimension(3) :: geomdim
real(pReal), intent(in), dimension(3,3) :: defgrad_av
real(pReal), intent(in), dimension( res(1),res(2),res(3),3,3) :: defgrad
! output variables
real(pReal), intent(out), dimension( res(1),res(2),res(3),3) :: coord_avgCorner
! variables with dimension depending on input
real(pReal), dimension(8,6,res(1),res(2),res(3),3) :: coord
real(pReal), dimension( 8,res(1),res(2),res(3),3) :: coord_avgOrder
! other variables
real(pReal), dimension(3) :: myStep, fones = 1.0_pReal, parameter_coords, negative, positive
integer(pInt), dimension(3) :: rear, init, ones = 1_pInt, oppo, me
integer(pInt) i, j, k, s, o
integer(pInt), dimension(3,8) :: corner = reshape((/ &
0_pInt, 0_pInt, 0_pInt,&
1_pInt, 0_pInt, 0_pInt,&
1_pInt, 1_pInt, 0_pInt,&
0_pInt, 1_pInt, 0_pInt,&
1_pInt, 1_pInt, 1_pInt,&
0_pInt, 1_pInt, 1_pInt,&
0_pInt, 0_pInt, 1_pInt,&
1_pInt, 0_pInt, 1_pInt &
/), &
(/3,8/))
integer(pInt), dimension(3,8) :: step = reshape((/ &
1_pInt, 1_pInt, 1_pInt,&
-1_pInt, 1_pInt, 1_pInt,&
-1_pInt,-1_pInt, 1_pInt,&
1_pInt,-1_pInt, 1_pInt,&
-1_pInt,-1_pInt,-1_pInt,&
1_pInt,-1_pInt,-1_pInt,&
1_pInt, 1_pInt,-1_pInt,&
-1_pInt, 1_pInt,-1_pInt &
/), &
(/3,8/))
integer(pInt), dimension(3,6) :: order = reshape((/ &
1_pInt, 2_pInt, 3_pInt,&
1_pInt, 3_pInt, 2_pInt,&
2_pInt, 1_pInt, 3_pInt,&
2_pInt, 3_pInt, 1_pInt,&
3_pInt, 1_pInt, 2_pInt,&
3_pInt, 2_pInt, 1_pInt &
/), &
(/3,6/))
if (debug_verbosity) then
print*, 'Restore geometry using linear integration'
print '(a,/,e12.5,e12.5,e12.5)', ' Dimension:', geomdim
print '(a,/,i5,i5,i5)', ' Resolution:', res
endif
coord_avgOrder = 0.0_pReal
do s = 0_pInt, 7_pInt ! corners (from 0 to 7)
init = corner(:,s+1_pInt)*(res-ones) +ones
oppo = corner(:,mod((s+4_pInt),8_pInt)+1_pInt)*(res-ones) +ones
do o=1_pInt,6_pInt ! orders (from 1 to 6)
do k = init(order(3,o)), oppo(order(3,o)), step(order(3,o),s+1_pInt)
rear(order(2,o)) = init(order(2,o))
do j = init(order(2,o)), oppo(order(2,o)), step(order(2,o),s+1_pInt)
rear(order(1,o)) = init(order(1,o))
do i = init(order(1,o)), oppo(order(1,o)), step(order(1,o),s+1_pInt)
me(order(1,o)) = i
me(order(2,o)) = j
me(order(3,o)) = k
if ( (me(1)==init(1)).and.(me(2)==init(2)).and. (me(3)==init(3)) ) then
coord(s+1_pInt,o,me(1),me(2),me(3),1:3) = geomdim * (matmul(defgrad_av,corner(1:3,s+1)) + &
matmul(defgrad(me(1),me(2),me(3),1:3,1:3),0.5*step(1:3,s+1_pInt)/res))
else
myStep = (me-rear)*geomdim/res
coord(s+1_pInt,o,me(1),me(2),me(3),1:3) = coord(s+1_pInt,o,rear(1),rear(2),rear(3),1:3) + &
0.5*matmul(defgrad(me(1),me(2),me(3),1:3,1:3) + &
defgrad(rear(1),rear(2),rear(3),1:3,1:3),myStep)
endif
rear = me
enddo; enddo; enddo; enddo
do i = 1_pInt,6_pInt
coord_avgOrder(s+1_pInt,1:res(1),1:res(2),1:res(3),1:3) = coord_avgOrder(s+1_pInt, 1:res(1),1:res(2),1:res(3),1:3)&
+ coord(s+1_pInt,i,1:res(1),1:res(2),1:res(3),1:3)/6.0
enddo
enddo
do k = 0_pInt, res(3)-1_pInt
do j = 0_pInt, res(2)-1_pInt
do i = 0_pInt, res(1)-1_pInt
parameter_coords = (2.0_pReal*(/real(i,pReal)+0.0_pReal,real(j,pReal)+0.0_pReal,real(k,pReal)+0.0_pReal/)&
-real(res,pReal)+fones)/(real(res,pReal)-fones)
positive = fones + parameter_coords
negative = fones - parameter_coords
coord_avgCorner(i+1_pInt,j+1_pInt,k+1_pInt,1:3)&
=(coord_avgOrder(1,i+1_pInt,j+1_pInt,k+1_pInt,1:3) *negative(1)*negative(2)*negative(3)&
+ coord_avgOrder(2,i+1_pInt,j+1_pInt,k+1_pInt,1:3) *positive(1)*negative(2)*negative(3)&
+ coord_avgOrder(3,i+1_pInt,j+1_pInt,k+1_pInt,1:3) *positive(1)*positive(2)*negative(3)&
+ coord_avgOrder(4,i+1_pInt,j+1_pInt,k+1_pInt,1:3) *negative(1)*positive(2)*negative(3)&
+ coord_avgOrder(5,i+1_pInt,j+1_pInt,k+1_pInt,1:3) *positive(1)*positive(2)*positive(3)&
+ coord_avgOrder(6,i+1_pInt,j+1_pInt,k+1_pInt,1:3) *negative(1)*positive(2)*positive(3)&
+ coord_avgOrder(7,i+1_pInt,j+1_pInt,k+1_pInt,1:3) *negative(1)*negative(2)*positive(3)&
+ coord_avgOrder(8,i+1_pInt,j+1_pInt,k+1_pInt,1:3) *positive(1)*negative(2)*positive(3))*0.125
enddo; enddo; enddo
end subroutine deformed_linear
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
subroutine deformed_fft(res,geomdim,defgrad_av,scaling,defgrad,coords)
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
! Routine to calculate coordinates in current configuration for given defgrad
! using integration in Fourier space (more accurate than deformed(...))
!
use numerics, only: fftw_timelimit, fftw_planner_flag
use debug, only: debug_verbosity
implicit none
! input variables
integer(pInt), intent(in), dimension(3) :: res
real(pReal), intent(in), dimension(3) :: geomdim
real(pReal), intent(in), dimension(3,3) :: defgrad_av
real(pReal), intent(in) :: scaling
real(pReal), intent(in), dimension(res(1), res(2),res(3),3,3) :: defgrad
! output variables
real(pReal), intent(out), dimension(res(1), res(2),res(3),3) :: coords
! allocatable arrays for fftw c routines
type(C_PTR) :: fftw_forth, fftw_back
type(C_PTR) :: coords_fftw, defgrad_fftw
real(pReal), dimension(:,:,:,:,:), pointer :: defgrad_real
complex(pReal), dimension(:,:,:,:,:), pointer :: defgrad_complex
real(pReal), dimension(:,:,:,:), pointer :: coords_real
complex(pReal), dimension(:,:,:,:), pointer :: coords_complex
! other variables
integer(pInt) :: i, j, k, res1_red
integer(pInt), dimension(3) :: k_s
complex(pReal), parameter :: integration_factor = cmplx(0.0_pReal,pi*2.0_pReal)
real(pReal), dimension(3) :: step, offset_coords
if (debug_verbosity) then
print*, 'Restore geometry using FFT-based integration'
print '(a,/,e12.5,e12.5,e12.5)', ' Dimension:', geomdim
print '(a,/,i5,i5,i5)', ' Resolution:', res
endif
res1_red = res(1)/2_pInt + 1_pInt ! size of complex array in first dimension (c2r, r2c)
step = geomdim/real(res, pReal)
if (pReal /= C_DOUBLE .or. pInt /= C_INT) call IO_error(error_ID=102)
call fftw_set_timelimit(fftw_timelimit)
defgrad_fftw = fftw_alloc_complex(int(res1_red *res(2)*res(3)*9_pInt,C_SIZE_T)) !C_SIZE_T is of type integer(8)
call c_f_pointer(defgrad_fftw, defgrad_real, [res(1)+2_pInt,res(2),res(3),3,3])
call c_f_pointer(defgrad_fftw, defgrad_complex,[res1_red ,res(2),res(3),3,3])
coords_fftw = fftw_alloc_complex(int(res1_red *res(2)*res(3)*3_pInt,C_SIZE_T)) !C_SIZE_T is of type integer(8)
call c_f_pointer(coords_fftw, coords_real, [res(1)+2_pInt,res(2),res(3),3])
call c_f_pointer(coords_fftw, coords_complex, [res1_red ,res(2),res(3),3])
fftw_forth = fftw_plan_many_dft_r2c(3,(/res(3),res(2) ,res(1)/),9_pInt,& ! dimensions , length in each dimension in reversed order
defgrad_real,(/res(3),res(2) ,res(1)+2_pInt/),& ! input data , physical length in each dimension in reversed order
1, res(3)*res(2)*(res(1)+2_pInt),& ! striding , product of physical lenght in the 3 dimensions
defgrad_complex,(/res(3),res(2) ,res1_red/),&
1, res(3)*res(2)* res1_red,fftw_planner_flag)
fftw_back = fftw_plan_many_dft_c2r(3,(/res(3),res(2) ,res(1)/),3_pInt,&
coords_complex,(/res(3),res(2) ,res1_red/),&
1, res(3)*res(2)* res1_red,&
coords_real,(/res(3),res(2) ,res(1)+2_pInt/),&
1, res(3)*res(2)*(res(1)+2_pInt),fftw_planner_flag)
do k = 1_pInt, res(3); do j = 1_pInt, res(2); do i = 1_pInt, res(1)
defgrad_real(i,j,k,1:3,1:3) = defgrad(i,j,k,1:3,1:3) ! ensure that data is aligned properly (fftw_alloc)
enddo; enddo; enddo
call fftw_execute_dft_r2c(fftw_forth, defgrad_real, defgrad_complex)
coords_complex = 0.0
do k = 1_pInt, res(3)
k_s(3) = k-1_pInt
if(k > res(3)/2_pInt+1_pInt) k_s(3) = k_s(3)-res(3)
do j = 1_pInt, res(2)
k_s(2) = j-1_pInt
if(j > res(2)/2_pInt+1_pInt) k_s(2) = k_s(2)-res(2)
do i = 1_pInt, res1_red
k_s(1) = i-1_pInt
if(i/=1_pInt) coords_complex(i,j,k,1:3) = coords_complex(i,j,k,1:3)&
+ defgrad_complex(i,j,k,1:3,1)*geomdim(1)/(real(k_s(1),pReal)*integration_factor)
if(j/=1_pInt) coords_complex(i,j,k,1:3) = coords_complex(i,j,k,1:3)&
+ defgrad_complex(i,j,k,1:3,2)*geomdim(2)/(real(k_s(2),pReal)*integration_factor)
if(k/=1_pInt) coords_complex(i,j,k,1:3) = coords_complex(i,j,k,1:3)&
+ defgrad_complex(i,j,k,1:3,3)*geomdim(3)/(real(k_s(3),pReal)*integration_factor)
enddo; enddo; enddo
call fftw_execute_dft_c2r(fftw_back,coords_complex,coords_real)
coords_real = coords_real/real(res(1)*res(2)*res(3))
do k = 1_pInt, res(3); do j = 1_pInt, res(2); do i = 1_pInt, res(1)
coords(i,j,k,1:3) = coords_real(i,j,k,1:3) ! ensure that data is aligned properly (fftw_alloc)
enddo; enddo; enddo
offset_coords = matmul(defgrad(1,1,1,1:3,1:3),step/2.0_pReal) - scaling*coords(1,1,1,1:3)
do k = 1_pInt, res(3); do j = 1_pInt, res(2); do i = 1_pInt, res(1)
coords(i,j,k,1:3) = scaling*coords(i,j,k,1:3) + offset_coords + matmul(defgrad_av,&
(/step(1)*real(i-1_pInt,pReal),&
step(2)*real(j-1_pInt,pReal),&
step(3)*real(k-1_pInt,pReal)/))
enddo; enddo; enddo
end subroutine deformed_fft
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
subroutine curl_fft(res,geomdim,vec_tens,field,curl)
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
! calculates curl field using differentation in Fourier space
! use vec_tens to decide if tensor (3) or vector (1)
use numerics, only: fftw_timelimit, fftw_planner_flag
use debug, only: debug_verbosity
implicit none
! input variables
integer(pInt), intent(in), dimension(3) :: res
real(pReal), intent(in), dimension(3) :: geomdim
integer(pInt), intent(in) :: vec_tens
real(pReal), intent(in), dimension(res(1), res(2),res(3),3,vec_tens) :: field
! output variables
real(pReal), intent(out), dimension(res(1), res(2),res(3),3,vec_tens) :: curl
! variables with dimension depending on input
real(pReal), dimension(res(1)/2_pInt+1_pInt,res(2),res(3),3) :: xi
! allocatable arrays for fftw c routines
type(C_PTR) :: fftw_forth, fftw_back
type(C_PTR) :: field_fftw, curl_fftw
real(pReal), dimension(:,:,:,:,:), pointer :: field_real
complex(pReal), dimension(:,:,:,:,:), pointer :: field_complex
real(pReal), dimension(:,:,:,:,:), pointer :: curl_real
complex(pReal), dimension(:,:,:,:,:), pointer :: curl_complex
! other variables
integer(pInt) i, j, k, l, res1_red
complex(pReal), parameter :: img =cmplx(0.0_pReal,1.0_pReal)
if (debug_verbosity) then
print*, 'Calculating curl of vector/tensor field'
print '(a,/,e12.5,e12.5,e12.5)', ' Dimension:', geomdim
print '(a,/,i5,i5,i5)', ' Resolution:', res
endif
res1_red = res(1)/2_pInt + 1_pInt ! size of complex array in first dimension (c2r, r2c)
if (pReal /= C_DOUBLE .or. pInt /= C_INT) call IO_error(error_ID=102)
call fftw_set_timelimit(fftw_timelimit)
field_fftw = fftw_alloc_complex(int(res1_red *res(2)*res(3)*vec_tens*3_pInt,C_SIZE_T)) !C_SIZE_T is of type integer(8)
call c_f_pointer(field_fftw, field_real, [res(1)+2_pInt,res(2),res(3),3,vec_tens])
call c_f_pointer(field_fftw, field_complex,[res1_red ,res(2),res(3),3,vec_tens])
curl_fftw = fftw_alloc_complex(int(res1_red *res(2)*res(3)*vec_tens*3_pInt,C_SIZE_T)) !C_SIZE_T is of type integer(8)
call c_f_pointer(curl_fftw, curl_real, [res(1)+2_pInt,res(2),res(3),3,vec_tens])
call c_f_pointer(curl_fftw, curl_complex, [res1_red ,res(2),res(3),3,vec_tens])
fftw_forth = fftw_plan_many_dft_r2c(3,(/res(3),res(2) ,res(1)/),vec_tens*3_pInt,& ! dimensions , length in each dimension in reversed order
field_real,(/res(3),res(2) ,res(1)+2_pInt/),& ! input data , physical length in each dimension in reversed order
1, res(3)*res(2)*(res(1)+2_pInt),& ! striding , product of physical lenght in the 3 dimensions
field_complex,(/res(3),res(2) ,res1_red/),&
1, res(3)*res(2)* res1_red,fftw_planner_flag)
fftw_back = fftw_plan_many_dft_c2r(3,(/res(3),res(2) ,res(1)/),vec_tens*3_pInt,&
curl_complex,(/res(3),res(2) ,res1_red/),&
1, res(3)*res(2)* res1_red,&
curl_real,(/res(3),res(2) ,res(1)+2_pInt/),&
1, res(3)*res(2)*(res(1)+2_pInt),fftw_planner_flag)
do k = 1_pInt, res(3); do j = 1_pInt, res(2); do i = 1_pInt, res(1)
field_real(i,j,k,1:3,1:vec_tens) = field(i,j,k,1:3,1:vec_tens) ! ensure that data is aligned properly (fftw_alloc)
enddo; enddo; enddo
call fftw_execute_dft_r2c(fftw_forth, field_real, field_complex)
do k = 0_pInt, res(3)-1_pInt
do j = 0_pInt, res(2)-1_pInt
do i = 0_pInt, res(1)/2_pInt
xi(i+1_pInt,j+1_pInt,k+1_pInt,1:3) = real((/i,j,k/), pReal)/geomdim
if(k==res(3)/2_pInt) xi(i+1_pInt,j+1_pInt,k+1_pInt,3)= 0.0_pReal ! set highest frequencies to zero
if(j==res(2)/2_pInt) xi(i+1_pInt,j+1_pInt,k+1_pInt,2)= 0.0_pReal
if(i==res(1)/2_pInt) xi(i+1_pInt,j+1_pInt,k+1_pInt,1)= 0.0_pReal
enddo; enddo; enddo
do k = 1, res(3)
do j = 1, res(2)
do i = 1, res1_red
do l = 1, vec_tens
curl_complex(i,j,k,1,l) =( field_complex(i,j,k,3,l)*xi(i,j,k,2) - field_complex(i,j,k,2,l)*xi(i,j,k,3))&
*img*pi*2.0_pReal
curl_complex(i,j,k,2,l) =(- field_complex(i,j,k,3,l)*xi(i,j,k,1) + field_complex(i,j,k,1,l)*xi(i,j,k,3))&
*img*pi*2.0_pReal
curl_complex(i,j,k,3,l) =( field_complex(i,j,k,2,l)*xi(i,j,k,1) - field_complex(i,j,k,1,l)*xi(i,j,k,2))&
*img*pi*2.0_pReal
enddo
enddo; enddo; enddo
call fftw_execute_dft_c2r(fftw_back, curl_complex, curl_real)
do k = 1_pInt, res(3); do j = 1_pInt, res(2); do i = 1_pInt, res(1)
curl(i,j,k,1:3,1:vec_tens) = curl_real(i,j,k,1:3,1:vec_tens) ! ensure that data is aligned properly (fftw_alloc)
enddo; enddo; enddo
end subroutine curl_fft
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
subroutine divergence_fft(res,geomdim,vec_tens,field,divergence)
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
! calculates divergence field using integration in Fourier space
! use vec_tens to decide if tensor (3) or vector (1)
use numerics, only: fftw_timelimit, fftw_planner_flag
use debug, only: debug_verbosity
implicit none
! input variables
integer(pInt), intent(in), dimension(3) :: res
real(pReal), intent(in), dimension(3) :: geomdim
integer(pInt), intent(in) :: vec_tens
real(pReal), intent(in), dimension(res(1), res(2),res(3),vec_tens,3) :: field
! output variables
real(pReal), intent(out), dimension(res(1), res(2),res(3),vec_tens) :: divergence
! variables with dimension depending on input
real(pReal), dimension(res(1)/2_pInt+1_pInt,res(2),res(3),3) :: xi
! allocatable arrays for fftw c routines
type(C_PTR) :: fftw_forth, fftw_back
type(C_PTR) :: field_fftw, divergence_fftw
real(pReal), dimension(:,:,:,:,:), pointer :: field_real
complex(pReal), dimension(:,:,:,:,:), pointer :: field_complex
real(pReal), dimension(:,:,:,:), pointer :: divergence_real
complex(pReal), dimension(:,:,:,:), pointer :: divergence_complex
! other variables
integer(pInt) :: i, j, k, res1_red
complex(pReal), parameter :: img = cmplx(0.0_pReal,1.0_pReal)
if (debug_verbosity) then
print '(a)', 'Calculating divergence of tensor/vector field using FFT'
print '(a,/,e12.5,e12.5,e12.5)', ' Dimension:', geomdim
print '(a,/,i5,i5,i5)', ' Resolution:', res
endif
res1_red = res(1)/2_pInt + 1_pInt ! size of complex array in first dimension (c2r, r2c)
if (pReal /= C_DOUBLE .or. pInt /= C_INT) call IO_error(error_ID=102)
call fftw_set_timelimit(fftw_timelimit)
field_fftw = fftw_alloc_complex(int(res1_red *res(2)*res(3)*vec_tens*3_pInt,C_SIZE_T)) !C_SIZE_T is of type integer(8)
call c_f_pointer(field_fftw, field_real, [res(1)+2_pInt,res(2),res(3),3,vec_tens])
call c_f_pointer(field_fftw, field_complex, [res1_red ,res(2),res(3),3,vec_tens])
divergence_fftw = fftw_alloc_complex(int(res1_red *res(2)*res(3)*vec_tens,C_SIZE_T)) !C_SIZE_T is of type integer(8)
call c_f_pointer(divergence_fftw, divergence_real, [res(1)+2_pInt,res(2),res(3),vec_tens])
call c_f_pointer(divergence_fftw, divergence_complex,[res1_red ,res(2),res(3),vec_tens])
fftw_forth = fftw_plan_many_dft_r2c(3,(/res(3),res(2) ,res(1)/),vec_tens*3_pInt,& ! dimensions , length in each dimension in reversed order
field_real,(/res(3),res(2) ,res(1)+2_pInt/),& ! input data , physical length in each dimension in reversed order
1, res(3)*res(2)*(res(1)+2_pInt),& ! striding , product of physical lenght in the 3 dimensions
field_complex,(/res(3),res(2) ,res1_red/),&
1, res(3)*res(2)* res1_red,fftw_planner_flag)
fftw_back = fftw_plan_many_dft_c2r(3,(/res(3),res(2) ,res(1)/),vec_tens,&
divergence_complex,(/res(3),res(2) ,res1_red/),&
1, res(3)*res(2)* res1_red,&
divergence_real,(/res(3),res(2) ,res(1)+2_pInt/),&
1, res(3)*res(2)*(res(1)+2_pInt),fftw_planner_flag)
do k = 1_pInt, res(3); do j = 1_pInt, res(2); do i = 1_pInt, res(1)
field_real(i,j,k,1:3,1:vec_tens) = field(i,j,k,1:3,1:vec_tens) ! ensure that data is aligned properly (fftw_alloc)
enddo; enddo; enddo
call fftw_execute_dft_r2c(fftw_forth, field_real, field_complex)
! Alternative calculation of discrete frequencies k_s, ordered as in FFTW (wrap around)
! do k = 0,res(3)/2 -1
! do j = 0,res(2)/2 -1
! do i = 0,res(1)/2 -1
! xi(1+mod(res(1)-i,res(1)),1+mod(res(2)-j,res(2)),1+mod(res(3)-k,res(3)),:) = (/-i,-j,-k/)/geomdim
! xi(1+i, 1+mod(res(2)-j,res(2)),1+mod(res(3)-k,res(3)),:) = (/ i,-j,-k/)/geomdim
! xi(1+mod(res(1)-i,res(1)),1+j, 1+mod(res(3)-k,res(3)),:) = (/-i, j,-k/)/geomdim
! xi(1+i, 1+j, 1+mod(res(3)-k,res(3)),:) = (/ i, j,-k/)/geomdim
! xi(1+mod(res(1)-i,res(1)),1+mod(res(2)-j,res(2)),1+k, :) = (/-i,-j, k/)/geomdim
! xi(1+i, 1+mod(res(2)-j,res(2)),1+k, :) = (/ i,-j, k/)/geomdim
! xi(1+mod(res(1)-i,res(1)),1+j, 1+k, :) = (/-i, j, k/)/geomdim
! xi(1+i, 1+j, 1+k, :) = (/ i, j, k/)/geomdim
! xi(1+i, 1+j, 1+k, :) = (/ i, j, k/)/geomdim
! xi(1+mod(res(1)-i,res(1)),1+j, 1+k, :) = (/-i, j, k/)/geomdim
! xi(1+i, 1+mod(res(2)-j,res(2)),1+k, :) = (/ i,-j, k/)/geomdim
! xi(1+mod(res(1)-i,res(1)),1+mod(res(2)-j,res(2)),1+k, :) = (/-i,-j, k/)/geomdim
! xi(1+i, 1+j, 1+mod(res(3)-k,res(3)),:) = (/ i, j,-k/)/geomdim
! xi(1+mod(res(1)-i,res(1)),1+j, 1+mod(res(3)-k,res(3)),:) = (/-i, j,-k/)/geomdim
! xi(1+i, 1+mod(res(2)-j,res(2)),1+mod(res(3)-k,res(3)),:) = (/ i,-j,-k/)/geomdim
! xi(1+mod(res(1)-i,res(1)),1+mod(res(2)-j,res(2)),1+mod(res(3)-k,res(3)),:) = (/-i,-j,-k/)/geomdim
! enddo; enddo; enddo
do k = 0_pInt, res(3)-1_pInt
do j = 0_pInt, res(2)-1_pInt
do i = 0_pInt, res(1)/2_pInt
xi(i+1_pInt,j+1_pInt,k+1_pInt,1:3) = (/real(i,pReal),real(j,pReal),real(k,pReal)/)/geomdim
if(k==res(3)/2_pInt) xi(i+1_pInt,j+1_pInt,k+1_pInt,3)= 0.0_pReal ! set highest frequencies to zero
if(j==res(2)/2_pInt) xi(i+1_pInt,j+1_pInt,k+1_pInt,2)= 0.0_pReal
if(i==res(1)/2_pInt) xi(i+1_pInt,j+1_pInt,k+1_pInt,1)= 0.0_pReal
enddo; enddo; enddo
do k = 1_pInt, res(3)
do j = 1_pInt, res(2)
do i = 1_pInt, res1_red
divergence_complex(i,j,k,1) = sum(field_complex(i,j,k,1:3,1)*xi(i,j,k,1:3)) !ToDo: check formula!!!
if(vec_tens == 3_pInt) then
divergence_complex(i,j,k,2) = sum(field_complex(i,j,k,1:3,2)*xi(i,j,k,1:3))
divergence_complex(i,j,k,3) = sum(field_complex(i,j,k,1:3,3)*xi(i,j,k,1:3))
endif
enddo; enddo; enddo
divergence_complex = divergence_complex*img*2.0_pReal*pi
call fftw_execute_dft_c2r(fftw_back, divergence_complex, divergence_real)
do k = 1_pInt, res(3); do j = 1_pInt, res(2); do i = 1_pInt, res(1)
divergence(i,j,k,1:vec_tens) = divergence_real(i,j,k,1:vec_tens) ! ensure that data is aligned properly (fftw_alloc)
enddo; enddo; enddo
! why not weighting the divergence field?
end subroutine divergence_fft
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
subroutine divergence_fdm(res,geomdim,vec_tens,order,field,divergence)
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
! calculates divergence field using FDM with variable accuracy
! use vec_tes to decide if tensor (3) or vector (1)
use debug, only: debug_verbosity
implicit none
integer(pInt), intent(in), dimension(3) :: res
integer(pInt), intent(in) :: vec_tens
integer(pInt), intent(inout) :: order
real(pReal), intent(in), dimension(3) :: geomdim
real(pReal), intent(in), dimension(res(1),res(2),res(3),vec_tens,3) :: field
! output variables
real(pReal), intent(out), dimension(res(1),res(2),res(3),vec_tens) :: divergence
! other variables
integer(pInt), dimension(6,3) :: coordinates
integer(pInt) i, j, k, m, l
real(pReal), dimension(4,4), parameter :: FDcoefficient = reshape((/ &
1.0_pReal/2.0_pReal, 0.0_pReal, 0.0_pReal, 0.0_pReal,& !from http://en.wikipedia.org/wiki/Finite_difference_coefficients
2.0_pReal/3.0_pReal,-1.0_pReal/12.0_pReal, 0.0_pReal, 0.0_pReal,&
3.0_pReal/4.0_pReal,-3.0_pReal/20.0_pReal,1.0_pReal/ 60.0_pReal, 0.0_pReal,&
4.0_pReal/5.0_pReal,-1.0_pReal/ 5.0_pReal,4.0_pReal/105.0_pReal,-1.0_pReal/280.0_pReal/),&
(/4,4/))
if (debug_verbosity) then
print*, 'Calculating divergence of tensor/vector field using FDM'
print '(a,/,e12.5,e12.5,e12.5)', ' Dimension:', geomdim
print '(a,/,i5,i5,i5)', ' Resolution:', res
endif
divergence = 0.0_pReal
order = order + 1_pInt
do k = 0_pInt, res(3)-1_pInt; do j = 0_pInt, res(2)-1_pInt; do i = 0_pInt, res(1)-1_pInt
do m = 1_pInt, order
coordinates(1,1:3) = mesh_location(mesh_index((/i+m,j,k/),(/res(1),res(2),res(3)/)),(/res(1),res(2),res(3)/))&
+ (/1_pInt,1_pInt,1_pInt/)
coordinates(2,1:3) = mesh_location(mesh_index((/i-m,j,k/),(/res(1),res(2),res(3)/)),(/res(1),res(2),res(3)/))&
+ (/1_pInt,1_pInt,1_pInt/)
coordinates(3,1:3) = mesh_location(mesh_index((/i,j+m,k/),(/res(1),res(2),res(3)/)),(/res(1),res(2),res(3)/))&
+ (/1_pInt,1_pInt,1_pInt/)
coordinates(4,1:3) = mesh_location(mesh_index((/i,j-m,k/),(/res(1),res(2),res(3)/)),(/res(1),res(2),res(3)/))&
+ (/1_pInt,1_pInt,1_pInt/)
coordinates(5,1:3) = mesh_location(mesh_index((/i,j,k+m/),(/res(1),res(2),res(3)/)),(/res(1),res(2),res(3)/))&
+ (/1_pInt,1_pInt,1_pInt/)
coordinates(6,1:3) = mesh_location(mesh_index((/i,j,k-m/),(/res(1),res(2),res(3)/)),(/res(1),res(2),res(3)/))&
+ (/1_pInt,1_pInt,1_pInt/)
do l = 1_pInt, vec_tens
divergence(i+1_pInt,j+1_pInt,k+1_pInt,l) = divergence(i+1_pInt,j+1_pInt,k+1_pInt,l) + FDcoefficient(m,order) * &
((field(coordinates(1,1),coordinates(1,2),coordinates(1,3),l,1)- &
field(coordinates(2,1),coordinates(2,2),coordinates(2,3),l,1))*real(res(1),pReal)/geomdim(1) +&
(field(coordinates(3,1),coordinates(3,2),coordinates(3,3),l,2)- &
field(coordinates(4,1),coordinates(4,2),coordinates(4,3),l,2))*real(res(2),pReal)/geomdim(2) +&
(field(coordinates(5,1),coordinates(5,2),coordinates(5,3),l,3)- &
field(coordinates(6,1),coordinates(6,2),coordinates(6,3),l,3))*real(res(3),pReal)/geomdim(3))
enddo
enddo
enddo; enddo; enddo
end subroutine divergence_fdm
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
subroutine tensor_avg(res,tensor,avg)
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
!calculate average of tensor field
!
implicit none
! input variables
integer(pInt), intent(in), dimension(3) :: res
real(pReal), intent(in), dimension(res(1),res(2),res(3),3,3) ::tensor
! output variables
real(pReal), intent(out), dimension(3,3) :: avg
! other variables
real(pReal) wgt
integer(pInt) m,n
wgt = 1.0_pReal/real(res(1)*res(2)*res(3), pReal)
do m = 1_pInt,3_pInt; do n = 1_pInt,3_pInt
avg(m,n) = sum(tensor(1:res(1),1:res(2),1:res(3),m,n)) * wgt
enddo; enddo
end subroutine tensor_avg
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
subroutine logstrain_spat(res,defgrad,logstrain_field)
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
!calculate logarithmic strain in spatial configuration for given defgrad field
!
implicit none
! input variables
integer(pInt), intent(in), dimension(3) :: res
real(pReal), intent(in), dimension(res(1),res(2),res(3),3,3) :: defgrad
! output variables
real(pReal), intent(out), dimension(res(1),res(2),res(3),3,3) :: logstrain_field
! other variables
real(pReal), dimension(3,3) :: temp33_Real, temp33_Real2
real(pReal), dimension(3,3,3) :: eigenvectorbasis
real(pReal), dimension(3) :: eigenvalue
integer(pInt) :: i, j, k
logical :: errmatinv
do k = 1_pInt, res(3); do j = 1_pInt, res(2); do i = 1_pInt, res(1)
call math_pDecomposition(defgrad(i,j,k,1:3,1:3),temp33_Real2,temp33_Real,errmatinv) !store R in temp33_Real
temp33_Real2 = math_inv3x3(temp33_Real)
temp33_Real = math_mul33x33(defgrad(i,j,k,1:3,1:3),temp33_Real2) ! v = F o inv(R), store in temp33_Real2
call math_spectral1(temp33_Real,eigenvalue(1), eigenvalue(2), eigenvalue(3),&
eigenvectorbasis(1,1:3,1:3),eigenvectorbasis(2,1:3,1:3),eigenvectorbasis(3,1:3,1:3))
eigenvalue = log(sqrt(eigenvalue))
logstrain_field(i,j,k,1:3,1:3) = eigenvalue(1)*eigenvectorbasis(1,1:3,1:3)+&
eigenvalue(2)*eigenvectorbasis(2,1:3,1:3)+&
eigenvalue(3)*eigenvectorbasis(3,1:3,1:3)
enddo; enddo; enddo
end subroutine logstrain_spat
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
subroutine logstrain_mat(res,defgrad,logstrain_field)
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
!calculate logarithmic strain in material configuration for given defgrad field
!
implicit none
! input variables
integer(pInt), intent(in), dimension(3) :: res
real(pReal), intent(in), dimension(res(1),res(2),res(3),3,3) :: defgrad
! output variables
real(pReal), intent(out), dimension(res(1),res(2),res(3),3,3) :: logstrain_field
! other variables
real(pReal), dimension(3,3) :: temp33_Real, temp33_Real2
real(pReal), dimension(3,3,3) :: eigenvectorbasis
real(pReal), dimension(3) :: eigenvalue
integer(pInt) :: i, j, k
logical :: errmatinv
do k = 1_pInt, res(3); do j = 1_pInt, res(2); do i = 1_pInt, res(1)
call math_pDecomposition(defgrad(i,j,k,1:3,1:3),temp33_Real,temp33_Real2,errmatinv) !store U in temp33_Real
call math_spectral1(temp33_Real,eigenvalue(1), eigenvalue(2), eigenvalue(3),&
eigenvectorbasis(1,1:3,1:3),eigenvectorbasis(2,1:3,1:3),eigenvectorbasis(3,1:3,1:3))
eigenvalue = log(sqrt(eigenvalue))
logstrain_field(i,j,k,1:3,1:3) = eigenvalue(1)*eigenvectorbasis(1,1:3,1:3)+&
eigenvalue(2)*eigenvectorbasis(2,1:3,1:3)+&
eigenvalue(3)*eigenvectorbasis(3,1:3,1:3)
enddo; enddo; enddo
end subroutine logstrain_mat
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
subroutine calculate_cauchy(res,defgrad,p_stress,c_stress)
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
!calculate cauchy stress for given PK1 stress and defgrad field
!
implicit none
! input variables
integer(pInt), intent(in), dimension(3) :: res
real(pReal), intent(in), dimension(res(1),res(2),res(3),3,3) :: defgrad
real(pReal), intent(in), dimension(res(1),res(2),res(3),3,3) :: p_stress
! output variables
real(pReal), intent(out), dimension(res(1),res(2),res(3),3,3) :: c_stress
! other variables
real(pReal) :: jacobi
integer(pInt) :: i, j, k
c_stress = 0.0_pInt
do k = 1_pInt, res(3); do j = 1_pInt, res(2); do i = 1_pInt, res(1)
jacobi = math_det3x3(defgrad(i,j,k,1:3,1:3))
c_stress(i,j,k,1:3,1:3) = matmul(p_stress(i,j,k,1:3,1:3),transpose(defgrad(i,j,k,1:3,1:3)))/jacobi
enddo; enddo; enddo
end subroutine calculate_cauchy
END MODULE math
!#############################################################################################################################
! BEGIN KDTREE2
!#############################################################################################################################
!(c) Matthew Kennel, Institute for Nonlinear Science (2004)
!
! Licensed under the Academic Free License version 1.1 found in file LICENSE
! with additional provisions found in that same file.
!
!#######################################################
! modifications: changed precision according to prec.f90
! k.komerla, m.diehl
!#######################################################
module kdtree2_priority_queue_module
use prec
!
! maintain a priority queue (PQ) of data, pairs of 'priority/payload',
! implemented with a binary heap. This is the type, and the 'dis' field
! is the priority.
!
type kdtree2_result
! a pair of distances, indexes
real(pReal) :: dis !=0.0
integer(pInt) :: idx !=-1 Initializers cause some bugs in compilers.
end type kdtree2_result
!
! A heap-based priority queue lets one efficiently implement the following
! operations, each in log(N) time, as opposed to linear time.
!
! 1) add a datum (push a datum onto the queue, increasing its length)
! 2) return the priority value of the maximum priority element
! 3) pop-off (and delete) the element with the maximum priority, decreasing
! the size of the queue.
! 4) replace the datum with the maximum priority with a supplied datum
! (of either higher or lower priority), maintaining the size of the
! queue.
!
!
! In the k-d tree case, the 'priority' is the square distance of a point in
! the data set to a reference point. The goal is to keep the smallest M
! distances to a reference point. The tree algorithm searches terminal
! nodes to decide whether to add points under consideration.
!
! A priority queue is useful here because it lets one quickly return the
! largest distance currently existing in the list. If a new candidate
! distance is smaller than this, then the new candidate ought to replace
! the old candidate. In priority queue terms, this means removing the
! highest priority element, and inserting the new one.
!
! Algorithms based on Cormen, Leiserson, Rivest, _Introduction
! to Algorithms_, 1990, with further optimization by the author.
!
! Originally informed by a C implementation by Sriranga Veeraraghavan.
!
! This module is not written in the most clear way, but is implemented such
! for speed, as it its operations will be called many times during searches
! of large numbers of neighbors.
!
type pq
!
! The priority queue consists of elements
! priority(1:heap_size), with associated payload(:).
!
! There are heap_size active elements.
! Assumes the allocation is always sufficient. Will NOT increase it
! to match.
integer(pInt) :: heap_size = 0
type(kdtree2_result), pointer :: elems(:)
end type pq
public :: kdtree2_result
public :: pq
public :: pq_create
public :: pq_delete, pq_insert
public :: pq_extract_max, pq_max, pq_replace_max, pq_maxpri
private
contains
function pq_create(results_in) result(res)
!
! Create a priority queue from ALREADY allocated
! array pointers for storage. NOTE! It will NOT
! add any alements to the heap, i.e. any existing
! data in the input arrays will NOT be used and may
! be overwritten.
!
! usage:
! real(pReal), pointer :: x(:)
! integer(pInt), pointer :: k(:)
! allocate(x(1000),k(1000))
! pq => pq_create(x,k)
!
type(kdtree2_result), target:: results_in(:)
type(pq) :: res
!
!
integer(pInt) :: nalloc
nalloc = size(results_in,1)
if (nalloc .lt. 1) then
write (*,*) 'PQ_CREATE: error, input arrays must be allocated.'
end if
res%elems => results_in
res%heap_size = 0
return
end function pq_create
!
! operations for getting parents and left + right children
! of elements in a binary heap.
!
!
! These are written inline for speed.
!
! integer(pInt) function parent(i)
! integer(pInt), intent(in) :: i
! parent = (i/2)
! return
! end function parent
! integer(pInt) function left(i)
! integer(pInt), intent(in) ::i
! left = (2*i)
! return
! end function left
! integer(pInt) function right(i)
! integer(pInt), intent(in) :: i
! right = (2*i)+1
! return
! end function right
! logical function compare_priority(p1,p2)
! real(pReal), intent(in) :: p1, p2
!
! compare_priority = (p1 .gt. p2)
! return
! end function compare_priority
subroutine heapify(a,i_in)
!
! take a heap rooted at 'i' and force it to be in the
! heap canonical form. This is performance critical
! and has been tweaked a little to reflect this.
!
type(pq),pointer :: a
integer(pInt), intent(in) :: i_in
!
integer(pInt) :: i, l, r, largest
real(pReal) :: pri_i, pri_l, pri_r, pri_largest
type(kdtree2_result) :: temp
i = i_in
bigloop: do
l = 2*i ! left(i)
r = l+1 ! right(i)
!
! set 'largest' to the index of either i, l, r
! depending on whose priority is largest.
!
! note that l or r can be larger than the heap size
! in which case they do not count.
! does left child have higher priority?
if (l .gt. a%heap_size) then
! we know that i is the largest as both l and r are invalid.
exit
else
pri_i = a%elems(i)%dis
pri_l = a%elems(l)%dis
if (pri_l .gt. pri_i) then
largest = l
pri_largest = pri_l
else
largest = i
pri_largest = pri_i
endif
!
! between i and l we have a winner
! now choose between that and r.
!
if (r .le. a%heap_size) then
pri_r = a%elems(r)%dis
if (pri_r .gt. pri_largest) then
largest = r
endif
endif
endif
if (largest .ne. i) then
! swap data in nodes largest and i, then heapify
temp = a%elems(i)
a%elems(i) = a%elems(largest)
a%elems(largest) = temp
!
! Canonical heapify() algorithm has tail-ecursive call:
!
! call heapify(a,largest)
! we will simulate with cycle
!
i = largest
cycle bigloop ! continue the loop
else
return ! break from the loop
end if
enddo bigloop
return
end subroutine heapify
subroutine pq_max(a,e)
!
! return the priority and its payload of the maximum priority element
! on the queue, which should be the first one, if it is
! in heapified form.
!
type(pq),pointer :: a
type(kdtree2_result),intent(out) :: e
if (a%heap_size .gt. 0) then
e = a%elems(1)
else
write (*,*) 'PQ_MAX: ERROR, heap_size < 1'
stop
endif
return
end subroutine pq_max
real(pReal) function pq_maxpri(a)
type(pq), pointer :: a
if (a%heap_size .gt. 0) then
pq_maxpri = a%elems(1)%dis
else
write (*,*) 'PQ_MAX_PRI: ERROR, heapsize < 1'
stop
endif
return
end function pq_maxpri
subroutine pq_extract_max(a,e)
!
! return the priority and payload of maximum priority
! element, and remove it from the queue.
! (equivalent to 'pop()' on a stack)
!
type(pq),pointer :: a
type(kdtree2_result), intent(out) :: e
if (a%heap_size .ge. 1) then
!
! return max as first element
!
e = a%elems(1)
!
! move last element to first
!
a%elems(1) = a%elems(a%heap_size)
a%heap_size = a%heap_size-1
call heapify(a,1)
return
else
write (*,*) 'PQ_EXTRACT_MAX: error, attempted to pop non-positive PQ'
stop
end if
end subroutine pq_extract_max
real(pReal) function pq_insert(a,dis,idx)
!
! Insert a new element and return the new maximum priority,
! which may or may not be the same as the old maximum priority.
!
type(pq),pointer :: a
real(pReal), intent(in) :: dis
integer(pInt), intent(in) :: idx
! type(kdtree2_result), intent(in) :: e
!
integer(pInt) :: i, isparent
real(pReal) :: parentdis
!
! if (a%heap_size .ge. a%max_elems) then
! write (*,*) 'PQ_INSERT: error, attempt made to insert element on full PQ'
! stop
! else
a%heap_size = a%heap_size + 1
i = a%heap_size
do while (i .gt. 1)
isparent = int(i/2)
parentdis = a%elems(isparent)%dis
if (dis .gt. parentdis) then
! move what was in i's parent into i.
a%elems(i)%dis = parentdis
a%elems(i)%idx = a%elems(isparent)%idx
i = isparent
else
exit
endif
end do
! insert the element at the determined position
a%elems(i)%dis = dis
a%elems(i)%idx = idx
pq_insert = a%elems(1)%dis
return
! end if
end function pq_insert
subroutine pq_adjust_heap(a,i)
type(pq),pointer :: a
integer(pInt), intent(in) :: i
!
! nominally arguments (a,i), but specialize for a=1
!
! This routine assumes that the trees with roots 2 and 3 are already heaps, i.e.
! the children of '1' are heaps. When the procedure is completed, the
! tree rooted at 1 is a heap.
real(pReal) :: prichild
integer(pInt) :: parent, child, N
type(kdtree2_result) :: e
e = a%elems(i)
parent = i
child = 2*i
N = a%heap_size
do while (child .le. N)
if (child .lt. N) then
if (a%elems(child)%dis .lt. a%elems(child+1)%dis) then
child = child+1
endif
endif
prichild = a%elems(child)%dis
if (e%dis .ge. prichild) then
exit
else
! move child into parent.
a%elems(parent) = a%elems(child)
parent = child
child = 2*parent
end if
end do
a%elems(parent) = e
return
end subroutine pq_adjust_heap
real(pReal) function pq_replace_max(a,dis,idx)
!
! Replace the extant maximum priority element
! in the PQ with (dis,idx). Return
! the new maximum priority, which may be larger
! or smaller than the old one.
!
type(pq),pointer :: a
real(pReal), intent(in) :: dis
integer(pInt), intent(in) :: idx
! type(kdtree2_result), intent(in) :: e
! not tested as well!
integer(pInt) :: parent, child, N
real(pReal) :: prichild, prichildp1
type(kdtree2_result) :: etmp
if (.true.) then
N=a%heap_size
if (N .ge. 1) then
parent =1
child=2
loop: do while (child .le. N)
prichild = a%elems(child)%dis
!
! posibly child+1 has higher priority, and if
! so, get it, and increment child.
!
if (child .lt. N) then
prichildp1 = a%elems(child+1)%dis
if (prichild .lt. prichildp1) then
child = child+1
prichild = prichildp1
endif
endif
if (dis .ge. prichild) then
exit loop
! we have a proper place for our new element,
! bigger than either children's priority.
else
! move child into parent.
a%elems(parent) = a%elems(child)
parent = child
child = 2*parent
end if
end do loop
a%elems(parent)%dis = dis
a%elems(parent)%idx = idx
pq_replace_max = a%elems(1)%dis
else
a%elems(1)%dis = dis
a%elems(1)%idx = idx
pq_replace_max = dis
endif
else
!
! slower version using elementary pop and push operations.
!
call pq_extract_max(a,etmp)
etmp%dis = dis
etmp%idx = idx
pq_replace_max = pq_insert(a,dis,idx)
endif
return
end function pq_replace_max
subroutine pq_delete(a,i)
!
! delete item with index 'i'
!
type(pq),pointer :: a
integer(pInt) :: i
if ((i .lt. 1) .or. (i .gt. a%heap_size)) then
write (*,*) 'PQ_DELETE: error, attempt to remove out of bounds element.'
stop
endif
! swap the item to be deleted with the last element
! and shorten heap by one.
a%elems(i) = a%elems(a%heap_size)
a%heap_size = a%heap_size - 1
call heapify(a,i)
end subroutine pq_delete
end module kdtree2_priority_queue_module
module kdtree2_module
use prec
use kdtree2_priority_queue_module
! K-D tree routines in Fortran 90 by Matt Kennel.
! Original program was written in Sather by Steve Omohundro and
! Matt Kennel. Only the Euclidean metric is supported.
!
!
! This module is identical to 'kd_tree', except that the order
! of subscripts is reversed in the data file.
! In otherwords for an embedding of N D-dimensional vectors, the
! data file is here, in natural Fortran order data(1:D, 1:N)
! because Fortran lays out columns first,
!
! whereas conventionally (C-style) it is data(1:N,1:D)
! as in the original kd_tree module.
!
!-------------DATA TYPE, CREATION, DELETION---------------------
public :: pReal
public :: kdtree2, kdtree2_result, tree_node, kdtree2_create, kdtree2_destroy
!---------------------------------------------------------------
!-------------------SEARCH ROUTINES-----------------------------
public :: kdtree2_n_nearest,kdtree2_n_nearest_around_point
! Return fixed number of nearest neighbors around arbitrary vector,
! or extant point in dataset, with decorrelation window.
!
public :: kdtree2_r_nearest, kdtree2_r_nearest_around_point
! Return points within a fixed ball of arb vector/extant point
!
public :: kdtree2_sort_results
! Sort, in order of increasing distance, rseults from above.
!
public :: kdtree2_r_count, kdtree2_r_count_around_point
! Count points within a fixed ball of arb vector/extant point
!
public :: kdtree2_n_nearest_brute_force, kdtree2_r_nearest_brute_force
! brute force of kdtree2_[n|r]_nearest
!----------------------------------------------------------------
integer(pInt), parameter :: bucket_size = 12
! The maximum number of points to keep in a terminal node.
type interval
real(pReal) :: lower,upper
end type interval
type :: tree_node
! an internal tree node
private
integer(pInt) :: cut_dim
! the dimension to cut
real(pReal) :: cut_val
! where to cut the dimension
real(pReal) :: cut_val_left, cut_val_right
! improved cutoffs knowing the spread in child boxes.
integer(pInt) :: l, u
type (tree_node), pointer :: left, right
type(interval), pointer :: box(:) => null()
! child pointers
! Points included in this node are indexes[k] with k \in [l,u]
end type tree_node
type :: kdtree2
! Global information about the tree, one per tree
integer(pInt) :: dimen=0, n=0
! dimensionality and total # of points
real(pReal), pointer :: the_data(:,:) => null()
! pointer to the actual data array
!
! IMPORTANT NOTE: IT IS DIMENSIONED the_data(1:d,1:N)
! which may be opposite of what may be conventional.
! This is, because in Fortran, the memory layout is such that
! the first dimension is in sequential order. Hence, with
! (1:d,1:N), all components of the vector will be in consecutive
! memory locations. The search time is dominated by the
! evaluation of distances in the terminal nodes. Putting all
! vector components in consecutive memory location improves
! memory cache locality, and hence search speed, and may enable
! vectorization on some processors and compilers.
integer(pInt), pointer :: ind(:) => null()
! permuted index into the data, so that indexes[l..u] of some
! bucket represent the indexes of the actual points in that
! bucket.
logical :: sort = .false.
! do we always sort output results?
logical :: rearrange = .false.
real(pReal), pointer :: rearranged_data(:,:) => null()
! if (rearrange .eqv. .true.) then rearranged_data has been
! created so that rearranged_data(:,i) = the_data(:,ind(i)),
! permitting search to use more cache-friendly rearranged_data, at
! some initial computation and storage cost.
type (tree_node), pointer :: root => null()
! root pointer of the tree
end type kdtree2
type :: tree_search_record
!
! One of these is created for each search.
!
private
!
! Many fields are copied from the tree structure, in order to
! speed up the search.
!
integer(pInt) :: dimen
integer(pInt) :: nn, nfound
real(pReal) :: ballsize
integer(pInt) :: centeridx=999, correltime=9999
! exclude points within 'correltime' of 'centeridx', iff centeridx >= 0
integer(pInt) :: nalloc ! how much allocated for results(:)?
logical :: rearrange ! are the data rearranged or original?
! did the # of points found overflow the storage provided?
logical :: overflow
real(pReal), pointer :: qv(:) ! query vector
type(kdtree2_result), pointer :: results(:) ! results
type(pq) :: pq
real(pReal), pointer :: data(:,:) ! temp pointer to data
integer(pInt), pointer :: ind(:) ! temp pointer to indexes
end type tree_search_record
private
! everything else is private.
type(tree_search_record), save, target :: sr ! A GLOBAL VARIABLE for search
contains
function kdtree2_create(input_data,dim,sort,rearrange) result (mr)
!
! create the actual tree structure, given an input array of data.
!
! Note, input data is input_data(1:d,1:N), NOT the other way around.
! THIS IS THE REVERSE OF THE PREVIOUS VERSION OF THIS MODULE.
! The reason for it is cache friendliness, improving performance.
!
! Optional arguments: If 'dim' is specified, then the tree
! will only search the first 'dim' components
! of input_data, otherwise, dim is inferred
! from SIZE(input_data,1).
!
! if sort .eqv. .true. then output results
! will be sorted by increasing distance.
! default=.false., as it is faster to not sort.
!
! if rearrange .eqv. .true. then an internal
! copy of the data, rearranged by terminal node,
! will be made for cache friendliness.
! default=.true., as it speeds searches, but
! building takes longer, and extra memory is used.
!
! .. Function Return Cut_value ..
type (kdtree2), pointer :: mr
integer(pInt), intent(in), optional :: dim
logical, intent(in), optional :: sort
logical, intent(in), optional :: rearrange
! ..
! .. Array Arguments ..
real(pReal), target :: input_data(:,:)
!
integer(pInt) :: i
! ..
allocate (mr)
mr%the_data => input_data
! pointer assignment
if (present(dim)) then
mr%dimen = dim
else
mr%dimen = size(input_data,1)
end if
mr%n = size(input_data,2)
if (mr%dimen > mr%n) then
! unlikely to be correct
write (*,*) 'KD_TREE_TRANS: likely user error.'
write (*,*) 'KD_TREE_TRANS: You passed in matrix with D=',mr%dimen
write (*,*) 'KD_TREE_TRANS: and N=',mr%n
write (*,*) 'KD_TREE_TRANS: note, that new format is data(1:D,1:N)'
write (*,*) 'KD_TREE_TRANS: with usually N >> D. If N =approx= D, then a k-d tree'
write (*,*) 'KD_TREE_TRANS: is not an appropriate data structure.'
stop
end if
call build_tree(mr)
if (present(sort)) then
mr%sort = sort
else
mr%sort = .false.
endif
if (present(rearrange)) then
mr%rearrange = rearrange
else
mr%rearrange = .true.
endif
if (mr%rearrange) then
allocate(mr%rearranged_data(mr%dimen,mr%n))
do i=1,mr%n
mr%rearranged_data(:,i) = mr%the_data(:, &
mr%ind(i))
enddo
else
nullify(mr%rearranged_data)
endif
end function kdtree2_create
subroutine build_tree(tp)
type (kdtree2), pointer :: tp
! ..
integer(pInt) :: j
type(tree_node), pointer :: dummy => null()
! ..
allocate (tp%ind(tp%n))
forall (j=1:tp%n)
tp%ind(j) = j
end forall
tp%root => build_tree_for_range(tp,1,tp%n, dummy)
end subroutine build_tree
recursive function build_tree_for_range(tp,l,u,parent) result (res)
! .. Function Return Cut_value ..
type (tree_node), pointer :: res
! ..
! .. Structure Arguments ..
type (kdtree2), pointer :: tp
type (tree_node),pointer :: parent
! ..
! .. Scalar Arguments ..
integer(pInt), intent (In) :: l, u
! ..
! .. Local Scalars ..
integer(pInt) :: i, c, m, dimen
logical :: recompute
real(pReal) :: average
!!$ If (.False.) Then
!!$ If ((l .Lt. 1) .Or. (l .Gt. tp%n)) Then
!!$ Stop 'illegal L value in build_tree_for_range'
!!$ End If
!!$ If ((u .Lt. 1) .Or. (u .Gt. tp%n)) Then
!!$ Stop 'illegal u value in build_tree_for_range'
!!$ End If
!!$ If (u .Lt. l) Then
!!$ Stop 'U is less than L, thats illegal.'
!!$ End If
!!$ Endif
!!$
! first compute min and max
dimen = tp%dimen
allocate (res)
allocate(res%box(dimen))
! First, compute an APPROXIMATE bounding box of all points associated with this node.
if ( u < l ) then
! no points in this box
nullify(res)
return
end if
if ((u-l)<=bucket_size) then
!
! always compute true bounding box for terminal nodes.
!
do i=1,dimen
call spread_in_coordinate(tp,i,l,u,res%box(i))
end do
res%cut_dim = 0
res%cut_val = 0.0
res%l = l
res%u = u
res%left =>null()
res%right => null()
else
!
! modify approximate bounding box. This will be an
! overestimate of the true bounding box, as we are only recomputing
! the bounding box for the dimension that the parent split on.
!
! Going to a true bounding box computation would significantly
! increase the time necessary to build the tree, and usually
! has only a very small difference. This box is not used
! for searching but only for deciding which coordinate to split on.
!
do i=1,dimen
recompute=.true.
if (associated(parent)) then
if (i .ne. parent%cut_dim) then
recompute=.false.
end if
endif
if (recompute) then
call spread_in_coordinate(tp,i,l,u,res%box(i))
else
res%box(i) = parent%box(i)
endif
end do
c = maxloc(res%box(1:dimen)%upper-res%box(1:dimen)%lower,1)
!
! c is the identity of which coordinate has the greatest spread.
!
if (.false.) then
! select exact median to have fully balanced tree.
m = (l+u)/2
call select_on_coordinate(tp%the_data,tp%ind,c,m,l,u)
else
!
! select point halfway between min and max, as per A. Moore,
! who says this helps in some degenerate cases, or
! actual arithmetic average.
!
if (.true.) then
! actually compute average
average = sum(tp%the_data(c,tp%ind(l:u))) / real(u-l+1,pReal)
else
average = (res%box(c)%upper + res%box(c)%lower)/2.0
endif
res%cut_val = average
m = select_on_coordinate_value(tp%the_data,tp%ind,c,average,l,u)
endif
! moves indexes around
res%cut_dim = c
res%l = l
res%u = u
! res%cut_val = tp%the_data(c,tp%ind(m))
res%left => build_tree_for_range(tp,l,m,res)
res%right => build_tree_for_range(tp,m+1,u,res)
if (associated(res%right) .eqv. .false.) then
res%box = res%left%box
res%cut_val_left = res%left%box(c)%upper
res%cut_val = res%cut_val_left
elseif (associated(res%left) .eqv. .false.) then
res%box = res%right%box
res%cut_val_right = res%right%box(c)%lower
res%cut_val = res%cut_val_right
else
res%cut_val_right = res%right%box(c)%lower
res%cut_val_left = res%left%box(c)%upper
res%cut_val = (res%cut_val_left + res%cut_val_right)/2
! now remake the true bounding box for self.
! Since we are taking unions (in effect) of a tree structure,
! this is much faster than doing an exhaustive
! search over all points
res%box%upper = max(res%left%box%upper,res%right%box%upper)
res%box%lower = min(res%left%box%lower,res%right%box%lower)
endif
end if
end function build_tree_for_range
integer(pInt) function select_on_coordinate_value(v,ind,c,alpha,li,ui) &
result(res)
! Move elts of ind around between l and u, so that all points
! <= than alpha (in c cooordinate) are first, and then
! all points > alpha are second.
!
! Algorithm (matt kennel).
!
! Consider the list as having three parts: on the left,
! the points known to be <= alpha. On the right, the points
! known to be > alpha, and in the middle, the currently unknown
! points. The algorithm is to scan the unknown points, starting
! from the left, and swapping them so that they are added to
! the left stack or the right stack, as appropriate.
!
! The algorithm finishes when the unknown stack is empty.
!
! .. Scalar Arguments ..
integer(pInt), intent (In) :: c, li, ui
real(pReal), intent(in) :: alpha
! ..
real(pReal) :: v(1:,1:)
integer(pInt) :: ind(1:)
integer(pInt) :: tmp
! ..
integer(pInt) :: lb, rb
!
! The points known to be <= alpha are in
! [l,lb-1]
!
! The points known to be > alpha are in
! [rb+1,u].
!
! Therefore we add new points into lb or
! rb as appropriate. When lb=rb
! we are done. We return the location of the last point <= alpha.
!
!
lb = li; rb = ui
do while (lb < rb)
if ( v(c,ind(lb)) <= alpha ) then
! it is good where it is.
lb = lb+1
else
! swap it with rb.
tmp = ind(lb); ind(lb) = ind(rb); ind(rb) = tmp
rb = rb-1
endif
end do
! now lb .eq. ub
if (v(c,ind(lb)) <= alpha) then
res = lb
else
res = lb-1
endif
end function select_on_coordinate_value
subroutine select_on_coordinate(v,ind,c,k,li,ui)
! Move elts of ind around between l and u, so that the kth
! element
! is >= those below, <= those above, in the coordinate c.
! .. Scalar Arguments ..
integer(pInt), intent (In) :: c, k, li, ui
! ..
integer(pInt) :: i, l, m, s, t, u
! ..
real(pReal) :: v(:,:)
integer(pInt) :: ind(:)
! ..
l = li
u = ui
do while (l<u)
t = ind(l)
m = l
do i = l + 1, u
if (v(c,ind(i))<v(c,t)) then
m = m + 1
s = ind(m)
ind(m) = ind(i)
ind(i) = s
end if
end do
s = ind(l)
ind(l) = ind(m)
ind(m) = s
if (m<=k) l = m + 1
if (m>=k) u = m - 1
end do
end subroutine select_on_coordinate
subroutine spread_in_coordinate(tp,c,l,u,interv)
! the spread in coordinate 'c', between l and u.
!
! Return lower bound in 'smin', and upper in 'smax',
! ..
! .. Structure Arguments ..
type (kdtree2), pointer :: tp
type(interval), intent(out) :: interv
! ..
! .. Scalar Arguments ..
integer(pInt), intent (In) :: c, l, u
! ..
! .. Local Scalars ..
real(pReal) :: last, lmax, lmin, t, smin,smax
integer(pInt) :: i, ulocal
! ..
! .. Local Arrays ..
real(pReal), pointer :: v(:,:)
integer(pInt), pointer :: ind(:)
! ..
v => tp%the_data(1:,1:)
ind => tp%ind(1:)
smin = v(c,ind(l))
smax = smin
ulocal = u
do i = l + 2, ulocal, 2
lmin = v(c,ind(i-1))
lmax = v(c,ind(i))
if (lmin>lmax) then
t = lmin
lmin = lmax
lmax = t
end if
if (smin>lmin) smin = lmin
if (smax<lmax) smax = lmax
end do
if (i==ulocal+1) then
last = v(c,ind(ulocal))
if (smin>last) smin = last
if (smax<last) smax = last
end if
interv%lower = smin
interv%upper = smax
end subroutine spread_in_coordinate
subroutine kdtree2_destroy(tp)
! Deallocates all memory for the tree, except input data matrix
! .. Structure Arguments ..
type (kdtree2), pointer :: tp
! ..
call destroy_node(tp%root)
deallocate (tp%ind)
nullify (tp%ind)
if (tp%rearrange) then
deallocate(tp%rearranged_data)
nullify(tp%rearranged_data)
endif
deallocate(tp)
return
contains
recursive subroutine destroy_node(np)
! .. Structure Arguments ..
type (tree_node), pointer :: np
! ..
! .. Intrinsic Functions ..
intrinsic ASSOCIATED
! ..
if (associated(np%left)) then
call destroy_node(np%left)
nullify (np%left)
end if
if (associated(np%right)) then
call destroy_node(np%right)
nullify (np%right)
end if
if (associated(np%box)) deallocate(np%box)
deallocate(np)
return
end subroutine destroy_node
end subroutine kdtree2_destroy
subroutine kdtree2_n_nearest(tp,qv,nn,results)
! Find the 'nn' vectors in the tree nearest to 'qv' in euclidean norm
! returning their indexes and distances in 'indexes' and 'distances'
! arrays already allocated passed to this subroutine.
type (kdtree2), pointer :: tp
real(pReal), target, intent (In) :: qv(:)
integer(pInt), intent (In) :: nn
type(kdtree2_result), target :: results(:)
sr%ballsize = huge(1.0)
sr%qv => qv
sr%nn = nn
sr%nfound = 0
sr%centeridx = -1
sr%correltime = 0
sr%overflow = .false.
sr%results => results
sr%nalloc = nn ! will be checked
sr%ind => tp%ind
sr%rearrange = tp%rearrange
if (tp%rearrange) then
sr%Data => tp%rearranged_data
else
sr%Data => tp%the_data
endif
sr%dimen = tp%dimen
call validate_query_storage(nn)
sr%pq = pq_create(results)
call search(tp%root)
if (tp%sort) then
call kdtree2_sort_results(nn, results)
endif
! deallocate(sr%pqp)
return
end subroutine kdtree2_n_nearest
subroutine kdtree2_n_nearest_around_point(tp,idxin,correltime,nn,results)
! Find the 'nn' vectors in the tree nearest to point 'idxin',
! with correlation window 'correltime', returing results in
! results(:), which must be pre-allocated upon entry.
type (kdtree2), pointer :: tp
integer(pInt), intent (In) :: idxin, correltime, nn
type(kdtree2_result), target :: results(:)
allocate (sr%qv(tp%dimen))
sr%qv = tp%the_data(:,idxin) ! copy the vector
sr%ballsize = huge(1.0) ! the largest real(pReal) number
sr%centeridx = idxin
sr%correltime = correltime
sr%nn = nn
sr%nfound = 0
sr%dimen = tp%dimen
sr%nalloc = nn
sr%results => results
sr%ind => tp%ind
sr%rearrange = tp%rearrange
if (sr%rearrange) then
sr%Data => tp%rearranged_data
else
sr%Data => tp%the_data
endif
call validate_query_storage(nn)
sr%pq = pq_create(results)
call search(tp%root)
if (tp%sort) then
call kdtree2_sort_results(nn, results)
endif
deallocate (sr%qv)
return
end subroutine kdtree2_n_nearest_around_point
subroutine kdtree2_r_nearest(tp,qv,r2,nfound,nalloc,results)
! find the nearest neighbors to point 'idxin', within SQUARED
! Euclidean distance 'r2'. Upon ENTRY, nalloc must be the
! size of memory allocated for results(1:nalloc). Upon
! EXIT, nfound is the number actually found within the ball.
!
! Note that if nfound .gt. nalloc then more neighbors were found
! than there were storage to store. The resulting list is NOT
! the smallest ball inside norm r^2
!
! Results are NOT sorted unless tree was created with sort option.
type (kdtree2), pointer :: tp
real(pReal), target, intent (In) :: qv(:)
real(pReal), intent(in) :: r2
integer(pInt), intent(out) :: nfound
integer(pInt), intent (In) :: nalloc
type(kdtree2_result), target :: results(:)
!
sr%qv => qv
sr%ballsize = r2
sr%nn = 0 ! flag for fixed ball search
sr%nfound = 0
sr%centeridx = -1
sr%correltime = 0
sr%results => results
call validate_query_storage(nalloc)
sr%nalloc = nalloc
sr%overflow = .false.
sr%ind => tp%ind
sr%rearrange= tp%rearrange
if (tp%rearrange) then
sr%Data => tp%rearranged_data
else
sr%Data => tp%the_data
endif
sr%dimen = tp%dimen
!
!sr%dsl = Huge(sr%dsl) ! set to huge positive values
!sr%il = -1 ! set to invalid indexes
!
call search(tp%root)
nfound = sr%nfound
if (tp%sort) then
call kdtree2_sort_results(nfound, results)
endif
if (sr%overflow) then
write (*,*) 'KD_TREE_TRANS: warning! return from kdtree2_r_nearest found more neighbors'
write (*,*) 'KD_TREE_TRANS: than storage was provided for. Answer is NOT smallest ball'
write (*,*) 'KD_TREE_TRANS: with that number of neighbors! I.e. it is wrong.'
endif
return
end subroutine kdtree2_r_nearest
subroutine kdtree2_r_nearest_around_point(tp,idxin,correltime,r2,&
nfound,nalloc,results)
!
! Like kdtree2_r_nearest, but around a point 'idxin' already existing
! in the data set.
!
! Results are NOT sorted unless tree was created with sort option.
!
type (kdtree2), pointer :: tp
integer(pInt), intent (In) :: idxin, correltime, nalloc
real(pReal), intent(in) :: r2
integer(pInt), intent(out) :: nfound
type(kdtree2_result), target :: results(:)
! ..
! .. Intrinsic Functions ..
intrinsic HUGE
! ..
allocate (sr%qv(tp%dimen))
sr%qv = tp%the_data(:,idxin) ! copy the vector
sr%ballsize = r2
sr%nn = 0 ! flag for fixed r search
sr%nfound = 0
sr%centeridx = idxin
sr%correltime = correltime
sr%results => results
sr%nalloc = nalloc
sr%overflow = .false.
call validate_query_storage(nalloc)
! sr%dsl = HUGE(sr%dsl) ! set to huge positive values
! sr%il = -1 ! set to invalid indexes
sr%ind => tp%ind
sr%rearrange = tp%rearrange
if (tp%rearrange) then
sr%Data => tp%rearranged_data
else
sr%Data => tp%the_data
endif
sr%rearrange = tp%rearrange
sr%dimen = tp%dimen
!
!sr%dsl = Huge(sr%dsl) ! set to huge positive values
!sr%il = -1 ! set to invalid indexes
!
call search(tp%root)
nfound = sr%nfound
if (tp%sort) then
call kdtree2_sort_results(nfound,results)
endif
if (sr%overflow) then
write (*,*) 'KD_TREE_TRANS: warning! return from kdtree2_r_nearest found more neighbors'
write (*,*) 'KD_TREE_TRANS: than storage was provided for. Answer is NOT smallest ball'
write (*,*) 'KD_TREE_TRANS: with that number of neighbors! I.e. it is wrong.'
endif
deallocate (sr%qv)
return
end subroutine kdtree2_r_nearest_around_point
function kdtree2_r_count(tp,qv,r2) result(nfound)
! Count the number of neighbors within square distance 'r2'.
type (kdtree2), pointer :: tp
real(pReal), target, intent (In) :: qv(:)
real(pReal), intent(in) :: r2
integer(pInt) :: nfound
! ..
! .. Intrinsic Functions ..
intrinsic HUGE
! ..
sr%qv => qv
sr%ballsize = r2
sr%nn = 0 ! flag for fixed r search
sr%nfound = 0
sr%centeridx = -1
sr%correltime = 0
nullify(sr%results) ! for some reason, FTN 95 chokes on '=> null()'
sr%nalloc = 0 ! we do not allocate any storage but that's OK
! for counting.
sr%ind => tp%ind
sr%rearrange = tp%rearrange
if (tp%rearrange) then
sr%Data => tp%rearranged_data
else
sr%Data => tp%the_data
endif
sr%dimen = tp%dimen
!
!sr%dsl = Huge(sr%dsl) ! set to huge positive values
!sr%il = -1 ! set to invalid indexes
!
sr%overflow = .false.
call search(tp%root)
nfound = sr%nfound
return
end function kdtree2_r_count
function kdtree2_r_count_around_point(tp,idxin,correltime,r2) &
result(nfound)
! Count the number of neighbors within square distance 'r2' around
! point 'idxin' with decorrelation time 'correltime'.
!
type (kdtree2), pointer :: tp
integer(pInt), intent (In) :: correltime, idxin
real(pReal), intent(in) :: r2
integer(pInt) :: nfound
! ..
! ..
! .. Intrinsic Functions ..
intrinsic HUGE
! ..
allocate (sr%qv(tp%dimen))
sr%qv = tp%the_data(:,idxin)
sr%ballsize = r2
sr%nn = 0 ! flag for fixed r search
sr%nfound = 0
sr%centeridx = idxin
sr%correltime = correltime
nullify(sr%results)
sr%nalloc = 0 ! we do not allocate any storage but that's OK
! for counting.
sr%ind => tp%ind
sr%rearrange = tp%rearrange
if (sr%rearrange) then
sr%Data => tp%rearranged_data
else
sr%Data => tp%the_data
endif
sr%dimen = tp%dimen
!
!sr%dsl = Huge(sr%dsl) ! set to huge positive values
!sr%il = -1 ! set to invalid indexes
!
sr%overflow = .false.
call search(tp%root)
nfound = sr%nfound
return
end function kdtree2_r_count_around_point
subroutine validate_query_storage(n)
!
! make sure we have enough storage for n
!
integer(pInt), intent(in) :: n
if (size(sr%results,1) .lt. n) then
write (*,*) 'KD_TREE_TRANS: you did not provide enough storage for results(1:n)'
stop
return
endif
return
end subroutine validate_query_storage
function square_distance(d, iv,qv) result (res)
! distance between iv[1:n] and qv[1:n]
! .. Function Return Value ..
! re-implemented to improve vectorization.
real(pReal) :: res
! ..
! ..
! .. Scalar Arguments ..
integer(pInt) :: d
! ..
! .. Array Arguments ..
real(pReal) :: iv(:),qv(:)
! ..
! ..
res = sum( (iv(1:d)-qv(1:d))**2 )
end function square_distance
recursive subroutine search(node)
!
! This is the innermost core routine of the kd-tree search. Along
! with "process_terminal_node", it is the performance bottleneck.
!
! This version uses a logically complete secondary search of
! "box in bounds", whether the sear
!
type (Tree_node), pointer :: node
! ..
type(tree_node),pointer :: ncloser, nfarther
!
integer(pInt) :: cut_dim, i
! ..
real(pReal) :: qval, dis
real(pReal) :: ballsize
real(pReal), pointer :: qv(:)
type(interval), pointer :: box(:)
if ((associated(node%left) .and. associated(node%right)) .eqv. .false.) then
! we are on a terminal node
if (sr%nn .eq. 0) then
call process_terminal_node_fixedball(node)
else
call process_terminal_node(node)
endif
else
! we are not on a terminal node
qv => sr%qv(1:)
cut_dim = node%cut_dim
qval = qv(cut_dim)
if (qval < node%cut_val) then
ncloser => node%left
nfarther => node%right
dis = (node%cut_val_right - qval)**2
! extra = node%cut_val - qval
else
ncloser => node%right
nfarther => node%left
dis = (node%cut_val_left - qval)**2
! extra = qval- node%cut_val_left
endif
if (associated(ncloser)) call search(ncloser)
! we may need to search the second node.
if (associated(nfarther)) then
ballsize = sr%ballsize
! dis=extra**2
if (dis <= ballsize) then
!
! we do this separately as going on the first cut dimen is often
! a good idea.
! note that if extra**2 < sr%ballsize, then the next
! check will also be false.
!
box => node%box(1:)
do i=1,sr%dimen
if (i .ne. cut_dim) then
dis = dis + dis2_from_bnd(qv(i),box(i)%lower,box(i)%upper)
if (dis > ballsize) then
return
endif
endif
end do
!
! if we are still here then we need to search mroe.
!
call search(nfarther)
endif
endif
end if
end subroutine search
real(pReal) function dis2_from_bnd(x,amin,amax) result (res)
real(pReal), intent(in) :: x, amin,amax
if (x > amax) then
res = (x-amax)**2;
return
else
if (x < amin) then
res = (amin-x)**2;
return
else
res = 0.0
return
endif
endif
return
end function dis2_from_bnd
logical function box_in_search_range(node, sr) result(res)
!
! Return the distance from 'qv' to the CLOSEST corner of node's
! bounding box
! for all coordinates outside the box. Coordinates inside the box
! contribute nothing to the distance.
!
type (tree_node), pointer :: node
type (tree_search_record), pointer :: sr
integer(pInt) :: dimen, i
real(pReal) :: dis, ballsize
real(pReal) :: l, u
dimen = sr%dimen
ballsize = sr%ballsize
dis = 0.0
res = .true.
do i=1,dimen
l = node%box(i)%lower
u = node%box(i)%upper
dis = dis + (dis2_from_bnd(sr%qv(i),l,u))
if (dis > ballsize) then
res = .false.
return
endif
end do
res = .true.
return
end function box_in_search_range
subroutine process_terminal_node(node)
!
! Look for actual near neighbors in 'node', and update
! the search results on the sr data structure.
!
type (tree_node), pointer :: node
!
real(pReal), pointer :: qv(:)
integer(pInt), pointer :: ind(:)
real(pReal), pointer :: data(:,:)
!
integer(pInt) :: dimen, i, indexofi, k, centeridx, correltime
real(pReal) :: ballsize, sd, newpri
logical :: rearrange
type(pq), pointer :: pqp
!
! copy values from sr to local variables
!
!
! Notice, making local pointers with an EXPLICIT lower bound
! seems to generate faster code.
! why? I don't know.
qv => sr%qv(1:)
pqp => sr%pq
dimen = sr%dimen
ballsize = sr%ballsize
rearrange = sr%rearrange
ind => sr%ind(1:)
data => sr%Data(1:,1:)
centeridx = sr%centeridx
correltime = sr%correltime
! doing_correl = (centeridx >= 0) ! Do we have a decorrelation window?
! include_point = .true. ! by default include all points
! search through terminal bucket.
mainloop: do i = node%l, node%u
if (rearrange) then
sd = 0.0
do k = 1,dimen
sd = sd + (data(k,i) - qv(k))**2
if (sd>ballsize) cycle mainloop
end do
indexofi = ind(i) ! only read it if we have not broken out
else
indexofi = ind(i)
sd = 0.0
do k = 1,dimen
sd = sd + (data(k,indexofi) - qv(k))**2
if (sd>ballsize) cycle mainloop
end do
endif
if (centeridx > 0) then ! doing correlation interval?
if (abs(indexofi-centeridx) < correltime) cycle mainloop
endif
!
! two choices for any point. The list so far is either undersized,
! or it is not.
!
! If it is undersized, then add the point and its distance
! unconditionally. If the point added fills up the working
! list then set the sr%ballsize, maximum distance bound (largest distance on
! list) to be that distance, instead of the initialized +infinity.
!
! If the running list is full size, then compute the
! distance but break out immediately if it is larger
! than sr%ballsize, "best squared distance" (of the largest element),
! as it cannot be a good neighbor.
!
! Once computed, compare to best_square distance.
! if it is smaller, then delete the previous largest
! element and add the new one.
if (sr%nfound .lt. sr%nn) then
!
! add this point unconditionally to fill list.
!
sr%nfound = sr%nfound +1
newpri = pq_insert(pqp,sd,indexofi)
if (sr%nfound .eq. sr%nn) ballsize = newpri
! we have just filled the working list.
! put the best square distance to the maximum value
! on the list, which is extractable from the PQ.
else
!
! now, if we get here,
! we know that the current node has a squared
! distance smaller than the largest one on the list, and
! belongs on the list.
! Hence we replace that with the current one.
!
ballsize = pq_replace_max(pqp,sd,indexofi)
endif
end do mainloop
!
! Reset sr variables which may have changed during loop
!
sr%ballsize = ballsize
end subroutine process_terminal_node
subroutine process_terminal_node_fixedball(node)
!
! Look for actual near neighbors in 'node', and update
! the search results on the sr data structure, i.e.
! save all within a fixed ball.
!
type (tree_node), pointer :: node
!
real(pReal), pointer :: qv(:)
integer(pInt), pointer :: ind(:)
real(pReal), pointer :: data(:,:)
!
integer(pInt) :: nfound
integer(pInt) :: dimen, i, indexofi, k
integer(pInt) :: centeridx, correltime, nn
real(pReal) :: ballsize, sd
logical :: rearrange
!
! copy values from sr to local variables
!
qv => sr%qv(1:)
dimen = sr%dimen
ballsize = sr%ballsize
rearrange = sr%rearrange
ind => sr%ind(1:)
data => sr%Data(1:,1:)
centeridx = sr%centeridx
correltime = sr%correltime
nn = sr%nn ! number to search for
nfound = sr%nfound
! search through terminal bucket.
mainloop: do i = node%l, node%u
!
! two choices for any point. The list so far is either undersized,
! or it is not.
!
! If it is undersized, then add the point and its distance
! unconditionally. If the point added fills up the working
! list then set the sr%ballsize, maximum distance bound (largest distance on
! list) to be that distance, instead of the initialized +infinity.
!
! If the running list is full size, then compute the
! distance but break out immediately if it is larger
! than sr%ballsize, "best squared distance" (of the largest element),
! as it cannot be a good neighbor.
!
! Once computed, compare to best_square distance.
! if it is smaller, then delete the previous largest
! element and add the new one.
! which index to the point do we use?
if (rearrange) then
sd = 0.0
do k = 1,dimen
sd = sd + (data(k,i) - qv(k))**2
if (sd>ballsize) cycle mainloop
end do
indexofi = ind(i) ! only read it if we have not broken out
else
indexofi = ind(i)
sd = 0.0
do k = 1,dimen
sd = sd + (data(k,indexofi) - qv(k))**2
if (sd>ballsize) cycle mainloop
end do
endif
if (centeridx > 0) then ! doing correlation interval?
if (abs(indexofi-centeridx)<correltime) cycle mainloop
endif
nfound = nfound+1
if (nfound .gt. sr%nalloc) then
! oh nuts, we have to add another one to the tree but
! there isn't enough room.
sr%overflow = .true.
else
sr%results(nfound)%dis = sd
sr%results(nfound)%idx = indexofi
endif
end do mainloop
!
! Reset sr variables which may have changed during loop
!
sr%nfound = nfound
end subroutine process_terminal_node_fixedball
subroutine kdtree2_n_nearest_brute_force(tp,qv,nn,results)
! find the 'n' nearest neighbors to 'qv' by exhaustive search.
! only use this subroutine for testing, as it is SLOW! The
! whole point of a k-d tree is to avoid doing what this subroutine
! does.
type (kdtree2), pointer :: tp
real(pReal), intent (In) :: qv(:)
integer(pInt), intent (In) :: nn
type(kdtree2_result) :: results(:)
integer(pInt) :: i, j, k
real(pReal), allocatable :: all_distances(:)
! ..
allocate (all_distances(tp%n))
do i = 1, tp%n
all_distances(i) = square_distance(tp%dimen,qv,tp%the_data(:,i))
end do
! now find 'n' smallest distances
do i = 1, nn
results(i)%dis = huge(1.0)
results(i)%idx = -1
end do
do i = 1, tp%n
if (all_distances(i)<results(nn)%dis) then
! insert it somewhere on the list
do j = 1, nn
if (all_distances(i)<results(j)%dis) exit
end do
! now we know 'j'
do k = nn - 1, j, -1
results(k+1) = results(k)
end do
results(j)%dis = all_distances(i)
results(j)%idx = i
end if
end do
deallocate (all_distances)
end subroutine kdtree2_n_nearest_brute_force
subroutine kdtree2_r_nearest_brute_force(tp,qv,r2,nfound,results)
! find the nearest neighbors to 'qv' with distance**2 <= r2 by exhaustive search.
! only use this subroutine for testing, as it is SLOW! The
! whole point of a k-d tree is to avoid doing what this subroutine
! does.
type (kdtree2), pointer :: tp
real(pReal), intent (In) :: qv(:)
real(pReal), intent (In) :: r2
integer(pInt), intent(out) :: nfound
type(kdtree2_result) :: results(:)
integer(pInt) :: i, nalloc
real(pReal), allocatable :: all_distances(:)
! ..
allocate (all_distances(tp%n))
do i = 1, tp%n
all_distances(i) = square_distance(tp%dimen,qv,tp%the_data(:,i))
end do
nfound = 0
nalloc = size(results,1)
do i = 1, tp%n
if (all_distances(i)< r2) then
! insert it somewhere on the list
if (nfound .lt. nalloc) then
nfound = nfound+1
results(nfound)%dis = all_distances(i)
results(nfound)%idx = i
endif
end if
enddo
deallocate (all_distances)
call kdtree2_sort_results(nfound,results)
end subroutine kdtree2_r_nearest_brute_force
subroutine kdtree2_sort_results(nfound,results)
! Use after search to sort results(1:nfound) in order of increasing
! distance.
integer(pInt), intent(in) :: nfound
type(kdtree2_result), target :: results(:)
!
!
!THIS IS BUGGY WITH INTEL FORTRAN
! If (nfound .Gt. 1) Call heapsort(results(1:nfound)%dis,results(1:nfound)%ind,nfound)
!
if (nfound .gt. 1) call heapsort_struct(results,nfound)
return
end subroutine kdtree2_sort_results
subroutine heapsort(a,ind,n)
!
! Sort a(1:n) in ascending order, permuting ind(1:n) similarly.
!
! If ind(k) = k upon input, then it will give a sort index upon output.
!
integer(pInt),intent(in) :: n
real(pReal), intent(inout) :: a(:)
integer(pInt), intent(inout) :: ind(:)
!
!
real(pReal) :: value ! temporary for a value from a()
integer(pInt) :: ivalue ! temporary for a value from ind()
integer(pInt) :: i,j
integer(pInt) :: ileft,iright
ileft=n/2+1
iright=n
! do i=1,n
! ind(i)=i
! Generate initial idum array
! end do
if(n.eq.1) return
do
if(ileft > 1)then
ileft=ileft-1
value=a(ileft); ivalue=ind(ileft)
else
value=a(iright); ivalue=ind(iright)
a(iright)=a(1); ind(iright)=ind(1)
iright=iright-1
if (iright == 1) then
a(1)=value;ind(1)=ivalue
return
endif
endif
i=ileft
j=2*ileft
do while (j <= iright)
if(j < iright) then
if(a(j) < a(j+1)) j=j+1
endif
if(value < a(j)) then
a(i)=a(j); ind(i)=ind(j)
i=j
j=j+j
else
j=iright+1
endif
end do
a(i)=value; ind(i)=ivalue
end do
end subroutine heapsort
subroutine heapsort_struct(a,n)
!
! Sort a(1:n) in ascending order
!
!
integer(pInt),intent(in) :: n
type(kdtree2_result),intent(inout) :: a(:)
!
!
type(kdtree2_result) :: value ! temporary value
integer(pInt) :: i,j
integer(pInt) :: ileft,iright
ileft=n/2+1
iright=n
! do i=1,n
! ind(i)=i
! Generate initial idum array
! end do
if(n.eq.1) return
do
if(ileft > 1)then
ileft=ileft-1
value=a(ileft)
else
value=a(iright)
a(iright)=a(1)
iright=iright-1
if (iright == 1) then
a(1) = value
return
endif
endif
i=ileft
j=2*ileft
do while (j <= iright)
if(j < iright) then
if(a(j)%dis < a(j+1)%dis) j=j+1
endif
if(value%dis < a(j)%dis) then
a(i)=a(j);
i=j
j=j+j
else
j=iright+1
endif
end do
a(i)=value
end do
end subroutine heapsort_struct
end module kdtree2_module
!#############################################################################################################################
! END KDTREE2
!#############################################################################################################################
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