mirror of
https://github.com/TREX-CoE/irpjast.git
synced 2024-12-22 04:14:54 +01:00
Added nelec as command line argument
This commit is contained in:
commit
04113a93d5
6
Makefile
6
Makefile
@ -1,7 +1,7 @@
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IRPF90 = irpf90/bin/irpf90 # --codelet=jastrow_full:1000 #-s nelec:10 -s nnuc:2 -s ncord:5 #-a -d
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FC = ifort -xHost -g -mkl=sequential
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IRPF90 = irpf90/bin/irpf90 --codelet=factor_een:2 #-s nelec:10 -s nnuc:2 -s ncord:5 #-a -d
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FC = ifort -xCORE-AVX512 -g -mkl=sequential
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FCFLAGS= -O2 -I .
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AR = ar
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NINJA = ninja
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ARCHIVE = ar crs
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RANLIB = ranlib
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19
README.org
19
README.org
@ -34,3 +34,22 @@
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\sum_{i=1}^{Ne} \sum_{pkl} \sum_a^{Nn}
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c_{apkl} R_{ia}^{p-k-l}\, C_{i,a(p-k+l)}^k
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$$
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* Running
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#+begin_src bash :var Ratio=5 Natoms=500
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python ./generateData.py -a $Natoms -r $Ratio
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#+end_src
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Cela genere les trois fichiers:
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geometry.txt
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elec_coords.txt
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jast_coeffs.txt
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#+begin_src bash :var Natoms=500
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./codelet_factor_een_blas $Natoms
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#+end_src
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@ -1,24 +1,26 @@
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program codelet_factor_een
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program codelet_factor_een_blas
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implicit none
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integer :: i
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double precision :: ticks_0, ticks_1, cpu_0, cpu_1
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integer, parameter :: irp_imax = 100000
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integer, parameter :: irp_imax = 200
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PROVIDE factor_een_blas tmp_c
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call provide_factor_een
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call provide_factor_een_blas
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double precision :: irp_rdtsc
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call cpu_time(cpu_0)
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ticks_0 = irp_rdtsc()
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do i=1,irp_imax
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call bld_factor_een
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call bld_tmp_c
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call bld_factor_een_blas
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enddo
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ticks_1 = irp_rdtsc()
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call cpu_time(cpu_1)
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print *, 'factor_een'
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print *, 'factor_een_blas'
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print *, '-----------'
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print *, 'Cycles:'
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print *, (ticks_1-ticks_0)/dble(irp_imax)
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@ -95,24 +95,24 @@ BEGIN_PROVIDER [ double precision, factor_een_deriv_e, (4, nelec) ]
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rescale_een_e(i,j,k) * &
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rescale_een_n(i,a,m+l)
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daccu(1:4) = daccu(1:4) + &
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rescale_een_e_deriv_e_t(1:4,i,j,k) * &
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rescale_een_e_deriv_e_t(i,1:4,j,k) * &
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rescale_een_n(i,a,m)
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daccu2(1:4) = daccu2(1:4) + &
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rescale_een_e_deriv_e_t(1:4,i,j,k) * &
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rescale_een_e_deriv_e_t(i,1:4,j,k) * &
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rescale_een_n(i,a,m+l)
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enddo
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factor_een_deriv_e(1:4,j) = factor_een_deriv_e(1:4,j) + &
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(accu * rescale_een_n_deriv_e(1:4,j,a,m+l) + daccu(1:4) * rescale_een_n(j,a,m+l) +&
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daccu2(1:4)* rescale_een_n(j,a,m) + accu2*rescale_een_n_deriv_e(1:4,j,a,m)) * cn
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(accu * rescale_een_n_deriv_e(j,1:4,a,m+l) + daccu(1:4) * rescale_een_n(j,a,m+l) +&
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daccu2(1:4)* rescale_een_n(j,a,m) + accu2*rescale_een_n_deriv_e(j,1:4,a,m)) * cn
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factor_een_deriv_e(4,j) = factor_een_deriv_e(4,j) + 2.d0*( &
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daccu (1) * rescale_een_n_deriv_e(1,j,a,m+l) + &
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daccu (2) * rescale_een_n_deriv_e(2,j,a,m+l) + &
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daccu (3) * rescale_een_n_deriv_e(3,j,a,m+l) + &
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daccu2(1) * rescale_een_n_deriv_e(1,j,a,m ) + &
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daccu2(2) * rescale_een_n_deriv_e(2,j,a,m ) + &
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daccu2(3) * rescale_een_n_deriv_e(3,j,a,m ) )*cn
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daccu (1) * rescale_een_n_deriv_e(j,1,a,m+l) + &
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daccu (2) * rescale_een_n_deriv_e(j,2,a,m+l) + &
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daccu (3) * rescale_een_n_deriv_e(j,3,a,m+l) + &
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daccu2(1) * rescale_een_n_deriv_e(j,1,a,m ) + &
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daccu2(2) * rescale_een_n_deriv_e(j,2,a,m ) + &
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daccu2(3) * rescale_een_n_deriv_e(j,3,a,m ) )*cn
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enddo
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enddo
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enddo
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@ -152,8 +152,8 @@ BEGIN_PROVIDER [ double precision, factor_een_deriv_e_ref, (4, nelec) ]
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rjam_cn = rescale_een_n(j, a, m) * cn
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do ii = 1, 4
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drjal(ii) = rescale_een_n_deriv_e(ii, j, a, l)
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drjam_cn(ii) = rescale_een_n_deriv_e(ii, j, a, m) * cn
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drjal(ii) = rescale_een_n_deriv_e(j, ii, a, l)
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drjam_cn(ii) = rescale_een_n_deriv_e(j, ii, a, m) * cn
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enddo
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do i = 1, nelec
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@ -162,7 +162,7 @@ BEGIN_PROVIDER [ double precision, factor_een_deriv_e_ref, (4, nelec) ]
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rijk = rescale_een_e(i, j, k)
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do ii = 1, 4
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drijk(ii) = rescale_een_e_deriv_e(ii, j, i, k)
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drijk(ii) = rescale_een_e_deriv_e(j, ii, i, k)
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enddo
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v1 = rijk * rial ! v(x)
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@ -1,19 +1,15 @@
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BEGIN_PROVIDER [ double precision, factor_een_blas ]
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&BEGIN_PROVIDER [ double precision, factor_een_deriv_e_blas, (4, nelec) ]
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BEGIN_PROVIDER [ double precision, tmp_c, (nelec,nnuc,0:ncord,0:ncord-1) ]
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&BEGIN_PROVIDER [ double precision, dtmp_c, (nelec,4,nnuc,0:ncord,0:ncord-1) ]
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implicit none
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BEGIN_DOC
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! Dimensions 1-3 : dx, dy, dz
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! Dimension 4 : d2x + d2y + d2z
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! Calculate the intermediate buffers
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! tmp_c:
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! r_{ij}^k . R_{ja}^l -> tmp_c_{ia}^{kl}
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!
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! dtmp_c:
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! dr_{ij}^k . R_{ja}^l -> dtmp_c_{ia}^{kl}
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END_DOC
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integer :: i, j, a, p, k, l, lmax, m, n
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double precision :: cn(ncord), accu
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double precision :: f(nnuc,0:ncord-2,0:ncord-2)
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double precision :: tmp_c(nelec,nnuc,0:ncord,0:ncord-1)
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double precision :: dtmp_c(4,nelec,nnuc,0:ncord,0:ncord-1)
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factor_een_blas = 0.0d0
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factor_een_deriv_e_blas(1:4,1:nelec) = 0.0d0
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integer :: k
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! r_{ij}^k . R_{ja}^l -> tmp_c_{ia}^{kl}
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do k=0,ncord-1
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@ -26,12 +22,30 @@
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! dr_{ij}^k . R_{ja}^l -> dtmp_c_{ia}^{kl}
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do k=0,ncord-1
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call dgemm('N','N', 4*nelec, nnuc*(ncord+1), nelec, 1.d0, &
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rescale_een_e_deriv_e(1,1,1,k), 4*size(rescale_een_e_deriv_e,2),&
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rescale_een_e_deriv_e(1,1,1,k), 4*size(rescale_een_e_deriv_e,1),&
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rescale_een_n(1,1,0), size(rescale_een_n,1), 0.d0, &
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dtmp_c(1,1,1,0,k), 4*size(dtmp_c,2))
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dtmp_c(1,1,1,0,k), 4*size(dtmp_c,1))
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enddo
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END_PROVIDER
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BEGIN_PROVIDER [ double precision, factor_een_blas ]
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&BEGIN_PROVIDER [ double precision, factor_een_deriv_e_blas, (nelec,4) ]
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implicit none
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BEGIN_DOC
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! Dimensions 1-3 : dx, dy, dz
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! Dimension 4 : d2x + d2y + d2z
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END_DOC
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integer :: i, j, a, p, k, l, lmax, m, n, ii
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double precision :: accu, cn
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! double precision,dimension(:),allocatable :: cn
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factor_een_blas = 0.0d0
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factor_een_deriv_e_blas(1:nelec,1:4) = 0.0d0
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do n = 1, dim_cord_vect
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l = lkpm_of_cindex(1,n)
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@ -40,33 +54,37 @@
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m = lkpm_of_cindex(4,n)
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do a = 1, nnuc
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cn(a) = cord_vect_full(n, a)
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enddo
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cn = cord_vect_full(n, a)
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if (cn == 0.d0) cycle
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do a = 1, nnuc
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accu = 0.d0
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do j=1,nelec
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accu = accu + rescale_een_n(j,a,m) * tmp_c(j,a,m+l,k)
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factor_een_deriv_e_blas(1:4,j) = factor_een_deriv_e_blas(1:4,j) + (&
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tmp_c(j,a,m,k) * rescale_een_n_deriv_e(1:4,j,a,m+l) + &
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dtmp_c(1:4,j,a,m,k) * rescale_een_n(j,a,m+l) + &
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dtmp_c(1:4,j,a,m+l,k) * rescale_een_n(j,a,m) + &
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tmp_c(j,a,m+l,k)*rescale_een_n_deriv_e(1:4,j,a,m) &
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) * cn(a)
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factor_een_deriv_e_blas(4,j) = factor_een_deriv_e_blas(4,j) + (&
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dtmp_c(1,j,a,m ,k) * rescale_een_n_deriv_e(1,j,a,m+l) + &
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dtmp_c(2,j,a,m ,k) * rescale_een_n_deriv_e(2,j,a,m+l) + &
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dtmp_c(3,j,a,m ,k) * rescale_een_n_deriv_e(3,j,a,m+l) + &
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dtmp_c(1,j,a,m+l,k) * rescale_een_n_deriv_e(1,j,a,m ) + &
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dtmp_c(2,j,a,m+l,k) * rescale_een_n_deriv_e(2,j,a,m ) + &
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dtmp_c(3,j,a,m+l,k) * rescale_een_n_deriv_e(3,j,a,m ) &
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)*cn(a)*2.d0
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enddo
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factor_een_blas = factor_een_blas + accu * cn(a)
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factor_een_blas = factor_een_blas + accu * cn
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do ii=1,4
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do j=1,nelec
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factor_een_deriv_e_blas(j,ii) = factor_een_deriv_e_blas(j,ii) + (&
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tmp_c(j,a,m,k) * rescale_een_n_deriv_e(j,ii,a,m+l) + &
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dtmp_c(j,ii,a,m,k) * rescale_een_n(j,a,m+l) + &
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dtmp_c(j,ii,a,m+l,k) * rescale_een_n(j,a,m) + &
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tmp_c(j,a,m+l,k)*rescale_een_n_deriv_e(j,ii,a,m) &
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) * cn
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enddo
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enddo
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cn = cn+cn
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do j=1,nelec
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factor_een_deriv_e_blas(j,4) = factor_een_deriv_e_blas(j,4) + (&
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dtmp_c(j,1,a,m ,k) * rescale_een_n_deriv_e(j,1,a,m+l) + &
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dtmp_c(j,2,a,m ,k) * rescale_een_n_deriv_e(j,2,a,m+l) + &
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dtmp_c(j,3,a,m ,k) * rescale_een_n_deriv_e(j,3,a,m+l) + &
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dtmp_c(j,1,a,m+l,k) * rescale_een_n_deriv_e(j,1,a,m ) + &
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dtmp_c(j,2,a,m+l,k) * rescale_een_n_deriv_e(j,2,a,m ) + &
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dtmp_c(j,3,a,m+l,k) * rescale_een_n_deriv_e(j,3,a,m ) &
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)*cn
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enddo
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enddo
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enddo
|
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2510
elec_coord.txt
2510
elec_coord.txt
File diff suppressed because it is too large
Load Diff
@ -3,7 +3,16 @@ BEGIN_PROVIDER [ integer, nelec ]
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BEGIN_DOC
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! Number of electrons
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END_DOC
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||||
nelec = 10
|
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|
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character*(32) :: buffer
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integer, external :: iargc
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if (iargc() == 0) then
|
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nelec = 10
|
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else
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call getarg(1,buffer)
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read(buffer,*)nelec
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endif
|
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END_PROVIDER
|
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|
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BEGIN_PROVIDER [ integer, nelec_up ]
|
||||
@ -11,7 +20,7 @@ BEGIN_PROVIDER [ integer, nelec_up ]
|
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BEGIN_DOC
|
||||
! Number of alpha and beta electrons
|
||||
END_DOC
|
||||
nelec_up = 5
|
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nelec_up = nelec/2
|
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END_PROVIDER
|
||||
|
||||
|
||||
|
||||
60
generateData.py
Executable file
60
generateData.py
Executable file
@ -0,0 +1,60 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
import numpy as np
|
||||
import sys, getopt
|
||||
|
||||
from jastlib import getCoefList, get_sphere_distribution, generateElsAroundPoints
|
||||
|
||||
def main(argv):
|
||||
Natom = 2
|
||||
Ratio = 5
|
||||
inputfile = ''
|
||||
outputfile = ''
|
||||
try:
|
||||
opts, args = getopt.getopt(argv,"ha:r:",["atom=","ratio="])
|
||||
except getopt.GetoptError:
|
||||
print('test.py -a <natoms> -r <ratio>')
|
||||
sys.exit(2)
|
||||
for opt, arg in opts:
|
||||
if opt == '-h':
|
||||
print('test.py -a <inputfile> -r <outputfile>')
|
||||
sys.exit()
|
||||
elif opt in ("-a", "--atom"):
|
||||
Natom = int(arg)
|
||||
elif opt in ("-r", "--ratio"):
|
||||
Ratio = int(arg)
|
||||
|
||||
|
||||
Nelec = Ratio
|
||||
Nord = 5
|
||||
|
||||
L1 = 20.0
|
||||
n = Natom # number of points
|
||||
dmin = 0.1 # min dist between points
|
||||
Ls = np.array([L1,L1,L1]) # lengths of the box
|
||||
shift = -10.0
|
||||
kappa = 2.0
|
||||
filename_atom = "geometry.txt"
|
||||
filename_coeffsa = "jast_coeffs.txt"
|
||||
(coeffsa, coeffsb, coeffsc) = getCoefList(Nord,n)
|
||||
coeffsall = np.concatenate((coeffsa,coeffsb,coeffsc))
|
||||
|
||||
atomList,_,_ = get_sphere_distribution(n, dmin, Ls, maxiter=1e4, allow_wall=True)
|
||||
|
||||
L1 = 15.0
|
||||
n = Nelec # number of points
|
||||
dmin = 0.1 # min dist between points
|
||||
Ls = np.array([L1,L1,L1]) # lengths of the box
|
||||
shift = -10.0
|
||||
kappa = 2.0
|
||||
filename_elec = "elec_coord.txt"
|
||||
|
||||
rlist = generateElsAroundPoints(n,atomList,dmin)
|
||||
|
||||
# Save file
|
||||
np.savetxt(filename_elec,rlist)
|
||||
np.savetxt(filename_atom,atomList)
|
||||
np.savetxt(filename_coeffsa,coeffsall)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main(sys.argv[1:])
|
||||
502
geometry.txt
502
geometry.txt
@ -1,2 +1,500 @@
|
||||
0.000000 0.000000 0.000000
|
||||
0.000000 0.000000 2.059801
|
||||
9.169699233477617284e+00 1.577356709974558768e+01 1.026790877215631070e+01
|
||||
1.709096115690725526e+01 1.887196960617206543e+01 9.799886585728481592e+00
|
||||
2.759679637155061371e+00 9.340816860053413606e-01 1.427913730962294281e+01
|
||||
8.466339359065724324e+00 9.231835516571540445e-01 1.640641208255591366e+01
|
||||
7.239083748223058556e+00 1.725722749662392630e+01 1.787551375153963562e+01
|
||||
1.474534281245462353e+01 1.833951609869788513e+01 6.010741396284879912e-02
|
||||
1.513197838597113787e+01 1.124136766210814820e-01 8.422615822432087285e+00
|
||||
1.565537492181737633e+01 1.748573069812977554e+00 6.284695229046888265e+00
|
||||
1.723692999612131871e+01 7.388450412650704457e+00 4.172286160665874988e+00
|
||||
1.715795128560187877e+01 1.450030322639299385e+01 1.976514987287575309e+01
|
||||
1.102229137605018749e+01 5.401791402001889786e+00 9.274795594529734899e+00
|
||||
1.615006086012495956e+00 1.633069527897562523e+01 1.910553292984471341e+01
|
||||
6.385605171265195779e+00 1.316589720661558260e+01 5.436216733982728755e+00
|
||||
1.292626807035237491e+01 5.886232823823796423e+00 1.715754087186913068e+01
|
||||
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1.171702620882371804e+01 2.276401260494658185e+00 1.810096945087098419e+01
|
||||
7.313860623077066414e+00 1.136536331465612548e+01 4.333435179470209064e+00
|
||||
1.161897344231803864e+01 1.519603298759204435e+01 3.382249439070197372e+00
|
||||
1.156896961606582153e+01 8.965684216027224096e+00 1.827736372208834581e+01
|
||||
1.167370077791304439e+01 1.051009905261099675e+01 8.807622667959403628e+00
|
||||
7.606672067973761386e+00 1.265725343657043211e+01 3.979380870847071261e+00
|
||||
8.892335704184702649e+00 5.104533485456581587e+00 2.453042442841448789e+00
|
||||
1.672416470286611556e+01 7.264109419389366273e+00 2.155153508137672702e+00
|
||||
1.300313228937827859e+01 5.226936495928229398e+00 7.224382256964512194e+00
|
||||
1.526044201138132372e+01 7.523756127501057378e+00 1.253176100436383944e+00
|
||||
1.503140840626626584e+01 2.281189329522508302e+00 1.566487391834201759e+01
|
||||
1.725284145996638685e+01 1.729211335181137787e+00 1.102979596681249852e+01
|
||||
6.760573654131977861e+00 1.293522977412822073e+01 7.663858940594245439e+00
|
||||
1.621239281076305971e+01 1.713461380275644075e+01 2.214231878610852799e+00
|
||||
5.697872101122003485e-01 7.850066496542680738e+00 1.270615299210492033e+01
|
||||
1.385054130936484285e+01 4.748277023904399208e+00 5.879718410124894490e+00
|
||||
1.947694008113235498e+01 9.848355111751477153e+00 1.037328615483129113e+01
|
||||
1.096948629944599851e+01 2.456217269844021001e+00 2.394115664983085257e+00
|
||||
7.238305823828159902e+00 1.929903683394915692e+00 1.077155935473052040e+01
|
||||
1.166342212104290077e+01 1.407302366702650787e+01 7.389359595792202029e+00
|
||||
5.092331392100135190e+00 1.952424587496226493e+01 1.923154861807247329e+01
|
||||
9.366425180191411570e+00 1.615164355335112845e+01 1.911607876834722219e+01
|
||||
3.315452033839434431e+00 1.473878392392635561e+01 1.812393203845930145e+01
|
||||
1.900480110888890906e+01 7.074939479502684314e+00 1.223207699986319952e+01
|
||||
1.112062688587192039e+00 1.350382159552646399e+01 1.788244529975350972e+01
|
||||
1.208543849838354234e+01 1.917538945159808250e+01 2.065915709806946943e+00
|
||||
9.560953012010797281e+00 7.998329148897871832e+00 7.450779254324775280e+00
|
||||
1.083078929122250500e+01 6.773165572482309216e-02 4.050027038129977441e+00
|
||||
1.520035442423352201e+01 2.823423909758457917e+00 1.660600955629038467e+01
|
||||
7.457920666287114031e+00 1.589040576353029088e+01 8.032331946108683951e+00
|
||||
6.218894466525117792e+00 1.902130382216541449e+01 1.903258778343918678e+01
|
||||
1.185352537044184373e+01 2.131383638373813838e+00 1.705043604149870617e+01
|
||||
7.907877423623723701e+00 2.355629672603098967e-01 1.768134544461914004e+01
|
||||
9.180573509978016133e+00 1.056672002888144668e+01 1.615673900391059448e+01
|
||||
9.758028775418940981e+00 1.897910123530181536e+01 3.232856588131445275e+00
|
||||
9.529556848976369565e+00 5.533775831517859345e+00 1.005084109974087703e+01
|
||||
1.888463110367292686e+01 1.628556394454996692e+01 1.803847414451672293e+01
|
||||
6.238548605600606756e+00 1.732812294171118594e+01 1.390202976478331909e+00
|
||||
1.703968926632573044e+00 5.406395310068439208e+00 7.791335319289476757e+00
|
||||
5.318212580207630324e+00 1.959798809581453582e+01 1.623296528008798489e+01
|
||||
1.030974595776082481e+01 3.345118938173370360e+00 1.876368434371347149e+00
|
||||
6.726524294819553162e+00 1.710588877924181261e+01 3.587736141512962718e+00
|
||||
4.672488187259027370e-01 1.330291362956780787e+01 1.617364891779558178e+01
|
||||
1.823219818623249111e+01 6.472742230971137545e+00 2.236150071190512456e+00
|
||||
1.338693627635828776e+01 1.345878971051173156e+00 1.234222008199759202e+01
|
||||
1.947357313764631215e+01 4.948194784649516009e+00 3.232011569633046477e+00
|
||||
7.125923551155466384e-01 1.162057903825268568e+01 2.197833471575816322e+00
|
||||
5.765106905171362150e+00 2.344048545419590290e+00 1.009968143530120166e+01
|
||||
1.702982426306183328e+01 6.072617927489281087e+00 1.344762850718726988e+01
|
||||
1.953240139022602762e+01 6.355949293339637762e+00 5.018385607396793624e+00
|
||||
1.245453658156568366e+01 3.228496948834442826e+00 9.246036724649957961e+00
|
||||
1.874147434626193842e+01 1.288676320323209890e+00 1.084079430093304275e+01
|
||||
3.727357917213356409e+00 7.890228993383980871e+00 1.887220717306394491e+01
|
||||
7.321766473129764918e+00 1.447808428304094619e+01 3.497951812525244009e+00
|
||||
7.865472306426224414e-01 1.175719643886713506e+01 1.517215169072994208e+01
|
||||
1.813191620677649940e+00 1.420133879752781070e+01 2.130895718843435205e+00
|
||||
4.951315637270963244e+00 1.270505767711414435e+01 1.503488503815237820e+01
|
||||
1.968294423243641589e+01 8.070047226443377575e-01 1.569001423339116563e+01
|
||||
1.566772562717018147e+01 6.727297417688427039e-01 1.644914485797703207e+01
|
||||
1.872485440230902043e+01 5.507240933872878941e+00 1.370100621573215349e+01
|
||||
1.161658169643436622e+01 8.393490346501719657e-01 5.285574035829931461e+00
|
||||
2.735011847701449561e+00 9.285366500191569727e+00 3.563701714451903424e+00
|
||||
1.595953564417274961e+01 4.982221599869225415e+00 9.866112924259621053e+00
|
||||
8.236398937164604916e+00 1.225732164001447977e+01 1.073870054404710839e+00
|
||||
1.673230079118365410e+01 1.707138375264127816e+00 1.186127956517092308e+01
|
||||
1.133157190343477083e+01 6.748273940311153574e+00 1.735995103299493536e+01
|
||||
1.123515339456784190e+01 3.358381384637665334e-01 4.451683544357505795e+00
|
||||
4.637006946943795782e+00 1.053287841873853736e+01 1.890483257401232109e+01
|
||||
8.316004687068261347e+00 1.015832009654642221e+01 9.997387582388792993e+00
|
||||
1.155453639981338654e+00 1.119721790588304700e+01 1.015230889964557903e-01
|
||||
1.781615549334679827e+01 6.965508912105047301e+00 9.275527641105492549e+00
|
||||
1.080805166589502875e+01 1.058441706397450766e+01 8.304530153792464731e+00
|
||||
1.501464242245880598e+01 1.007311195971194273e+01 1.119912106401039775e+00
|
||||
1.419991605053262163e+01 1.775556257620701572e+01 1.286914118149863029e+01
|
||||
4.276250814546123102e+00 1.627273238694782975e+01 1.338681989661718585e+00
|
||||
1.854777207937439343e+01 1.925368107712428412e+01 1.792532699376242533e+01
|
||||
1.223975203151594915e+00 1.509404945054635405e+00 9.011561993634691348e+00
|
||||
2.194943875494046459e+00 1.877640576542419737e+01 9.705328951731640785e+00
|
||||
5.216227060122527348e+00 1.486080380567930703e+01 1.688088707737033545e+01
|
||||
8.212309994247849687e+00 1.287695777335280667e+01 6.582461320676003425e+00
|
||||
1.251332426363976680e+01 1.395650004243432818e+01 1.712772751477069022e+01
|
||||
1.276409123650453559e+01 9.267092658243401004e+00 1.117797986214748462e+01
|
||||
6.113755862360137350e+00 4.078389036120655398e-01 4.247092748998301914e+00
|
||||
1.242978019441407156e+01 1.326256672807486048e+01 1.169215723874168944e+01
|
||||
9.042791400820338055e+00 4.356422507665575594e+00 6.449680275291116871e+00
|
||||
1.099070526776987222e+01 7.993714068441293286e+00 6.805343837475432700e+00
|
||||
1.589809313358526488e+01 7.419391712002973982e+00 2.740259265591922233e+00
|
||||
1.403169879421245447e+01 8.972298589150595305e+00 8.031561069202838965e+00
|
||||
1.344854912285986792e+01 1.337958972288058135e+01 1.745916261721233198e+01
|
||||
1.929539884202950262e+01 5.880174791470014206e+00 6.137258875502791255e+00
|
||||
1.481705838303003375e+01 1.859961296602317127e+00 1.070658133606104556e+01
|
||||
1.747793859169851771e+01 1.802051836868805523e+01 3.046717052164580330e+00
|
||||
8.166905019318310366e+00 7.107870788028291642e+00 3.094851515184415813e+00
|
||||
2.911619256801625255e+00 1.748206940353133731e+01 1.667585480238292206e+01
|
||||
9.841559860317685704e+00 4.407466849272965170e+00 4.015772381534159052e+00
|
||||
8.534641826538900133e+00 7.001124362981947513e-03 1.083859604224306494e+01
|
||||
1.179950797532610274e+01 7.733610875448507116e+00 3.715706806233098902e+00
|
||||
1.031814661896667751e+01 1.504274861610019087e+01 8.641941454933366629e+00
|
||||
8.487179669857354725e+00 2.779087131125754784e+00 1.771467618721269588e+01
|
||||
1.817694302030358244e+01 1.368297972028561738e+01 1.459998433404883755e+01
|
||||
1.523108736137761809e+01 1.914783005037991259e+01 1.271830247155052618e+01
|
||||
1.676565671838121574e+01 1.137791753488018465e+01 1.149470907912997220e-01
|
||||
4.713786035411972719e+00 1.502631037162001082e+00 1.781772595544743876e+00
|
||||
1.496393319315559722e+01 1.907873074229365074e+01 9.267905967302020542e+00
|
||||
3.420605072294604643e+00 1.946138302136004228e+01 1.554521849678835999e+01
|
||||
5.059396683262584737e+00 1.179111420257443754e+01 1.694676442505262770e+01
|
||||
1.034505581870227076e+01 1.047187126578368321e+01 1.513149670850754447e+01
|
||||
5.766942056752768053e+00 9.343210501397724244e+00 7.221102977105225307e+00
|
||||
1.859697591806138917e+01 2.139702594727030949e+00 1.926127136089304059e+01
|
||||
1.758948960639834524e+01 8.716762176901982073e-01 1.994986541769686283e+01
|
||||
9.791295245901567412e+00 7.476110121679734988e+00 1.719085699736939787e+01
|
||||
5.237537596468319734e+00 1.114089935463471903e+01 2.413380171945278541e-01
|
||||
2.947436828339069503e+00 4.101110664128533756e+00 7.002043945902258315e+00
|
||||
1.432988014056335402e+01 1.921806287020138271e+01 3.934162851604201538e+00
|
||||
1.154680355918341128e+01 3.270462716187296781e+00 1.951812629060489712e+01
|
||||
4.846414428820264853e+00 1.087854833453446446e+01 7.050825938023857375e+00
|
||||
1.212728915878844838e+01 9.183471762621229217e+00 3.013213345939611543e+00
|
||||
8.256669085996799495e+00 2.639001216334704303e+00 1.964759874273240570e+01
|
||||
5.578484768657336446e+00 5.250179413170503295e+00 5.202500442144026849e+00
|
||||
3.628998141330312954e+00 5.424377769518844872e+00 1.685060418027228124e+01
|
||||
1.675031186141319139e+01 2.738918567328580966e+00 4.941011954075147372e-02
|
||||
7.800310628073869879e+00 1.009891445538099575e+01 1.608966164039095403e+01
|
||||
1.734493420202401737e+01 1.961265577635142066e+01 1.363516602159723412e+01
|
||||
6.630357772344597223e+00 3.263443697742078875e+00 4.760493490091127100e+00
|
||||
8.559244331671484574e+00 9.128667856159415450e+00 1.602140160223702026e+01
|
||||
2.679660835208141911e+00 5.250720355636633307e+00 1.341015448463152016e+00
|
||||
6.038176216444695044e+00 6.157414420908484232e+00 9.374931235597072643e-02
|
||||
1.794816126171273751e+01 1.999483621425812885e+01 2.852150505341961573e+00
|
||||
4.257699770888214275e+00 2.783806729552100734e+00 1.521570975371453471e+01
|
||||
3.969559505967985569e+00 1.893103996289927693e+01 1.641274544068463115e+01
|
||||
6.616243527471028507e+00 1.860375951597969291e+01 1.485333710912073002e+01
|
||||
1.906086127887575543e+01 4.294385959116819862e+00 1.636802165767673856e+01
|
||||
5.054294927043523344e-01 5.301083774118295899e+00 7.469612132794376080e+00
|
||||
1.012444166562786663e+01 1.499032934216872892e+00 1.588788869604790754e+01
|
||||
1.160365945173237812e+01 1.965862931449910889e+01 5.536512241907749043e+00
|
||||
6.199859829526282340e+00 9.282932393847531216e+00 8.980042228686686556e+00
|
||||
1.303740662239231085e+01 9.235762115290071961e+00 1.420503195579945910e+01
|
||||
1.104860128164335720e+01 5.683132552589700737e+00 1.026562735946136051e+01
|
||||
1.516211207459281773e+01 1.315441397925543754e+01 1.735247591384620947e+01
|
||||
|
||||
14541
jast_coeffs.txt
14541
jast_coeffs.txt
File diff suppressed because it is too large
Load Diff
271
jastlib.py
Normal file
271
jastlib.py
Normal file
@ -0,0 +1,271 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
def get_sphere_distribution(n, dmin, Ls, maxiter=1e4, allow_wall=True):
|
||||
"""Get random points in a box with given dimensions and minimum separation.
|
||||
|
||||
Parameters:
|
||||
|
||||
- n: number of points
|
||||
- dmin: minimum distance
|
||||
- Ls: dimensions of box, shape (3,) array
|
||||
- maxiter: maximum number of iterations.
|
||||
- allow_wall: whether to allow points on wall;
|
||||
(if False: points need to keep distance dmin/2 from the walls.)
|
||||
|
||||
Return:
|
||||
|
||||
- ps: array (n, 3) of point positions,
|
||||
with 0 <= ps[:, i] < Ls[i]
|
||||
- n_iter: number of iterations
|
||||
- dratio: average nearest-neighbor distance, divided by dmin.
|
||||
|
||||
Note: with a fill density (sphere volume divided by box volume) above about
|
||||
0.53, it takes very long. (Random close-packed spheres have a fill density
|
||||
of 0.64).
|
||||
|
||||
Author: Han-Kwang Nienhuys (2020)
|
||||
Copying: BSD, GPL, LGPL, CC-BY, CC-BY-SA
|
||||
See Stackoverflow: https://stackoverflow.com/a/62895898/6228891
|
||||
"""
|
||||
Ls = np.array(Ls).reshape(3)
|
||||
if not allow_wall:
|
||||
Ls -= dmin
|
||||
|
||||
# filling factor; 0.64 is for random close-packed spheres
|
||||
# This is an estimate because close packing is complicated near the walls.
|
||||
# It doesn't work well for small L/dmin ratios.
|
||||
sphere_vol = np.pi/6*dmin**3
|
||||
box_vol = np.prod(Ls + 0.5*dmin)
|
||||
fill_dens = n*sphere_vol/box_vol
|
||||
if fill_dens > 0.64:
|
||||
msg = f'Too many to fit in the volume, density {fill_dens:.3g}>0.64'
|
||||
raise ValueError(msg)
|
||||
|
||||
# initial try
|
||||
ps = np.random.uniform(size=(n, 3)) * Ls
|
||||
|
||||
# distance-squared matrix (diagonal is self-distance, don't count)
|
||||
dsq = ((ps - ps.reshape(n, 1, 3))**2).sum(axis=2)
|
||||
dsq[np.arange(n), np.arange(n)] = np.infty
|
||||
|
||||
for iter_no in range(int(maxiter)):
|
||||
# find points that have too close neighbors
|
||||
close_counts = np.sum(dsq < dmin**2, axis=1) # shape (n,)
|
||||
n_close = np.count_nonzero(close_counts)
|
||||
if n_close == 0:
|
||||
break
|
||||
|
||||
# Move the one with the largest number of too-close neighbors
|
||||
imv = np.argmax(close_counts)
|
||||
|
||||
# new positions
|
||||
newp = np.random.uniform(size=3)*Ls
|
||||
ps[imv]= newp
|
||||
|
||||
# update distance matrix
|
||||
new_dsq_row = ((ps - newp.reshape(1, 3))**2).sum(axis=-1)
|
||||
dsq[imv, :] = dsq[:, imv] = new_dsq_row
|
||||
dsq[imv, imv] = np.inf
|
||||
else:
|
||||
raise RuntimeError(f'Failed after {iter_no+1} iterations.')
|
||||
|
||||
if not allow_wall:
|
||||
ps += dmin/2
|
||||
|
||||
dratio = (np.sqrt(dsq.min(axis=1))/dmin).mean()
|
||||
return ps, iter_no+1, dratio
|
||||
|
||||
def generateElsAroundPoints(n,LS,dmin):
|
||||
"""
|
||||
Parameters:
|
||||
|
||||
- n: number of points
|
||||
- LS: list of position of all atoms
|
||||
- dmin: minimum intra block distance
|
||||
- shift: inter block distance
|
||||
|
||||
Return:
|
||||
|
||||
- r: array (n, 3) of point positions,
|
||||
|
||||
"""
|
||||
|
||||
xs = None
|
||||
for Ls in LS:
|
||||
|
||||
# Get list of random points around Ls
|
||||
distrib,a,b = get_sphere_distribution(n,dmin,Ls)
|
||||
|
||||
if xs is None:
|
||||
xs = distrib[:,0]
|
||||
ys = distrib[:,1]
|
||||
zs = distrib[:,2]
|
||||
else:
|
||||
xs = np.concatenate((xs,distrib[:,0]))
|
||||
ys = np.concatenate((ys,distrib[:,1]))
|
||||
zs = np.concatenate((zs,distrib[:,2]))
|
||||
|
||||
return((np.array((xs,ys,zs))).T)
|
||||
|
||||
def getCoefList(Nord,Natom):
|
||||
assert(Nord < 11)
|
||||
dict = {
|
||||
0 : lambda x,y:x-y-2,
|
||||
1 : lambda x,y:x-y,
|
||||
2 : lambda x,y:x-y,
|
||||
3 : lambda x,y:x-y,
|
||||
4 : lambda x,y:x-y,
|
||||
5 : lambda x,y:x-y,
|
||||
6 : lambda x,y:x-y,
|
||||
7 : lambda x,y:x-y,
|
||||
8 : lambda x,y:x-y,
|
||||
9 : lambda x,y:x-y,
|
||||
10 : lambda x,y:x-y,
|
||||
11 : lambda x,y:x-y,
|
||||
}
|
||||
count = 0
|
||||
for p in range(2,Nord+1):
|
||||
for k in range(p-1,-1,-1):
|
||||
lmax = dict[k](p,k)
|
||||
for l in range(lmax,-1,-1):
|
||||
if (p-k-l) & 1 is 0:
|
||||
count += 1
|
||||
coeflista = np.random.rand(Nord+1,Natom)
|
||||
coeflistb = np.random.rand(Nord+1)
|
||||
coeflistc = np.random.rand(count,Natom)
|
||||
return (coeflista.reshape((Nord+1)*Natom),coeflistb,coeflistc.reshape(count*Natom))
|
||||
#return (coeflista,coeflistb,coeflistc)
|
||||
|
||||
def get_sphere_distribution(n, dmin, Ls, maxiter=1e4, allow_wall=True):
|
||||
"""Get random points in a box with given dimensions and minimum separation.
|
||||
|
||||
Parameters:
|
||||
|
||||
- n: number of points
|
||||
- dmin: minimum distance
|
||||
- Ls: dimensions of box, shape (3,) array
|
||||
- maxiter: maximum number of iterations.
|
||||
- allow_wall: whether to allow points on wall;
|
||||
(if False: points need to keep distance dmin/2 from the walls.)
|
||||
|
||||
Return:
|
||||
|
||||
- ps: array (n, 3) of point positions,
|
||||
with 0 <= ps[:, i] < Ls[i]
|
||||
- n_iter: number of iterations
|
||||
- dratio: average nearest-neighbor distance, divided by dmin.
|
||||
|
||||
Note: with a fill density (sphere volume divided by box volume) above about
|
||||
0.53, it takes very long. (Random close-packed spheres have a fill density
|
||||
of 0.64).
|
||||
|
||||
Author: Han-Kwang Nienhuys (2020)
|
||||
Copying: BSD, GPL, LGPL, CC-BY, CC-BY-SA
|
||||
See Stackoverflow: https://stackoverflow.com/a/62895898/6228891
|
||||
"""
|
||||
Ls = np.array(Ls).reshape(3)
|
||||
if not allow_wall:
|
||||
Ls -= dmin
|
||||
|
||||
# filling factor; 0.64 is for random close-packed spheres
|
||||
# This is an estimate because close packing is complicated near the walls.
|
||||
# It doesn't work well for small L/dmin ratios.
|
||||
sphere_vol = np.pi/6*dmin**3
|
||||
box_vol = np.prod(Ls + 0.5*dmin)
|
||||
fill_dens = n*sphere_vol/box_vol
|
||||
if fill_dens > 0.64:
|
||||
msg = f'Too many to fit in the volume, density {fill_dens:.3g}>0.64'
|
||||
raise ValueError(msg)
|
||||
|
||||
# initial try
|
||||
ps = np.random.uniform(size=(n, 3)) * Ls
|
||||
|
||||
# distance-squared matrix (diagonal is self-distance, don't count)
|
||||
dsq = ((ps - ps.reshape(n, 1, 3))**2).sum(axis=2)
|
||||
dsq[np.arange(n), np.arange(n)] = np.infty
|
||||
|
||||
for iter_no in range(int(maxiter)):
|
||||
# find points that have too close neighbors
|
||||
close_counts = np.sum(dsq < dmin**2, axis=1) # shape (n,)
|
||||
n_close = np.count_nonzero(close_counts)
|
||||
if n_close == 0:
|
||||
break
|
||||
|
||||
# Move the one with the largest number of too-close neighbors
|
||||
imv = np.argmax(close_counts)
|
||||
|
||||
# new positions
|
||||
newp = np.random.uniform(size=3)*Ls
|
||||
ps[imv]= newp
|
||||
|
||||
# update distance matrix
|
||||
new_dsq_row = ((ps - newp.reshape(1, 3))**2).sum(axis=-1)
|
||||
dsq[imv, :] = dsq[:, imv] = new_dsq_row
|
||||
dsq[imv, imv] = np.inf
|
||||
else:
|
||||
raise RuntimeError(f'Failed after {iter_no+1} iterations.')
|
||||
|
||||
if not allow_wall:
|
||||
ps += dmin/2
|
||||
|
||||
dratio = (np.sqrt(dsq.min(axis=1))/dmin).mean()
|
||||
return ps, iter_no+1, dratio
|
||||
|
||||
|
||||
def scalingee(r,kappa=1.0):
|
||||
return (numpy.ones_like(r) - numpy.exp(-kappa*r))/kappa
|
||||
|
||||
def scalingen(r,kappa=1.0):
|
||||
return numpy.exp(-kappa*r)
|
||||
|
||||
if False:
|
||||
Nord = 5
|
||||
L1 = 2.0
|
||||
n = 2 # number of points
|
||||
dmin = 0.1 # min dist between points
|
||||
Ls = np.array([L1,L1,L1]) # lengths of the box
|
||||
shift = -10.0
|
||||
kappa = 2.0
|
||||
filename_atom = str(n) + "_geometry.txt"
|
||||
filename_elec = str(n)
|
||||
filename_coeffs = str(n) + "_jast_coeffs.txt"
|
||||
(coeffsa, coeffsb, coeffsc) = getCoefList(Nord,n)
|
||||
coeffsall = np.concatenate((coeffsa,coeffsb,coeffsc))
|
||||
print(coeffsa.shape,coeffsb.shape,coeffsc.shape)
|
||||
|
||||
atomList,_,_ = get_sphere_distribution(n, dmin, Ls, maxiter=1e4, allow_wall=True)
|
||||
#print(atomList)
|
||||
|
||||
L1 = 5.0
|
||||
n = 5 # number of points
|
||||
dmin = 0.1 # min dist between points
|
||||
Ls = np.array([L1,L1,L1]) # lengths of the box
|
||||
shift = -10.0
|
||||
kappa = 2.0
|
||||
filename_elec = filename_elec + "_" + str(n) + "_elec_coord.txt"
|
||||
|
||||
#rlist = generateBlockRandomPointsAtShftApart(n,L1,dmin,shift)
|
||||
rlist = generateElsAroundPoints(n,atomList,dmin)
|
||||
|
||||
|
||||
# Save file
|
||||
np.savetxt(filename_elec,rlist)
|
||||
np.savetxt(filename_atom,atomList)
|
||||
np.savetxt(filename_coeffs,coeffsall)
|
||||
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(111, projection='3d')
|
||||
|
||||
xs = rlist.T[0]
|
||||
ys = rlist.T[1]
|
||||
zs = rlist.T[2]
|
||||
ax.scatter(xs, ys, zs, marker='o')
|
||||
|
||||
plt.show()
|
||||
|
||||
rijScaled = np.array([[(lambda xval, yval: np.linalg.norm(xval-yval))(xval,yval) for yval in rlist] for xval in rlist])
|
||||
|
||||
plt.imshow(rijScaled)
|
||||
plt.colorbar()
|
||||
plt.show()
|
||||
@ -3,7 +3,9 @@ BEGIN_PROVIDER [ integer, nnuc ]
|
||||
BEGIN_DOC
|
||||
! Number of nuclei
|
||||
END_DOC
|
||||
nnuc = 2
|
||||
!nnuc = 2
|
||||
! read(*,*)nnuc
|
||||
nnuc = nelec/5
|
||||
END_PROVIDER
|
||||
|
||||
|
||||
@ -14,7 +16,8 @@ BEGIN_PROVIDER [ integer, typenuc ]
|
||||
! Type of the nuclei
|
||||
END_DOC
|
||||
typenuc = 1
|
||||
typenuc_arr = (/1, 1/)
|
||||
!typenuc_arr = (/1, 1/)
|
||||
typenuc_arr = 1
|
||||
END_PROVIDER
|
||||
|
||||
|
||||
|
||||
@ -186,7 +186,7 @@ BEGIN_PROVIDER [double precision, rescale_een_n, (nelec, nnuc, 0:ncord)]
|
||||
|
||||
END_PROVIDER
|
||||
|
||||
BEGIN_PROVIDER [double precision, rescale_een_n_deriv_e, (4, nelec, nnuc, 0:ncord)]
|
||||
BEGIN_PROVIDER [double precision, rescale_een_n_deriv_e, (nelec, 4, nnuc, 0:ncord)]
|
||||
implicit none
|
||||
BEGIN_DOC
|
||||
! Derivative of the scaled distance J_{een} wrt R_{ia}
|
||||
@ -198,23 +198,23 @@ BEGIN_PROVIDER [double precision, rescale_een_n_deriv_e, (4, nelec, nnuc, 0:ncor
|
||||
kappa_l = - dble(l) * kappa
|
||||
do a = 1, nnuc
|
||||
do i = 1, nelec
|
||||
! r'(x) \lor r''(x)
|
||||
do ii = 1, 4
|
||||
rescale_een_n_deriv_e(ii, i, a, l) = &
|
||||
! r'(x) \lor r''(x)
|
||||
rescale_een_n_deriv_e(i, ii, a, l) = &
|
||||
kappa_l * elnuc_dist_deriv_e(ii, i, a)
|
||||
!print *, "pp", ii, i, a, elnuc_dist_deriv_e(ii, i, a)
|
||||
enddo
|
||||
|
||||
! \left(r''(x)+r'(x)^2\right)
|
||||
rescale_een_n_deriv_e(4, i, a, l) = rescale_een_n_deriv_e(4, i, a, l) + &
|
||||
rescale_een_n_deriv_e(1, i, a, l) * rescale_een_n_deriv_e(1, i, a, l) + &
|
||||
rescale_een_n_deriv_e(2, i, a, l) * rescale_een_n_deriv_e(2, i, a, l) + &
|
||||
rescale_een_n_deriv_e(3, i, a, l) * rescale_een_n_deriv_e(3, i, a, l)
|
||||
rescale_een_n_deriv_e(i, 4, a, l) = rescale_een_n_deriv_e(i, 4, a, l) + &
|
||||
rescale_een_n_deriv_e(i, 1, a, l) * rescale_een_n_deriv_e(i, 1, a, l) + &
|
||||
rescale_een_n_deriv_e(i, 2, a, l) * rescale_een_n_deriv_e(i, 2, a, l) + &
|
||||
rescale_een_n_deriv_e(i, 3, a, l) * rescale_een_n_deriv_e(i, 3, a, l)
|
||||
|
||||
! \times e^{r(x)}
|
||||
do ii = 1, 4
|
||||
rescale_een_n_deriv_e(ii, i, a, l) = &
|
||||
rescale_een_n_deriv_e(ii, i, a, l) * rescale_een_n(i, a, l)
|
||||
rescale_een_n_deriv_e(i, ii, a, l) = &
|
||||
rescale_een_n_deriv_e(i, ii, a, l) * rescale_een_n(i, a, l)
|
||||
enddo
|
||||
enddo
|
||||
enddo
|
||||
@ -243,7 +243,7 @@ BEGIN_PROVIDER [double precision, elnuc_dist_deriv_e, (4, nelec, nnuc)]
|
||||
end do
|
||||
END_PROVIDER
|
||||
|
||||
BEGIN_PROVIDER [double precision, rescale_een_e_deriv_e, (4, nelec, nelec, 0:ncord)]
|
||||
BEGIN_PROVIDER [double precision, rescale_een_e_deriv_e, (nelec, 4, nelec, 0:ncord)]
|
||||
BEGIN_DOC
|
||||
! Derivative of the scaled distance J_{een} wrt R_{ia}
|
||||
END_DOC
|
||||
@ -259,27 +259,27 @@ BEGIN_PROVIDER [double precision, rescale_een_e_deriv_e, (4, nelec, nelec, 0:nco
|
||||
do i = 1, nelec
|
||||
! r'(x) \lor r''(x)
|
||||
do ii = 1, 4
|
||||
rescale_een_e_deriv_e(ii, i, j, l) = &
|
||||
rescale_een_e_deriv_e(i, ii, j, l) = &
|
||||
kappa_l * elec_dist_deriv_e(ii, i, j)
|
||||
enddo
|
||||
|
||||
! \left(r''(x)+r'(x)^2\right)
|
||||
rescale_een_e_deriv_e(4, i, j, l) = rescale_een_e_deriv_e(4, i, j, l) + &
|
||||
rescale_een_e_deriv_e(1, i, j, l) * rescale_een_e_deriv_e(1, i, j, l) + &
|
||||
rescale_een_e_deriv_e(2, i, j, l) * rescale_een_e_deriv_e(2, i, j, l) + &
|
||||
rescale_een_e_deriv_e(3, i, j, l) * rescale_een_e_deriv_e(3, i, j, l)
|
||||
rescale_een_e_deriv_e(i, 4, j, l) = rescale_een_e_deriv_e(i, 4, j, l) + &
|
||||
rescale_een_e_deriv_e(i, 1, j, l) * rescale_een_e_deriv_e(i, 1, j, l) + &
|
||||
rescale_een_e_deriv_e(i, 2, j, l) * rescale_een_e_deriv_e(i, 2, j, l) + &
|
||||
rescale_een_e_deriv_e(i, 3, j, l) * rescale_een_e_deriv_e(i, 3, j, l)
|
||||
|
||||
! \times e^{r(x)}
|
||||
do ii = 1, 4
|
||||
rescale_een_e_deriv_e(ii, i, j, l) = &
|
||||
rescale_een_e_deriv_e(ii, i, j, l) * rescale_een_e(i, j, l)
|
||||
rescale_een_e_deriv_e(i, ii, j, l) = &
|
||||
rescale_een_e_deriv_e(i, ii, j, l) * rescale_een_e(i, j, l)
|
||||
enddo
|
||||
enddo
|
||||
enddo
|
||||
enddo
|
||||
END_PROVIDER
|
||||
|
||||
BEGIN_PROVIDER [double precision, rescale_een_e_deriv_e_t, (4, nelec, nelec, 0:ncord)]
|
||||
BEGIN_PROVIDER [double precision, rescale_een_e_deriv_e_t, (nelec, 4, nelec, 0:ncord)]
|
||||
implicit none
|
||||
BEGIN_DOC
|
||||
! Transposed rescale_een_e_deriv_e
|
||||
@ -288,7 +288,7 @@ BEGIN_PROVIDER [double precision, rescale_een_e_deriv_e_t, (4, nelec, nelec, 0:n
|
||||
do l=0,ncord
|
||||
do j=1,nelec
|
||||
do i=1,nelec
|
||||
rescale_een_e_deriv_e_t(1:4,j,i,l) = rescale_een_e_deriv_e(1:4,i,j,l)
|
||||
rescale_een_e_deriv_e_t(j,1:4,i,l) = rescale_een_e_deriv_e(i,1:4,j,l)
|
||||
enddo
|
||||
enddo
|
||||
enddo
|
||||
|
||||
Loading…
Reference in New Issue
Block a user