mirror of
https://github.com/TREX-CoE/irpjast.git
synced 2024-12-22 12:23:57 +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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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 -xHost -g -mkl=sequential
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FC = ifort -xCORE-AVX512 -g -mkl=sequential
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FCFLAGS= -O2 -I .
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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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ARCHIVE = ar crs
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RANLIB = ranlib
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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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\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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c_{apkl} R_{ia}^{p-k-l}\, C_{i,a(p-k+l)}^k
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$$
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$$
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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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implicit none
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integer :: i
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integer :: i
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double precision :: ticks_0, ticks_1, cpu_0, cpu_1
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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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double precision :: irp_rdtsc
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call cpu_time(cpu_0)
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call cpu_time(cpu_0)
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ticks_0 = irp_rdtsc()
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ticks_0 = irp_rdtsc()
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do i=1,irp_imax
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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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enddo
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ticks_1 = irp_rdtsc()
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ticks_1 = irp_rdtsc()
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call cpu_time(cpu_1)
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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 *, '-----------'
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print *, 'Cycles:'
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print *, 'Cycles:'
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print *, (ticks_1-ticks_0)/dble(irp_imax)
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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_e(i,j,k) * &
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rescale_een_n(i,a,m+l)
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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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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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rescale_een_n(i,a,m)
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daccu2(1:4) = daccu2(1:4) + &
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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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rescale_een_n(i,a,m+l)
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enddo
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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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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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(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(1:4,j,a,m)) * cn
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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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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 (1) * rescale_een_n_deriv_e(j,1,a,m+l) + &
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daccu (2) * rescale_een_n_deriv_e(2,j,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(3,j,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(1,j,a,m ) + &
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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(2,j,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(3,j,a,m ) )*cn
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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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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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rjam_cn = rescale_een_n(j, a, m) * cn
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do ii = 1, 4
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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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drjal(ii) = rescale_een_n_deriv_e(j, ii, a, l)
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drjam_cn(ii) = rescale_een_n_deriv_e(ii, j, a, m) * cn
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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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enddo
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do i = 1, nelec
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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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rijk = rescale_een_e(i, j, k)
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do ii = 1, 4
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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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enddo
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v1 = rijk * rial ! v(x)
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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, tmp_c, (nelec,nnuc,0:ncord,0:ncord-1) ]
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&BEGIN_PROVIDER [ double precision, factor_een_deriv_e_blas, (4, nelec) ]
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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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implicit none
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BEGIN_DOC
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BEGIN_DOC
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! Dimensions 1-3 : dx, dy, dz
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! Calculate the intermediate buffers
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! Dimension 4 : d2x + d2y + d2z
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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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END_DOC
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integer :: k
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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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! r_{ij}^k . R_{ja}^l -> tmp_c_{ia}^{kl}
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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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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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! dr_{ij}^k . R_{ja}^l -> dtmp_c_{ia}^{kl}
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do k=0,ncord-1
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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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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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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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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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do n = 1, dim_cord_vect
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l = lkpm_of_cindex(1,n)
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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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m = lkpm_of_cindex(4,n)
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do a = 1, nnuc
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do a = 1, nnuc
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cn(a) = cord_vect_full(n, a)
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cn = cord_vect_full(n, a)
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enddo
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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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accu = 0.d0
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do j=1,nelec
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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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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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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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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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BEGIN_DOC
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||||||
! Number of electrons
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! Number of electrons
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END_DOC
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END_DOC
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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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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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|
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END_PROVIDER
|
END_PROVIDER
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||||||
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BEGIN_PROVIDER [ integer, nelec_up ]
|
BEGIN_PROVIDER [ integer, nelec_up ]
|
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@ -11,7 +20,7 @@ BEGIN_PROVIDER [ integer, nelec_up ]
|
|||||||
BEGIN_DOC
|
BEGIN_DOC
|
||||||
! Number of alpha and beta electrons
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! Number of alpha and beta electrons
|
||||||
END_DOC
|
END_DOC
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nelec_up = 5
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nelec_up = nelec/2
|
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END_PROVIDER
|
END_PROVIDER
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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
|
9.169699233477617284e+00 1.577356709974558768e+01 1.026790877215631070e+01
|
||||||
0.000000 0.000000 2.059801
|
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
|
||||||
|
1.208625197377544858e+01 9.786394515437207176e+00 1.717854136000790533e+01
|
||||||
|
1.494633023918510517e+01 1.603464738388862543e+01 1.406542597486318869e+01
|
||||||
|
1.667900812370894670e+01 1.251829711646267995e+01 4.270300401191871487e+00
|
||||||
|
1.586198715643285517e+01 2.875672063917811272e-01 8.891552400836387093e+00
|
||||||
|
9.419404525581425602e+00 1.389052206291107971e+01 1.108900939317432588e+01
|
||||||
|
1.963965760373481828e+01 1.146902995603590725e+01 1.282907948294069911e+01
|
||||||
|
1.198047511787546071e+01 5.562236588668721282e+00 1.915349450607307347e+01
|
||||||
|
1.488104353517301348e+01 5.804103516304268240e+00 6.937216682401476930e+00
|
||||||
|
1.081824688317853855e+01 1.816125330714973174e+01 3.402481046984189295e+00
|
||||||
|
1.719374956854118608e+01 4.884797944310266260e+00 1.920695796409663814e+01
|
||||||
|
1.793960241583939208e+01 1.653024768810333889e+00 3.298821317147142551e+00
|
||||||
|
1.897808829921623364e+00 8.140060824774359105e+00 1.961610203110009820e+01
|
||||||
|
1.522322197862856541e+01 1.141399000556607923e+01 9.491306080383932198e+00
|
||||||
|
1.885179661581180710e+01 1.492261282193172889e+01 6.484741121232293182e+00
|
||||||
|
1.116624933697523758e+01 6.358757303413402617e+00 6.916852334485867893e+00
|
||||||
|
1.641047798611942454e+01 8.078339459447054338e+00 3.974595146357355890e+00
|
||||||
|
1.839623071904149398e+01 1.747807382087439976e+01 1.224414893914888935e+01
|
||||||
|
1.013635602912024680e+01 4.994384310269368576e+00 1.919873788632172307e+01
|
||||||
|
3.532729893816910494e+00 1.177599028381978385e+01 1.364746894301321234e+01
|
||||||
|
6.364475283610449452e+00 2.756442052672813947e+00 1.993988542501183847e+00
|
||||||
|
1.986281358109961559e+01 1.591076223093103437e+01 8.678810962712169896e+00
|
||||||
|
1.175127911665890146e+01 7.772089039972498448e+00 7.187138704784985066e+00
|
||||||
|
1.193125527485955395e+01 1.431390781406945933e+01 1.265541473195264288e+01
|
||||||
|
1.133185649869511025e+01 7.714119831904899804e+00 1.988089589887153608e+01
|
||||||
|
3.970252576323316518e+00 1.945022550869681766e+01 1.250876195144565095e+01
|
||||||
|
8.316736617812782839e+00 1.175130684010653859e+01 1.051450652667101338e+01
|
||||||
|
2.363693618139217634e+00 2.468837030925852272e+00 1.005198216788683041e-02
|
||||||
|
6.296064183630758038e+00 1.379169961939031452e+01 1.087964194835752529e+01
|
||||||
|
1.404224396433654753e+01 1.676940411433356370e+00 1.264252268440966454e+01
|
||||||
|
7.748241981417103297e-01 1.214872603120482353e+01 8.330412247841345597e-01
|
||||||
|
1.482580840274279943e+01 1.223347895464069168e+01 3.415821930923017558e+00
|
||||||
|
1.583665650690541682e+01 1.353650167723072428e+01 2.697684980051406889e+00
|
||||||
|
7.228036394543392973e+00 1.856489259785730184e+01 5.412992838630334980e+00
|
||||||
|
1.250523870392217773e+01 1.930800659024364307e+01 1.323894588602757771e+01
|
||||||
|
1.476425588956215051e+01 1.973578521040820988e+01 1.845229986910515763e+01
|
||||||
|
1.453346429546398255e+00 8.164114196818228919e+00 1.096993647314665132e+01
|
||||||
|
1.645780314591159055e+01 3.609174059640158916e+00 1.214909757599990314e+01
|
||||||
|
1.503594622796937230e+01 8.361488631403306115e+00 4.885904156983071900e+00
|
||||||
|
4.607709491539058178e+00 1.711603235809798917e+01 1.077737795893560246e+01
|
||||||
|
5.444611672020791104e+00 4.650442422860228575e+00 1.909067263900083233e+01
|
||||||
|
5.313792477754875065e-01 8.018457454298415499e+00 3.294528391053614946e+00
|
||||||
|
2.008248149095013257e+00 5.871637319393618881e-01 1.274877050725333660e+01
|
||||||
|
4.236439749525723997e+00 1.622219492454691547e+01 1.673052983670413330e+00
|
||||||
|
4.863853221184999853e+00 3.211812899760422280e+00 1.196906268597565060e+01
|
||||||
|
1.423284986943139252e+01 5.125867755205759657e-01 1.957274773922198818e+01
|
||||||
|
8.788927972275073941e+00 1.690304861735086561e+01 1.986478748330291566e+01
|
||||||
|
1.873007149410335614e+01 1.202269311771925153e+01 1.238139871086807275e+01
|
||||||
|
1.332404831179675497e+01 2.430077255068527897e+00 5.774860805419890220e+00
|
||||||
|
2.860148112180942448e+00 5.699127286772864842e-01 3.785091713927268842e+00
|
||||||
|
3.169014233130353908e+00 1.187067878487777506e+01 5.904637425708301635e+00
|
||||||
|
6.485116764957301605e+00 1.449966765623678455e+01 1.093038093646896591e+01
|
||||||
|
1.638799794515688291e+01 5.828648438494672845e+00 6.770382163340869397e+00
|
||||||
|
6.488517205429760182e+00 9.709343068893831585e+00 1.716889983416628596e+01
|
||||||
|
7.335968100444041795e+00 8.477396516109221736e+00 1.244480223455498091e+01
|
||||||
|
1.287827118726432296e+01 1.462681359150602489e+01 4.404176750645225624e+00
|
||||||
|
1.177381305500459518e+01 6.986630747738691305e+00 8.980641095899795090e+00
|
||||||
|
7.780325482042506735e+00 1.464235462946512101e+01 1.190153382636764334e+01
|
||||||
|
7.363964503444961451e+00 9.378804346496965039e+00 1.963274323415326705e+01
|
||||||
|
5.776655941050856669e+00 5.339409256429639150e+00 6.151279230717858759e+00
|
||||||
|
3.116758779185213601e+00 1.742245812485627354e+01 1.569327886491995905e+01
|
||||||
|
8.567387735155199024e+00 1.208934780212089777e+00 5.196919625657359099e+00
|
||||||
|
1.415037591844026466e+01 2.847983412165777661e+00 1.233328815020159297e+01
|
||||||
|
1.636468986461008512e+01 1.451079484590765034e+01 1.207738244144391793e+01
|
||||||
|
7.417186920223405977e+00 1.310703527774940014e+01 6.177255611674361546e+00
|
||||||
|
7.707056020550233200e+00 1.551121691246043000e+01 1.191495277078642445e+00
|
||||||
|
6.066579737290198615e+00 1.398026616942766864e+01 1.670256441921655899e+00
|
||||||
|
9.404773402560927309e+00 8.163372380824096552e-02 9.563611400839750587e+00
|
||||||
|
3.572392081411224218e-01 1.349107317825030705e+01 1.442921281054204741e+01
|
||||||
|
1.046956357964869078e+01 1.720237551282311728e+01 1.976912018536546611e+01
|
||||||
|
3.587378947280008834e+00 1.982017106695849051e+01 1.800465657877865766e+01
|
||||||
|
1.233057899646373912e+01 5.017501465684251372e+00 6.245401926461646269e+00
|
||||||
|
1.223960883456811644e+00 4.752974531379106082e+00 6.328475477314010611e-01
|
||||||
|
9.772396890948849446e+00 5.571050679520041626e+00 5.226614381600134251e+00
|
||||||
|
6.975527861247479144e+00 1.870471225537538373e+01 9.192423017315331180e+00
|
||||||
|
9.240112207612600770e+00 1.896763728569224128e+01 1.101673199128806857e+01
|
||||||
|
1.341972145993210219e+01 1.870280684111147096e+01 1.795990955568402470e+01
|
||||||
|
1.035326854711128064e+01 4.878469341937064385e+00 3.751485854957772315e+00
|
||||||
|
1.870795449017081324e+01 1.831914660403077821e+00 8.373263276422404644e+00
|
||||||
|
8.087921672582453425e+00 1.950401904800463271e+01 1.454718045256741199e+01
|
||||||
|
1.027044331124545806e+01 3.508664047516378837e+00 1.445507762924474804e+01
|
||||||
|
1.554986522192784903e+01 1.020126281964536297e+01 1.835357595579026579e+01
|
||||||
|
7.168074592017841695e+00 1.100460329646218582e+01 1.428017210088657407e+01
|
||||||
|
6.485423174327742402e+00 1.322683084053914371e+01 1.992940432667338335e+00
|
||||||
|
6.553663472634834619e+00 4.229294242312933605e+00 1.569147564716142362e+00
|
||||||
|
1.481466695650968113e+00 7.831868024012984542e+00 1.127861742440441617e+01
|
||||||
|
1.335657649179298012e+01 1.647583063010893767e+01 1.138630873736699201e+01
|
||||||
|
1.379151973752265015e+01 1.871206752648178195e+01 1.427418709907454719e+01
|
||||||
|
1.832833852719470968e+01 9.370541708219858990e+00 1.306342934326463201e+01
|
||||||
|
4.831030605796211574e+00 1.953813384789268781e+01 1.357433411806962908e+01
|
||||||
|
1.424415448690996833e+01 1.767406449802044932e+01 6.249021198885172268e+00
|
||||||
|
3.905369047332567511e+00 3.269990121459864785e+00 7.224162243389821825e+00
|
||||||
|
1.358237647699499284e+01 7.315184490172310205e+00 2.425126693107051423e+00
|
||||||
|
1.212372619074920443e+01 7.230526657229456866e+00 6.173809719590686029e+00
|
||||||
|
1.189462711607931489e+00 1.436258062726703599e+01 1.388908238946449991e+01
|
||||||
|
5.791282582461516171e+00 1.154811124642241005e+01 4.853987867958833746e+00
|
||||||
|
1.580896164277743488e+01 1.336431055791168987e+01 1.464733318205402313e-01
|
||||||
|
1.461920345301095558e+01 2.868597875720253487e+00 6.843543709812403009e+00
|
||||||
|
6.466614211741039675e-01 2.591729448327484420e-01 9.027239297261210993e+00
|
||||||
|
9.797766150642786442e+00 1.073713633697784431e+01 6.555964178001707054e+00
|
||||||
|
7.720982694389178391e+00 7.626541579051428599e+00 3.378125038476549324e+00
|
||||||
|
5.629253920787151699e+00 2.939295942554405183e+00 7.733903921451464214e+00
|
||||||
|
6.583043287686914269e+00 1.922035337287248957e+00 3.584219282118321637e+00
|
||||||
|
1.489921294361408677e+01 1.407384696930397183e+01 1.706132384981712136e+01
|
||||||
|
1.884796602727871928e+01 1.412843909897030237e+01 4.981866736853817201e+00
|
||||||
|
5.257804257464737674e+00 9.596920031787581351e-01 4.521353899301237433e+00
|
||||||
|
8.035825356431727684e+00 5.683074702560486635e+00 7.592413901629657680e+00
|
||||||
|
8.674200728650191650e+00 1.989106949092978738e+01 4.798674908083784274e+00
|
||||||
|
1.196880775847672940e+00 1.881420583740770169e+01 5.170873776613637673e+00
|
||||||
|
1.273187955638934987e+01 1.194740895765314548e+01 1.332625470554491542e+00
|
||||||
|
1.135098960471650287e+01 2.938928551817328039e+00 7.838721421271126033e+00
|
||||||
|
1.716530594140219890e+01 1.648801429086308801e+01 1.242794559996758075e+01
|
||||||
|
1.059808339130593069e+01 4.094109684779065894e+00 1.252581464872687889e+01
|
||||||
|
1.630451438891242777e+00 1.581507340905474557e+01 1.241343681908335661e-01
|
||||||
|
1.153722834561401100e+01 2.198244146456629355e+00 6.327048268880295367e+00
|
||||||
|
1.273252708789018151e+01 1.974450378774391268e+01 6.961717815856731661e+00
|
||||||
|
5.526234739443327548e+00 2.096001487241014871e+00 8.073834430983314547e+00
|
||||||
|
8.083971380937386542e+00 2.941693099249573784e+00 9.457602991575523532e+00
|
||||||
|
6.242035997829011862e+00 8.171402498789335667e+00 1.457729081929404202e+01
|
||||||
|
1.889743919435310460e+01 1.215973328705928047e+01 7.291307883355515607e+00
|
||||||
|
1.066803974906196650e+01 6.740766286962682763e+00 1.468358927758984578e+01
|
||||||
|
2.502460941375919123e+00 5.335195575479789731e-01 1.692966502639559678e+01
|
||||||
|
1.048201056139653886e+01 6.589474791250122365e+00 2.907510843947571644e+00
|
||||||
|
1.284394055156609227e+01 8.013476693909149517e-01 7.799321414157689425e+00
|
||||||
|
1.275682590461077126e+01 5.346479388172761915e+00 5.472946928985457760e+00
|
||||||
|
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||||||
|
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
|
BEGIN_DOC
|
||||||
! Number of nuclei
|
! Number of nuclei
|
||||||
END_DOC
|
END_DOC
|
||||||
nnuc = 2
|
!nnuc = 2
|
||||||
|
! read(*,*)nnuc
|
||||||
|
nnuc = nelec/5
|
||||||
END_PROVIDER
|
END_PROVIDER
|
||||||
|
|
||||||
|
|
||||||
@ -14,7 +16,8 @@ BEGIN_PROVIDER [ integer, typenuc ]
|
|||||||
! Type of the nuclei
|
! Type of the nuclei
|
||||||
END_DOC
|
END_DOC
|
||||||
typenuc = 1
|
typenuc = 1
|
||||||
typenuc_arr = (/1, 1/)
|
!typenuc_arr = (/1, 1/)
|
||||||
|
typenuc_arr = 1
|
||||||
END_PROVIDER
|
END_PROVIDER
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
@ -186,7 +186,7 @@ BEGIN_PROVIDER [double precision, rescale_een_n, (nelec, nnuc, 0:ncord)]
|
|||||||
|
|
||||||
END_PROVIDER
|
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
|
implicit none
|
||||||
BEGIN_DOC
|
BEGIN_DOC
|
||||||
! Derivative of the scaled distance J_{een} wrt R_{ia}
|
! 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
|
kappa_l = - dble(l) * kappa
|
||||||
do a = 1, nnuc
|
do a = 1, nnuc
|
||||||
do i = 1, nelec
|
do i = 1, nelec
|
||||||
! r'(x) \lor r''(x)
|
|
||||||
do ii = 1, 4
|
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)
|
kappa_l * elnuc_dist_deriv_e(ii, i, a)
|
||||||
!print *, "pp", ii, i, a, elnuc_dist_deriv_e(ii, i, a)
|
!print *, "pp", ii, i, a, elnuc_dist_deriv_e(ii, i, a)
|
||||||
enddo
|
enddo
|
||||||
|
|
||||||
! \left(r''(x)+r'(x)^2\right)
|
! \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(i, 4, a, l) = rescale_een_n_deriv_e(i, 4, 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(i, 1, a, l) * rescale_een_n_deriv_e(i, 1, 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(i, 2, a, l) * rescale_een_n_deriv_e(i, 2, 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, 3, a, l) * rescale_een_n_deriv_e(i, 3, a, l)
|
||||||
|
|
||||||
! \times e^{r(x)}
|
! \times e^{r(x)}
|
||||||
do ii = 1, 4
|
do ii = 1, 4
|
||||||
rescale_een_n_deriv_e(ii, i, a, l) = &
|
rescale_een_n_deriv_e(i, ii, 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(i, a, l)
|
||||||
enddo
|
enddo
|
||||||
enddo
|
enddo
|
||||||
enddo
|
enddo
|
||||||
@ -243,7 +243,7 @@ BEGIN_PROVIDER [double precision, elnuc_dist_deriv_e, (4, nelec, nnuc)]
|
|||||||
end do
|
end do
|
||||||
END_PROVIDER
|
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
|
BEGIN_DOC
|
||||||
! Derivative of the scaled distance J_{een} wrt R_{ia}
|
! Derivative of the scaled distance J_{een} wrt R_{ia}
|
||||||
END_DOC
|
END_DOC
|
||||||
@ -259,27 +259,27 @@ BEGIN_PROVIDER [double precision, rescale_een_e_deriv_e, (4, nelec, nelec, 0:nco
|
|||||||
do i = 1, nelec
|
do i = 1, nelec
|
||||||
! r'(x) \lor r''(x)
|
! r'(x) \lor r''(x)
|
||||||
do ii = 1, 4
|
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)
|
kappa_l * elec_dist_deriv_e(ii, i, j)
|
||||||
enddo
|
enddo
|
||||||
|
|
||||||
! \left(r''(x)+r'(x)^2\right)
|
! \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(i, 4, j, l) = rescale_een_e_deriv_e(i, 4, 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(i, 1, j, l) * rescale_een_e_deriv_e(i, 1, 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(i, 2, j, l) * rescale_een_e_deriv_e(i, 2, 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, 3, j, l) * rescale_een_e_deriv_e(i, 3, j, l)
|
||||||
|
|
||||||
! \times e^{r(x)}
|
! \times e^{r(x)}
|
||||||
do ii = 1, 4
|
do ii = 1, 4
|
||||||
rescale_een_e_deriv_e(ii, i, j, l) = &
|
rescale_een_e_deriv_e(i, ii, 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(i, j, l)
|
||||||
enddo
|
enddo
|
||||||
enddo
|
enddo
|
||||||
enddo
|
enddo
|
||||||
enddo
|
enddo
|
||||||
END_PROVIDER
|
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
|
implicit none
|
||||||
BEGIN_DOC
|
BEGIN_DOC
|
||||||
! Transposed rescale_een_e_deriv_e
|
! 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 l=0,ncord
|
||||||
do j=1,nelec
|
do j=1,nelec
|
||||||
do i=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
|
enddo
|
||||||
enddo
|
enddo
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user