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irpjast
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Added nelec as command line argument

This commit is contained in:
Anthony Scemama 2021-03-10 14:52:04 +01:00
parent 26a69bea7e 0ea808722c
commit 04113a93d5
13 changed files with 17971 additions and 130 deletions
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6
Makefile
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@ -1,7 +1,7 @@
IRPF90 = irpf90/bin/irpf90 # --codelet=jastrow_full:1000 #-s nelec:10 -s nnuc:2 -s ncord:5 #-a -d
FC = ifort -xHost -g -mkl=sequential
IRPF90 = irpf90/bin/irpf90 --codelet=factor_een:2 #-s nelec:10 -s nnuc:2 -s ncord:5 #-a -d
FC = ifort -xCORE-AVX512 -g -mkl=sequential
FCFLAGS= -O2 -I .
AR = ar
NINJA = ninja
ARCHIVE = ar crs
RANLIB = ranlib

19
README.org
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@ -34,3 +34,22 @@
\sum_{i=1}^{Ne} \sum_{pkl} \sum_a^{Nn}
c_{apkl} R_{ia}^{p-k-l}\, C_{i,a(p-k+l)}^k
$$
* Running
#+begin_src bash :var Ratio=5 Natoms=500
python ./generateData.py -a $Natoms -r $Ratio
#+end_src
Cela genere les trois fichiers:
geometry.txt
elec_coords.txt
jast_coeffs.txt
#+begin_src bash :var Natoms=500
./codelet_factor_een_blas $Natoms
#+end_src

16
codelet_factor_een.f → codelet_factor_een_blas.irp.f
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@ -1,24 +1,26 @@
program codelet_factor_een
program codelet_factor_een_blas
implicit none
integer :: i
double precision :: ticks_0, ticks_1, cpu_0, cpu_1
integer, parameter :: irp_imax = 100000
integer, parameter :: irp_imax = 200
call provide_factor_een
PROVIDE factor_een_blas tmp_c
call provide_factor_een_blas
double precision :: irp_rdtsc
call cpu_time(cpu_0)
ticks_0 = irp_rdtsc()
do i=1,irp_imax
call bld_factor_een
call bld_tmp_c
call bld_factor_een_blas
enddo
ticks_1 = irp_rdtsc()
call cpu_time(cpu_1)
print *, 'factor_een'
print *, 'factor_een_blas'
print *, '-----------'
print *, 'Cycles:'
print *, (ticks_1-ticks_0)/dble(irp_imax)
@ -26,4 +28,4 @@ program codelet_factor_een
print *, (cpu_1-cpu_0)/dble(irp_imax)
end

26
el_nuc_el.irp.f
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@ -95,24 +95,24 @@ BEGIN_PROVIDER [ double precision, factor_een_deriv_e, (4, nelec) ]
rescale_een_e(i,j,k) * &
rescale_een_n(i,a,m+l)
daccu(1:4) = daccu(1:4) + &
rescale_een_e_deriv_e_t(1:4,i,j,k) * &
rescale_een_e_deriv_e_t(i,1:4,j,k) * &
rescale_een_n(i,a,m)
daccu2(1:4) = daccu2(1:4) + &
rescale_een_e_deriv_e_t(1:4,i,j,k) * &
rescale_een_e_deriv_e_t(i,1:4,j,k) * &
rescale_een_n(i,a,m+l)
enddo
factor_een_deriv_e(1:4,j) = factor_een_deriv_e(1:4,j) + &
(accu * rescale_een_n_deriv_e(1:4,j,a,m+l) + daccu(1:4) * rescale_een_n(j,a,m+l) +&
daccu2(1:4)* rescale_een_n(j,a,m) + accu2*rescale_een_n_deriv_e(1:4,j,a,m)) * cn
(accu * rescale_een_n_deriv_e(j,1:4,a,m+l) + daccu(1:4) * rescale_een_n(j,a,m+l) +&
daccu2(1:4)* rescale_een_n(j,a,m) + accu2*rescale_een_n_deriv_e(j,1:4,a,m)) * cn
factor_een_deriv_e(4,j) = factor_een_deriv_e(4,j) + 2.d0*( &
daccu (1) * rescale_een_n_deriv_e(1,j,a,m+l) + &
daccu (2) * rescale_een_n_deriv_e(2,j,a,m+l) + &
daccu (3) * rescale_een_n_deriv_e(3,j,a,m+l) + &
daccu2(1) * rescale_een_n_deriv_e(1,j,a,m ) + &
daccu2(2) * rescale_een_n_deriv_e(2,j,a,m ) + &
daccu2(3) * rescale_een_n_deriv_e(3,j,a,m ) )*cn
daccu (1) * rescale_een_n_deriv_e(j,1,a,m+l) + &
daccu (2) * rescale_een_n_deriv_e(j,2,a,m+l) + &
daccu (3) * rescale_een_n_deriv_e(j,3,a,m+l) + &
daccu2(1) * rescale_een_n_deriv_e(j,1,a,m ) + &
daccu2(2) * rescale_een_n_deriv_e(j,2,a,m ) + &
daccu2(3) * rescale_een_n_deriv_e(j,3,a,m ) )*cn
enddo
enddo
enddo
@ -152,8 +152,8 @@ BEGIN_PROVIDER [ double precision, factor_een_deriv_e_ref, (4, nelec) ]
rjam_cn = rescale_een_n(j, a, m) * cn
do ii = 1, 4
drjal(ii) = rescale_een_n_deriv_e(ii, j, a, l)
drjam_cn(ii) = rescale_een_n_deriv_e(ii, j, a, m) * cn
drjal(ii) = rescale_een_n_deriv_e(j, ii, a, l)
drjam_cn(ii) = rescale_een_n_deriv_e(j, ii, a, m) * cn
enddo
do i = 1, nelec
@ -162,7 +162,7 @@ BEGIN_PROVIDER [ double precision, factor_een_deriv_e_ref, (4, nelec) ]
rijk = rescale_een_e(i, j, k)
do ii = 1, 4
drijk(ii) = rescale_een_e_deriv_e(ii, j, i, k)
drijk(ii) = rescale_een_e_deriv_e(j, ii, i, k)
enddo
v1 = rijk * rial ! v(x)

92
el_nuc_el_blas.irp.f
View File

@ -1,19 +1,15 @@
BEGIN_PROVIDER [ double precision, factor_een_blas ]
&BEGIN_PROVIDER [ double precision, factor_een_deriv_e_blas, (4, nelec) ]
BEGIN_PROVIDER [ double precision, tmp_c, (nelec,nnuc,0:ncord,0:ncord-1) ]
&BEGIN_PROVIDER [ double precision, dtmp_c, (nelec,4,nnuc,0:ncord,0:ncord-1) ]
implicit none
BEGIN_DOC
! Dimensions 1-3 : dx, dy, dz
! Dimension 4 : d2x + d2y + d2z
! Calculate the intermediate buffers
! tmp_c:
! r_{ij}^k . R_{ja}^l -> tmp_c_{ia}^{kl}
!
! dtmp_c:
! dr_{ij}^k . R_{ja}^l -> dtmp_c_{ia}^{kl}
END_DOC
integer :: i, j, a, p, k, l, lmax, m, n
double precision :: cn(ncord), accu
double precision :: f(nnuc,0:ncord-2,0:ncord-2)
double precision :: tmp_c(nelec,nnuc,0:ncord,0:ncord-1)
double precision :: dtmp_c(4,nelec,nnuc,0:ncord,0:ncord-1)
factor_een_blas = 0.0d0
factor_een_deriv_e_blas(1:4,1:nelec) = 0.0d0
integer :: k
! r_{ij}^k . R_{ja}^l -> tmp_c_{ia}^{kl}
do k=0,ncord-1
@ -26,12 +22,30 @@
! dr_{ij}^k . R_{ja}^l -> dtmp_c_{ia}^{kl}
do k=0,ncord-1
call dgemm('N','N', 4*nelec, nnuc*(ncord+1), nelec, 1.d0, &
rescale_een_e_deriv_e(1,1,1,k), 4*size(rescale_een_e_deriv_e,2),&
rescale_een_e_deriv_e(1,1,1,k), 4*size(rescale_een_e_deriv_e,1),&
rescale_een_n(1,1,0), size(rescale_een_n,1), 0.d0, &
dtmp_c(1,1,1,0,k), 4*size(dtmp_c,2))
dtmp_c(1,1,1,0,k), 4*size(dtmp_c,1))
enddo
END_PROVIDER
BEGIN_PROVIDER [ double precision, factor_een_blas ]
&BEGIN_PROVIDER [ double precision, factor_een_deriv_e_blas, (nelec,4) ]
implicit none
BEGIN_DOC
! Dimensions 1-3 : dx, dy, dz
! Dimension 4 : d2x + d2y + d2z
END_DOC
integer :: i, j, a, p, k, l, lmax, m, n, ii
double precision :: accu, cn
! double precision,dimension(:),allocatable :: cn
factor_een_blas = 0.0d0
factor_een_deriv_e_blas(1:nelec,1:4) = 0.0d0
do n = 1, dim_cord_vect
l = lkpm_of_cindex(1,n)
@ -40,33 +54,37 @@
m = lkpm_of_cindex(4,n)
do a = 1, nnuc
cn(a) = cord_vect_full(n, a)
enddo
cn = cord_vect_full(n, a)
if (cn == 0.d0) cycle
do a = 1, nnuc
accu = 0.d0
do j=1,nelec
accu = accu + rescale_een_n(j,a,m) * tmp_c(j,a,m+l,k)
factor_een_deriv_e_blas(1:4,j) = factor_een_deriv_e_blas(1:4,j) + (&
tmp_c(j,a,m,k) * rescale_een_n_deriv_e(1:4,j,a,m+l) + &
dtmp_c(1:4,j,a,m,k) * rescale_een_n(j,a,m+l) + &
dtmp_c(1:4,j,a,m+l,k) * rescale_een_n(j,a,m) + &
tmp_c(j,a,m+l,k)*rescale_een_n_deriv_e(1:4,j,a,m) &
) * cn(a)
factor_een_deriv_e_blas(4,j) = factor_een_deriv_e_blas(4,j) + (&
dtmp_c(1,j,a,m ,k) * rescale_een_n_deriv_e(1,j,a,m+l) + &
dtmp_c(2,j,a,m ,k) * rescale_een_n_deriv_e(2,j,a,m+l) + &
dtmp_c(3,j,a,m ,k) * rescale_een_n_deriv_e(3,j,a,m+l) + &
dtmp_c(1,j,a,m+l,k) * rescale_een_n_deriv_e(1,j,a,m ) + &
dtmp_c(2,j,a,m+l,k) * rescale_een_n_deriv_e(2,j,a,m ) + &
dtmp_c(3,j,a,m+l,k) * rescale_een_n_deriv_e(3,j,a,m ) &
)*cn(a)*2.d0
enddo
factor_een_blas = factor_een_blas + accu * cn(a)
factor_een_blas = factor_een_blas + accu * cn
do ii=1,4
do j=1,nelec
factor_een_deriv_e_blas(j,ii) = factor_een_deriv_e_blas(j,ii) + (&
tmp_c(j,a,m,k) * rescale_een_n_deriv_e(j,ii,a,m+l) + &
dtmp_c(j,ii,a,m,k) * rescale_een_n(j,a,m+l) + &
dtmp_c(j,ii,a,m+l,k) * rescale_een_n(j,a,m) + &
tmp_c(j,a,m+l,k)*rescale_een_n_deriv_e(j,ii,a,m) &
) * cn
enddo
enddo
cn = cn+cn
do j=1,nelec
factor_een_deriv_e_blas(j,4) = factor_een_deriv_e_blas(j,4) + (&
dtmp_c(j,1,a,m ,k) * rescale_een_n_deriv_e(j,1,a,m+l) + &
dtmp_c(j,2,a,m ,k) * rescale_een_n_deriv_e(j,2,a,m+l) + &
dtmp_c(j,3,a,m ,k) * rescale_een_n_deriv_e(j,3,a,m+l) + &
dtmp_c(j,1,a,m+l,k) * rescale_een_n_deriv_e(j,1,a,m ) + &
dtmp_c(j,2,a,m+l,k) * rescale_een_n_deriv_e(j,2,a,m ) + &
dtmp_c(j,3,a,m+l,k) * rescale_een_n_deriv_e(j,3,a,m ) &
)*cn
enddo
enddo
enddo

2510
elec_coord.txt
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File diff suppressed because it is too large Load Diff

13
electrons.irp.f
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@ -3,7 +3,16 @@ BEGIN_PROVIDER [ integer, nelec ]
BEGIN_DOC
! Number of electrons
END_DOC
nelec = 10
character*(32) :: buffer
integer, external :: iargc
if (iargc() == 0) then
nelec = 10
else
call getarg(1,buffer)
read(buffer,*)nelec
endif
END_PROVIDER
BEGIN_PROVIDER [ integer, nelec_up ]
@ -11,7 +20,7 @@ BEGIN_PROVIDER [ integer, nelec_up ]
BEGIN_DOC
! Number of alpha and beta electrons
END_DOC
nelec_up = 5
nelec_up = nelec/2
END_PROVIDER

60
generateData.py Executable file
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@ -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
View File

@ -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
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
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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
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File diff suppressed because it is too large Load Diff

271
jastlib.py Normal file
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@ -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()

7
nuclei.irp.f
View File

@ -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

38
rescale.irp.f
View File

@ -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