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Introduced error bars in variance and norm

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This commit is contained in:
Anthony Scemama 2020-08-29 00:22:48 +02:00
parent 3ec31857f9
commit 32dd686f96
2 changed files with 39 additions and 25 deletions
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52
src/cipsi/pt2_stoch_routines.irp.f
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@ -360,9 +360,9 @@ subroutine pt2_collector(zmq_socket_pull, E, relative_error, pt2_data, b, N_)
integer, intent(in) :: N_ integer, intent(in) :: N_
double precision, allocatable :: eI(:,:), eI_task(:,:), S(:), S2(:) double precision, allocatable :: eI(:,:), eI_task(:,:), Se(:), Se2(:)
double precision, allocatable :: vI(:,:), vI_task(:,:), T2(:) double precision, allocatable :: vI(:,:), vI_task(:,:), Sv(:), Sv2(:)
double precision, allocatable :: nI(:,:), nI_task(:,:), T3(:) double precision, allocatable :: nI(:,:), nI_task(:,:), Sn(:), Sn2(:)
integer(ZMQ_PTR),external :: new_zmq_to_qp_run_socket integer(ZMQ_PTR),external :: new_zmq_to_qp_run_socket
integer(ZMQ_PTR) :: zmq_to_qp_run_socket integer(ZMQ_PTR) :: zmq_to_qp_run_socket
integer, external :: zmq_delete_tasks_async_send integer, external :: zmq_delete_tasks_async_send
@ -402,8 +402,9 @@ subroutine pt2_collector(zmq_socket_pull, E, relative_error, pt2_data, b, N_)
allocate(eI(N_states, N_det_generators), eI_task(N_states, pt2_n_tasks_max)) allocate(eI(N_states, N_det_generators), eI_task(N_states, pt2_n_tasks_max))
allocate(vI(N_states, N_det_generators), vI_task(N_states, pt2_n_tasks_max)) allocate(vI(N_states, N_det_generators), vI_task(N_states, pt2_n_tasks_max))
allocate(nI(N_states, N_det_generators), nI_task(N_states, pt2_n_tasks_max)) allocate(nI(N_states, N_det_generators), nI_task(N_states, pt2_n_tasks_max))
allocate(S(pt2_N_teeth+1), S2(pt2_N_teeth+1)) allocate(Se(pt2_N_teeth+1), Se2(pt2_N_teeth+1))
allocate(T2(pt2_N_teeth+1), T3(pt2_N_teeth+1)) allocate(Sv(pt2_N_teeth+1), Sv2(pt2_N_teeth+1))
allocate(Sn(pt2_N_teeth+1), Sn2(pt2_N_teeth+1))
@ -415,10 +416,12 @@ subroutine pt2_collector(zmq_socket_pull, E, relative_error, pt2_data, b, N_)
pt2_data % pt2_err(pt2_stoch_istate) = huge(1.) pt2_data % pt2_err(pt2_stoch_istate) = huge(1.)
pt2_data % variance(pt2_stoch_istate) = huge(1.) pt2_data % variance(pt2_stoch_istate) = huge(1.)
pt2_data % norm2(pt2_stoch_istate) = 0.d0 pt2_data % norm2(pt2_stoch_istate) = 0.d0
S(:) = 0d0 Se(:) = 0d0
S2(:) = 0d0 Sv(:) = 0d0
T2(:) = 0d0 Sn(:) = 0d0
T3(:) = 0d0 Se2(:) = 0d0
Sv2(:) = 0d0
Sn2(:) = 0d0
n = 1 n = 1
t = 0 t = 0
U = 0 U = 0
@ -474,14 +477,16 @@ subroutine pt2_collector(zmq_socket_pull, E, relative_error, pt2_data, b, N_)
x += eI(pt2_stoch_istate, i) * pt2_W_T / pt2_w(i) x += eI(pt2_stoch_istate, i) * pt2_W_T / pt2_w(i)
x2 += vI(pt2_stoch_istate, i) * pt2_W_T / pt2_w(i) x2 += vI(pt2_stoch_istate, i) * pt2_W_T / pt2_w(i)
x3 += nI(pt2_stoch_istate, i) * pt2_W_T / pt2_w(i) x3 += nI(pt2_stoch_istate, i) * pt2_W_T / pt2_w(i)
S(p) += x Se(p) += x
S2(p) += x*x Sv(p) += x2
T2(p) += x2 Sn(p) += x3
T3(p) += x3 Se2(p) += x*x
Sv2(p) += x2*x2
Sn2(p) += x3*3
end do end do
avg = E0 + S(t) / dble(c) avg = E0 + Se(t) / dble(c)
avg2 = v0 + T2(t) / dble(c) avg2 = v0 + Sv(t) / dble(c)
avg3 = n0 + T3(t) / dble(c) avg3 = n0 + Sn(t) / dble(c)
if ((avg /= 0.d0) .or. (n == N_det_generators) ) then if ((avg /= 0.d0) .or. (n == N_det_generators) ) then
do_exit = .true. do_exit = .true.
endif endif
@ -494,9 +499,18 @@ subroutine pt2_collector(zmq_socket_pull, E, relative_error, pt2_data, b, N_)
call wall_time(time) call wall_time(time)
! 1/(N-1.5) : see Brugger, The American Statistician (23) 4 p. 32 (1969) ! 1/(N-1.5) : see Brugger, The American Statistician (23) 4 p. 32 (1969)
if(c > 2) then if(c > 2) then
eqt = dabs((S2(t) / c) - (S(t)/c)**2) ! dabs for numerical stability eqt = dabs((Se2(t) / c) - (Se(t)/c)**2) ! dabs for numerical stability
eqt = sqrt(eqt / (dble(c) - 1.5d0)) eqt = sqrt(eqt / (dble(c) - 1.5d0))
pt2_data % pt2_err(pt2_stoch_istate) = eqt pt2_data % pt2_err(pt2_stoch_istate) = eqt
eqt = dabs((Sv2(t) / c) - (Sv(t)/c)**2) ! dabs for numerical stability
eqt = sqrt(eqt / (dble(c) - 1.5d0))
pt2_data % variance_err(pt2_stoch_istate) = eqt
eqt = dabs((Sn2(t) / c) - (Sn(t)/c)**2) ! dabs for numerical stability
eqt = sqrt(eqt / (dble(c) - 1.5d0))
pt2_data % norm2_err(pt2_stoch_istate) = eqt
if ((time - time1 > 1.d0) .or. (n==N_det_generators)) then if ((time - time1 > 1.d0) .or. (n==N_det_generators)) then
time1 = time time1 = time
print '(G10.3, 2X, F16.10, 2X, G10.3, 2X, F14.10, 2X, F14.10, 2X, F10.4, A10)', c, avg+E, eqt, avg2, avg3, time-time0, '' print '(G10.3, 2X, F16.10, 2X, G10.3, 2X, F14.10, 2X, F14.10, 2X, F10.4, A10)', c, avg+E, eqt, avg2, avg3, time-time0, ''
@ -520,7 +534,7 @@ subroutine pt2_collector(zmq_socket_pull, E, relative_error, pt2_data, b, N_)
call pull_pt2_results(zmq_socket_pull, index, eI_task, vI_task, nI_task, task_id, n_tasks, b2) call pull_pt2_results(zmq_socket_pull, index, eI_task, vI_task, nI_task, task_id, n_tasks, b2)
if(n_tasks > pt2_n_tasks_max)then if(n_tasks > pt2_n_tasks_max)then
print*,'PB !!!' print*,'PB !!!'
print*,'If you see this, send an email to Anthony scemama with the following content' print*,'If you see this, send a bug report with the following content'
print*,irp_here print*,irp_here
print*,'n_tasks,pt2_n_tasks_max = ',n_tasks,pt2_n_tasks_max print*,'n_tasks,pt2_n_tasks_max = ',n_tasks,pt2_n_tasks_max
stop -1 stop -1
@ -531,7 +545,7 @@ subroutine pt2_collector(zmq_socket_pull, E, relative_error, pt2_data, b, N_)
do i=1,n_tasks do i=1,n_tasks
if(index(i).gt.size(eI,2).or.index(i).lt.1)then if(index(i).gt.size(eI,2).or.index(i).lt.1)then
print*,'PB !!!' print*,'PB !!!'
print*,'If you see this, send an email to Anthony scemama with the following content' print*,'If you see this, send a bug report with the following content'
print*,irp_here print*,irp_here
print*,'i,index(i),size(ei,2) = ',i,index(i),size(ei,2) print*,'i,index(i),size(ei,2) = ',i,index(i),size(ei,2)
stop -1 stop -1

8
src/iterations/print_summary.irp.f
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@ -70,10 +70,10 @@ subroutine print_summary(e_,pt2_data,n_det_,n_occ_pattern_,n_st,s2_)
print*,'* State ',k print*,'* State ',k
print *, '< S^2 > = ', s2_(k) print *, '< S^2 > = ', s2_(k)
print *, 'E = ', e_(k) print *, 'E = ', e_(k)
print *, 'Variance = ', pt2_data % variance(k) print *, 'Variance = ', pt2_data % variance(k), ' +/- ', pt2_data % variance_err(k)
print *, 'PT norm = ', dsqrt(pt2_data % norm2(k)) print *, 'PT norm = ', dsqrt(pt2_data % norm2(k)), ' +/- ', 0.5d0*dsqrt(pt2_data % norm2(k)) * pt2_data % norm2_err(k) / pt2_data % norm2(k)
print *, 'PT2 = ', pt2_data % pt2(k) print *, 'PT2 = ', pt2_data % pt2(k), ' +/- ', pt2_data % pt2_err(k)
print *, 'rPT2 = ', pt2_data % pt2(k)*f(k) print *, 'rPT2 = ', pt2_data % pt2(k)*f(k), ' +/- ', pt2_data % rpt2_err(k)
print *, 'E+PT2 '//pt2_string//' = ', e_(k)+pt2_data % pt2(k), ' +/- ', pt2_data % pt2_err(k) print *, 'E+PT2 '//pt2_string//' = ', e_(k)+pt2_data % pt2(k), ' +/- ', pt2_data % pt2_err(k)
print *, 'E+rPT2'//pt2_string//' = ', e_(k)+pt2_data % pt2(k)*f(k), ' +/- ', pt2_data % pt2_err(k)*f(k) print *, 'E+rPT2'//pt2_string//' = ', e_(k)+pt2_data % pt2(k)*f(k), ' +/- ', pt2_data % pt2_err(k)*f(k)
print *, '' print *, ''