use the Box-Muller transform instead of random sampling
still needs testing.
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src/math.f90
45
src/math.f90
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@ -961,39 +961,36 @@ pure function math_3333toVoigt66(m3333)
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end function math_3333toVoigt66
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!--------------------------------------------------------------------------------------------------
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!> @brief draw a random sample from Gauss variable
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!> @brief Draw a sample from a normal distribution.
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!> @details https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
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!> https://masuday.github.io/fortran_tutorial/random.html
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!--------------------------------------------------------------------------------------------------
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real(pReal) function math_sampleGaussVar(mu, sigma, width)
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impure elemental subroutine math_normal(x,mu,sigma)
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real(pReal), intent(in) :: mu, & !< mean
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sigma !< standard deviation
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real(pReal), intent(in), optional :: width !< cut off as multiples of standard deviation
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real(pReal), intent(out) :: x
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real(pReal), intent(in), optional :: mu, sigma
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real(pReal), dimension(2) :: rnd ! random numbers
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real(pReal) :: scatter, & ! normalized scatter around mean
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width_
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real(pReal) :: sigma_, mu_
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real(pReal), dimension(2) :: rnd
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if (abs(sigma) < tol_math_check) then
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math_sampleGaussVar = mu
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if (present(mu)) then
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mu_ = mu
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else
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if (present(width)) then
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width_ = width
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else
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width_ = 3.0_pReal ! use +-3*sigma as default scatter
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endif
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mu_ = 0.0_pReal
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end if
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if (present(sigma)) then
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sigma_ = sigma
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else
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sigma_ = 1.0_pReal
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end if
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do
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call random_number(rnd)
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scatter = width_ * (2.0_pReal * rnd(1) - 1.0_pReal)
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if (rnd(2) <= exp(-0.5_pReal * scatter**2)) exit ! test if scattered value is drawn
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enddo
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x = mu_ + sigma_ * sqrt(-2.0_pReal*log(1.0_pReal-rnd(1)))*cos(2.0_pReal*PI*(1.0_pReal - rnd(2)))
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math_sampleGaussVar = scatter * sigma
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endif
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end function math_sampleGaussVar
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end subroutine math_normal
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!--------------------------------------------------------------------------------------------------
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@ -1592,21 +1592,15 @@ subroutine stateInit(ini,phase,Nentries)
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stt%rhoSglMobile(s,e) = densityBinning
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end do
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else ! homogeneous distribution with noise
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do e = 1, Nentries
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do f = 1,size(ini%N_sl,1)
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from = 1 + sum(ini%N_sl(1:f-1))
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upto = sum(ini%N_sl(1:f))
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do s = from,upto
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noise = [math_sampleGaussVar(0.0_pReal, ini%sigma_rho_u), &
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math_sampleGaussVar(0.0_pReal, ini%sigma_rho_u)]
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stt%rho_sgl_mob_edg_pos(s,e) = ini%rho_u_ed_pos_0(f) + noise(1)
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stt%rho_sgl_mob_edg_neg(s,e) = ini%rho_u_ed_neg_0(f) + noise(1)
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stt%rho_sgl_mob_scr_pos(s,e) = ini%rho_u_sc_pos_0(f) + noise(2)
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stt%rho_sgl_mob_scr_neg(s,e) = ini%rho_u_sc_neg_0(f) + noise(2)
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end do
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stt%rho_dip_edg(from:upto,e) = ini%rho_d_ed_0(f)
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stt%rho_dip_scr(from:upto,e) = ini%rho_d_sc_0(f)
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end do
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call math_normal(stt%rho_sgl_mob_edg_pos(from:upto,:),ini%rho_u_ed_pos_0(f),ini%sigma_rho_u)
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call math_normal(stt%rho_sgl_mob_edg_neg(from:upto,:),ini%rho_u_ed_neg_0(f),ini%sigma_rho_u)
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call math_normal(stt%rho_sgl_mob_scr_pos(from:upto,:),ini%rho_u_sc_pos_0(f),ini%sigma_rho_u)
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call math_normal(stt%rho_sgl_mob_scr_neg(from:upto,:),ini%rho_u_sc_neg_0(f),ini%sigma_rho_u)
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stt%rho_dip_edg(from:upto,:) = ini%rho_d_ed_0(f)
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stt%rho_dip_scr(from:upto,:) = ini%rho_d_sc_0(f)
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end do
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end if
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