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@ -1,9 +1,7 @@
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#+TITLE: Quantum Monte Carlo
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#+AUTHOR: Anthony Scemama, Claudia Filippi
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# SETUPFILE: https://fniessen.github.io/org-html-themes/org/theme-readtheorg.setup
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# SETUPFILE: https://fniessen.github.io/org-html-themes/org/theme-bigblow.setup
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#+LANGUAGE: en
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#+INFOJS_OPT: toc:t mouse:underline path:http://orgmode.org/org-info.js
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#+INFOJS_OPT: toc:t mouse:underline path:org-info.js
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#+STARTUP: latexpreview
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#+LATEX_CLASS: report
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#+LATEX_HEADER_EXTRA: \usepackage{minted}
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@ -47,7 +45,7 @@
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computes a statistical estimate of the expectation value of the energy
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associated with a given wave function.
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Finally, we introduce the diffusion Monte Carlo (DMC) method which
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gives the exact energy of the hydrogen atom and of the $H_2$ molecule.
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gives the exact energy of the hydrogen atom and of the H_2 molecule.
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Code examples will be given in Python and Fortran. You can use
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whatever language you prefer to write the program.
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@ -1156,17 +1154,17 @@ gfortran hydrogen.f90 qmc_stats.f90 qmc_uniform.f90 -o qmc_uniform
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initial position $\mathbf{r}_0$, we will realize a random walk as follows:
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$$
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\mathbf{r}_{n+1} = \mathbf{r}_{n} + \tau \mathbf{u}
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\mathbf{r}_{n+1} = \mathbf{r}_{n} + \delta t\, \mathbf{u}
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$$
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where $\tau$ is a fixed constant (the so-called /time-step/), and
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where $\delta t$ is a fixed constant (the so-called /time-step/), and
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$\mathbf{u}$ is a uniform random number in a 3-dimensional box
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$(-1,-1,-1) \le \mathbf{u} \le (1,1,1)$. We will then add the
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accept/reject step that guarantees that the distribution of the
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$\mathbf{r}_n$ is $\Psi^2$:
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1) Compute $\Psi$ at a new position $\mathbf{r'} = \mathbf{r}_n +
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\tau \mathbf{u}$
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\delta t\, \mathbf{u}$
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2) Compute the ratio $R = \frac{\left[\Psi(\mathbf{r'})\right]^2}{\left[\Psi(\mathbf{r}_{n})\right]^2}$
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3) Draw a uniform random number $v \in [0,1]$
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4) if $v \le R$, accept the move : set $\mathbf{r}_{n+1} = \mathbf{r'}$
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@ -1213,15 +1211,15 @@ gfortran hydrogen.f90 qmc_stats.f90 qmc_uniform.f90 -o qmc_uniform
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from hydrogen import *
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from qmc_stats import *
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def MonteCarlo(a,nmax,tau):
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def MonteCarlo(a,nmax,dt):
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# TODO
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return energy/nmax, N_accep/nmax
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# Run simulation
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a = 0.9
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nmax = 100000
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tau = 1.3
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X0 = [ MonteCarlo(a,nmax,tau) for i in range(30)]
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dt = 1.3
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X0 = [ MonteCarlo(a,nmax,dt) for i in range(30)]
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# Energy
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X = [ x for (x, _) in X0 ]
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@ -1236,11 +1234,11 @@ print(f"A = {A} +/- {deltaA}")
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*Fortran*
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#+BEGIN_SRC f90 :tangle none
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subroutine metropolis_montecarlo(a,nmax,tau,energy,accep)
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subroutine metropolis_montecarlo(a,nmax,dt,energy,accep)
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implicit none
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double precision, intent(in) :: a
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integer*8 , intent(in) :: nmax
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double precision, intent(in) :: tau
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double precision, intent(in) :: dt
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double precision, intent(out) :: energy
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double precision, intent(out) :: accep
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@ -1257,7 +1255,7 @@ end subroutine metropolis_montecarlo
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program qmc
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implicit none
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double precision, parameter :: a = 0.9d0
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double precision, parameter :: tau = 1.3d0
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double precision, parameter :: dt = 1.3d0
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integer*8 , parameter :: nmax = 100000
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integer , parameter :: nruns = 30
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@ -1266,7 +1264,7 @@ program qmc
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double precision :: ave, err
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do irun=1,nruns
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call metropolis_montecarlo(a,nmax,tau,X(irun),Y(irun))
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call metropolis_montecarlo(a,nmax,dt,X(irun),Y(irun))
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enddo
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call ave_error(X,nruns,ave,err)
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@ -1288,13 +1286,13 @@ gfortran hydrogen.f90 qmc_stats.f90 qmc_metropolis.f90 -o qmc_metropolis
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from hydrogen import *
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from qmc_stats import *
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def MonteCarlo(a,nmax,tau):
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def MonteCarlo(a,nmax,dt):
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energy = 0.
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N_accep = 0
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r_old = np.random.uniform(-tau, tau, (3))
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r_old = np.random.uniform(-dt, dt, (3))
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psi_old = psi(a,r_old)
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for istep in range(nmax):
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r_new = r_old + np.random.uniform(-tau,tau,(3))
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r_new = r_old + np.random.uniform(-dt,dt,(3))
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psi_new = psi(a,r_new)
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ratio = (psi_new / psi_old)**2
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v = np.random.uniform()
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@ -1308,8 +1306,8 @@ def MonteCarlo(a,nmax,tau):
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# Run simulation
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a = 0.9
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nmax = 100000
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tau = 1.3
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X0 = [ MonteCarlo(a,nmax,tau) for i in range(30)]
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dt = 1.3
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X0 = [ MonteCarlo(a,nmax,dt) for i in range(30)]
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# Energy
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X = [ x for (x, _) in X0 ]
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@ -1328,11 +1326,11 @@ print(f"A = {A} +/- {deltaA}")
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*Fortran*
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#+BEGIN_SRC f90 :exports code
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subroutine metropolis_montecarlo(a,nmax,tau,energy,accep)
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subroutine metropolis_montecarlo(a,nmax,dt,energy,accep)
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implicit none
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double precision, intent(in) :: a
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integer*8 , intent(in) :: nmax
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double precision, intent(in) :: tau
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double precision, intent(in) :: dt
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double precision, intent(out) :: energy
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double precision, intent(out) :: accep
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@ -1346,11 +1344,11 @@ subroutine metropolis_montecarlo(a,nmax,tau,energy,accep)
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energy = 0.d0
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n_accep = 0_8
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call random_number(r_old)
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r_old(:) = tau * (2.d0*r_old(:) - 1.d0)
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r_old(:) = dt * (2.d0*r_old(:) - 1.d0)
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psi_old = psi(a,r_old)
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do istep = 1,nmax
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call random_number(r_new)
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r_new(:) = r_old(:) + tau * (2.d0*r_new(:) - 1.d0)
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r_new(:) = r_old(:) + dt * (2.d0*r_new(:) - 1.d0)
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psi_new = psi(a,r_new)
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ratio = (psi_new / psi_old)**2
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call random_number(v)
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@ -1368,7 +1366,7 @@ end subroutine metropolis_montecarlo
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program qmc
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implicit none
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double precision, parameter :: a = 0.9d0
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double precision, parameter :: tau = 1.3d0
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double precision, parameter :: dt = 1.3d0
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integer*8 , parameter :: nmax = 100000
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integer , parameter :: nruns = 30
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@ -1377,7 +1375,7 @@ program qmc
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double precision :: ave, err
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do irun=1,nruns
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call metropolis_montecarlo(a,nmax,tau,X(irun),Y(irun))
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call metropolis_montecarlo(a,nmax,dt,X(irun),Y(irun))
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enddo
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call ave_error(X,nruns,ave,err)
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@ -1477,12 +1475,12 @@ end subroutine random_gauss
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Now, if instead of drawing uniform random numbers we
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choose to draw Gaussian random numbers with zero mean and variance
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$\tau$, the transition probability becomes:
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$\delta t$, the transition probability becomes:
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\[
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T(\mathbf{r}_{n} \rightarrow \mathbf{r}_{n+1}) =
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\frac{1}{(2\pi\,\tau)^{3/2}} \exp \left[ - \frac{\left(
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\mathbf{r}_{n+1} - \mathbf{r}_{n} \right)^2}{2\tau} \right]
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\frac{1}{(2\pi\,\delta t)^{3/2}} \exp \left[ - \frac{\left(
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\mathbf{r}_{n+1} - \mathbf{r}_{n} \right)^2}{2\delta t} \right]
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\]
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@ -1500,19 +1498,19 @@ end subroutine random_gauss
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The numerical scheme becomes a drifted diffusion:
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\[
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\mathbf{r}_{n+1} = \mathbf{r}_{n} + \tau \frac{\nabla
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\mathbf{r}_{n+1} = \mathbf{r}_{n} + \delta t\, \frac{\nabla
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\Psi(\mathbf{r})}{\Psi(\mathbf{r})} + \chi
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\]
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where $\chi$ is a Gaussian random variable with zero mean and
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variance $\tau$.
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variance $\delta t$.
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The transition probability becomes:
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\[
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T(\mathbf{r}_{n} \rightarrow \mathbf{r}_{n+1}) =
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\frac{1}{(2\pi\,\tau)^{3/2}} \exp \left[ - \frac{\left(
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\frac{1}{(2\pi\,\delta t)^{3/2}} \exp \left[ - \frac{\left(
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\mathbf{r}_{n+1} - \mathbf{r}_{n} - \frac{\nabla
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\Psi(\mathbf{r}_n)}{\Psi(\mathbf{r}_n)} \right)^2}{2\,\tau} \right]
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\Psi(\mathbf{r}_n)}{\Psi(\mathbf{r}_n)} \right)^2}{2\,\delta t} \right]
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\]
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@ -1570,14 +1568,14 @@ end subroutine drift
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from hydrogen import *
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from qmc_stats import *
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def MonteCarlo(a,nmax,tau):
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def MonteCarlo(a,nmax,dt):
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# TODO
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# Run simulation
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a = 0.9
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nmax = 100000
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tau = 1.3
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X0 = [ MonteCarlo(a,nmax,tau) for i in range(30)]
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dt = 1.3
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X0 = [ MonteCarlo(a,nmax,dt) for i in range(30)]
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# Energy
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X = [ x for (x, _) in X0 ]
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@ -1592,14 +1590,14 @@ print(f"A = {A} +/- {deltaA}")
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*Fortran*
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#+BEGIN_SRC f90 :tangle none
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subroutine variational_montecarlo(a,tau,nmax,energy,accep_rate)
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subroutine variational_montecarlo(a,dt,nmax,energy,accep_rate)
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implicit none
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double precision, intent(in) :: a, tau
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double precision, intent(in) :: a, dt
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integer*8 , intent(in) :: nmax
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double precision, intent(out) :: energy, accep_rate
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integer*8 :: istep
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double precision :: sq_tau, chi(3)
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double precision :: sq_dt, chi(3)
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double precision :: psi_old, psi_new
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double precision :: r_old(3), r_new(3)
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double precision :: d_old(3), d_new(3)
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@ -1612,7 +1610,7 @@ end subroutine variational_montecarlo
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program qmc
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implicit none
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double precision, parameter :: a = 0.9
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double precision, parameter :: tau = 1.0
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double precision, parameter :: dt = 1.0
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integer*8 , parameter :: nmax = 100000
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integer , parameter :: nruns = 30
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@ -1621,7 +1619,7 @@ program qmc
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double precision :: ave, err
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do irun=1,nruns
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call variational_montecarlo(a,tau,nmax,X(irun),accep(irun))
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call variational_montecarlo(a,dt,nmax,X(irun),accep(irun))
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enddo
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call ave_error(X,nruns,ave,err)
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print *, 'E = ', ave, '+/-', err
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@ -1641,23 +1639,23 @@ gfortran hydrogen.f90 qmc_stats.f90 vmc_metropolis.f90 -o vmc_metropolis
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from hydrogen import *
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from qmc_stats import *
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def MonteCarlo(a,nmax,tau):
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def MonteCarlo(a,nmax,dt):
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energy = 0.
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accep_rate = 0.
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sq_tau = np.sqrt(tau)
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sq_dt = np.sqrt(dt)
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r_old = np.random.normal(loc=0., scale=1.0, size=(3))
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d_old = drift(a,r_old)
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d2_old = np.dot(d_old,d_old)
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psi_old = psi(a,r_old)
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for istep in range(nmax):
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chi = np.random.normal(loc=0., scale=1.0, size=(3))
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r_new = r_old + tau * d_old + sq_tau * chi
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r_new = r_old + dt * d_old + sq_dt * chi
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d_new = drift(a,r_new)
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d2_new = np.dot(d_new,d_new)
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psi_new = psi(a,r_new)
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# Metropolis
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prod = np.dot((d_new + d_old), (r_new - r_old))
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argexpo = 0.5 * (d2_new - d2_old)*tau + prod
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argexpo = 0.5 * (d2_new - d2_old)*dt + prod
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q = psi_new / psi_old
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q = np.exp(-argexpo) * q*q
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if np.random.uniform() < q:
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@ -1673,8 +1671,8 @@ def MonteCarlo(a,nmax,tau):
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# Run simulation
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a = 0.9
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nmax = 100000
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tau = 1.3
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X0 = [ MonteCarlo(a,nmax,tau) for i in range(30)]
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dt = 1.3
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X0 = [ MonteCarlo(a,nmax,dt) for i in range(30)]
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# Energy
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X = [ x for (x, _) in X0 ]
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@ -1693,20 +1691,20 @@ print(f"A = {A} +/- {deltaA}")
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*Fortran*
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#+BEGIN_SRC f90
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subroutine variational_montecarlo(a,tau,nmax,energy,accep_rate)
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subroutine variational_montecarlo(a,dt,nmax,energy,accep_rate)
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implicit none
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double precision, intent(in) :: a, tau
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double precision, intent(in) :: a, dt
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integer*8 , intent(in) :: nmax
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double precision, intent(out) :: energy, accep_rate
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integer*8 :: istep
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double precision :: sq_tau, chi(3), d2_old, prod, u
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double precision :: sq_dt, chi(3), d2_old, prod, u
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double precision :: psi_old, psi_new, d2_new, argexpo, q
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double precision :: r_old(3), r_new(3)
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double precision :: d_old(3), d_new(3)
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double precision, external :: e_loc, psi
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sq_tau = dsqrt(tau)
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sq_dt = dsqrt(dt)
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! Initialization
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energy = 0.d0
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@ -1718,7 +1716,7 @@ subroutine variational_montecarlo(a,tau,nmax,energy,accep_rate)
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do istep = 1,nmax
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call random_gauss(chi,3)
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r_new(:) = r_old(:) + tau * d_old(:) + chi(:)*sq_tau
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r_new(:) = r_old(:) + dt * d_old(:) + chi(:)*sq_dt
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call drift(a,r_new,d_new)
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d2_new = d_new(1)*d_new(1) + d_new(2)*d_new(2) + d_new(3)*d_new(3)
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psi_new = psi(a,r_new)
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@ -1726,7 +1724,7 @@ subroutine variational_montecarlo(a,tau,nmax,energy,accep_rate)
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prod = (d_new(1) + d_old(1))*(r_new(1) - r_old(1)) + &
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(d_new(2) + d_old(2))*(r_new(2) - r_old(2)) + &
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(d_new(3) + d_old(3))*(r_new(3) - r_old(3))
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argexpo = 0.5d0 * (d2_new - d2_old)*tau + prod
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argexpo = 0.5d0 * (d2_new - d2_old)*dt + prod
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q = psi_new / psi_old
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q = dexp(-argexpo) * q*q
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call random_number(u)
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@ -1746,7 +1744,7 @@ end subroutine variational_montecarlo
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program qmc
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implicit none
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double precision, parameter :: a = 0.9
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double precision, parameter :: tau = 1.0
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double precision, parameter :: dt = 1.0
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integer*8 , parameter :: nmax = 100000
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integer , parameter :: nruns = 30
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@ -1755,7 +1753,7 @@ program qmc
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double precision :: ave, err
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do irun=1,nruns
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call variational_montecarlo(a,tau,nmax,X(irun),accep(irun))
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call variational_montecarlo(a,dt,nmax,X(irun),accep(irun))
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enddo
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call ave_error(X,nruns,ave,err)
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print *, 'E = ', ave, '+/-', err
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@ -1845,7 +1843,7 @@ gfortran hydrogen.f90 qmc_stats.f90 vmc_metropolis.f90 -o vmc_metropolis
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- a rate equation for the potential.
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The diffusion equation can be simulated by a Brownian motion:
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\[ \mathbf{r}_{n+1} = \mathbf{r}_{n} + \sqrt{\tau}\, \chi \]
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\[ \mathbf{r}_{n+1} = \mathbf{r}_{n} + \sqrt{\delta t}\, \chi \]
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where $\chi$ is a Gaussian random variable, and the rate equation
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can be simulated by creating or destroying particles over time (a
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so-called branching process).
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@ -1871,7 +1869,7 @@ gfortran hydrogen.f90 qmc_stats.f90 vmc_metropolis.f90 -o vmc_metropolis
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\left[ -\frac{1}{2} \Delta \psi(\mathbf{r},\tau) + V(\mathbf{r}) \psi(\mathbf{r},\tau) \right] \Psi_T(\mathbf{r})
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\]
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Defining $\Pi(\mathbf{r},t) = \psi(\mathbf{r},\tau)
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Defining $\Pi(\mathbf{r},\tau) = \psi(\mathbf{r},\tau)
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\Psi_T(\mathbf{r})$,
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\[
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@ -1881,10 +1879,15 @@ gfortran hydrogen.f90 qmc_stats.f90 vmc_metropolis.f90 -o vmc_metropolis
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\right] + E_L(\mathbf{r}) \Pi(\mathbf{r},\tau)
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\]
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The new "kinetic energy" can be simulated by the drifted diffusion
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scheme presented in the previous section (VMC).
|
||||
The new "potential" is the local energy, which has smaller fluctuations
|
||||
as $\Psi_T$ tends to the exact wave function. The new "kinetic energy"
|
||||
can be simulated by the drifted diffusion scheme presented in the
|
||||
previous section (VMC).
|
||||
when $\Psi_T$ gets closer to the exact wave function. It can be simulated by
|
||||
changing the number of particles according to $\exp\left[ -\delta t\,
|
||||
\left(E_L(\mathbf{r}) - E_\text{ref}\right)\right]$
|
||||
where $E_{\text{ref}}$ is a constant introduced so that the average
|
||||
of this term is close to one, keeping the number of particles rather
|
||||
constant.
|
||||
|
||||
This equation generates the /N/-electron density $\Pi$, which is the
|
||||
product of the ground state with the trial wave function. It
|
||||
@ -1895,7 +1898,7 @@ gfortran hydrogen.f90 qmc_stats.f90 vmc_metropolis.f90 -o vmc_metropolis
|
||||
systems where the wave function has nodes (systems with 3 or more
|
||||
electrons), this scheme introduces an error known as the /fixed node
|
||||
error/.
|
||||
|
||||
|
||||
*** Appendix : Details of the Derivation
|
||||
|
||||
\[
|
||||
@ -1942,15 +1945,87 @@ gfortran hydrogen.f90 qmc_stats.f90 vmc_metropolis.f90 -o vmc_metropolis
|
||||
\right] + E_L(\mathbf{r}) \Pi(\mathbf{r},\tau)
|
||||
\]
|
||||
|
||||
|
||||
** Fixed-node DMC energy
|
||||
|
||||
Now that we have a process to sample $\Pi(\mathbf{r},\tau) =
|
||||
\psi(\mathbf{r},\tau) \Psi_T(\mathbf{r})$, we can compute the exact
|
||||
energy of the system, within the fixed-node constraint, as:
|
||||
|
||||
\[
|
||||
E = \lim_{\tau \to \infty} \frac{\int \Pi(\mathbf{r},\tau) E_L(\mathbf{r}) d\mathbf{r}}
|
||||
{\int \Pi(\mathbf{r},\tau) d\mathbf{r}} = \lim_{\tau \to
|
||||
\infty} E(\tau).
|
||||
\]
|
||||
|
||||
Indeed, this is equivalent to
|
||||
|
||||
\[
|
||||
E(\tau) = \frac{\int \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) E_L(\mathbf{r}) d\mathbf{r}}
|
||||
{\int \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) d\mathbf{r}}
|
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= \frac{\int \psi(\mathbf{r},\tau) \hat{H} \Psi_T(\mathbf{r}) d\mathbf{r}}
|
||||
{\int \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) d\mathbf{r}}
|
||||
= \frac{\langle \psi_\tau | \hat{H} | \Psi_T \rangle}
|
||||
{\langle \psi_\tau | \Psi_T \rangle}
|
||||
\]
|
||||
|
||||
As $\hat{H}$ is Hermitian,
|
||||
|
||||
\[
|
||||
E(\tau) = \frac{\langle \psi_\tau | \hat{H} | \Psi_T \rangle}
|
||||
{\langle \psi_\tau | \Psi_T \rangle}
|
||||
= \frac{\langle \Psi_T | \hat{H} | \psi_\tau \rangle}
|
||||
{\langle \Psi_T | \psi_\tau \rangle}
|
||||
= E[\psi_\tau] \frac{\langle \Psi_T | \psi_\tau \rangle}
|
||||
{\langle \Psi_T | \psi_\tau \rangle}
|
||||
= E[\psi_\tau]
|
||||
\]
|
||||
|
||||
So computing the energy within DMC consists in generating the
|
||||
density $\Pi$, and simply averaging the local energies computed
|
||||
with the trial wave function.
|
||||
|
||||
** Pure Diffusion Monte Carlo
|
||||
|
||||
Instead of having a variable number of particles to simulate the
|
||||
branching process, one can choose to sample $[\Psi_T(\mathbf{r})]^2$ instead of
|
||||
$\psi(\mathbf{r},\tau) \Psi_T(\mathbf{r})$, and consider the term
|
||||
$\exp \left( -\delta t\,( E_L(\mathbf{r}) - E_{\text{ref}} \right)$ as a
|
||||
cumulative product of branching weights:
|
||||
|
||||
\[
|
||||
W(\mathbf{r}_n, \tau)
|
||||
= \exp \left( \int_0^\tau - (E_L(\mathbf{r}_t) - E_{\text{ref}}) dt \right)
|
||||
\approx \prod_{i=1}^{n} \exp \left( -\delta t\, (E_L(\mathbf{r}_i) - E_{\text{ref}}) \right).
|
||||
\]
|
||||
|
||||
The wave function becomes
|
||||
|
||||
\[
|
||||
\psi(\mathbf{r},\tau) = \Psi_T(\mathbf{r}) W(\mathbf{r},\tau)
|
||||
\]
|
||||
|
||||
and the expression of the fixed-node DMC energy is
|
||||
|
||||
\begin{eqnarray*}
|
||||
E(\tau) & = & \frac{\int \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) E_L(\mathbf{r}) d\mathbf{r}}
|
||||
{\int \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) d\mathbf{r}} \\
|
||||
& = & \frac{\int \left[ \Psi_T(\mathbf{r}) \right]^2 W(\mathbf{r},\tau) E_L(\mathbf{r}) d\mathbf{r}}
|
||||
{\int \left[ \Psi_T(\mathbf{r}) \right]^2 W(\mathbf{r},\tau) d\mathbf{r}} \\
|
||||
\end{eqnarray*}
|
||||
|
||||
This algorithm is less stable than the branching algorithm: it
|
||||
requires to have a value of $E_\text{ref}$ which is close to the
|
||||
fixed-node energy, and a good trial wave function. Its big
|
||||
advantage is that it is very easy to program starting from a VMC code.
|
||||
|
||||
** TODO Hydrogen atom
|
||||
|
||||
*** Exercise
|
||||
|
||||
#+begin_exercise
|
||||
Modify the Metropolis VMC program to introduce the PDMC weight.
|
||||
In the limit $\tau \rightarrow 0$, you should recover the exact
|
||||
In the limit $\delta t \rightarrow 0$, you should recover the exact
|
||||
energy of H for any value of $a$.
|
||||
#+end_exercise
|
||||
|
||||
@ -2094,7 +2169,7 @@ gfortran hydrogen.f90 qmc_stats.f90 vmc_metropolis.f90 -o vmc_metropolis
|
||||
: A = 0.78861366666666655 +/- 3.5096729498002445E-004
|
||||
|
||||
|
||||
** TODO Dihydrogen
|
||||
** TODO H_2
|
||||
|
||||
We will now consider the H_2 molecule in a minimal basis composed of the
|
||||
$1s$ orbitals of the hydrogen atoms:
|
||||
@ -2108,7 +2183,7 @@ gfortran hydrogen.f90 qmc_stats.f90 vmc_metropolis.f90 -o vmc_metropolis
|
||||
the nuclei.
|
||||
|
||||
|
||||
* Appendix :noexport:
|
||||
* Old sections to be removed :noexport:
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||||
|
||||
** Gaussian sampling :noexport:
|
||||
:PROPERTIES:
|
||||
|
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288
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}}else{if("?"==c||"¿"==c){b.showHelp();}else{if("C"==c){if(b.ST.length){b.showTagsIndex();
|
||||
}else{b.warn("No Tags found.");}}else{if("H"==c&&b.LINK_HOME){window.document.location.href=b.LINK_HOME;
|
||||
}else{if("h"==c&&b.LINK_UP){window.document.location.href=b.LINK_UP;}else{if("l"==c){if(""!=b.OCCUR){b.sR(b.RC_H,"Choose HTML-link type: 's' = section, 'o' = occur");
|
||||
}else{b.sR(c,"HTML-link:",'<a href="'+b.BU+b.dT()+'">'+document.title+", Sec. '"+b.rT(b.N.H.innerHTML)+"'</a>","C-c to copy, ");
|
||||
window.setTimeout(function(){org_html_manager.CI.select();},100);}return;}else{if("L"==c){if(""!=b.OCCUR){b.sR(b.RC_O,"Choose Org-link type: 's' = section, 'o' = occur");
|
||||
}else{b.sR(c,"Org-link:","[["+b.BU+b.dT()+"]["+document.title+", Sec. '"+b.rT(b.N.H.innerHTML)+"']]","C-c to copy, ");
|
||||
window.setTimeout(function(){org_html_manager.CI.select();},100);}return;}else{if("U"==c){if(""!=b.OCCUR){b.sR(b.RC_P,"Choose Org-link type: 's' = section, 'o' = occur");
|
||||
}else{b.sR(c,"Plain URL Link:",b.BU+b.dT(),"C-c to copy, ");window.setTimeout(function(){org_html_manager.CI.select();
|
||||
},100);}return;}else{if("g"==c){b.sR(c,"Enter section number:");return;}else{if("o"==c){if(""!=b.OCCUR){b.sR(c,"Occur:",b.OCCUR,"RET to use previous, DEL ");
|
||||
}else{b.sR(c,"Occur:",b.OCCUR);}window.setTimeout(function(){org_html_manager.CI.value=org_html_manager.OCCUR;
|
||||
org_html_manager.CI.select();},100);return;}else{if("s"==c){if(""!=b.OCCUR){b.sR(c,"Search forward:",b.OCCUR,"RET to use previous, DEL ");
|
||||
}else{b.sR(c,"Search forward:",b.OCCUR);}window.setTimeout(function(){org_html_manager.CI.value=org_html_manager.OCCUR;
|
||||
org_html_manager.CI.select();},100);return;}else{if("S"==c){if(""==b.OCCUR){c="s";
|
||||
b.sR(c,"Search forward:");}else{b.RC=c;b.eRC();}return;}else{if("r"==c){if(""!=b.OCCUR){b.sR(c,"Search backwards:",b.OCCUR,"RET to use previous, DEL ");
|
||||
}else{b.sR(c,"Search backwards:",b.OCCUR);}window.setTimeout(function(){org_html_manager.CI.value=org_html_manager.OCCUR;
|
||||
org_html_manager.CI.select();},100);return;}else{if("R"==c){if(""==b.OCCUR){c="r";
|
||||
b.sR(c,"Search backwards:");}else{b.RC=c;b.eRC();}return;}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}return;
|
||||
},eRC:function(){var j=this;var a=j.RC;var k=j.trim(j.CI.value);j.eR();if(""==a||""==k){j.hC();
|
||||
return;}if(a=="g"){var f=j.SN_MAP[k];if(null!=f){j.hC();j.go(f.J);return;}j.warn("Goto section: no such section.",false,k);
|
||||
return;}else{if(a=="s"){if(""==k){return false;}if(j.SHO){j.rSH();}var g=j.OCCUR;
|
||||
var e=0;if(k==j.OCCUR){e++;}j.OCCUR=k;j.mSR();for(var d=j.N.J+e;d<j.S.length;++d){if(j.sIO(d)){j.OCCUR=k;
|
||||
j.hC();j.go(j.S[d].J);return;}}j.warn("Search forwards: text not found.",false,j.OCCUR);
|
||||
j.OCCUR=g;return;}else{if(a=="S"){for(var d=j.N.J+1;d<j.S.length;++d){if(j.sIO(d)){j.hC();
|
||||
j.go(j.S[d].J);return;}}j.warn("Search forwards: text not found.",false,j.OCCUR);
|
||||
return;}else{if(a=="r"){if(""==k){return false;}if(j.SHO){j.rSH();}var g=j.OCCUR;
|
||||
j.OCCUR=k;var e=0;if(k==j.OCCUR){e++;}j.mSR();for(var d=j.N.J-e;d>-1;--d){if(j.sIO(d)){j.hC();
|
||||
j.go(j.S[d].J);return;}}j.warn("Search backwards: text not found.",false,j.OCCUR);
|
||||
j.OCCUR=g;return;}else{if(a=="R"){for(var d=j.N.J-1;d>-1;--d){k=j.rT(j.S[d].H.innerHTML);
|
||||
if(j.sIO(d)){j.hC();j.go(j.S[d].J);return;}}j.warn("Search backwards: text not found.",false,j.OCCUR);
|
||||
return;}else{if(a=="o"){if(""==k){return false;}if(j.SHO){j.rSH();}var g=j.OCCUR;
|
||||
j.OCCUR=k;j.mSR();var b=new Array();for(var d=0;d<j.S.length;++d){if(j.sIO(d)){b.push(d);
|
||||
}}if(0==b.length){j.warn("Occur: text not found.",false,j.OCCUR);j.OCCUR=g;return;
|
||||
}j.hC();if(j.PLAIN_VIEW!=j.VIEW){j.pV();}j.R.DRT=true;j.tG();for(var d=0;d<j.S.length;
|
||||
++d){OrgNode.sE(j.S[d].D);OrgNode.hE(j.S[d].F);}for(var d=(b.length-1);d>=1;--d){OrgNode.sE(j.S[b[d]].F);
|
||||
}j.ss(b[0]);}else{if(a==j.RC_O){var h=k.charAt(0);if("s"==h){j.sR(j.RC_NULL,"Org-link to this section:","[["+j.BU+j.dT()+"]["+document.title+", Sec. '"+j.rT(j.N.H.innerHTML)+"']]","C-c to copy, ");
|
||||
window.setTimeout(function(){org_html_manager.CI.select();},100);}else{if("o"==h){j.sR(j.RC_NULL,"Org-link, occurences of <i>""+j.OCCUR+""</i>:","[["+j.BU+"?OCCUR="+j.OCCUR+"]["+document.title+", occurences of '"+j.OCCUR+"']]","C-c to copy, ");
|
||||
window.setTimeout(function(){org_html_manager.CI.select();},100);}else{j.warn(h+": No such link type!");
|
||||
}}}else{if(a==j.RC_H){var h=k.charAt(0);if("s"==h){j.sR(j.RC_NULL,"HTML-link to this section:",'<a href="'+j.BU+j.dT()+'">'+document.title+", Sec. '"+j.rT(j.N.H.innerHTML)+"'</a>","C-c to copy, ");
|
||||
window.setTimeout(function(){org_html_manager.CI.select();},100);}else{if("o"==h){j.sR(j.RC_NULL,"HTML-link, occurences of <i>""+j.OCCUR+""</i>:",'<a href="'+j.BU+"?OCCUR="+j.OCCUR+'">'+document.title+", occurences of '"+j.OCCUR+"'</a>","C-c to copy, ");
|
||||
window.setTimeout(function(){org_html_manager.CI.select();},100);}else{j.warn(h+": No such link type!");
|
||||
}}}else{if(a==j.RC_P){var h=k.charAt(0);if("s"==h){j.sR(j.RC_NULL,"Plain-link to this section:",j.BU+j.dT(),"C-c to copy, ");
|
||||
window.setTimeout(function(){org_html_manager.CI.select();},100);}else{if("o"==h){j.sR(j.RC_NULL,"Plain-link, occurences of <i>""+j.OCCUR+""</i>:",j.BU+"?OCCUR="+j.OCCUR,"C-c to copy, ");
|
||||
window.setTimeout(function(){org_html_manager.CI.select();},100);}else{j.warn(h+": No such link type!");
|
||||
}}}}}}}}}}}},dT:function(b){if(null==b){b=this.N;}var c="#"+this.N.I;for(var a in b.iTF){if(!a.match(this.SIDX)){c=a;
|
||||
break;}}return c;},mSR:function(){var a=this.OCCUR.replace(/>/g,">").replace(/</g,"<").replace(/=/g,"\\=").replace(/\\/g,"\\\\").replace(/\?/g,"\\?").replace(/\)/g,"\\)").replace(/\(/g,"\\(").replace(/\./g,"[^<>]").replace(/\"/g,""");
|
||||
this.SCX=new RegExp(">([^<]*)?("+a+")([^>]*)?<","ig");},sIO:function(c){var b=this;
|
||||
var a=false;if(null!=b.S[c]){if(b.SCX.test(b.S[c].H.innerHTML)){a=true;b.sSH(b.S[c].H);
|
||||
b.S[c].HH=true;b.SHO=true;}if(b.SCX.test(b.S[c].F.innerHTML)){a=true;b.sSH(b.S[c].F);
|
||||
b.S[c].HH=true;b.SHO=true;}return a;}return false;},sSH:function(b){var a=b.innerHTML;
|
||||
b.innerHTML=a.replace(this.SCX,'>$1<span class="org-info-js_search-highlight">$2</span>$3<');
|
||||
},rSH:function(){var c=this;for(var b=0;b<c.S.length;++b){if(c.S[b].HH){while(c.SC_HLX.test(c.S[b].H.innerHTML)){var a=c.S[b].H.innerHTML;
|
||||
c.S[b].H.innerHTML=a.replace(c.SC_HLX,"$2");}while(c.SC_HLX.test(c.S[b].F.innerHTML)){var a=c.S[b].F.innerHTML;
|
||||
c.S[b].F.innerHTML=a.replace(c.SC_HLX,"$2");}c.S[b].HH=false;}}c.SHO=false;},hH:function(b){var a=parseInt(b);
|
||||
if(this.PLAIN_VIEW==this.VIEW&&this.MOUSE_HINT){if("underline"==this.MOUSE_HINT){this.S[a].H.style.borderBottom="1px dashed #666666";
|
||||
}else{this.S[a].H.style.backgroundColor=this.MOUSE_HINT;}}},unhH:function(b){var a=parseInt(b);
|
||||
if("underline"==this.MOUSE_HINT){this.S[a].H.style.borderBottom="";}else{this.S[a].H.style.backgroundColor="";
|
||||
}},showHelp:function(){var a=this;if(a.Rg){a.eR();}else{if(a.Mg){a.rW();}}a.Hg=a.Hg?0:1;
|
||||
if(a.Hg){a.LVM=a.VIEW;if(a.PLAIN_VIEW==a.VIEW){a.iV(true);}a.W.innerHTML='Press any key or <a href="javascript:org_html_manager.showHelp();">click here</a> to proceed.<h2>Keyboard Shortcuts</h2><table cellpadding="3" rules="groups" frame="hsides" style="caption-side:bottom;margin:20px;border-style:none;" border="0";><caption><small>org-info.js, v. 0.1.7.1</small></caption><tbody><tr><td><code><b>? / ¿</b></code></td><td>show this help screen</td></tr></tbody><tbody><tr><td><code><b></b></code></td><td><b>Moving around</b></td></tr><tr><td><code><b>n / p</b></code></td><td>goto the next / previous section</td></tr><tr><td><code><b>N / P</b></code></td><td>goto the next / previous sibling</td></tr><tr><td><code><b>t / E</b></code></td><td>goto the first / last section</td></tr><tr><td><code><b>g</b></code></td><td>goto section...</td></tr><tr><td><code><b>u</b></code></td><td>go one level up (parent section)</td></tr><tr><td><code><b>i / C</b></code></td><td>show table of contents / tags index</td></tr><tr><td><code><b>b / B</b></code></td><td>go back to last / forward to next visited section.</td></tr><tr><td><code><b>h / H</b></code></td><td>go to main index in this directory / link HOME page</td></tr></tbody><tbody><tr><td><code><b></b></code></td><td><b>View</b></td></tr><tr><td><code><b>m / x</b></code></td><td>toggle the view mode between info and plain / slides</td></tr><tr><td><code><b>f / F</b></code></td><td>fold current section / whole document (plain view only)</td></tr></tbody><tbody><tr><td><code><b></b></code></td><td><b>Searching</b></td></tr><tr><td><code><b>s / r</b></code></td><td>search forward / backward....</td></tr><tr><td><code><b>S / R</b></code></td><td>search again forward / backward</td></tr><tr><td><code><b>o</b></code></td><td>occur-mode</td></tr><tr><td><code><b>c</b></code></td><td>clear search-highlight</td></tr></tbody><tbody><tr><td><code><b></b></code></td><td><b>Misc</b></td></tr><tr><td><code><b>l / L / U</b></code></td><td>display HTML link / Org link / Plain-URL</td></tr><tr><td><code><b>v / V</b></code></td><td>scroll down / up</td></tr><tr><td><code><b>W</b></code></td><td>Print</td></tr></tbody></table><br />Press any key or <a href="javascript:org_html_manager.showHelp();">click here</a> to proceed.';
|
||||
window.scrollTo(0,0);}else{if(a.PLAIN_VIEW==a.LVM){a.pV();}else{if(a.SLIDE_VIEW==a.LVM){a.sV();
|
||||
}}a.ss(a.N.J);}},showTagsIndex:function(){var e=this;if(e.Rg){e.eR();}else{if(e.Mg){e.rW();
|
||||
}}e.Hg=e.Hg?0:1;if(e.Hg){e.LVM=e.VIEW;if(e.PLAIN_VIEW==e.VIEW){e.iV(true);}if(null==e.TAGS_INDEX){e.TAGS_INDEX='Press any key or <a href="javascript:org_html_manager.showTagsIndex();">click here</a> to proceed.<br /><br />Click the headlines to expand the contents.<h2>Index of Tags</h2>';
|
||||
for(var d=0;d<e.ST.length;++d){var b=e.ST[d];var f="org-html-manager-sorted-tags-"+b;
|
||||
e.TAGS_INDEX+="<a href=\"javascript:OrgNode.toggleElement(document.getElementById('"+f+"'));\"><h3>"+b+'</h3></a><div id="'+f+'" style="visibility:hidden;display:none;"><ul>';
|
||||
for(var c=0;c<e.TAGS[b].length;++c){var a=e.TAGS[b][c];e.TAGS_INDEX+='<li><a href="javascript:org_html_manager.ss('+a+');">'+e.S[a].H.innerHTML+"</a></li>";
|
||||
}e.TAGS_INDEX+="</ul></div>";}e.TAGS_INDEX+='<br />Press any key or <a href="javascript:org_html_manager.showTagsIndex();">click here</a> to proceed.';
|
||||
}e.W.innerHTML=e.TAGS_INDEX;window.scrollTo(0,0);}else{if(e.PLAIN_VIEW==e.LVM){e.pV();
|
||||
}else{if(e.SLIDE_VIEW==e.LVM){e.sV();}}e.ss(e.N.J);}},runHooks:function(b,a){if(this.HOOKS.run_hooks&&this.HOOKS[b]){for(var c=0;
|
||||
c<this.HOOKS[b].length;++c){this.HOOKS[b][c](this,a);}}},addHook:function(a,b){if("run_hooks"!=a){this.HOOKS[a].push(b);
|
||||
}},removeHook:function(a,c){if(this.HOOKS[a]){for(var b=this.HOOKS[a].length-1;b>=0;
|
||||
--b){if(this.HOOKS[a][b]==c){this.HOOKS[a].splice(b,1);}}}}};function OrgHtmlManagerKeyEvent(b){var d;
|
||||
if(!b){b=window.event;}if(b.which){d=b.which;}else{if(b.keyCode){d=b.keyCode;}}if(b.ctrlKey){return;
|
||||
}var a=String.fromCharCode(d);if(b.shiftKey){org_html_manager.CI.value=org_html_manager.CI.value+a;
|
||||
}else{org_html_manager.CI.value=org_html_manager.CI.value+a.toLowerCase();}org_html_manager.getKey();
|
||||
}function OrgHtmlManagerLoadCheck(){org_html_manager.init();}
|
||||
Loading…
Reference in New Issue
Block a user