examples/TimeStepperDemo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " *** OPENMP n_threads =           8\n"
     ]
    }
   ],
   "source": [
    "using TSSM\n",
    "include(\"time_stepper.jl\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TSSM.Schroedinger1D{Float64}(Ptr{Void} @0x0000000003b2c870)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cubic_coupling = -1.0\n",
    "xmin=-16.0\n",
    "xmax=16.0\n",
    "Nx=1024\n",
    "\n",
    "m=Schroedinger1D(Nx,xmin, xmax,cubic_coupling=cubic_coupling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TSSM.WfSchroedinger1D{Float64}(Ptr{Void} @0x0000000003c672f0,TSSM.Schroedinger1D{Float64}(Ptr{Void} @0x0000000003b2c870))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psi=wave_function(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "soliton (generic function with 1 method)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "const a=[2.0, 2.0,  0.0,  0.0] #aplitude\n",
    "const b=[1.0,-3.0,  1.0, -9.0] #speed\n",
    "const c=[5.0,-5.0,  0.0, -10.0] #offset x\n",
    "\n",
    "function soliton(x)\n",
    "    (a[1]/cosh(a[1]*(x-c[1]))) * exp(-b[1]*x*1im)+\n",
    "    (a[2]/cosh(a[2]*(x-c[2]))) * exp(-b[2]*x*1im)+\n",
    "    (a[3]/cosh(a[3]*(x-c[3]))) * exp(-b[3]*x*1im)+\n",
    "    (a[4]/cosh(a[4]*(x-c[4]))) * exp(-b[4]*x*1im)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PalindromicScheme((0.20165104431232422,0.578800656272665,0.5626159753565692,0.2731288360565245,0.25387403824755483,-0.10273380314843214,-0.8353516931903706,0.0680149460931651,0.0680149460931651,-0.8353516931903706,-0.10273380314843214,0.25387403824755483,0.2731288360565245,0.5626159753565692,0.578800656272665,0.20165104431232422),5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "palindromic_scheme_34 = PalindromicScheme( \n",
    "          ( 0.268330095781759925,  0.919661523017399857, \n",
    "           -0.187991618799159782, -0.187991618799159782, \n",
    "            0.919661523017399857,  0.268330095781759925 ),\n",
    "            3 )\n",
    "\n",
    "palindromic_scheme_56 = PalindromicScheme(\n",
    "          ( 0.201651044312324230,   0.578800656272664932, \n",
    "            0.562615975356569200,   0.273128836056524479, \n",
    "            0.253874038247554845,  -0.102733803148432142, \n",
    "           -0.835351693190370636,   0.068014946093165092, \n",
    "            0.068014946093165092,  -0.835351693190370636,\n",
    "           -0.102733803148432142,   0.253874038247554845, \n",
    "            0.273128836056524479,   0.562615975356569200, \n",
    "            0.578800656272664932,   0.201651044312324230 ),            \n",
    "            5 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = get_nodes(m);\n",
    "to_real_space!(psi);\n",
    "u = get_data(psi, true);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0e-8"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "  tend=10.0\n",
    "  t0=0.0\n",
    "  out=1\n",
    "  tol=1e-8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "215."
     ]
    }
   ],
   "source": [
    "set!(psi, soliton)\n",
    "\n",
    "plotdata=abs(u).^2\n",
    "steps=[0.0]\n",
    "mytime=[0.0]\n",
    "told=0.0\n",
    "nsteps=0\n",
    "\n",
    "\n",
    "@time  for t in adaptive_time_stepper(psi, t0, tend, 0.01, tol, palindromic_scheme_56, \"AB\")\n",
    "       #readline(STDIN)\n",
    "      push!(steps,t-told)\n",
    "      push!(mytime,t)\n",
    "      told=t\n",
    "      nsteps=nsteps+1\n",
    "  \n",
    "      if out==1\n",
    "      to_real_space!(psi)\n",
    "        plotdata = [plotdata abs(u).^2]\n",
    "         #@printf(\"t=%5.3f\\n\", t)\n",
    "         #figure(2)\n",
    "         #hold(false)\n",
    "         #plot(x, abs(u).^2)\n",
    "         #hold(true)\n",
    "      end\n",
    "  end\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Recompiling stale cache file /home/juser/.julia/lib/v0.4/PyPlot.ji for module PyPlot.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "933363 seconds (508.24 k allocations: 3.217 GB, 0.12% gc time)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Recompiling stale cache file /home/juser/.julia/lib/v0.4/PyCall.ji for module PyCall.\n",
      "INFO: Recompiling stale cache file /opt/julia_packages/.julia/lib/v0.4/Conda.ji for module Conda.\n",
      "INFO: Recompiling stale cache file /opt/julia_packages/.julia/lib/v0.4/BinDeps.ji for module BinDeps.\n",
      "INFO: Recompiling stale cache file /opt/julia_packages/.julia/lib/v0.4/URIParser.ji for module URIParser.\n",
      "INFO: Recompiling stale cache file /opt/julia_packages/.julia/lib/v0.4/SHA.ji for module SHA.\n",
      "WARNING: Module Compat uuid did not match cache file\n",
      "  This is likely because module Compat does not support  precompilation but is imported by a module that does.\n",
      "WARNING: deserialization checks failed while attempting to load cache from /opt/julia_packages/.julia/lib/v0.4/SHA.ji\n",
      "WARNING: Module Compat uuid did not match cache file\n",
      "  This is likely because module Compat does not support  precompilation but is imported by a module that does.\n",
      "WARNING: deserialization checks failed while attempting to load cache from /opt/julia_packages/.julia/lib/v0.4/BinDeps.ji\n",
      "INFO: Recompiling stale cache file /home/juser/.julia/lib/v0.4/BinDeps.ji for module BinDeps.\n",
      "WARNING: Module SHA uuid did not match cache file\n",
      "  This is likely because module SHA does not support  precompilation but is imported by a module that does.\n",
      "WARNING: deserialization checks failed while attempting to load cache from /opt/julia_packages/.julia/lib/v0.4/Conda.ji\n",
      "INFO: Recompiling stale cache file /home/juser/.julia/lib/v0.4/Colors.ji for module Colors.\n",
      "INFO: Recompiling stale cache file /home/juser/.julia/lib/v0.4/FixedPointNumbers.ji for module FixedPointNumbers.\n",
      "INFO: Recompiling stale cache file /home/juser/.julia/lib/v0.4/ColorTypes.ji for module ColorTypes.\n",
      "INFO: Recompiling stale cache file /home/juser/.julia/lib/v0.4/Reexport.ji for module Reexport.\n",
      "INFO: Recompiling stale cache file /home/juser/.julia/lib/v0.4/LaTeXStrings.ji for module LaTeXStrings.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f0be42a8d10>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "  using PyPlot\n",
    "  figure(1)\n",
    "  hold(false)\n",
    "  if out==1\n",
    "  subplot(2, 1, 1)\n",
    "  pcolormesh(mytime,x,plotdata)\n",
    "  xlabel(\"t\")\n",
    "  ylabel(\"x\")\n",
    "  #colorbar()\n",
    "  axis([t0,tend,xmin,xmax])\n",
    "  title(\"abs(u)^2\")\n",
    "\n",
    "  subplot(2, 1, 2)\n",
    "  end\n",
    "  plot(mytime[1:end-2], steps[2:end-1])\n",
    "  xlabel(\"t\")\n",
    "  ylabel(\"stepsize\")\n",
    "  savefig(\"step.png\", bbox_inches=\"tight\")\n",
    "  #colorbar( )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.4.5",
   "language": "julia",
   "name": "julia-0.4"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.4.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}