{ "metadata": { "name": "", "signature": "sha256:ea4fd684090ed8b533496c628f1f10207a45c5964120b1cd122007d3d9aeaa5a" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Schrodinger equation in 1D\n", "===\n", "\n", "$$\n", "i\\hbar\\frac{\\partial\\psi}{\\partial t} = \\hat H\\psi\n", "$$\n", "In particular\n", "$$\n", "i\\hbar\\frac{\\partial\\psi}{\\partial t} = \\left[-\\frac{\\hbar^2}{2m}\\Delta + V\\right]\\psi\n", "$$\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "from pylab import *\n", "from numpy import *" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "#physical constants\n", "hbar = 1.0\n", "m = 1.0" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "#discretization parameters\n", "nx = 100 # number of unknown grid points in spatial direction\n", "SC = 1.0 # stability criterion SC = 2*D*dt/dx^2, for FTCS should be SC <= 1\n", "\n", "def schr_init(maxx, maxt, maxV, nx, SC):\n", " #choose time step according to CFL condition\n", " dx = maxx/(nx+1)\n", " dt = hbar/(2*hbar**2/(m*dx**2)+maxV)*SC\n", " #dt = SC*dx**2*m/hbar # for V = 0\n", " nt = int(maxt/dt)+1\n", " \n", " #define array for storing the solution\n", " Ur = zeros((nt, nx+2))\n", " Ui = zeros((nt, nx+2))\n", " \n", " x = arange(nx+2)*dx\n", " t = arange(nt)*dt\n", " return Ur, Ui, dx, dt, x, t" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "def schr_solve(Ur, Ui, dx, dt, nx, nt):\n", " xint = arange(1, nx+1)\n", " for it in range(0,nt-1):\n", " Ui[it+1,xint] = Ui[it,xint] + hbar*dt/(2*m*dx**2)*(Ur[it,xint+1] - 2*Ur[it, xint] + Ur[it, xint-1])\\\n", " - dt/hbar*V[xint]*Ur[it,xint]\n", " Ur[it+1,xint] = Ur[it,xint] - hbar*dt/(2*m*dx**2)*(Ui[it+1,xint+1] - 2*Ui[it+1, xint] + Ui[it+1, xint-1])\\\n", " + dt/hbar*V[xint]*Ui[it+1,xint]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assume the initial wave packet will be in the form\n", "$$\n", "\\psi(x, t_0) = \\frac{1}{\\sqrt{\\sigma\\sqrt{\\pi}}}e^{-(x-x_0)^2/2\\sigma}e^{ik_0x}\n", "$$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "maxx = 2.\n", "maxt = 0.05\n", "nx = 200\n", "Ur, Ui, dx, dt, x, t = schr_init(maxx, maxt, 0, nx, 1.0)\n", "V = zeros_like(x)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "sigma = 0.1\n", "k0 = 50.\n", "x0 = maxx/3.\n", "U0 = 1./sqrt(sigma*sqrt(pi))*exp(-(x-x0)**2/(2*sigma**2)) * exp(1j*k0*x)\n", "def Ut(t, x, x0=x0, k0=k0):\n", " p0 = hbar*k0\n", " norm = sqrt(sigma/(sqrt(pi)*(sigma**2+1j*hbar*t/m)))\n", " envelope = exp(-0.5*(x-x0-p0*t/m)**2/(sigma**2+1j*hbar*t/m))\n", " #phase = exp(1j/hbar*(p0*x-p0**2*t)/(2*m))\n", " phase = exp(1j*k0*(x-hbar*k0*t/(2*m)))\n", " return norm*envelope*phase\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "#plot(U0.real)\n", "#plot(U0.imag)\n", "plot(Ut(0, x).imag)\n", "plot(Ut(0, x).real)\n", "plot(abs(Ut(0, x)))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "Ur[0, :] = Ut(0, x).real\n", "Ui[0, :] = Ut(-0.5*dt, x).imag\n", "#Ui[0, :] = U0.imag\n", "schr_solve(Ur, Ui, dx, dt, nx, len(t))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "#pcolormesh(sqrt(Ui**2+Ur**2)[:100,:], rasterized=True)\n", "pcolormesh(Ui[:200,:], rasterized=True)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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wlkQj3nrRtxvZpCZDPne2RPbPSNOpAFZkYEh3omT/sNwuxSplzT42VJnofk0W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\nl3hlEdcOkOcsNIexsm8ynGkbnSffeFXvcLwZONm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"text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# http://nbviewer.ipython.org/github/empet/Math/blob/master/DomainColoring.ipynb\n", "\n", "def Hcomplex(z):# computes the hue corresponding to the complex number z\n", " H=np.angle(z)/(2*np.pi)+1\n", " return np.mod(H,1)\n", "def g(x):\n", " return (1.0-1/(1+x**2))**0.2\n", "\n", "U = Ur[:200,:] + 1j*Ui[:200,:]\n", "\n", "from matplotlib.colors import hsv_to_rgb\n", "H = Hcomplex(U)\n", "V=g(abs(U)*0.5)\n", "S = ones_like(H, float)\n", "\n", "HSV = dstack((H,S,V))# V**0.2>V for V in[0,1];this choice avoids too dark colors\n", "RGB=hsv_to_rgb(HSV)\n", "imshow(RGB)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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xDFaLEVlZarViGNtgoBHqTMmqZhhufFayJDCqxaimA1uWpN38XmdTb9x579m7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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "from matplotlib import animation\n", "from JSAnimation.IPython_display import display_animation" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = figure()\n", "ax = axes(xlim=(0,maxx),ylim=(-3,3))\n", "line1, = ax.plot([],[],lw=1)\n", "line2, = ax.plot([],[],lw=1)\n", "line3, = ax.plot([],[],lw=2)\n", "line4, = ax.plot([],[],lw=2)\n", "\n", "def animate(data):\n", " line1.set_data(x,data[:len(x)])\n", " line2.set_data(x,data[len(x):])\n", " line3.set_data(x,sqrt(data[:len(x)]**2 + data[len(x):]**2))\n", " return line1," ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "Ui_int = 0.5*(Ui[:-1,:]+Ui[1:,:])\n", "anim = animation.FuncAnimation(fig, animate, frames=hstack((Ui_int, Ur[:-1,:]))[:200:2,:], interval=100)\n", "display_animation(anim, default_mode='loop')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "\n", "
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