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 "metadata": {
  "name": ""
 },
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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "###04/04/2014 tokurakawa Fusied silica dispersion. \n",
      "# coding:shift-jis\n",
      "%pylab inline\n",
      "from matplotlib.pyplot import*\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "import sympy as sy\n",
      "import numpy as nm\n",
      "import scipy as sc\n",
      "import pylab as plt\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "l = sy.Symbol('l')\n",
      "c=3*10**8\n",
      "n1 = ([0.6961663,0.4079426,0.8974794,0.0684043,0.1162414,9.896161])  ### sellmeier CaF2\n",
      "  ###\n",
      "x = plt.linspace(1.0, 2.5, 50)                   ### waveelngth\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "##refractive index at g,m,p axis\n",
      "def N(l):\n",
      "    return (1+n1[0]/(1-(n1[3]/l)**2)+n1[1]/(1-(n1[4]/l)**2)+n1[2]/(1-(n1[5]/l)**2))**0.5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "##dn/dl \n",
      "sy.diff(N(l),l);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "##d2n/dl2\n",
      "sy.diff(N(l),l,2);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "##d23n/dl3\n",
      "sy.diff(N(l),l,3);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "l = plt.linspace(1.0, 2.2, 50)###wavelkength range\n",
      "GD=(-0.00325746533026443/(l**3*(1 - 0.00467914825849/l**2)**2) - 0.00551214614175523/(l**3*(1 - 0.01351206307396/l**2)**2) - 87.8937498373318/(l**3*(1 - 97.934002537921/l**2)**2))*(1 + 0.6961663/(1 - 0.00467914825849/l**2) + 0.4079426/(1 - 0.01351206307396/l**2) + 0.8974794/(1 - 97.934002537921/l**2))**(-0.5)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "GDD=(-0.00325746533026443/(l**3*(1 - 0.00467914825849/l**2)**2) - 0.00551214614175523/(l**3*(1 - 0.01351206307396/l**2)**2) - 87.8937498373318/(l**3*(1 - 97.934002537921/l**2)**2))*(0.00325746533026443/(l**3*(1 - 0.00467914825849/l**2)**2) + 0.00551214614175524/(l**3*(1 - 0.01351206307396/l**2)**2) + 87.8937498373318/(l**3*(1 - 97.934002537921/l**2)**2))*(1 + 0.6961663/(1 - 0.00467914825849/l**2) + 0.4079426/(1 - 0.01351206307396/l**2) + 0.8974794/(1 - 97.934002537921/l**2))**(-1.5) + (1 + 0.6961663/(1 - 0.00467914825849/l**2) + 0.4079426/(1 - 0.01351206307396/l**2) + 0.8974794/(1 - 97.934002537921/l**2))**(-0.5)*(0.00977239599079329/(l**4*(1 - 0.00467914825849/l**2)**2) + 0.0165364384252657/(l**4*(1 - 0.01351206307396/l**2)**2) + 263.681249511995/(l**4*(1 - 97.934002537921/l**2)**2) + 6.09686529087934e-5/(l**6*(1 - 0.00467914825849/l**2)**3) + 0.000297921865361128/(l**6*(1 - 0.01351206307396/l**2)**3) + 34431.1468785466/(l**6*(1 - 97.934002537921/l**2)**3))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TOD=(-0.00325746533026443/(l**3*(1 - 0.00467914825849/l**2)**2) - 0.00551214614175523/(l**3*(1 - 0.01351206307396/l**2)**2) - 87.8937498373318/(l**3*(1 - 97.934002537921/l**2)**2))*(0.00325746533026443/(l**3*(1 - 0.00467914825849/l**2)**2) + 0.00551214614175524/(l**3*(1 - 0.01351206307396/l**2)**2) + 87.8937498373318/(l**3*(1 - 97.934002537921/l**2)**2))*(0.00977239599079328/(l**3*(1 - 0.00467914825849/l**2)**2) + 0.0165364384252657/(l**3*(1 - 0.01351206307396/l**2)**2) + 263.681249511995/(l**3*(1 - 97.934002537921/l**2)**2))*(1 + 0.6961663/(1 - 0.00467914825849/l**2) + 0.4079426/(1 - 0.01351206307396/l**2) + 0.8974794/(1 - 97.934002537921/l**2))**(-2.5) + (-0.00325746533026443/(l**3*(1 - 0.00467914825849/l**2)**2) - 0.00551214614175523/(l**3*(1 - 0.01351206307396/l**2)**2) - 87.8937498373318/(l**3*(1 - 97.934002537921/l**2)**2))*(1 + 0.6961663/(1 - 0.00467914825849/l**2) + 0.4079426/(1 - 0.01351206307396/l**2) + 0.8974794/(1 - 97.934002537921/l**2))**(-1.5)*(-0.00977239599079329/(l**4*(1 - 0.00467914825849/l**2)**2) - 0.0165364384252657/(l**4*(1 - 0.01351206307396/l**2)**2) - 263.681249511995/(l**4*(1 - 97.934002537921/l**2)**2) - 6.09686529087934e-5/(l**6*(1 - 0.00467914825849/l**2)**3) - 0.000297921865361128/(l**6*(1 - 0.01351206307396/l**2)**3) - 34431.1468785466/(l**6*(1 - 97.934002537921/l**2)**3)) + (0.00325746533026443/(l**3*(1 - 0.00467914825849/l**2)**2) + 0.00551214614175524/(l**3*(1 - 0.01351206307396/l**2)**2) + 87.8937498373318/(l**3*(1 - 97.934002537921/l**2)**2))*(1 + 0.6961663/(1 - 0.00467914825849/l**2) + 0.4079426/(1 - 0.01351206307396/l**2) + 0.8974794/(1 - 97.934002537921/l**2))**(-1.5)*(0.00977239599079329/(l**4*(1 - 0.00467914825849/l**2)**2) + 0.0165364384252657/(l**4*(1 - 0.01351206307396/l**2)**2) + 263.681249511995/(l**4*(1 - 97.934002537921/l**2)**2) + 6.09686529087934e-5/(l**6*(1 - 0.00467914825849/l**2)**3) + 0.000297921865361128/(l**6*(1 - 0.01351206307396/l**2)**3) + 34431.1468785466/(l**6*(1 - 97.934002537921/l**2)**3)) + (0.00325746533026443/(l**3*(1 - 0.00467914825849/l**2)**2) + 0.00551214614175524/(l**3*(1 - 0.01351206307396/l**2)**2) + 87.8937498373318/(l**3*(1 - 97.934002537921/l**2)**2))*(1 + 0.6961663/(1 - 0.00467914825849/l**2) + 0.4079426/(1 - 0.01351206307396/l**2) + 0.8974794/(1 - 97.934002537921/l**2))**(-1.5)*(0.00977239599079329/(l**4*(1 - 0.00467914825849/l**2)**2) + 0.0165364384252657/(l**4*(1 - 0.01351206307396/l**2)**2) + 263.681249511995/(l**4*(1 - 97.934002537921/l**2)**2) + 6.09686529087934e-5/(l**6*(1 - 0.00467914825849/l**2)**3) + 0.000297921865361128/(l**6*(1 - 0.01351206307396/l**2)**3) + 34431.1468785466/(l**6*(1 - 97.934002537921/l**2)**3)) + (1 + 0.6961663/(1 - 0.00467914825849/l**2) + 0.4079426/(1 - 0.01351206307396/l**2) + 0.8974794/(1 - 97.934002537921/l**2))**(-0.5)*(-0.0390895839631732/(l**5*(1 - 0.00467914825849/l**2)**2) - 0.0661457537010628/(l**5*(1 - 0.01351206307396/l**2)**2) - 1054.72499804798/(l**5*(1 - 97.934002537921/l**2)**2) - 0.000548717876179141/(l**7*(1 - 0.00467914825849/l**2)**3) - 0.00268129678825015/(l**7*(1 - 0.01351206307396/l**2)**3) - 309880.321906919/(l**7*(1 - 97.934002537921/l**2)**3) - 1.71168819648397e-6/(l**9*(1 - 0.00467914825849/l**2)**4) - 2.41532342152283e-5/(l**9*(1 - 0.01351206307396/l**2)**4) - 20231880.1547227/(l**9*(1 - 97.934002537921/l**2)**4))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "##GVD\n",
      "GVD=l**3/(2*3.14*c**2)*GDD*10**(-9)*10**30     #### 1mm per pass dispersion\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "##TVD\n",
      "TVD=-l**4/(4*3.14**2*c**3)*(3*GDD+l*TOD)*10**(-15)*10**45    #### 1mm per pass dispersion\n",
      " "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n=N(l)\n",
      "plt.plot(l, n, color=\"k\", marker=\"o\")\n",
      "plt.ylabel(\"Refractive index\",fontsize=22)\n",
      "plt.xlabel(\"wavelength (um)\",fontsize=22)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.text.Text at 0xb6a9940>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gsujCwkL07NkTGRkZsLGxQaNGjXDnzh20atVK62FQLy8vkwZ4+fJlbN68GXv2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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb5077f0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(l, GVD, color=\"k\", marker=\"o\")\n",
      "plt.ylabel(\"fs2\",fontsize=22)\n",
      "plt.xlabel(\"wavelength (um)\",fontsize=22)\n",
      "legend(('GVD',)) # \u51e1\u4f8b\n",
      "title(\"Fusied silica material GVD\", fontsize=22, fontname='serif') # \u30bf\u30a4\u30c8\u30eb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.text.Text at 0xc1dab38>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bn/Ewxio6TjblwBBnPFevXuUzHsZYhcUFAiYUHR0tecYjNU+bLkqlUmPaHcYY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\nY0bHyYYxxpjRcbJhjDFmdP8H/ydXTRSUsG4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc1a2c88>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(l, TVD, color=\"k\", marker=\"o\")\n",
      "plt.ylabel(\"fs3\",fontsize=22)\n",
      "plt.xlabel(\"wavelength (um)\",fontsize=22)\n",
      "legend(('TVD',)) # \u51e1\u4f8b\n",
      "title(\"Fusied silica materia TVD\", fontsize=22, fontname='serif') # \u30bf\u30a4\u30c8\u30eb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.text.Text at 0xb8389e8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UqFG6DqdV0aBFQghpQYqR7uXl5UhPT0efPn3afSJpDkomhBBSh9BI95qaGhrp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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb6dd748>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lp=400 ##distance mm\n",
      "lps=10   ##insertion depth mm"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PGVD=l**3/(2*3.14**2*c**2)*(4*(GDD+(2*n-n**(-3))*GD**2)*lps-8*GD**2*lp)*10**(-9)*10**30"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "##GVD prism pair\n",
      "plt.plot(l, PGVD, color=\"k\", marker=\"o\")\n",
      "plt.ylabel(\"fs2\",fontsize=22)\n",
      "plt.xlabel(\"wavelength (um)\",fontsize=22)\n",
      "legend(('GVD',)) # \u51e1\u4f8b\n",
      "title(\"Fusied silica prism pair GVD\", fontsize=22, fontname='serif') # \u30bf\u30a4\u30c8\u30eb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.text.Text at 0xbe28198>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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88stmly2Xy2FnZ6dVHG3WqvOnLox3yd80XbP28fFRexnB2NiYbGxs+B4S6zI0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\nw8b/AH8WypLEf7dZAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb747940>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PTVD=-l**4/(2*3.14**2*c**3)*(3*(4*(GDD+(2*n-n**(-3))*GD**2)*lps-8*GD**2*lp)+l*(4*TOD*lps-24*GD*GDD*lp))*10**(-15)*10**45"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "##TVD prism pair\n",
      "plt.plot(l, PTVD, color=\"k\", marker=\"o\")\n",
      "plt.ylabel(\"fs3\",fontsize=22)\n",
      "plt.xlabel(\"wavelength (um)\",fontsize=22)\n",
      "legend(('TVD',)) # \u51e1\u4f8b\n",
      "title(\"Fusied silica prism pair TVD\", fontsize=22, fontname='serif') # \u30bf\u30a4\u30c8\u30eb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "<matplotlib.text.Text at 0xc80d358>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ijLEm6tq1KwRB0HUYeqNr164a3R+PY1IDj1lgjGlKTEwMjh07hl27duk6FK3j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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc88a358>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}