Plotting Final report.ipynb

{
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  },
  "name": "",
  "signature": "sha256:8b0eda6be57e69d22f0e75826ea4e9e71ba2700561777444ebd53e7d03cfbb17"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline  \n",
      "import analyse_tool \n",
      "import numpy as np\n",
      "import simulation\n",
      "from timeit import Timer\n",
      "#import revreaddy as rdy\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "import matplotlib.cm as cm\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "e=analyse_tool.Analyse(D=1,particles=3,length=1000,alpha=0.5,dt=1.0,version='lowen')\n",
      "a=e.compute_trajectory()\n",
      "    \n",
      "plt.plot(a[0])\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/janek/miniconda/lib/python2.7/site-packages/numpy/core/numeric.py:482: ComplexWarning: Casting complex values to real discards the imaginary part\n",
        "  return array(a, dtype, copy=False, order=order)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PdVTlOA3Hn0vr3lfzmNtItUS+6S35hx7yl40rhUpawzytulRxbqbZmFOmkNUd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\nRV5RFKWB+f+sBldbi/QaeQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f52521cb750>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Plot of 3 trajectories with different alpha"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "fig = plt.figure()\n",
      "\n",
      "colors=['r','b','g','k','c','w','b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "ax=plt.axes(projection='3d')\n",
      "ax.set_axis_off()\n",
      "ain=1\n",
      "for i in (np.logspace(0.02,1.0,num=3,base=2)-1):\n",
      "    e=analyse_tool.Analyse(D=1,particles=3,length=1000,alpha=i,dt=1.0,version='lowen')\n",
      "    \n",
      "#e=analyse_tool.Analyse(D=2,particles=3,length=2000,alpha=0.8,dt=0.1,version='lowencpp')\n",
      "    a=e.compute_trajectory()\n",
      "    plt.plot(a[0],a[1],a[2],color=colors[ain],alpha=1-e.alpha/2, label=\"$\\\\alpha $=$%.2f $\" %(e.alpha))\n",
      "    ain=ain+1\n",
      "#plt.axis_off()\n",
      "plt.legend()\n",
      "#plt.savefig('Abschlussarbeit/data/trajectories_differentalpha.png',dpi=300)   # save the figure to file \n",
      "\n",
      "\n",
      "#plt.plot(range(len(a[0])),a[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": null
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Alogithmic scaling"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aneu=np.logspace(1,8.5,num=16,base=2)\n",
      "time_n=[]\n",
      "for i in aneu[1:]:\n",
      "    print i\n",
      "    timen=Timer(lambda: analyse_tool.Analyse(D=2,particles=1,length=int(i),alpha=0.5,dt=1,version='nhoch3').compute_trajectory())\n",
      "    time_n.append(timen.timeit(number=1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.82842712475\n",
        "4.0\n",
        "5.65685424949\n",
        "8.0\n",
        "11.313708499\n",
        "16.0\n",
        "22.627416998"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "32.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "45.2548339959"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "64.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "90.5096679919"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "128.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "181.019335984"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "256.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "362.038671968"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "time_python1=[]\n",
      "time_cpp1=[]\n",
      "time_b1=[]\n",
      "time_a1=[]\n",
      "time_n1=[]\n",
      "time_lowen_modified=[]\n",
      "a1=np.logspace(1,10,num=10,base=2)\n",
      "for i in a1:\n",
      "    print i\n",
      "    timec=Timer(lambda: analyse_tool.Analyse(D=2,particles=int(i),length=1000,alpha=0.5,dt=1,version='cpp').compute_trajectory())\n",
      "    timen=Timer(lambda: analyse_tool.Analyse(D=2,particles=int(i),length=128,alpha=0.5,dt=1,version='nhoch3').compute_trajectory())\n",
      "    timep=Timer(lambda: analyse_tool.Analyse(D=2,particles=int(i),length=1000,alpha=0.5,dt=1,version='lowen').compute_trajectory())\n",
      "    timeb=Timer(lambda: analyse_tool.Analyse(D=2,particles=int(i),length=1000,alpha=0.5,dt=1,version='lowen').compute_trajectory())\n",
      "    timelowencpp=Timer(lambda: analyse_tool.Analyse(D=2,particles=int(i),length=1000,alpha=0.5,dt=1,version='lowencpp').compute_trajectory())  \n",
      "    timelowenmodified=Timer(lambda: analyse_tool.Analyse(D=2,particles=int(i),length=1000,alpha=0.5,dt=1,version='lowenmodified').compute_trajectory())  \n",
      "    time_n1.append(timen.timeit(number=1)*3)\n",
      "    time_python1.append(timep.timeit(number=1)*3)\n",
      "    time_cpp1.append(timec.timeit(number=1))\n",
      "    time_b1.append(timeb.timeit(number=1)*3)\n",
      "    time_a1.append(timelowencpp.timeit(number=1))\n",
      "    time_lowen_modified.append(timelowenmodified.timeit(number=1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.0\n",
        "4.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "8.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "16.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "32.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "64.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "128.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "256.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "512.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1024.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "time_python=[]\n",
      "time_cpp=[]\n",
      "time_b=[]\n",
      "time_a=[]\n",
      "time_modif=[]\n",
      "a=np.logspace(1,15,num=15,base=2)\n",
      "for i in a:\n",
      "    timec=Timer(lambda: analyse_tool.Analyse(D=2,particles=1,length=int(i),alpha=0.5,dt=1,version='cpp').compute_trajectory())\n",
      "    timeb=Timer(lambda: analyse_tool.Analyse(D=2,particles=3,length=int(i),alpha=0.5,dt=1,version='lowen').compute_trajectory())\n",
      "    timea=Timer(lambda: analyse_tool.Analyse(D=2,particles=1,length=int(i),alpha=0.5,dt=1,version='lowencpp').compute_trajectory())\n",
      "    time_modifa=Timer(lambda: analyse_tool.Analyse(D=2,particles=1,length=int(i),alpha=0.5,dt=1,version='lowenmodified').compute_trajectory())\n",
      "    timep=Timer(lambda: analyse_tool.Analyse(D=2,particles=3,length=int(i),alpha=0.5,dt=1,version=\"python_notrick\").compute_trajectory())\n",
      "    time_python.append(timep.timeit(number=1))\n",
      "    time_cpp.append(timec.timeit(number=1))\n",
      "    time_b.append(timeb.timeit(number=1))\n",
      "    time_a.append(timea.timeit(number=1))\n",
      "    time_modif.append(time_modifa.timeit(number=1))\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "32768.0\n",
        "0.14328789711\n",
        "0.476837158203\n",
        "0.0683249936628\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#ax1 = plt.subplot(221)\n",
      "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True ,figsize=(9,9))\n",
      "colors=['r','b','g','k','c',\"burlywood\",'b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "\n",
      "\n",
      "textneu='\\propto N'\n",
      "b=5*a1\n",
      "\n",
      "ax1.plot(a1,time_python1,label=\"Naive\",c=colors[1])\n",
      "ax1.plot(a1,time_cpp1,\"--\",c=colors[1])\n",
      "ax1.plot(a1,time_b1,label=\"Lowen\",c=colors[2])\n",
      "ax1.plot(a1,time_a1,\"--\",c=colors[2])\n",
      "ax1.plot(a1[:len(time_n1)],time_n1,label=\"Choleski \",c=colors[2])\n",
      "#ax1.loglog(a1[1:],b[1:]/(1.0*10**3),\"--\", c=\"c\",label= \"$%s$  \" %(textneu))\n",
      "\n",
      "#ax1.set_title('a) t  vs. Traj. amount (N)', fontsize=10)\n",
      "\n",
      "\n",
      "#ax1.set_ylim([min(a1[3:]*np.log(a1[3:])/(0.5*10**7)),max(time_b)])\n",
      "\n",
      "#ax1.set_xlabel('N', fontsize=10)\n",
      "#ax1.set_ylabel('t in sec', fontsize=10)\n",
      "#ax1.legend(loc=4,fontsize='small')\n",
      "#ax1.set_xlim([0,max(a1)])\n",
      "\n",
      "#plt.savefig('Abschlussarbeit/data/profiling_particle.png',dpi=300)   # save the figure to file \n",
      "#plt.show()\n",
      "#ax1.setp(ax1.get_xticklabels(), fontsize=6)\n",
      "\n",
      "# share y only\n",
      "# = plt.subplot()\n",
      "#ax2=plt.subplot(2, sharey=ax1)\n",
      "text='\\\\propto M log(M)'\n",
      "plt.text(10**4, 10**(-4), \"$%s$  \" %(text),verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "plt.text(10**2, 10**(1), \"$%s$  \" %(text1) ,verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "\n",
      "ax2.loglog(a,time_python,label=\"Naive (p)\")\n",
      "ax2.loglog(a,time_cpp,label=\"Our (c++)\")\n",
      "ax2.loglog(a,time_b,label=\"lowen (p)\")\n",
      "ax2.loglog(a,time_a,label=\"lowen (c++)\")\n",
      "ax2.loglog(a,time_modif,label=\"lowen* (c++)\")\n",
      "ax2.loglog(aneu[1:],time_n,label=\"Choleski (p)\")\n",
      "text1='\\\\propto M^3'\n",
      "#ax2.loglog(a[1:],a[1:]**3/(5*10**5),\"--\", label= \"$%s$  \" %(text1))\n",
      "#ax2.loglog(a[3:],a[3:]*np.log(a[3:])/(0.6*10**7) ,\"--\",c=\"g\", label= \"$%s$  \" %(text))\n",
      "#ax2.loglog(10**10*a1[1:],10**10*b[1:]/(1.0*10**3),\"--\", c=\"c\",label= \"$%s$  \" %(textneu))\n",
      "#plt.setp(ax2.get_xticklabels(), visible=False)\n",
      "\n",
      "\n",
      "ax2.set_xlabel('M',fontsize=10)\n",
      "ax2.set_xlim([0,max(a)])\n",
      "ax2.set_title('b) t  vs. Traj. Length (M)', fontsize=10)\n",
      "\n",
      "\n",
      "#ax2.ylabel('t in sec',fontsize=10)\n",
      "ax2.legend(ncol=1, fontsize='small')\n",
      "plt.savefig('Abschlussarbeit/data/nongaussiandouble.png',dpi=300)\n",
      "plt.tight_layout()\n",
      "\n",
      "  # save the figure to file \n",
      "\n",
      "# make these tick labels invisible\n",
      "#plt.set_setp(ax2.get_yticklabels(), visible=False)\n",
      "\n",
      "\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FCxfSunVr3n77bd5//31Gjx7NokWLGDVqVO62L7zwAqtWrcq9/VxUC+SRI0dy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\nIu82exdzs+Ir0RKYmopvdDQX09P5zsWFr5ycsDHhXdhC6RK7ho0nPn4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ONtl3sokYH0Hq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ZfFlNVTiD0Sy+whsy/1I0sJKQiKiFRiISHdyMzcQV5eGj4+F97W64yMjB0kJX1O9+6/lek9jx07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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fad1f754090>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Scaling trajectory length"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(6,3))\n",
      "colors=['r','b','g','k','c',\"burlywood\",'b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "\n",
      "\n",
      "textneu='\\propto N'\n",
      "b=5*a1\n",
      "\n",
      "text='\\\\propto M log(M)'\n",
      "plt.text(10**4, 10**(-4), \"$%s$  \" %(text),verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "plt.text(10**2, 10**(1), \"$%s$  \" %(text1) ,verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "\n",
      "plt.loglog(a,time_python,label=\"Naive\",c=colors[1])\n",
      "plt.loglog(a,time_cpp,\"--\",c=colors[1])\n",
      "plt.loglog(a,time_b,label=\"Lowen\",c=colors[2])\n",
      "plt.loglog(a,time_a,\"--\",c=colors[2])\n",
      "plt.loglog(a,time_modif,\"--\",c=colors[3],label=\"Lowen *\")\n",
      "plt.loglog(aneu[1:],time_n,label=\"Choleski \",c=colors[4])\n",
      "text1='\\\\propto M^3'\n",
      "#ax2.loglog(a[1:],a[1:]**3/(5*10**5),\"--\", label= \"$%s$  \" %(text1))\n",
      "#ax2.loglog(a[3:],a[3:]*np.log(a[3:])/(0.6*10**7) ,\"--\",c=\"g\", label= \"$%s$  \" %(text))\n",
      "#plt.loglog(0,0, c=\"c\",label= \"$%s$  \" %(textneu))\n",
      "#plt.setp(ax2.get_xticklabels(), visible=False)\n",
      "\n",
      "plt.ylabel('t/[sec] ', fontsize=10)\n",
      "\n",
      "plt.xlabel('M',fontsize=10)\n",
      "plt.xlim([0,1.5*10**6])\n",
      "#plt.title('b) t  vs. Traj. Length (M)', fontsize=10)\n",
      "\n",
      "\n",
      "#ax2.ylabel('t in sec',fontsize=10)\n",
      "plt.legend(ncol=1, fontsize=12)\n",
      "plt.savefig('Abschlussarbeit/data/nongaussiandouble1.png',dpi=300)\n",
      "plt.tight_layout()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ycjIXL15kz549pKenU7t2bczMzDC+b556xowZTJgwgYEDBxIfH1+kbSWluN59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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fad1c03c9d0>"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(6,3))\n",
      "colors=['r','b','g','k','c',\"burlywood\",'b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "\n",
      "\"\"\"\n",
      "textneu='\\propto N'\n",
      "b=5*a1\n",
      "\n",
      "text='\\\\propto M log(M)'\n",
      "plt.text(10**4, 10**(-3), \"$%s$  \" %(text),verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "plt.text(10**2, 10**(1), \"$%s$  \" %(text1) ,verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "\n",
      "plt.loglog(a,time_python,label=\"Naive\",c=colors[1])\n",
      "plt.loglog(a,time_cpp,\"--\",c=colors[1])\n",
      "plt.loglog(a,time_b,label=\"Lowen\",c=colors[2])\n",
      "plt.loglog(a,time_a,\"--\",c=colors[2])\n",
      "plt.loglog(a,time_modif,\"--\",c=colors[3],label=\"Lowen *\")\n",
      "plt.loglog(aneu[1:],time_n,label=\"Choleski \",c=colors[4])\n",
      "text1='\\\\propto M^3'\n",
      "\n",
      "\"\"\"\n",
      "\n",
      "\n",
      "plt.loglog(a1,time_python1,label=\"Naive\",c=colors[1])\n",
      "plt.plot(a1,time_cpp1,\"--\",c=colors[1])\n",
      "plt.plot(a1,time_b1,label=\"Lowen\",c=colors[2])\n",
      "plt.plot(a1,time_a1,\"--\",c=colors[2])\n",
      "plt.plot(a1[:len(time_n1)],time_n1,label=\"Choleski \",c=colors[4])\n",
      "plt.loglog(a1,time_lowen_modified,\"--\",c=colors[3],label=\"Lowen *\")\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "#ax2.loglog(a[1:],a[1:]**3/(5*10**5),\"--\", label= \"$%s$  \" %(text1))\n",
      "#ax2.loglog(a[3:],a[3:]*np.log(a[3:])/(0.6*10**7) ,\"--\",c=\"g\", label= \"$%s$  \" %(text))\n",
      "#plt.loglog(0,0, c=\"c\",label= \"$%s$  \" %(textneu))\n",
      "#plt.setp(ax2.get_xticklabels(), visible=False)\n",
      "\n",
      "plt.ylabel('t/[sec] ', fontsize=10)\n",
      "\n",
      "plt.xlabel('M',fontsize=10)\n",
      "plt.xlim([0,1.5*10**4])\n",
      "#plt.title('b) t  vs. Traj. Length (M)', fontsize=10)\n",
      "\n",
      "\n",
      "#ax2.ylabel('t in sec',fontsize=10)\n",
      "plt.legend(ncol=1, fontsize=12)\n",
      "plt.savefig('Abschlussarbeit/data/nongaussiandouble2.png',dpi=300)\n",
      "plt.tight_layout()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LvaudK4gk3V13756VsuhRs+LeunuLv3/+dwKmBpD/Tn5eHv4yr7/yOm5ubllT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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fad168fd3d0>"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Accuarcy with alpha"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#f,  ((ax1, ax2), (ax3,)) = plt.subplots(2, 2, sharey=True ,figsize=(6,3))\n",
      "\n",
      "plt.figure(figsize=(6,6))\n",
      "ax3=plt.subplot(223)   #bottom left\n",
      "ax2=plt.subplot(221,sharex=ax3)   #top left\n",
      "ax1=plt.subplot(224,sharey=ax3)   #top right\n",
      "\n",
      "\n",
      "msd_text='MSD=2 K_\\\\alpha t^\\\\alpha'\n",
      "t='t=\\\\Delta t \\\\cdot n'\n",
      "colors=['r','b','g','k','c','w','b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "for i in (np.logspace(0.02,1.0,num=5,base=2)-1):\n",
      "    e=analyse_tool.Analyse(D=2,particles=4000,length=200,alpha=i,dt=0.1,version='lowennew')\n",
      "    msd,std=e.msd_ensemble()\n",
      "    ax1.plot(e.t*e.dt, msd,label=\"$\\\\alpha $=$%.2f $\" %(e.alpha))\n",
      "    ax1.loglog(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1])\n",
      "\n",
      "ax1.loglog(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1], label=\"$%s$ \" %(msd_text))\n",
      "ax1.set_xlabel('t', fontsize=12)\n",
      "plt.setp(ax1.get_yticklabels(), visible=False)\n",
      "\n",
      "ax1.set_title('c) Our Algorithm', fontsize=10)\n",
      "\n",
      "colors=['r','b','g','k','c','w','b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "for i in (np.logspace(0.02,1.0,num=5,base=2)-1):\n",
      "    e=analyse_tool.Analyse(D=2,particles=4000,length=200,alpha=i,dt=0.1,version='lowencpp')\n",
      "    msd,std=e.msd_ensemble()\n",
      "    ax2.plot(e.t*e.dt, msd,label=\"$\\\\alpha $=$%.2f $\" %(e.alpha))\n",
      "    ax2.loglog(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1])\n",
      "plt.setp(ax2.get_xticklabels(), visible=False)\n",
      "ax2.loglog(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1], label=\"$%s$ \" %(msd_text))\n",
      "#ax2.set_xlabel('t', fontsize=12)\n",
      "ax2.set_ylabel('$\\delta r^{2}(t)$', fontsize=12)\n",
      "\n",
      "ax2.set_title('a) Lowen', fontsize=10)\n",
      "\n",
      "\n",
      "lgd=ax2.legend(bbox_to_anchor=(1.2,1.04), loc='upper left', ncol=1,fontsize='small')\n",
      "colors=['r','b','g','k','c','w','b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "for i in (np.logspace(0.02,1.0,num=5,base=2)-1):\n",
      "    e=analyse_tool.Analyse(D=2,particles=4000,length=128/2,alpha=i,dt=0.1,version='nhoch3')\n",
      "    msd,std=e.msd_ensemble()\n",
      "    ax3.plot(e.t*e.dt, msd,label=\"$\\\\alpha $=$%.2f $\" %(e.alpha))\n",
      "    ax3.loglog(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1])\n",
      "\n",
      "ax3.loglog(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1], label=\"$%s$ \" %(msd_text))\n",
      "ax3.set_xlabel('t', fontsize=12)\n",
      "ax3.set_ylabel('$\\delta r^{2}(t)$', fontsize=12)\n",
      "ax3.set_title('b) Cholesky Algorithm', fontsize=10)\n",
      "\n",
      "\n",
      "#ax2.ylabel('t in sec',fontsize=10)\n",
      "#plt.savefig('Abschlussarbeit/data/alpha_changethree.png', bbox_extra_artists=(lgd,), bbox_inches='tight',dpi=300)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jxPwlhpZvtKT+DTmDyt698I9/wKhRbrh8efd8S7quXbM+dS+lsBAhvfn6008/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\nFVgURVEUvfo/gn2s1uBcG2AAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f5f731a4c10>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a=np.logspace(0.02,1.25,num=6,base=2)-1\n",
      "a[4]=1\n",
      "print a\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.01395948  0.20246925  0.42602572  0.69114457  1.          1.37841423]\n"
       ]
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "# Accuracy with Alpha different representation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(9,9))\n",
      "msd_text='MSD=2 K_\\\\alpha t^\\\\alpha'\n",
      "t='t=\\\\Delta t \\\\cdot n'\n",
      "coloin=-1\n",
      "a=np.logspace(0.02,1.25,num=6,base=2)-1\n",
      "a[4]=1\n",
      "colors=['r','b','g','k','c','w','b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "plt.text(100, 10**5, 'Naive',verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "for i in a[:-1]:\n",
      "    coloin=coloin+1\n",
      "    e=analyse_tool.Analyse(D=2,particles=400,length=2000,alpha=i,dt=0.1,version='cpp')\n",
      "    msd,std=e.msd_ensemble()\n",
      "    plt.loglog(e.t/e.dt,10000*msd,color=colors[coloin])\n",
      "    plt.loglog(e.t/e.dt,10000*e.msdanalyt(),\":\",color=\"k\")\n",
      "\n",
      "#ax1.loglog(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1], label=\"$%s$ \" %(msd_text))\n",
      "\n",
      "#ax1.set_title('c) Our Algorithm', fontsize=10)\n",
      "coloin=-1\n",
      "colors=['r','b','g','k','c','w','b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "plt.text(100, 10, 'Lowen',verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "\n",
      "for i in a[:-1]:\n",
      "    coloin=coloin+1\n",
      "    e=analyse_tool.Analyse(D=2,particles=400,length=2000,alpha=i,dt=0.1,version='lowencpp')\n",
      "    msd,std=e.msd_ensemble()\n",
      "    plt.loglog(e.t/e.dt,1*msd,color=colors[coloin])\n",
      "    plt.loglog(e.t/e.dt,1*e.msdanalyt(),\":\",color=\"k\")\n",
      "#ax2.loglog(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1], label=\"$%s$ \" %(msd_text))\n",
      "\n",
      "#ax2.set_title('a) Lowen', fontsize=10)\n",
      "\n",
      "\n",
      "coloin=-1\n",
      "\n",
      "colors=['r','b','g','k','c',\"burlywood\",'b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "plt.text(100, 10**(-2), 'Cholesky',verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "\n",
      "for i in a[:]:\n",
      "    coloin=coloin+1\n",
      "    e=analyse_tool.Analyse(D=2,particles=400,length=128*1/2,alpha=i,dt=0.1,version='nhoch3')\n",
      "    msd,std=e.msd_ensemble()\n",
      "    plt.plot(e.t/e.dt,0.001*msd,color=colors[coloin])\n",
      "    plt.loglog(e.t/e.dt,0.001*e.msdanalyt(),\":\",color=\"k\")\n",
      "    \n",
      "coloin=-1\n",
      "\n",
      "colors=['r','b','g','k','c',\"burlywood\",'b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "plt.text(100, 10**(-5), 'Davis-Harte',verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "for i in a[:]:\n",
      "    coloin=coloin+1\n",
      "    e=analyse_tool.Analyse(D=2,particles=400,length=2000,alpha=i,dt=0.1,version='hartecppverion2')\n",
      "    msd,std=e.msd_ensemble()\n",
      "    plt.plot(e.t/e.dt,0.000001*msd,color=colors[coloin],label=\"$\\\\alpha $=$%.2f $\" %(e.alpha))\n",
      "    plt.loglog(e.t/e.dt,0.000001*e.msdanalyt(),\":\",color=\"k\")\n",
      "\n",
      "plt.xlim((0,2000000))\n",
      "#plt.loglog(e.t/e.dt,e.msdanalyt(),\":\",color=colors[1], label=\"$%s$ \" %(msd_text))\n",
      "\n",
      "plt.legend( ncol=1,fontsize=16)\n",
      "\n",
      "plt.xlabel('$t/ \\Delta t$', fontsize=16)\n",
      "plt.ylabel('$\\delta r^{2}(t)/ \\Gamma^2$', fontsize=16)\n",
      "\n",
      "\n",
      "#ax3.set_title('b) Cholesky Algorithm', fontsize=10)\n",
      "#ax2plt.xlim((0,200000))\n",
      "plt.savefig('Abschlussarbeit/data/alpha_changethreeneu.png', bbox_inches='tight',dpi=300)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4UPU2uypql5VCUSBITEzkWEgIXyYn41qqFJs6daJOjRqFMrdLREQE/v7+nDlzhqioKKZN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gXs7Ozo2rUr8+fPL1hn5u/S0tIIDAyke/fuiAi7ju6ib7++5Lycg5lmhreFN92sumFf3579Eftp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\nSZI6y6AjSZI66z//ZwWECsmRZAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4fd9285bd0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "msd_text='MSD=2 K_\\\\alpha t^\\\\alpha'\n",
      "t='t=\\\\Delta t \\\\cdot n'\n",
      "colors=['r','b','g','k','c','w','b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "for i in (np.logspace(0.02,1.4,num=5,base=2)-1):\n",
      "    e=analyse_tool.Analyse(D=2,particles=4000,length=2000,alpha=i,dt=0.1,version='hartecppverion2')\n",
      "    msd,std=e.msd_ensemble()\n",
      "    plt.plot(e.t*e.dt, msd,label=\"$\\\\alpha $=$%.2f $\" %(e.alpha))\n",
      "    plt.loglog(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1])\n",
      "plt.legend()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "def gen_log_space(limit, n):\n",
      "    result = [1]\n",
      "    if n>1:  # just a check to avoid ZeroDivisionError\n",
      "        ratio = (float(limit)/result[-1]) ** (1.0/(n-len(result)))\n",
      "    while len(result)<n:\n",
      "        next_value = result[-1]*ratio\n",
      "        if next_value - result[-1] >= 1:\n",
      "            # safe zone. next_value will be a different integer\n",
      "            result.append(next_value)\n",
      "        else:\n",
      "            # problem! same integer. we need to find next_value by artificially incrementing previous value\n",
      "            result.append(result[-1]+1)\n",
      "            # recalculate the ratio so that the remaining values will scale correctly\n",
      "            ratio = (float(limit)/result[-1]) ** (1.0/(n-len(result)))\n",
      "    # round, re-adjust to 0 indexing (i.e. minus 1) and return np.uint64 array\n",
      "    return np.array(map(lambda x: round(x)-1, result), dtype=np.uint64)\n",
      "\n",
      "y = gen_log_space(2000, 50)\n",
      "y1 = gen_log_space(300, 20)\n",
      "\n",
      "print y1\n",
      "def gamma(D,dt):\n",
      "     return 2*D*dt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": null
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Backward Forward Msd"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "msd_text='2 K_\\\\alpha t^\\\\alpha'\n",
      "t='t=\\\\Delta t \\\\cdot n'\n",
      "colors=['r','b','g','k','c','w','b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "plt.figure(figsize=(8,4))\n",
      "e=analyse_tool.Analyse(D=20,particles=1000,length=2000,alpha=0.3,dt=1,version='cpp')\n",
      "\n",
      "msd,std=e.msd_ensemble()\n",
      "\n",
      "plt.loglog(e.t,10*e.msdanalyt()/gamma(e.D,e.dt),color=\"k\")\n",
      "plt.errorbar(e.t[y], 10*msd[y]/gamma(e.D,e.dt),yerr=10*std[y]/gamma(e.D,e.dt), color=\"c\",linestyle=\"\",label= \" Forward Naive\" )\n",
      "e.invert_time()\n",
      "msd1,std1=e.msd_ensemble()\n",
      "plt.errorbar(e.t[y], 10*msd1[y]/gamma(e.D,e.dt),yerr=10*std1[y]/gamma(e.D,e.dt), color=\"g\",label= \" Backward Naive\" )\n",
      "\n",
      "e=analyse_tool.Analyse(D=200,particles=1000,length=2000,alpha=0.3,dt=1,version='lowencpp')\n",
      "\n",
      "msd,std3=e.msd_ensemble()\n",
      "\n",
      "plt.loglog(e.t,100*e.msdanalyt()/gamma(e.D,e.dt),color=\"k\")\n",
      "plt.errorbar(e.t[y], 100*msd[y]/gamma(e.D,e.dt),yerr=100*std3[y]/gamma(e.D,e.dt), color=\"c\",linestyle=\"\",label= \" Forward Lowen\" )\n",
      "e.invert_time()\n",
      "msd1,std4=e.msd_ensemble()\n",
      "plt.errorbar(e.t[y], 100*msd1[y]/gamma(e.D,e.dt),yerr=100*std4[y]/gamma(e.D,e.dt), color=\"b\",label= \" Backward Lowen\" )\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "e=analyse_tool.Analyse(D=2,particles=1000,length=300,alpha=0.3,dt=1,version='nhoch3')\n",
      "msd,std=e.msd_ensemble()\n",
      "\n",
      "plt.loglog(e.t,1.0*e.msdanalyt()/gamma(e.D,e.dt),color=\"k\")\n",
      "plt.errorbar(e.t[y1], 1*msd[y1]/gamma(e.D,e.dt),yerr=1*std[y1]/gamma(e.D,e.dt), color=\"c\",linestyle=\"\",label= \" Forward Cholesky\" )\n",
      "e.invert_time()\n",
      "msd1,std1=e.msd_ensemble()\n",
      "plt.errorbar(e.t[y1], 1*msd1[y1]/gamma(e.D,e.dt),yerr=1*std1[y1]/gamma(e.D,e.dt), color=\"r\",label= \" Backward Cholesky\" )\n",
      "\n",
      "\n",
      "e=analyse_tool.Analyse(D=200,particles=1000,length=2000,alpha=0.3,dt=1.0,version='hartecppverion2')\n",
      "msd,std=e.msd_ensemble()\n",
      "plt.loglog(e.t/e.dt,0.1*e.msdanalyt()/gamma(e.D,e.dt),color=\"k\")\n",
      "plt.errorbar(e.t[y]/e.dt, 0.1*msd[y]/gamma(e.D,e.dt),yerr=0.1*std3[y]/gamma(e.D,e.dt), color=\"c\",linestyle=\"\",label= \" Forward Davis-Harte\" )\n",
      "e.invert_time()\n",
      "msd1,std1=e.msd_ensemble()\n",
      "plt.errorbar(e.t[y], 0.1*msd1[y]/gamma(e.D,e.dt),yerr=0.1*std4[y]/gamma(e.D,e.dt), color=\"burlywood\",label= \" Backward Davis-Harte\" )\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "plt.xlim((0,200000))\n",
      "\n",
      "plt.xlabel('$t/ \\Delta t$', fontsize=14)\n",
      "plt.ylabel('$\\delta r^{2}(t)/ \\Gamma^2$', fontsize=14)\n",
      "lgd=plt.legend(  ncol=1,fontsize='small')\n",
      "plt.savefig('Abschlussarbeit/data/changeintimenew.png', bbox_inches='tight',dpi=300)   # save the figure to file \n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kBG9vb1JTU+298oaeM+DQTIG/+c1vWLlyJa+++ipgWxMwbNiwJtdpyPQ3YMAA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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1ff6471a10>"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Rescaled FUnction"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(6,6))\n",
      "ax3=plt.subplot(223)   #bottom left\n",
      "ax2=plt.subplot(221,sharex=ax3)   #top left\n",
      "ax1=plt.subplot(224,sharey=ax3)   #bottom right\n",
      "\n",
      "colornum=-1\n",
      "length=1100\n",
      "steps=5\n",
      "shades=np.linspace(0,1,steps+1)\n",
      "colors = iter(cm.rainbow(np.linspace(0, 1, 6)))\n",
      "analt=c.rescaled_analytical_distribution(1,5)\n",
      "c=analyse_tool.Analyse(D=2,particles=10000,length=length+1,alpha=0.5,dt=1,version=\"cpp\")\n",
      "for j in range(100,1100,1000/steps):\n",
      "        colornum=1+colornum\n",
      "        shade=shades[colornum]\n",
      "        #d=c.rescaled_analytical_distribution(t=j,r_dis=50)\n",
      "        h=c.rescaled_function(t=j,histpoints=35)\n",
      "        ax1.loglog(h[0],h[1],color=next(colors), label=\"rescaled function at $t=%d$\" %(j))\n",
      "        #plt.plot(d[0],d[1],color='%f' %(shade))\n",
      "ax1.plot(analt[0],analt[1],\"--\", label=\"analytical rescaled function\")\n",
      "ax1.set_xlabel('rescaled distance $ r_{res} $ ', fontsize=10)\n",
      "#ax1.set_ylabel('rescaled distribution', fontsize=10)\n",
      "#plt.legend(loc=3, fontsize='small')\n",
      "ax1.set_xlim([0.06,5])\n",
      "ax1.set_ylim([0.0003,1])\n",
      "plt.setp(ax1.get_yticklabels(), visible=False)\n",
      "ax1.set_title('c) Our Algorithm', fontsize=10)\n",
      "\n",
      "colornum=-1\n",
      "index=0\n",
      "length=1100\n",
      "steps=5\n",
      "shades=np.linspace(0,1,steps+1)\n",
      "colors = iter(cm.rainbow(np.linspace(0, 1, 6)))\n",
      "choleskirange=[100,150,200,250,350]\n",
      "c=analyse_tool.Analyse(D=2,particles=10000,length=length+1,alpha=0.5,dt=1,version=\"lowen\")\n",
      "for j in range(100,1100,1000/steps):   \n",
      "        colornum=1+colornum\n",
      "        shade=shades[colornum]\n",
      "        #d=c.rescaled_analytical_distribution(t=j,r_dis=50)\n",
      "        h=c.rescaled_function(t=j,histpoints=35)\n",
      "        ax2.loglog(h[0],h[1],color=next(colors), label=\"rescaled function at $t=%d$ (for Choleski $t=%d$)\" %(j,choleskirange[index]))\n",
      "        #plt.plot(d[0],d[1],color='%f' %(shade))\n",
      "        index=index+1\n",
      "ax2.plot(analt[0],analt[1],\"--\", label=\"analytical rescaled function\")\n",
      "#ax2.set_xlabel('rescaled distance $ r_{res} $ ', fontsize=10)\n",
      "ax2.set_ylabel('rescaled distribution', fontsize=10)\n",
      "#plt.legend(loc=3, fontsize='small')\n",
      "ax2.set_xlim([0.06,5])\n",
      "ax2.set_ylim([0.0003,1])\n",
      "plt.setp(ax2.get_xticklabels(), visible=False)\n",
      "ax2.set_title('a) Lowen', fontsize=10)\n",
      "lgd=ax2.legend(bbox_to_anchor=(1.2,1.04), loc='upper left', ncol=1,fontsize='small')\n",
      "\n",
      "colornum=-1\n",
      "length=1100\n",
      "steps=5\n",
      "shades=np.linspace(0,1,steps+1)\n",
      "colors = iter(cm.rainbow(np.linspace(0, 1, 6)))\n",
      "c=analyse_tool.Analyse(D=2,particles=10000,length=351,alpha=0.5,dt=1,version=\"nhoch3\")\n",
      "for j in [100,150,200,250,350]:\n",
      "        colornum=1+colornum\n",
      "        shade=shades[colornum]\n",
      "        #d=c.rescaled_analytical_distribution(t=j,r_dis=50)\n",
      "        h=c.rescaled_function(t=j,histpoints=35)\n",
      "        ax3.loglog(h[0],h[1],color=next(colors), label=\"rescaled function at $t=%d$\" %(j))\n",
      "        #plt.plot(d[0],d[1],color='%f' %(shade))\n",
      "ax3.plot(analt[0],analt[1],\"--\", label=\"analytical rescaled function\")\n",
      "ax3.set_xlabel('rescaled distance $ r_{res} $ ', fontsize=10)\n",
      "ax3.set_ylabel('rescaled distribution', fontsize=10)\n",
      "#plt.legend(loc=3, fontsize='small')\n",
      "ax3.set_xlim([0.06,5])\n",
      "ax3.set_ylim([0.0003,1])\n",
      "ax3.set_title('b) Cholesky Algorithm', fontsize=10)\n",
      "\n",
      "\n",
      "#ax2.ylabel('t in sec',fontsize=10)\n",
      "plt.savefig('Abschlussarbeit/data/scaledfunctionneu12.png', bbox_extra_artists=(lgd,), bbox_inches='tight',dpi=300)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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V4LUu7bF87bXXdMmSJXry5EkdOXKk7tq1S1NSUvLnezuWf/3rX3XmzJleyyvq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\nmliVUkqpEPr/oMllns/tVuYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1ff7050b90>"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": null
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "This is a new represesentation of the rescaled function"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''\n",
      "plt.figure(figsize=(9,9))\n",
      "msd_text='MSD=2 K_\\\\alpha t^\\\\alpha'\n",
      "t='t=\\\\Delta t \\\\cdot n'\n",
      "coloin=-1\n",
      "a=np.logspace(0.02,1.25,num=6,base=2)-1\n",
      "a[4]=1\n",
      "colors=['r','b','g','k','c','w','b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "plt.text(100, 10**5, 'Naive',verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "for i in a[:-1]:\n",
      "    coloin=coloin+1\n",
      "    e=analyse_tool.Analyse(D=2,particles=400,length=2000,alpha=i,dt=0.1,version='cpp')\n",
      "    msd,std=e.msd_ensemble()\n",
      "    plt.loglog(e.t/e.dt,10000*msd,color=colors[coloin])\n",
      "    plt.loglog(e.t/e.dt,10000*e.msdanalyt(),\":\",color=\"k\")\n",
      "\n",
      "#ax1.loglog(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1], label=\"$%s$ \" %(msd_text))\n",
      "\n",
      "#ax1.set_title('c) Our Algorithm', fontsize=10)\n",
      "coloin=-1\n",
      "colors=['r','b','g','k','c','w','b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "plt.text(100, 10, 'Lowen',verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "\n",
      "for i in a[:-1]:\n",
      "    coloin=coloin+1\n",
      "    e=analyse_tool.Analyse(D=2,particles=400,length=2000,alpha=i,dt=0.1,version='lowencpp')\n",
      "    msd,std=e.msd_ensemble()\n",
      "    plt.loglog(e.t/e.dt,1*msd,color=colors[coloin])\n",
      "    plt.loglog(e.t/e.dt,1*e.msdanalyt(),\":\",color=\"k\")\n",
      "#ax2.loglog(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1], label=\"$%s$ \" %(msd_text))\n",
      "\n",
      "#ax2.set_title('a) Lowen', fontsize=10)\n",
      "\n",
      "\n",
      "coloin=-1\n",
      "\n",
      "colors=['r','b','g','k','c',\"burlywood\",'b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "plt.text(100, 10**(-2), 'Cholesky',verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "\n",
      "for i in a[:]:\n",
      "    coloin=coloin+1\n",
      "    e=analyse_tool.Analyse(D=2,particles=400,length=128*1/2,alpha=i,dt=0.1,version='nhoch3')\n",
      "    msd,std=e.msd_ensemble()\n",
      "    plt.plot(e.t/e.dt,0.001*msd,color=colors[coloin])\n",
      "    plt.loglog(e.t/e.dt,0.001*e.msdanalyt(),\":\",color=\"k\")\n",
      "    \n",
      "coloin=-1\n",
      "\n",
      "colors=['r','b','g','k','c',\"burlywood\",'b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "plt.text(100, 10**(-5), 'Davis-Harte',verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "for i in a[:]:\n",
      "    coloin=coloin+1\n",
      "    e=analyse_tool.Analyse(D=2,particles=400,length=2000,alpha=i,dt=0.1,version='hartecppverion2')\n",
      "    msd,std=e.msd_ensemble()\n",
      "    plt.plot(e.t/e.dt,0.000001*msd,color=colors[coloin],label=\"$\\\\alpha $=$%.2f $\" %(e.alpha))\n",
      "    plt.loglog(e.t/e.dt,0.000001*e.msdanalyt(),\":\",color=\"k\")\n",
      "\n",
      "plt.xlim((0,2000000))\n",
      "#plt.loglog(e.t/e.dt,e.msdanalyt(),\":\",color=colors[1], label=\"$%s$ \" %(msd_text))\n",
      "\n",
      "plt.legend( ncol=1,fontsize=16)\n",
      "\n",
      "plt.xlabel('$t/ \\Delta t$', fontsize=16)\n",
      "plt.ylabel('$\\delta r^{2}(t)/ \\Gamma^2$', fontsize=16)\n",
      "\n",
      "'''\n",
      "plt.figure(figsize=(9,9))\n",
      "\n",
      "colornum=-1\n",
      "length=1100\n",
      "steps=5\n",
      "shades=np.linspace(0,1,steps+1)\n",
      "colors = iter(cm.rainbow(np.linspace(0, 1, 6)))\n",
      "plt.text(0.5, 1, 'Naive',verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "\n",
      "c=analyse_tool.Analyse(D=0.2,particles=10000,length=length+1,alpha=0.5,dt=1,version=\"cpp\")\n",
      "analt=c.rescaled_analytical_distribution(1,5)\n",
      "\n",
      "for j in range(100,1100,1000/steps):\n",
      "        colornum=1+colornum\n",
      "        shade=shades[colornum]\n",
      "        #d=c.rescaled_analytical_distribution(t=j,r_dis=50)\n",
      "        h=c.rescaled_function(t=j,histpoints=35)\n",
      "        plt.loglog(h[0],h[1],color=next(colors))\n",
      "        #plt.plot(d[0],d[1],color='%f' %(shade))\n",
      "plt.plot(analt[0],analt[1],\"--\")\n",
      "plt.xlabel('$r_{res} \\Gamma$ ', fontsize=14)\n",
      "\n",
      "#ax1.set_xlim([0.06,5])\n",
      "#ax1.set_ylim([0.0003,1])\n",
      "#plt.setp(ax1.get_yticklabels(), visible=False)\n",
      "#ax1.set_title('c) Our Algorithm', fontsize=14)\n",
      "\n",
      "colornum=-1\n",
      "index=0\n",
      "length=1100\n",
      "steps=5\n",
      "shades=np.linspace(0,1,steps+1)\n",
      "colors = iter(cm.rainbow(np.linspace(0, 1, 6)))\n",
      "choleskirange=[100,150,200,250,350]\n",
      "c=analyse_tool.Analyse(D=2,particles=10000,length=length+1,alpha=0.5,dt=1,version=\"lowen\")\n",
      "plt.text(0.5, 1000, 'Lowen',verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "\n",
      "for j in range(100,1100,1000/steps):   \n",
      "        colornum=1+colornum\n",
      "        shade=shades[colornum]\n",
      "        #d=c.rescaled_analytical_distribution(t=j,r_dis=50)\n",
      "        h=c.rescaled_function(t=j,histpoints=35)\n",
      "        plt.loglog(h[0],1000*h[1],color=next(colors))\n",
      "        #plt.plot(d[0],d[1],color='%f' %(shade))\n",
      "        index=index+1\n",
      "plt.plot(analt[0],1000*analt[1],\"--\")\n",
      "#ax2.set_xlabel('rescaled distance $ r_{res} $ ', fontsize=10)\n",
      "plt.ylabel('$ \\mathcal{P}_{gauss}(\\hat{r}) $', fontsize=16)\n",
      "plt.xlabel(' $ \\hat{r}/ \\Gamma $ ', fontsize=16)\n",
      "\n",
      "#plt.legend(loc=3, fontsize='small')\n",
      "#ax2.set_xlim([0.06,5])\n",
      "#ax2.set_ylim([0.0003,1])\n",
      "#plt.setp(ax2.get_xticklabels(), visible=False)\n",
      "#ax2.set_title('a) Lowen', fontsize=10)\n",
      "#lgd=ax2.legend(bbox_to_anchor=(1.2,1.04), loc='upper left', ncol=1,fontsize='small')\n",
      "\n",
      "colornum=-1\n",
      "length=1100\n",
      "steps=5\n",
      "shades=np.linspace(0,1,steps+1)\n",
      "colors = iter(cm.rainbow(np.linspace(0, 1, 6)))\n",
      "plt.text(0.5, 0.0001, 'Cholesky',verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "c=analyse_tool.Analyse(D=2,particles=10000,length=351,alpha=0.5,dt=1,version=\"nhoch3\")\n",
      "for j in [100,150,200,250,350]:\n",
      "        colornum=1+colornum\n",
      "        shade=shades[colornum]\n",
      "        #d=c.rescaled_analytical_distribution(t=j,r_dis=50)\n",
      "        h=c.rescaled_function(t=j,histpoints=35)\n",
      "        plt.loglog(h[0],0.0001*h[1],color=next(colors))\n",
      "        #plt.plot(d[0],d[1],color='%f' %(shade))\n",
      "plt.loglog(analt[0],0.0001*analt[1],\"--\")\n",
      "\n",
      "colornum=-1\n",
      "index=0\n",
      "length=1100\n",
      "steps=5\n",
      "shades=np.linspace(0,1,steps+1)\n",
      "colors = iter(cm.rainbow(np.linspace(0, 1, 6)))\n",
      "choleskirange=[100,150,200,250,350]\n",
      "plt.text(0.5, 0.0000001, 'Davis-Harte',verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "\n",
      "c=analyse_tool.Analyse(D=2,particles=10000,length=length+1,alpha=0.5,dt=1,version=\"hartecppverion2\")\n",
      "for j in range(100,1100,1000/steps):   \n",
      "        colornum=1+colornum\n",
      "        shade=shades[colornum]\n",
      "        #d=c.rescaled_analytical_distribution(t=j,r_dis=50)\n",
      "        h=c.rescaled_function(t=j,histpoints=35)\n",
      "        plt.loglog(h[0],0.0000001*h[1],color=next(colors), label=\"$ \\mathcal{P}_{gauss}(\\hat{r}) $, $t=%d$ (Choleski: $t=%d$)\" %(j,choleskirange[index]))\n",
      "        #plt.plot(d[0],d[1],color='%f' %(shade))\n",
      "        index=index+1\n",
      "plt.plot(analt[0],0.0000001*analt[1],\"--\")\n",
      "#ax2.set_xlabel('rescaled distance $ r_{res} $ ', fontsize=10)\n",
      "#ax3.set_ylabel('rescaled distribution', fontsize=10)\n",
      "plt.legend(loc=3,fontsize=11)\n",
      "\n",
      "plt.xlim([0.5*10**(-2),10])\n",
      "plt.ylim([10**(-15),10000])\n",
      "\n",
      "#ax3.set_ylim([0.0003,1])\n",
      "#ax3.set_title('b) Cholesky Algorithm', fontsize=10)\n",
      "\n",
      "\n",
      "#ax2.ylabel('t in sec',fontsize=10)\n",
      "plt.savefig('Abschlussarbeit/data/scaledfunctionneu12.png',bbox_inches='tight',dpi=300)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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N4ve//z3z5s1j+vTpXHzxxdxzzz088cQTBIPBL2335JNPJikpieeee+7wvvLy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AirVq2yGldcXJzV+o72+e9+9zucnZ0pLy8nJSWFpKQkANasWQPAlClTCAwM5MiRI7fs\n9+HDh1NcXMzw4cOttimEEN2NJEbt0JHEpqMyMzMJDAxk9erVBAUFGT9Unamurs6i/PjjjxMTE0NM\nTAyAxahF//79KSsrY8iQIa1ur7q6mrq6OosfaIBHH32UlStXtimm+Ph4/vKXvxjloUOHGhN7Kysr\ncXV1ZdiwYS3q2mL8+PEUFBRQXl6Os7Oz1WXs7e0BjASgtra2TduGhkRh7NixfPON9fuN5ufn8/XX\nX5OWlkZUVBSnTp0y+tjPz4/9+/ezbNmyFv13K53R53V1daxfv56PPvqIgQMH8sUXX5CRkcE777xD\nv379yMvLA6zviybyoFghhK2RxMiGHDt2DG9vb+bMmcOcOXMsXvvggw9wc3Pj1KlTvPrqq8TFxdGn\nTx8eeOAB3N3dSU1N5Y033iA6Opro6Gh2797NtWvXmD9/Pk5OThZla/OVioqKeOSRR1rUm0wmXFxc\nAAgLC2Pu3LktYjt+/Dje3t6YTCYKCgqMeUBFRUXs3bvX6nt1dHRk8eLFQMPppLNnz1q87u/vT3Jy\nMgB5eXk8/fTT+Pn5taizxt3dndLSUu677z6gYQ5NUFAQERERbNu2jQEDBlBXV8fHH39sdc5Pkyee\neIIXXniBH374gZEjR5KQkMCECRMsRosAfHx8KCgoID093bgJWdO+PH/+PC4uLsyePZuAgADc3Nz4\n5ZdfjEQzOjqazZs388wzz7Bv3z4GDhzYajw362ifQ8NVaatWrWLQoEF8+umnuLi4GKcKCwsL8fHx\nAazviyalpaVWjxshhOiuJDGyIUlJSYSHh7eoT09PZ+DAgYwaNYrLly8TGxtLWFgYGRkZjBkzhgED\nBnDt2jW01sak3R07dvDSSy8Z82puLjs6OlJfX0+vXg1T0C5evGjMcWpOa03fvn0B+O6774iMjGyx\nzJAhQzCbzaSlpRmnpKBhQnlbRi+qqqpwd3e3qPPx8WHPnj0kJyeTk5NDTEwMw4YNa1FnjZ+fH9nZ\n2YwdO9aoS0hI4K233mLixInY2dlRX1/PzJkzsbe3b5EoNpXvv/9+EhMTWbhwIbW1tQwePNjiFgZN\ny7m4uJCSksJrr71GZGQkZrMZDw8PUlJSOHnyJGvXrgUaRmiioqKMpKhp/ddff6wjg64AABKNSURB\nVB0HBwcCAgJIS0uziCc8PJygoCBmzZrVqX2+e/du1q1bx/r16wGYPn06H374Idu2bePkyZOUlJSw\nc+fOVvcFwLlz57C3t+ehhx66bXtCCNFtdOSStp7wh41crq+1Ni6Pbm7Tpk26sLBQJyUl6dzcXL14\n8WKttdZr1qzRFy5c0FevXtUbN27U+fn5Oi4uTr/99tv63LlzesmSJVprrbds2WJRTklJ0VlZWbeN\np6qqSq9bt05rrfWVK1d0YGBgZ7zNu+7EiRPaz8+vq8Po8WJjY/WGDRtafb27fs6EELYNuVz/3tF0\neXRzvr6+ZGdnk5yczOjRo3nqqadIS0ujtraWs2fPYm9vT3V1NVlZWZSVlRkTfpsm+rq7u1uUZ82a\n1abnx23fvp2IiAgAXF1dOXDgQOe80bvM09OTQYMGcezYsa4Opce6ceMGu3btMq5gFEIIW6H0PT4x\nUimlm/eBrU0YNZlMODk5ERkZybvvvtsp2ywpKaGkpAR/f3+rrxcXF1NYWEhgYGCntCfuPbb2ORNC\n2IbG75Z239xPEqMekBgtX74cPz8/HB0djfv8CNHd2drnTAhhGyQx6qCekBgJYYvkcyaEuBs6mhjJ\nHCMhhBBCiEaSGAkhhBBCNJLESAghhBCikSRGQgghhBCNJDESQgghhGgkiZEQQgghRCNJjIQQQggh\nGkliJIQQQgjRSBIjIYQQQohGkhiJVlVXVxv/3rp1K+fPn7f6mhBCCNFTSGJkQw4dOkRUVBQuLi48\n++yzTJo0CW9vb7766qtOb+vbb78lNzcXgL179+Lj40NKSgoVFRUAXL16le3bt3d6u9b8+c9/JiEh\ngejoaK5du2bUx8TE8Omnn7J27VquXr3aap01wcHB5OTkGOWamhrefPNNRo4cybhx45gwYQKrV6+m\npqaGXr16UVlZecdxt3e9260/fvx4bty40e7ttkVdXR0fffQRn332GcuXL+fs2bMArFixApPJxKlT\np9iyZYuxfPN+v3HjBl5eXsbxIoQQNkNrfU//NXSBJWt1XW337t06ISFBV1dX64cfftio37dvn3Zw\ncNDXr1/vtLbMZrPetGnTbZfbtWuXzsvLa9M2q6qq9Ny5c+84luPHj+tly5ZprbX+4Ycf9OrVq7XW\nWmdkZOgXX3xRa6314cOH9dKlS3VmZmaLOmtyc3O1r6+vRV1oaKieP3++0Y+1tbU6Li5OX79+XSul\n2tW/SildUVFxx+t11vrt7XOtG/q9qY+2bNmiX331Va211p6entrV1VWHhIRok8mktba+L7TWOjY2\nVm/cuLHVNrrj50wIYfsav1vanRfIiJGN8PDwICwsjMLCQh577DGjfsaMGTg7O6NUu5+X18KuXbuY\nMWPGbZebN28eiYmJbdrm4cOHLeJuq7/85S8MHz4cgEceecQYHcvIyMDb2xsALy8vDh48SEZGBl5e\nXhZ11sTHx7NgwQKjXFBQwJ49e4iPj6d///4A9O7dmyVLlhjl9957j0mTJuHh4cHnn39urLt//34m\nTJjAuHHjmDp1KkVFRVbbPHr0KP7+/nh5eeHl5cWXX34JQGVlJc899xyjR4/G09OT559/vsW69fX1\nvPLKK4SGhmI2m9s8EtXePgeYOHEin332GQC5ublMnToVgMjISK5cucKOHTtwdHQEIDMzs8W+gIZR\nuW3btrWrfSGE6CqSGNmI8ePHAw2nuCZPnmzU5+fnExwcTL9+/TqtrezsbEaNGmWUMzMzWb9+PcHB\nwURHR/Pyyy8DDad6qqqqbru9gwcPEhsbi1KKI0eO3FEsAwYMoLa2FoCqqip++uknAH7++WfjPdvZ\n2VFWVsalS5eMRKapzppDhw7x5JNPGuUTJ04wYsQInJycWo3DycmJnJwcEhMTWbFiBQCXL18mLCyM\npKQk8vLyWLhwIaGhoS3WLS8vZ+nSpSQlJXH8+HFSU1OJiIjAZDJx4MABfv31V06fPk1ubi5bt261\nWLeqqorg4GDs7Oz45JNPsLOzs3g9PDyc1NTUFm12pM+b2Nvbs2PHDpycnJg2bRoAFy9eZP/+/WzY\nsIHvv/8egEuXLrXYFwDu7u5UVFRQWlrarvaFEKIr/K6rAxB3Jjs7m3nz5gFw+vRpUlNT2bRpU6e2\n0Xxi9aBBgxg8eDBTpkxh5cqVmM1m4zU7Oztqamro06dPq9sLCAggNjaWdevW4eDgYNQXFxdb/VGH\nhkRk0aJFzJkzh6ioKAC+++47evfuDTTMgWn6NzScErZWZ01JSQlubm6txmtNSEgIAJMnT+bChQuY\nzWaOHj3KuHHjjFGZRYsWsWzZMioqKowEDSArK4uSkhKmT59u1PXq1YuioiI8PT05c+YMy5cvx9fX\nl5kzZ1q0O23aNEJCQli1apXVuOLi4qzWd6TPm/Tr188YWVu1ahV/+tOfWLNmDQBTpkwhMDCQI0eO\n3LLfhw8fTnFxsTHqJ4QQ3Z0kRu3x/pyOrb90T7tXzczMJDAwkNWrVxMUFGT8UHWmuro6i/Ljjz9O\nTEwMMTExABajFv3796esrIwhQ4a0ur3q6mrq6uosfqABHn30UVauXHnLWIYNG8bLL7/M3r17cXZ2\nNkbOhg4dakzsraysxNXVlWHDhrWoa4vx48dTUFBAeXk5zs7OVpext7cHMBKAplGsttBaM3bsWL75\n5hurr+fn5/P111+TlpZGVFQUp06dMvrYz8+P/fv3s2zZshb9dysd6fPmJk6cyJIlS3j66af55ptv\neOedd+jXrx95eXmA9X3RRCnVaoIqhBDdkSRG7dGBxKYjjh07hre3N3PmzGHOHMvk7IMPPsDNzY1T\np07x6quvEhcXR58+fXjggQdwd3cnNTWVN954g+joaKKjo9m9ezfXrl1j/vz5ODk5WZStzVcqKiri\nkUceaVFvMplwcXEBICwsjLlz57aI7fjx43h7e2MymSgoKDDmARUVFbF3716r79XR0ZHFixfzww8/\nEBcXxwcffMAXX3zBH/7wBwD8/f1JTk4GIC8vj6effho/P78Wdda4u7tTWlrKfffdB8CIESMICgoi\nIiKCbdu2MWDAAOrq6vj444+tzvlp8sQTT/DCCy/www8/MHLkSBISEpgwYYLFaBGAj48PBQUFpKen\n4+vrC/x9X54/fx4XFxdmz55NQEAAbm5u/PLLL0aiGR0dzebNm3nmmWfYt28fAwcObDWem3WkzwFi\nY2MpKipi69atXLhwgdGjR+Po6GicKiwsLMTHxwewvi+alJaWWj1uhBCiu5LEyIYkJSURHh7eoj49\nPZ2BAwcyatQoLl++TGxsLGFhYWRkZDBmzBgGDBjAtWvX0Fobk3Z37NjBSy+9ZMyrubns6OhIfX09\nvXo1TEG7ePGiMVLTnNaavn37Ag2nuiIjI1ssM2TIEMxmM2lpacYpKWiYUH670Qs3NzcefPBB/uu/\n/ovy8nJjfo+Pjw979uwhOTmZnJwcYmJiGDZsWIs6a/z8/MjOzmbs2LFGXUJCAm+99RYTJ07Ezs6O\n+vp6Zs6cib29fYtEsal8//33k5iYyMKFC6mtrWXw4MEWtzBoWs7FxYWUlBRee+01IiMjMZvNeHh4\nkJKSwsmTJ1m7di3QMFIXFRVlJEVN67/++us4ODgQEBBAWlqaRTzh4eEEBQUxa9asTutzgAULFpCe\nns6OHTs4fPgwn332GQ8++CDbtm3j5MmTlJSUsHPnzlb3BcC5c+ewt7fnoYceum17QgjRbXTkkrae\n8IeNXK6vtTYuj25u06ZNurCwUCclJenc3Fy9ePFirbXWa9as0RcuXNBXr17VGzdu1Pn5+TouLk6/\n/fbb+ty5c3rJkiVa64bLsW8up6Sk6KysrNvGU1VVpdetW6e11vrKlSs6MDCwM97mXXfixAnt5+fX\n1WH0eLGxsXrDhg2tvt5dP2dCCNuGXK5/72i6PLo5X19fsrOzSU5OZvTo0Tz11FOkpaVRW1vL2bNn\nsbe3p7q6mqysLMrKyowJv00Tfd3d3S3Ks2bNIj09/bbxbN++nYiICABcXV05cOBA57zRu8zT05NB\ngwZx7Nixrg6lx7px4wa7du0yrmAUQghbofQ9PjFSKaWb94GtTRg1mUw4OTkRGRnJu+++2ynbLCkp\noaSkBH9/f6uvFxcXU1hYSGBgYKe0J+49tvY5E0LYhsbvlnbf3E8Sox6QGC1fvhw/Pz8cHR0JCAjo\n6nCEaBNb+5wJIWyDJEYd1BMSIyFskXzOhBB3Q0cTI5ljJIQQQgjRSBIjIYQQQohGkhgJIYQQQjSS\nxEgIIYQQopEkRkIIIYQQjSQxEkIIIYRoJImREEIIIUQjeYhsK6w9YV4IIYQQPVuPSYyUUo8A6wAn\nrfVzSilf4P8ETgE7tdbftHVbctM5IYQQ4t7UY06laa1LtNZLbqqqB34F+gKlXRPV3dOWh7wK0V5y\nfIm7SY4v0Z11u8RIKfWhUupnpdT/alY/TSn1vVKqQCn1b23YVKbWegawBnjrrgTbheSLRdxNcnyJ\nu0mOL9GddbvECPgImHZzhVKqN7C5sX4UsEAp9bhS6l+UUu8opYY138hND0Arp2HUqFO09QPdluVu\nt0xP+/L4Ld9PZ7bVkW3d6bpyfHXMb/We5PiS48tW2pHj6851u8RIa50JXG1WPQko1Fr/qLWuAXYC\ns7XWiVrrV7TWF5RS9ymlPgA8lVJrlFLPNpb/C/ifnRVfT9nxXUESo85bXo4v6+SHq3OWl+PLOjm+\nOmf57n58qe440Vgp5Q6kaq3HNJbnA89orcMby/87MFlr/XIntNX9OkAIIYQQ7aa1bvel5bZyVdpd\nS1460nlCCCGE6Fm63am0VpwHHryp/CA98EozIYQQQnQtW0mMjgMjlFLuSik74HkgpYtjEkIIIUQP\n0+0SI6XUDiAL+Ael1E9KqcVa61pgOXAAyAeStdZnujJOIYQQQvQ83XLytRBCCCFEV+h2I0ai/ZRS\ns5VSW5VSO5VSAV0dj+hZlFKPKKXilVK7uzoW0bMopforpRIav78WdnU8ome50+8uGTHqgZRSzsD/\n1ewRKUJ0CqXUbq31c10dh+g5lFL/Avyitd6nlNqptQ7p6phEz9PW7y4ZMeqGOuGxKP9Ow53ChWih\nEx+7I0Sr7vA4cwN+avx33W8aqLBJd/N7TBKj7qldj0VRDWKBNK117m8ftrARnfLYHSFuo83HGQ23\nX2m6JYv8Lom2uJPj647IAdgNtfexKMDLwO+B+UqpiN82amErOumxOzKiJG7pTo4z4HNgnlJqC3Ir\nFtEGd3J83el3l63c+VpYDjVDw/+wJt+8gNb6PeC93zIo0WO05fj6BfjDbxmU6HGsHmda60rgX7sm\nJNGDtHZ83dF3l4wY2Q6ZJS/uJjm+xG9BjjNxN3XK8SWJke2Qx6KIu0mOL/FbkONM3E2dcnxJYmQ7\n5LEo4m6S40v8FuQ4E3dTpxxfkhh1Q/JYFHE3yfElfgtynIm76W4eX3KDRyGEEEKIRjJiJIQQQgjR\nSBIjIYQQQohGkhgJIYQQQjSSxEgIIYQQopEkRkIIIYQQjSQxEkIIIYRoJImREEIIIUQjSYyEEEII\nIRpJYiSEEEII0UgSIyFEj6CUWqiUCmnjsv+3Uqrf3Y5JCGF7ftfVAQghREcppf4FcG34p1qotU66\nxbJ2wANa60ql1DQgjIaHTZ4HdgEaGAD4AEO11oPv+hsQQnQb8qw0IYRNU0r1Afy01l81lp8B/tL4\nQElry88BHLTWO26qKwEOaa3/9aY6BewF5mutq+7mexBCdB9yKk0IYdO01jVNSVFj+UBrSVGjOcCe\nNmxXAwmAe4eDFELYDEmMhBD3DKWUM2C+gxGg40CfuxiSEKKbkTlGQgibp5SKAkYDGwBv4D7ASWv9\nZrNFnwN2t3W7WuviTgtSCGETZMRICGHTlFKBQApQCvxPIBlwBKZbWfz3wNe/XXRCCFsjiZEQwtYN\n0lqfAqYAH2mtK4FtwPybF1JKPQz8pOWKEyHELcipNCGETdNaJyml+tNwCi2kse6vVhZdCHzyW8Ym\nhLA9MmIkhOgJ/gfwV6116S2WmaC1zv2tAhJC2CZJjIQQPYE/8JfWXlRKTaThCrNWF2n8E0Lc4yQx\nEkL0BP8AfH6L1xdg5TSaUmqaUuoTYDgwWyn1oVIq7C7FKISwAXLnayFEj6aU6g18orVu03PUhBD3\nNhkxEkL0dAHAwa4OQghhGyQxEkL0dPO4g5s6CiHubZIYCSF6rMYHwdZora91dSxCCNsgc4yEEEII\nIRrJiJEQQgghRCNJjIQQQgghGkliJIQQQgjRSBIjIYQQQohGkhgJIYQQQjSSxEgIIYQQopEkRkII\nIYQQjSQxEkIIIYRo9P8DTXRPjmVbmZUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2d213343d0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Non-gaussian parameter"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "length=1000\n",
      "steps=50\n",
      "\n",
      "\n",
      "gaussianparamter3=[]\n",
      "for i in range(steps):\n",
      "        print 3,i\n",
      "        c=analyse_tool.Analyse(D=2,particles=5000,length=356,alpha=0.5,dt=0.1,version=\"nhoch3\")\n",
      "        b=c.nongaussian_parameter()\n",
      "        gaussianparamter3.append(b)\n",
      "gaussianparamter3=np.array(gaussianparamter3)\n",
      "\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3 0\n",
        "3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "analyse_tool.py:133: RuntimeWarning: invalid value encountered in divide\n",
        "  nongaussianparamter=(1/3.)*moment4/moment2poten2-1\n"
       ]
      },
      {
       "ename": "KeyboardInterrupt",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-9-041bad90ab3b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msteps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[1;32mprint\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m         \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0manalyse_tool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAnalyse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mD\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mparticles\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlength\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m356\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mversion\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"nhoch3\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m         \u001b[0mb\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnongaussian_parameter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[0mgaussianparamter3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/janek/Dokumente/Masterarbeit/Masterarbeit_fbm/analyse_tool.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, D, particles, length, alpha, dt, x, version)\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mD\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mparticles\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlength\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mversion\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"python\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[0msimulation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFelix_Method\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mD\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mD\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparticles\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparticles\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlength\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlength\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mversion\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mversion\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrajectory\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msimulation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFelix_Method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mD\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mparticles\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlength\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mversion\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mversion\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_trajectory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/janek/Dokumente/Masterarbeit/Masterarbeit_fbm/simulation.pyc\u001b[0m in \u001b[0;36mcompute_trajectory\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    190\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    191\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 192\u001b[1;33m                     \u001b[0mA\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    193\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mparticle\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparticles\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    194\u001b[0m                 \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/janek/Dokumente/Masterarbeit/Masterarbeit_fbm/simulation.pyc\u001b[0m in \u001b[0;36m<genexpr>\u001b[1;34m((k,))\u001b[0m\n\u001b[0;32m    190\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    191\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 192\u001b[1;33m                     \u001b[0mA\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    193\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mparticle\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparticles\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    194\u001b[0m                 \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "length=2**7\n",
      "steps =1\n",
      "print 2**8\n",
      "\n",
      "\n",
      "gaussianparamter2=[]\n",
      "for i in range(steps):\n",
      "        print 3,i\n",
      "        c=analyse_tool.Analyse(D=2,particles=10000,length=length,alpha=1.1,dt=0.1,version=\"hartecpp\")\n",
      "        c.invert_time()\n",
      "        b=c.nongaussian_parameter()\n",
      "        gaussianparamter2.append(b)\n",
      "gaussianparamter2=np.array(gaussianparamter2)\n",
      "\n",
      "steps=1\n",
      "\n",
      "\n",
      "gaussianparamter3=[]\n",
      "for i in range(steps):\n",
      "        print 3,i\n",
      "        c=analyse_tool.Analyse(D=2,particles=10000,length=length,alpha=1.1,dt=0.1,version=\"hartecppverion2\")\n",
      "        #c.invert_time()\n",
      "        b=c.nongaussian_parameter()\n",
      "        gaussianparamter3.append(b)\n",
      "gaussianparamter3=np.array(gaussianparamter3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.errorbar(range(length), gaussianparamter2.mean(axis=0), yerr=gaussianparamter2.std(axis=0),errorevery=10,label=\"$\\\\alpha $=$%.2f $\" %(c.alpha))\n",
      "plt.errorbar(range(length), gaussianparamter3.mean(axis=0), yerr=gaussianparamter3.std(axis=0),errorevery=10,label=\"$\\\\alpha $=$%.2f $\" %(c.alpha))\n",
      "\n",
      "    #plt.errorbar(range(length+1), gaussianparamter1.mean(axis=0), yerr=gaussianparamter1.std(axis=0),errorevery=10,label=\"Our\")\n",
      "\n",
      "plt.xlabel('t', fontsize=10)\n",
      "plt.ylabel('$\\\\alpha_2(t)$ non-Gaussian parameter', fontsize=10)\n",
      "plt.legend(loc=2,fontsize='small')\n",
      "#plt.savefig('Abschlussarbeit/data/nongaussianlowenalpha.png',dpi=300)\n",
      "plt.show()\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "length=101\n",
      "steps=2\n",
      "\n",
      "\n",
      "plt.figure(figsize=(6.5,4))\n",
      "\n",
      "for alpha in [1.0,0.7,0.3,0.1]:\n",
      "    gaussianparamter2=[]\n",
      "    msd_all=[]\n",
      "    for i in range(steps):\n",
      "            print 2,i\n",
      "            c=analyse_tool.Analyse(D=2,particles=5000,length=length+1,alpha=alpha,dt=1.0,version=\"python_trick\")\n",
      "            b=c.nongaussian_parameter()\n",
      "            gaussianparamter2.append(b)\n",
      "            msd,std=c.msd_ensemble()\n",
      "            msd_all.append(msd)\n",
      "    msd_all=np.array(msd_all)        \n",
      "    gaussianparamter2=np.array(gaussianparamter2)\n",
      "    #plt.errorbar(range(356), gaussianparamter3.mean(axis=0), yerr=gaussianparamter3.std(axis=0),errorevery=10,label=\"Choleski\")\n",
      "    plt.errorbar(range(length+1), gaussianparamter2.mean(axis=0), yerr=gaussianparamter2.std(axis=0),errorevery=10,label=\"$\\\\alpha $=$%.2f $\" %(c.alpha))\n",
      "    #plt.errorbar(range(length+1), gaussianparamter1.mean(axis=0), yerr=gaussianparamter1.std(axis=0),errorevery=10,label=\"Our\")\n",
      "\n",
      "plt.xlabel('t', fontsize=10)\n",
      "plt.ylabel('$\\\\alpha_2(t)$ non-Gaussian parameter', fontsize=10)\n",
      "plt.legend(loc=2,fontsize='small')\n",
      "#plt.savefig('Abschlussarbeit/data/nongaussianlowenalpha.png',dpi=300)\n",
      "plt.show()\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2 0\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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elCI+YrXchIDt22HRouDj6elw991w5pnwox/BCy9ASgp87GPa88uWdYtbfT0c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\nAWaOSRDp0bPTAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2625217710>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True ,figsize=(8,4))\n",
      "\n",
      "length=100\n",
      "steps=30\n",
      "\n",
      "for alpha in [1.0,0.7,0.3,0.1]:\n",
      "    gaussianparamter2=[]\n",
      "    msd_all=[]\n",
      "    for i in range(steps):\n",
      "            print 2,i\n",
      "            c=analyse_tool.Analyse(D=2,particles=5000,length=length+1,alpha=alpha,dt=1.0,version=\"lowen\")\n",
      "            b=c.nongaussian_parameter()\n",
      "            gaussianparamter2.append(b)\n",
      "            msd,std=c.msd_ensemble()\n",
      "            msd_all.append(msd)\n",
      "    msd_all=np.array(msd_all)        \n",
      "    gaussianparamter2=np.array(gaussianparamter2)\n",
      "    plt.errorbar(range(length+1), gaussianparamter2.mean(axis=0), yerr=gaussianparamter2.std(axis=0),errorevery=5,label=\"$\\\\alpha $=$%.2f $\" %(c.alpha))\n",
      "\n",
      "ax2.set_xlabel('$t/\\Delta t$', fontsize=15)\n",
      "#ax2.set_ylabel('$\\\\alpha_2(t)$ non-Gaussian parameter', fontsize=10)\n",
      "ax2.legend(loc=2,fontsize=12)\n",
      "#ax2.set_title('a) $\\\\alpha_2(t)$  vs. $\\\\alpha$', fontsize=10)\n",
      "plt.setp(ax2.get_yticklabels(), visible=False)\n",
      "\n",
      "\n",
      "\n",
      "gaussianparamter1=[]\n",
      "for i in range(steps):\n",
      "        print 1,i\n",
      "        c=analyse_tool.Analyse(D=2,particles=5000,length=length+1,alpha=0.5,dt=0.1,version=\"python_trick\")\n",
      "        b=c.nongaussian_parameter()\n",
      "        gaussianparamter1.append(b)\n",
      "gaussianparamter1=np.array(gaussianparamter1)\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gaussianparamter2=[]\n",
      "for i in range(steps):\n",
      "        print 3,i\n",
      "        c=analyse_tool.Analyse(D=2,particles=5000,length=length+1,alpha=0.5,dt=0.1,version=\"lowen\")\n",
      "        b=c.nongaussian_parameter()\n",
      "        gaussianparamter2.append(b)\n",
      "gaussianparamter2=np.array(gaussianparamter2)\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gaussianparamter3=[]\n",
      "for i in range(steps):\n",
      "        print 3,i\n",
      "        c=analyse_tool.Analyse(D=2,particles=5000,length=356/8,alpha=0.5,dt=0.1,version=\"nhoch3\")\n",
      "        b=c.nongaussian_parameter()\n",
      "        gaussianparamter3.append(b)\n",
      "gaussianparamter3=np.array(gaussianparamter3)\n",
      "\n",
      "gaussianparamter4=[]\n",
      "for i in range(1):\n",
      "        print 3,i\n",
      "        c=analyse_tool.Analyse(D=2,particles=20000,length=length+1,alpha=0.5,dt=0.1,version=\"hartecppverion2\")\n",
      "        b=c.nongaussian_parameter()\n",
      "        gaussianparamter4.append(b)\n",
      "gaussianparamter4=np.array(gaussianparamter4)\n",
      "\n",
      "\n",
      "ax1.errorbar(range(356/8), gaussianparamter3.mean(axis=0), yerr=gaussianparamter3.std(axis=0),errorevery=5,label=\"Choleski\")\n",
      "ax1.errorbar(range(length+1), gaussianparamter2.mean(axis=0), yerr=gaussianparamter2.std(axis=0),errorevery=5,label=\"Lowen\")\n",
      "ax1.errorbar(range(length+1), gaussianparamter1.mean(axis=0), yerr=gaussianparamter1.std(axis=0),errorevery=5,label=\"Naive\")\n",
      "ax1.errorbar(range(length+1), gaussianparamter4.mean(axis=0), yerr=gaussianparamter1.std(axis=0),errorevery=5,label=\"Davis Harte\")\n",
      "\n",
      "ax1.set_xlabel('$t/\\Delta t$', fontsize=15)\n",
      "ax1.set_ylabel('NGP(t)', fontsize=15)\n",
      "ax1.legend(loc=2,fontsize='small')\n",
      "#ax1.set_title('b) $\\\\alpha_2(t)$  vs. Algorithms', fontsize=10)\n",
      "\n",
      "\n",
      "#ax2.ylabel('t in sec',fontsize=10)\n",
      "plt.savefig('Abschlussarbeit/data/nongaussianlowenalphaneu.png',dpi=300)\n",
      "plt.show()\n",
      "  # save the figure to file \n",
      "\n",
      "# make these tick labels invisible\n",
      "#plt.set_setp(ax2.get_yticklabels(), visible=False)\n",
      "\n",
      "\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
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/qlCRQqFQKNJk2zYth8DMmVnPcKewb9TMgEKhcBoMa4noIyLQJVbtM6WWiCXa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\ntHGUMaBQKBQKRRvn/wFdlAto1hFi/wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9d49ea9c50>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "modified lowen"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, (ax1, ax2) = plt.subplots(1, 2,figsize=(8,4))\n",
      "colors=['r','b','g','k','c',\"burlywood\",'b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
      "\n",
      "a=np.logspace(0.02,1.25,num=6,base=2)-1\n",
      "a[4]=1\n",
      "length=100\n",
      "steps=20\n",
      "coloin=-1\n",
      "for alpha in [1.0,0.7,0.3,0.1]:\n",
      "    gaussianparamter2=[]\n",
      "    gaussianparamter6=[]\n",
      "    coloin=coloin+1\n",
      "\n",
      "    msd_all=[]\n",
      "    for i in range(steps):\n",
      "            print 2,i\n",
      "            c=analyse_tool.Analyse(D=2,particles=5000,length=length+1,alpha=alpha,dt=1.0,version=\"lowennew\")\n",
      "            b=c.nongaussian_parameter()\n",
      "            gaussianparamter6.append(b)\n",
      "            #d=analyse_tool.Analyse(D=2,particles=5000,length=length+1,alpha=alpha,dt=1.0,version=\"lowen\")\n",
      "            #e=d.nongaussian_parameter()\n",
      "            #gaussianparamter6.append(e)\n",
      "                  \n",
      "    #gaussianparamter2=np.array(gaussianparamter2)\n",
      "    gaussianparamter6=np.array(gaussianparamter6)\n",
      "\n",
      "    #plt.errorbar(range(length+1), gaussianparamter2.mean(axis=0), yerr=gaussianparamter2.std(axis=0),errorevery=5)\n",
      "    plt.errorbar(range(length+1), gaussianparamter6.mean(axis=0), yerr=gaussianparamter6.std(axis=0),errorevery=5,color=colors[coloin],label=\"$\\\\alpha $=$%.2f $\" %(c.alpha))\n",
      "\n",
      "ax2.set_xlabel('$t/\\Delta t$', fontsize=15)\n",
      "ax2.set_ylabel('NGP(t)', fontsize=15)\n",
      "ax2.legend(loc=2,fontsize=12)\n",
      "#ax2.set_title('a) $\\\\alpha_2(t)$  vs. $\\\\alpha$', fontsize=10)lowennew\n",
      "#plt.setp(ax2.get_yticklabels(), visible=False)\n",
      "\n",
      "\n",
      "coloin=-1\n",
      "\n",
      "for i in [1.0,0.7,0.3,0.1]:\n",
      "    coloin=coloin+1\n",
      "    e=analyse_tool.Analyse(D=2,particles=2000,length=2000,alpha=i,dt=0.1,version='lowenmodified')\n",
      "    msd,std=e.msd_ensemble()\n",
      "    ax1.plot(e.t/e.dt,msd,color=colors[coloin],label=\"$\\\\alpha $=$%.2f $\" %(e.alpha))\n",
      "    ax1.loglog(e.t/e.dt,e.msdanalyt(),\":\",color=\"k\")\n",
      "#plt.loglog(e.t/e.dt,e.msdanalyt(),\":\",color=colors[1], label=\"$%s$ \" %(msd_text))\n",
      "\n",
      "\n",
      "ax1.set_xlabel('$t/ \\Delta t$', fontsize=15)\n",
      "ax1.set_ylabel('$\\delta r^{2}(t)/ \\Gamma^2$', fontsize=15)\n",
      "\n",
      "ax1.legend(loc=2,fontsize=12)\n",
      "#ax1.set_title('b) $\\\\alpha_2(t)$  vs. Algorithms', fontsize=10)\n",
      "\n",
      "#ax2.ylabel('t in sec',fontsize=10)\n",
      "plt.tight_layout()\n",
      "plt.savefig('Abschlussarbeit/data/nongaussianlowenalphaneumodifed.png',dpi=300)\n",
      "plt.show()\n",
      "  # save the figure to file \n",
      "\n",
      "# make these tick labels invisible\n",
      "#plt.set_setp(ax2.get_yticklabels(), visible=False)\n",
      "\n",
      "\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2 0\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        " 14\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        " 9\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        " 11\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        " 13\n",
        "2"
       ]
      },
      {
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IKTOMDTYM1nbmZSNDHE3fVCkmbq1QXbFmFqcycpyEsWPHsnv3bqOfDRw4kHXr\n1hEbG4unp2dhu6enJ5GRkaXOqRnQtzB3fEXIz89n25Yt9P/gA3weffTWdt8V5NQp6NQJwBN4DJ3O\nTdW9qd7EAX4Gx35AbDl9WujbEELUBFYB30sp15R2EmfLSHM2fVPlGBgzoaGhhASH2FIau6P4g0BF\nigEqI8eBuHz5MkuWLKFbt27s2LGDmTNn4u3tTUZGBhs2bCh3fGpqKrUN0oXc3NzIyDD64Alo66AV\nGV8RXGNj+XzbNujVy2qBxufPX6FTp7+oV28I27cLuncfrgwcxUEgUAjhD1wGHgYmFeuzDngOWC6E\n6AukSikThfYFCQNOSSk/rTqRq4bqpG/sFcNsJAi212LLJlPEQ7MUQvQeLGPeTcP4nYpQbY0cS4oB\nFv8SWkrxJxZTyMzMZMKECWzatAkvLy98fHyYNWsWU6ZMYezYsSbN4eHhQXJycuFxdnY2TcpYAiou\np7njLWHJkiX09/Wl7ZNParVwXn3VKiWGz56F9u3jgb1cuzaEGtX2f37FcLZigFLKPCHEc8BvaCnk\nYVLK00KIGfrPF0spNwkhRgshzgOZwDT98P5oMTzHhRBH9G1vSil/tZZ81tI55lJd9I3d4x8BQRHa\n++2ht/Zt8A/i1oHjUHyrhrI8nIbByKEoT47ZWOI+tsQ4sRYrVqygZ8+eeHl5AdC4cWNOnjyJEAI3\nNzdGjRrFrl27jI4dNGgQGzduJCAggIMHDxa2JyUl0aNHj1LPWVzBmjveEmplZVFz+nR45x2YMaPC\n8924cYP33pN88EEdoCs3b3ZVBk4FKFBSFXEf2xtSys3A5mJti4sdP2dk3C4quaBqRXWOpUZSddE3\n9k6RH/q7QgkJD7GpPI6IUvcOQm5uLm3atCk8zszMxNXVlQkTJgCwefPm0oYWMmjQIF577bXC48OH\nDzNnjlb37MKFC9x2221FFE1xBVvWeKuQnc0j69bBo49axcABGDEilB07AnjmmSdYsEDtO6VQmIKz\n65vK2FDSHD79NII1a7Tzc3QwQXrjZfz4IF58sfLPX52otnVyjF23rWtKlEVaWhpz5syhf//+5Obm\n4u7uTlhYGMOGDWPixInUrWtadtCyZcuIjo5Gp9MREBDA5MmTAejRowdhYWF0794dgAULFvDTTz8R\nExPD1KlTmTVrFvXr1y91vDFMvZ/Lly8nNzeXKfv2QVwcrFgBtWqZdD3GyMmB06dh1Sp4770cBg6s\nyY4dyrqX140CAAAgAElEQVSxJs5SDLAqcMQ6OY6mbypSJ8daNWAs6WfNOQ3jd0JDQwjWBzOXFb/j\nYPeocooBCiHcgQ+A1sAfwBf6dez7gK5SymBLTmxLHNHIcURMvZ+RkZHkr1pF+8WL4fBhaNjQ4nNu\n2SIZMeIfQChwG6dPQ/v2Fk+nKAVl5JiOIxo5joYyciyc047vUVGjzXJ9Y8p68hfAOWAx4A2sFELU\nl1KuBp6x5KQKBdxyTwcmJtL+k09gw4YKGThjxsCIEYInnniW5GQ/UlOVgaNQKBSOSFAQhIRARasv\nmBKTs0dK+aX+/Ub9fi3/J4T4d1mDFIqy2LVrF0uWLCHslVdg8mT45puCAjZmEx2dzcCBq4mJmcz+\n/dC7d18rS6tQVA2G2WuDBw8uTJCwdO8qS+dQKJwFU4ycfCFEL7RUydlSygQhxFvAdMBh92i2JIVc\nYT369OlD0/r1YexYeOstzQ1jAZcuQWBgHrm5h9ix42F691ax9JWJs6WQ2xvW0ElKr9mGInVdgiCk\nioOZrY05NW3sGZMCj4UQg9BicpZJKXUG7SOtWROiqlAxOVWDsft58+ZNatWqpW2IN2kS1KkDS5aY\nPXd+vo4nnkhm6dLGvPACfPKJypyqSlRMjukofVP5qJgcC+e05eagVRR4bNJjr5RyB7DDSLvDGTgK\n25GQkMCoUaM48OWX1Jg3D/7+G7ZvN3uenByYOHEjf/yxjp07v2LAgEoQVqGwIrffDmvWwG232VoS\nhbPibJ4ka2G2b18I8SDaPi41gZNSyvLreysUQFMfH8KHDKHGuHHw9NPw1VeaJ8cMPv4YXn4ZRo8e\ny8mTo2jRopKEVdgN+u0TMOoOcRBcXeH6dVtLoXBmVOFA41gSwPA/YLyUcpO1hVE4J/Hx8TRr2BCm\nTcPz0iU4fhy8vc2e55VXPmDBgkD27HmQvn0FQqj4G2dECNEMmAIMAXoAXoAUQiQDR4BtaEvn8baT\n0jzq14e0NFtLoVAU2w8rIpgQ/aNDRfbDsuc9tswuBiiE+ElK+VAlyVMllLVGrrAuubm53NmlC+uy\ns2naty8sXQq1zYtXz86Gtm0hNjaSN95owocf1q8cYRUmUxkxOUKIAOA94EG0ncD3AieBZEAAjYDO\nQD/AF1gJ/J+U8oI15bA2Qgg5dqzkqafgnnuKtNtOKCdExeRYMKeKyTFKrCmdhBDtpZRnLJjfZjiw\nN9x+OXyYvVev4vrttzB6tFnRwVJKvvzyK9atm0JsbB3mzQtk1qxKlFVha44Ay4C+UspDZXXUZ3xO\n04+xe6u3QYOSy1UlfpSd7QfUhj/KFSE5Kxmvul6Veg5F1WGJkZNvYr/ngWctmF/hBBw7doy2SUnU\neeQRXL/6yuwUcSm1bRmefjqJ5s2vk51dx1wHkMLxaGfqEpSU8iBwUAjxr0qWySrY43KVPS8x2IoL\nKRfo/01/El5JKGxT98mxscTI6S+E+Ec5fQRwtwVzK5yEsLffZtKuXfT76ScYPtyssQsXxrBwoR8n\nTghq136LLVvMXuFyeC6nX+azfZ/xnz3/Aaj0p1d7wNDAEUK0BBKklDnF+wkhagLNpJSXHCUuxx6N\nHMMf6dDQ0MKigY5MRWu7HIo/RGJmIuk30/Go5aEfq73G/jAWvDY6xX2qTlhi5PTVv8rDrrWyKgZY\niXzyCfMPH4YtW6B3b5OHSQnPP3+ZRYseIixsN1OmuCBE9at/E50ajf9n/tpBOnBNW9qwhxiOKiwG\nGIWmZ/408llXYD/gao0TCSFGAp/q5/taSlliq2shxHxgFJAFTJVSHtG3fwOMAa5IKbuUdo4qNXIM\nXA/BQHVyPRjqdEsMtyPxRwCIvh5NZ5/ORT7bG7sXqkEJgDxdHicST9C9WXdbi2IVLDFy1kopJ5TX\nSQjxiwVzVxnKGrc+R44cwXXbNm6fOxf27gV/f5PHSilZvlywYkVzTpzYTYcOpmyr5lzEXI/h4Z8e\nZu/6vVpOkUCrKZ5nP0GqBT8ioaGhthSjFlDCw2MJQghXYAGa5zkOOCCEWCelPG3QZzTQRkoZKITo\nAyzk1oPeEuBz4LuyztOgAcTFWUNiEzAwZuzRQ7Pu7DrSb6bTskFLBrQcYDf/twGOJByhlmstolKj\nihg52bnZpGSnaMVTnJzwi+GMXzGemFkxNKrTyNbiVBhLjJxvrNxP4ST8/dNP1Fi4kNsjIswycDZu\n3MgHH4Rz7Nhcdu3C6Q0cKSUf7/2YqNQonu39LE99+RQ7s3ZqlackcBXIhdWPrqaddzs6Nu5oY4mr\nBiFEVzQvTcGv3hghRPEtVmsDD6NtGmwNegPnpZRRehmWA+OA0wZ97gW+BZBS7hdCeAohmkopE6SU\nO4UQ/uWdpDRPTnWL98jNz2XSqknc0/Yedsfs5qNhHzGx80RbiwVo38sjCUcYFjCMqNSoIp9dTr+M\nm6sbOX5Wsa1twsVrF2lSrwl/7v6zzCW9yJRIsnKzWHhgIW8PettW4loNs4wcIcQ7QLgpfaWU6y2S\nSOGYbN3K/WFh2hJVt25mDc3MHERk5O0cPqylijsTSVlJ7I3Zy+H4w0zqMol2C9pBLpox4wYLDiyA\nNUB/oDHaz/tIuP7GderXsvukIWszAZhtcPxOKf0uAk9b6Zy+QIzBcSzQx4Q+vkACJlKakTNgUB4D\nBwlcXVwr7HWZNm0aL7zwAt3M/P5VJccTj9PaszXLH1jO8r+WE3YkzG6MnPiMePJ1+QzwG1DCyIlL\nj6NX817sSd/D5fTLNPdobhshK8DUtVN5tMujPBn0ZJlLeueSzzGt2zQWHFjAy3e+TO0ajh0Qaa4n\n53NgvBDiMeAasFpKuc/6YikchdOnTxPxySfMXLMGVq82OQZn1apVdOzYkb17O/D66x6sWuXhNAbO\n3pi93PnNnUUbJYRsD9He/4q2E1yBN3z8rW7nnz9PQKOAyhfSPnkfmKt/n4ZWDPBgsT45xoKRK4Cp\nsYPF11TMijksMHJWn15Nr+a9aNmgJbn5uQxeOpjHuj7GjF4zzJnOKDt37mTUqFF2beTsj9tP3xba\nSt/49uN5dtOzJQyK4pgTTFwRz9jRhKN0b9ad1g1bc+DygSKfxaXF0aJ+C4iB3Zd282CnB8uezM7I\n0+VxIO4AnRt3LrdvZEokM3rOICEjgWXHlvFkzyerQMLKwywjR0qZCiwFlgohvID7hBBPAEnASn1a\np6K6cP069WbPxmfXLli/HvoUfwAunfz8fI4du8lbb8G2bdCl1JBNxyD1RirB4cHM/3N+yQ8PoT0S\nFOQbjgFcIPm1ZDzcPHARLri6WCWG1qGRUuai+bkAqmrNMo6ikRZ+lKwFVrxPC32bySxfHsKZM/Dk\nS5/j3tadIx8e4ZN9n3A04Si9fU0Pzi8NnU5HTEwMV69erfBclcm+2H0MbDkQgNo1ajOp8ySWHl1a\n5pj2vdqT65fLsIBh5Xq7KpIxdiT+CN2bdsff09+oJ8fXwxdiYE/MHoczck4kniAnP4cjCUfK7RuZ\nHElbr7a8cucrvPjrizYxcqyZ4GBxXXwpZTLwFfCVEMIHzeCZiebC/VlKedQqEirskzNnYNQo/EaM\nwC8yEurVK3fI+fPnadOmDdu3w6FDD/H11xAa6tgGTtjhMJ5Y/0TRxsvACWhyXxMGtBxAx94deeuu\nt4jJjCHQK9AmcjoCQoj+UsrdZo4ZIKXcVYHTHgQC9XE1l9HifSYV67MOeA5YLoToC6RKKRPNOcmr\nr4bw+7Z8rvX/N5N7Tmbw0sGkZKcwb/g8tvy9pQLiayQkJJCTk0NSUlKF57IaNUs27Y/bzyt3vlJ4\n/Hj3xxm/YnxJP5kBWy5s4Z3wd4h6IcrkU89YP0Nb/jWDIwlHuK/DfbRq0KqkkWPoyYkx67+oXbAv\ndh/j2o9jy4Ut5OvyS32oys3P5dL1S9zW8DbaNGrD+ZTzZOZkVrG0Jb10FUl0sMrTkpTyipRykZTy\ncbRMhQFCiG+EEO8KIcr3jykciviICKb06EH+O+/AokUmGTg6nY7p059g2LA4HnkE5s6F1q1h5swq\nELgSuJl3ExEqNAMniyKRamfeOcOhBYdIeCWBlQ+t5N1R71K7dm1l4JTPKiHENiHEZCGEe2mdhBAe\nQojHhBA7gJ8rckIpZR6aAfMbcApYIaU8LYSYIYSYoe+zCfhbCHEeWAw8YyDLj8AeoK0QIkYIMc3Y\neerXh1RdDI3dG/PpyE8Z2WYkyyYso0+LPuUu15jCpUuXACrkyZFSMn3tdHLyK74auOXCFvin9qNZ\nQEp2CvHp8XRq3KmwrXuz7jSs3RBalj5XQkYCl65f4s84Y9UEjLMhcgM0M0/mIwmaJ8fH3Yes3Cwy\ncjIKP4tLj9PicOLg5NWTZOVmmTd5Rdm8mTfB4joE++L2MTJgJI3rNuZ8yvlS+0WlRtHcozlurm7U\ncKlBO+92nLp6qtT+D/78IPtj91skU1VhdZewlDJeSrlASjkd+BK4WwixRAjxprXPpbABx47h8+CD\nTJ81C9fp08vtXlCqPTLSBQ+PcJo08SUqCvLz4eBBbXdmRyLtZhoiWFD7/tqg09oaNGjAO6PeIef/\ncpDBknYt2tGjRw/bCuqYtAEigI+Ba0KIQ0KIZUKIz4QQ84UQPwghjgEpwL+BLfoxFUJKuVlK2U5K\n2UZK+aG+bbGUcrFBn+f0n3eVUh42aJ8kpWwupawlpfSTUi4xdo769SGtxgUCGgbgIlyYO3wuQ28b\nanRpxFTmzJlDTo5mkERHR+Pq6lohT05SVhJLji5h28VtFs9RwIe7PoRasPXvrYVtf8b9Sc/mPUt4\nEe70uxOalD5XYkYijeo04udTptmz6TfTuZx+GTyNfLhwIUyZAikpRZqv37hOYkYibb3aIoSglWcr\nolOjCz+PS4/Dt74v5EFnn84cumyw68jmzbBmjUmyWcTq1TBtmhbCFxgI339v9hT7YvfRt0VfujXt\nxtGE0hdZIlMiizyMdfbpzF9X/jLaNzEjkVWnVrE9ervZ8lQllbruLaWMlVJ+KqWchmbwKBwYuXUr\n3H03rgsXctf775fbPyoqiqFDh7JokaRzZ/DzE3z9NdQ04sa2Zzae20j72e355egvNPh3A+1bkwTc\nhOy3s0l9K5V3X3+Xmq4OdmF2hpQyQ0r5LlrMy4PALrRn/LuBoWjP5n+ghWr7SSn/JaWsel+6BdSu\nDdLzAv4NigaVN6zdkHxdPqk3Us2aLycnhzfffJNTp7Sn7EuXLtGpU6cKeXLi0rUwo1WnVlk8B2g/\nqFGpUfAHfH/81g/y/tj99PEtGbfXplEbbevVUkjITOCJ7k+YbOScS9ZXFyhu5GRlaevjQkDXroYR\nypxLPkdbr7aFBlhx4/Ny+mUtJgfo3LjzLe9Gfj48+yzMmEHE48sICZb6YGct6DkkpMhpzGfNGnjm\nGdi8mcmgZa++8sqtSfPzQe/FK43krGTi0+Pp2Lgj3Zt2L2HkJGYkciLxBKC/D41uZYB08enCiSsn\njM677uw6PGp5cPCyfYfiVllBEn0Mj8JByV2yhAGjR3Pl66/hgQdMGlO/fiuGD/+a//s/wcGD2sqW\nvW/PcCPvBscTj7Pr712ItwQiVDD2x7Gc3XyW+xbdV9hv49KNxL4V6/DplfaIlDJXSrlWSvmClHKw\nlLKT/nWXlPIlKeVG/TKTQ+HW5AK+dYoaOUII/D39i3gNTCE2NhYpJX/9pT1lR0dH07Nnzwp5cuLS\n4ujg3YE1Z9eQp7P89s7ZPYeX+70MJ2BT5CbSb6YD2pJJQWaVIeUaORkJDL1tKHVq1NES98vhXPI5\nbQmsuJHzzTfQrx989x2EhcFDD8EvWs3apKwkGrvfCuLxb3DLyJFSEp8eX5g23tar7S1D6pdfoFkz\nOHqUoGOfERI/g5DZOiC00MgJ6pUBP/xQvuDF2bYNnnoKNm2C7vrqw127wrJl8Mgj2ucDB2qf6XSl\nTvNn3J/c4XsHri6udGvarUTw8Tvh7zDllylIKYlMNt2Ts/rMal7p9wqH4svcS9fmWMXIEUL4CyHu\nEEL0FkIECCHqWGNehZ2waBE1Q0P5bu1afMaNK7PrgQMHWLp0FdOng5eXYP3621i5Uvtu2jMiVCBC\nBHXer0PXRV0ZOHmgFqGhp/3U9oVr/DJYMjpwtOa+VlgVodFTCHG/EKK/EMJprEgX7ws0qVmyPEAr\nz5KBrqWhrf6OIjpaM4oKjJxLly7Rs2fPCnty7vS7E39Pf3ZE77BojsjkSPbE7GF69+mQBQNbDWTN\nmTUcSzjG3pi9lnlyMhJoVq8ZD3V6CDqV3q+As8lnGRYwDBoYNObmwkcfwZv6qInhw+HXX+GZZ5h0\nHJKzk/Gqc2vnccO/SVJWEu5u7tSpqf2stfVqy7mUc9ofY+5cPpjizxcxqzXvyrlzMH160R/WxYth\n8mRYsaJ84Qs4dAgmToSffoLiS9/DhsFzz8G992rzenvD8eOlTrUvdh99fTXjssRylRv8fOpnrmRe\n4XD8Yc6lnCOw0S0jpzRPTuqNVHZf2s0LfV/gauZVrRq0nWJRdpUQwgXNnfwo4A9cQVsnzwEaAo2E\nEDloa+YL9KnnlY4QojXwNtBASulYOX72iE5H1iuvUHfVKggPJ+C28jduOXCgNq+/XouHHoL0dJNi\nkquc6zeuk5ydTL4un1e3vsras2vhBFri8Ch9p+Fwb4d7WXd2HTn/l0NN15rczLtJrRq1bCi5c6PP\n0lyHVoW4gEtCiAecoTyFrsEFGgnNyNmwYQO33347LVu2LOI1KI+EBIBNnD//LQ0aNCjiyenRowdJ\nSUkW73MWl6alSbdp1IaVp1YypPUQs+fYdWkXw24bRt2adQF4tMujvPb7a2TlZvH5qM9p5lEyGvi2\nhreBp1bLpYZLyZ+k2GsJfLewKdcZB4ELy6598/PPnM3/i7sDR/DTsZ/QSR0uwkXzpAQGFtbxKkxR\nnjAB38UL+V4sIKWWjgivCIKCgvD39C/0UBSmj+tp591O8+Ts2QNJSWyr407+6ZU82/tZ2LgR7r2X\nLwo65+XB559rsUDPP6+dv3Xrsm9iVhY8/DAsWFB6cZ8339Tm8/CAEyc0A6uU+kj74/bzzB1arHyL\n+i3I0+WRkJFA03pNoRMMajWIXs16EXYkrDB9vIAW9VuQnZsNdYvOueHcBu5qfRf1a9Wne7PuHI4/\nzN232eee3GYbOUKIHmiGxBbgqdJ2AdZ7cwYA84UQR6WUH1dIUhOQUl4EnhBCVCjjQqEnOJjJ337L\ny0uXMqAMA+fs2b/Ztq0JGza4s39/FzZt6sLAgVUopxmM/t9oNp/frJnlB4HR+g8CgXba29J2/FYG\nTqXzb7QyiY8CR9AeoP4DhKFt9+CwSCnJqXeBBvmakbNgwQIeeughpmdn4++WQZRLlEnznDmj/Xvy\nZDQjRoxg/34ts+XSpUu0bduWWrVqkZaWRoMGDcqYxThx6XH09u3NXf53MWjpIN4c8CZ+DczbrOmv\nK3/RxedWTYh7293L7pjdvNzvZVp5tjI6pnaN2pCp7d3WumFRAyAnP4dsXTpzgr3I13ny1ewkXn7z\n1g7hRfj8c3j1Vc69WIdZA16FbIhPj8fXoznMnw//+ldhV8MU5XsWLqRP7inE1KcK2wxjcuLS4op4\nbQOuQXTSBXIfup8awe9yNPGtwmyseu71YO1ahnl4wNq1mgepRQt4+mnNeJk0CXbuLAxMjEszUm4p\nNBR69dKW00pDCPDw0JYs+wXQZXU4vPii0a5RqVGatwxtebRb024cjj/M6MDR0AOe6P4E3Zp2o+ui\nrmTlZhX5Owkh6OzTmd0+RdPmV59ezX3tteX7ns16cvDyQbs1csxarhJCDEQLAHxAn31g1MABkFJm\nSym3Sin/AewXQoRYIqA+FT1RCHGiWPtIIcQZIUSkEOJ1S+ZWlMGqVfDdd3x34AAD7rnHaJejR2H2\nbBg06DMWLtzL1Knw11/YnYGTp8tjz/k9iEFCM3BAc2XfDi7ChWfveJY9z+xBvi9LNXAUVcIQ4G0p\n5Q9SytNSys3AFKCLEMLbxrJViKSsJFyogcxuCEBKSgrx8fHw44/4bzlA1PUok+YpMHIiI6O56667\nuHr1KnFxceTm5tKoUSMaN25s8ZJVgcci0CuQiZ0m0uPLHty+8HZirseUP1jPiSsn6NLklpFTp2Yd\n5o+aX6qBU0gKJVKbk5KS+PPEnzR2b4yLcNEC+xPhcHxhchvx6fqfoA0b4MMPkUeOcK5mGm2Xb4VU\n7Qeegwe1bKoRIwrHRURQGDOzgWA2Nx7C/i8SiPgjHyhm5KTH0byeFo/TE6g1IIjmsh5RK7/m8sQx\nuAgX+rboS0RUhDZ5vXo8Bpph88EHt4yPF18ELy/4v/8DtPT6jv/tCA9wKyX98GFYulQzykxgb+xe\nRl75WDOc8vON9in02uh5uNPDTF0zlTd+fwM8YVTgKPwa+NGnRR9aebYq4U3r7NMZfIrOeTj+MANa\nDgCgV/Nedh2XY25MznEp5UeyIC/YRPQFvj4x81wFLAFGGjYY7Bw8EugITBJCdLBwfkUxUhYuJP3p\np2H1ajxK8eC8/baOu+/WvleLFn3GkSN38+CD0LSp0e5VRr4un2lrp/H737/z36//S/vP2lPzvZr0\nX9Yf6gE6uPLKFf474b+c+uAU+bPzWTB6Af38+tlWcAVoVYWLRzkWREY5dADUhWsX8MgLKCxzkpKS\nQnxcHBw9iv/xaKKumLbf6Nmz2r/R0dG0bt2a9u3bs3nzZlq2bIkQgsaNG1scfGzosfhk5CckvJxA\nr+a9+OGE6UGzJ66cKOLJMRkjRs6yZct4N/hdmrgb5JdfpnDLhejUaFp/1prMU0dh2jRYvZrLvvWp\nW68RnnMX4FNg5CxaBDNmgMutn7ugoFtGDoQSMKI2j3oeJWjHuwA0cW+Ce0139p78jbgdG/C9kgUR\nEWwA+Oor2gb25ZyX4GjCUbo17caIgBH8dv63wvl3Azz2mGZcjdfv2+LiAt9+qy2d/forBy8fxK++\nH+TDgG8GkLIvHO6/Xysi5lPMqiiFvTF7ic9KJKd5Ezh2rMTn2bnZZOdla4HYep7s+SThj4Vz8upJ\n2EuhUfPcHc/R369/iTmMGTlXs67SpJ72dynw5Ngr5m7rcL14mxDiJcOlKCHEXUCelHJneWNNPKex\nXX6N7hwshEgEPgC6CSFel1LOKW1ew3LfxvZAqbZs2sSXr71Gg5deYmbPnka7/Pe/ecyb15fDhzfT\nsaOZZUUrCSklQz8eSvjVcKiDVir+d6AvmnHjAt+8/w3Tumu12mbe4aBVCG2ENcusl4GgsPpQIQWP\npw5WUakoF1Iu0IgAruu1YEpKCgmRkeDnh//gO4hKMW2FXfPknCUx8RItW7akc+fObNq0iVatNE+J\nt7c3n/7xKdMbT2d4wHCzZCwee+Lq4srkLpN54483eH1A2c5ynU5H/PV4snOztcrA5mLEyElMTOT8\nufO0f8hgI/q4W0bOxsiN3My/yaF/Psigd9+Fvn05dzGctj7tYfl7PPyvu4j6axesWkXe6ZPcyMnA\nvaa70Xil5BspeM16C8a/Ar16IYDX/27Ovz4aS7MGvtyRUgfen8rzwM/jxtF28x+cSz5HVm4W3Zp2\nY3jAcCauKrbR6Pvvw8svQw2Dn1lvb63OzcSJhC+azPCA4Zx89iSd+tXm83fHEvyfpfCg6eGke2P3\nIpFcDuqJf0REiSDlxMxEmrg3KXHNnXw6sX7SesQjt9rHtB3DmLZjSpyji0+XIkZOZk4mOqnDvaZW\nrzPQK5CU7BSSs5LxqutVYjxo5QN+OPEDn436zORrKyAuzqwdVEpgaeDxWuACsJ1iT1hSynB9VsRk\nKeX/KiRd6RjdOVhKmYKJuxNXZLdfpyU2FqZN47VNmxADBpT4OD5eSxT45psabN26xiYGTlZuFvm6\nfDxqeRCdEo3MlbRepF/H3wx0gq53dOVY4rHCvaKWjlvKY90eq3JZnQlrllkvhyVCiIJyspJbBf+/\nE0JkG7RJKWXFN32qIi5cu0DjGponJz8/n9TUVOIvXoQ776TRI0+Qt+V/YCTkS0pJcnYy3nW11TrN\nyNnKtWsxhUbOu+++yyOPPAJA48aN2XRyEy5HXcwzcmpoP14F5ylgsP9golKjiE6NLnPJ6X//+x9h\ny8PofH9ni4KeSYHz14oaOVevXiX2YiyD6w6+1XiZQq/BxnMbaZlfj32BdRj0tKb2zyafpZ1XOwgK\n4vAr0OHnL2Hkg8z4czbLji8D4N93/5uX+r1U9PTZKXj5Bmrp2fffDz16MG30vfyLi5ysAeOf/Qja\njmWl/traerXl5JWTJGUnMaH9BLo27UrqjVSiUqOola3/Q7q6QmMjOnLwYJg9m22bX+bF6V/iAUz8\nMZagh+rw2vixmJqaLKVkb+xemtVrRkyrQPxXhcNLRa+r+FKVJRR4cgoC2q9mXaVx3caFf2cX4UL3\npt05ePkgI9qMMDrHt8e+ZcuFLRYZOW+88UaF5Lc0hfxDIBN4CXhRCHFSCLFICPGoEMJfvzzVsOwp\nKoQKnLAyabt3c6R3b3jtNVwGDiyiqHJyYOzYWFq3/pCoKK1sw8CBFjytWYH3d7yP3yd+DFs2DP8J\n/rSeditQ8e0P3kb3tY6jTx9FBkt0s3XIYKkMHMfhO+AAcFL/OqX/9zu0MHHDtpM2ktEiLly7QLNa\nt5GWBqmpqVrtlStXoHdvxIAB+Ge4EmikQu+emD2M+F774cjKgsREgG24utbD3d2dzp07k5GRUejJ\nqdugLqkpqfx6/tciWyqUS31o5tGM48ePF4npqeFSg/HtxrPqdNkFAv/44w9OHj9p2VIVGPXkXLly\nhdybudTJqlOQOw/JWnxT7LmD7Dy7hTeOebCvfystEBc4m6Q3coDdqRDdK5Ds119i9ZnVRL8Yzdf3\nfl1ih3HQp5DX9SLtjjt4efp02LaN2q+8wasDXif6enQRDxfcSiMvWK5yEZpR+dv531i3bl25l3vj\nySfCfPoAACAASURBVGnsb5rPoHH/ZBzQYfMBerfqV2iImUJsWiy5+bkMbDWQmDY+sGsXXLlSpI81\njByvul7gAuk5Wr2jq5lXi9QUArij+R1G7yuATur45cwvRF+PtmjLkIrux2aRkSOl3CelfEdKORj4\nAngS+Bttc7uDQogUoDLDT03ZObhMQkJCqsL97hhcvMixceP4+Y47NPeqAZmZWkKATufJnDlNWLJE\nq15cleyL3afVsZkk+OC1D6hfqz6///07c9+fyyezP+Hcc+e48fYN/jXkX0WMM4ueKBVlEhERUWle\nUCnlVDNeRveIslcupFyghbu2XJWSkkKTJk1IyMhA9uoFLi60atiamZ7AgaI/FKeTTnPx2kUAIiMh\nIADgb2rW1IyazvovY8uW2uZPmTUy8XXVgofNKrfvAV43vejXrx+BgYEEBgaSqFlUPNDxAVaeWml0\n2PfHv+f1ra+zc+dOki8n07Z+W6P9pJT885//JC+vlCKDKfD3tb/RyVurlVeuXMGtrhtyxyltF9/o\naJDQo2YrPnpjEN1dmjPyy23sTTxYuH3MuZRzt1KgUyGqvo6NtS7Ro1kPmnk047aGtxlN10/O0urk\nbNu2jfmff45OX1zvqZ5PFe5M/vDDDxf2b+vVlqMJR4lLiys8350t7uRQ/CEStDz/MtkXu4+Ovt2o\n/9F8ggCaNOHVO19l3t55Re5BWeyN3Us/v3741fcjhjR4/HGYNQuA5gB33EHC+h9pWq+MPTNMJf1W\nkHeBJ8eQvi36si92n3E5Y/biXdebVg1a8ffx7YUylsf5lPPcyLthGyOnGPuklHuklP+RUt6Dtvdr\nBynlw+UNrACFOwcLIdzQjKvyzWcDQkJCVBwOQHg49O/PwPfe44O1a4t8tH9/Ch06XKBBA/jll3q8\n8ML0Sjcc9sTs4eXfXmbmhpncv+h+RC9BvzB9UHBr8LzXk79f+BsZLHl5wMu82PdFAr0CVXp3FREU\nFKSWei2gab2mBDQMJC1NM3JatmiBq5Sk6Wum+Pe6mx890Qq8TZ2qWTRoxfWu3bhGZk4mZ85A+/YA\n8eh0mlHTokULarvXxrOJ5ga6whU8dZ6MazeOtWfWlhSkNOrDzaM3mTp1KteuXaNfv378739atMFd\nre/i1KlTnL9ScmPHNWfWMG/LPFKupVCnWS3qfv0rvPZaiX6nTp3i888/JyWllKJxudoWF4Yp1Veu\nXKFRYEOytuyHoUNhwADmAHfs/pvF3fMZM+xZ/H3aoZM6YtJiyMnP4dDlQ9ryCsB1uHT9Ev878T8m\nddY2lm/t2brQaCzEFbLzsqlfqz5//PEHeXl5XNF7ROrWrMvhGYdpWKchv//+e+EQv/p+ZOZk0smn\nU2HgbssGLYlJizHJyAm/GK7VIXr0UdL1bQNbDsTDzYOtF7aWObaAvTF76ddCb+SkxWip53v3wrJl\nbAG46y4Sju6k6YbtWtaWeflCRcmA+Ay9kZN5FR/3opHIfVr0YX/c/kJj05BVp1dxf4f7aevVlsiv\n58CnnxoNki7OfSvuY/3Z9bY3cqSUPxQ7llLKxIrOW4CxXX5L2znYWuesLmTt2sW3996L/O67EtuB\nL10Kw4Ztpl+/tXzzDdSqZBti64WtPLP2Gfo/0p+P937MokOLWB27Gm7XPg9/LJwrb1/hWvA1owXD\nFI6PEKKmEGK6EGKwQZsQQvwshPhJ//pZX1bCoQKRVz60Ev9GvqSlQXJyMl41a9KsVi3ir/0/e+cd\nHkXZtfHfpJDeC+kkIaTSAoHQa4DQlF5EEQREELC9AoogKgrSVFSkgw0pgijSCQSkhxpaAukJIb33\nss/3x2Q32RQI7X31M/d17QU7+8wzZTczZ865z31nAuBl5U1IY+D2bXB1hU6dYNEiWVkXuTQRFgYe\nHgDpFBc3QaGQdWmKAoq4JOQW3tjSWHSKdRjiOUQWuawvDCHxdCLjxo1DkiQmTJjAjz/KpZPSolJK\nNpYw9sOxajcxIQSn4k7RQdEBY1tNSsyKKEkTsGGDzO+rghMn5KxSenrd7j5u5m7cSb9Dwom9FGz/\niZSUFHQtCsnUt4avvoJly2gF+L25jGJFCQObDUSSJFUW4cdrP9KycUuamleoSpeBqa4phyIOMdxr\nOCCX5LKKsmSBOyV05QBLkiSOHj2KgYEBCdX2v6CgQC1A09TQpJlFM1o3rhTgczRxJD47XpUBy8vL\noy4cizlWQ2xRkiSGeQ2rlLl4CJQ2GY4mjiTkJICBAaxbB+PHy11gS5eSNHogNvYesrhgkybw7ruy\neGBV3LwJQUEP3liVTE5KfkqNTI6DsQM6mjpEZUapLRdCsPv2boZ7DaeZdmPuhJ+RW+nXrHng5uKz\n47mecp2IjIgnUvGGR9fJ6StJ0iOT/SRJMpckqWZ4Xw/U5fJbm3Pwo+BfX67680/yhw4lsn9/RK/K\nP7bS0jJWrZJN5k6dGsf27W/XPcdTwLZt2whLDKPvT3357sp3jO44mrR30lAsUHB91nW+fPVLUv6T\nQg/nHjXqwA347+NZlquACcBqoKr+liYwHHBFzhJbAc8D/6hyFVQ4kVdkcsxLSrA1N1c99Xd37g5N\nAFNTWXzq5ElYv5676Xcx1zMnISeB8HBlJqccPb0mpKbCF+e+YNgLw9h8azNFZUVEl0RTnl+Ol6WX\nnN2sLx1DAlEq6NSpEyBn7NLT0wkNDWXNmjVoKbSIvBbJhssbVKtEZUahqaGJZ74nyTYZGDjoE+Xh\nI99Qt2xRm155rX1QkONt5U3fn/riFTSUt36ZgigqotQkl/v6FfTOMWMIBDq3fo7uTbrjbeUNQAf7\nDpyJP8OS00uY13We2pzW+db0sOmBmZ48h4akgZOJk3rJSl/mndy7d4+UlBR69OhBfLy6NlBtHT6e\nlp742vqq3juZOBGXHaf6TqvPoURpeSkXEy/K7uvV0Me1D0ejjtayljqKy4oJTQ6lnV27ykwOQEAA\nWcePo6TqJhWkYjNyomw3ceCALELYv7+cLSwtxQFk/aDRo+VyYF2omskpqMnJgcpsTlVc3rMa7cwc\nmjdywP18BHc7ecoGo9u28SAx/IMRB9HR1CE8KZzS0kfgltWCRwpyhBCHAVdJklZKkuT5sPGSJBlI\nkvQG8D7w5WPu4zPBv7pctW0bvPYaVrt38/GOHWhU6EcoFODtPYEvvjjK0aPQsuXT33RkZCSpaakI\nIShXlLNgxwK8lnvRx7UPRR8UsW3pNiyMLFRKm290eONfGdxcvHgRXV1dJEn6W3GLnnG5agzwvRCi\nNtGYqRUGnT2QryVjahnzt4axMSpOjnl+PjYODrIgIPINHj05YwOApyeK/DyiMiLp3qQ78TnxleUq\nPbC0ciQ0Kpnfwn5j7aC1WBtYs/ivxTSxa0JGWgaSJDHAbQC4wtWrVzl3rna+hApp0H1Q98qOGQ0N\nXnzxRdbOmsWyJUtYs2YNxsnGvH/sfahowDoVd4ouTl24eOwko4QB7i19uH79OkyeDBs3qtrihBAE\nBwfTsmXLBwY5XwV+Rc6sJK5u1mWXpz7WZmZkNdUhNi5RbZy9sT3BE4JV+9rBoQPrLq3D2sCabk26\nqY3N25eHU4yT2rLqDuPooeLj9OzZkyZNmtTI5FR/D/DdwO9kj64KmOiYyO3c9+X9javDHTwuOw5r\nA2sMGhnU+OyPtX8QHxlPYm5iLWtW4mrSVdwt3DFoZKDKIIF8rl2GDlWNU/p+IUng4yOLE965A+np\n8Nxz7AM5s/LOOzKnp3q5Sal5kCfPBbVzckAONtV4OefOseWn/zA2ww7J2xv3oKvccTIAe3vo1YsX\nq66ckCC33BfIjZUHIg4wpvkYwhPCsbR8Mh3QRy5XCSG2AZ8DUyVJOiZJ0gZJkuZIkvSaJEmTJUl6\nV5KkZZIkHQF+Ai4JIf4jhHh0WnUDnjpKg4OZ+8or5OzcqSZNHB4OgYFgZfUFoaEBuLk9ne2Vl5eT\nm5tLWkEaOcU5jJo7Cuu3rWm/oT3Lzywn0S+R3179jQPjDvy/5NWUlZVRUlJCXl6e2hNJcHAwv1fh\nQJWVlXHo0CHOnz+PJEm0a9eO4uLi/8Uu/y/RGuTrbi2oevW9CPjWMe6RUR/1dEmSVlV8fk2SJN9H\nWVcJE5MqmZzCQmydnFRBjoakATFwIuaEcmISuvtiJunhaelJXFY8d+6AqUMSGENGl49Zfmk+o7xH\nYalvyfR201l8ajEd3TuqOAyelp5gAUuWLGHjxo117pdCoYAEGDpiiNryl2xtWX3iBJ1zchijqUl2\nRjbTPKdBRTHxdPxpfE19iYyO5rt+77Nh8gY5yGnTBkxNUeaHb9++jYGBAW3atHlgkKOjpYPBiTM0\ndffHSrKmkZ05ZSaCosKiurk8yIq7xeXFzOs6r8YDgX6BPkXxRWrLXExdiM6qwsupyOQcPXqU3r17\n4+DgUGuQ41btomipbylbUlRAkiQcjR1V5aq6MjlRmVE0Natp1BoSEsKiTxbhlu9GUNSDy0dXk67i\nayP/DK0NrMkpzqGorIj8/HyysiqtImvtrtLXl93TnZ0JAjnAefdd+cf52WdyO21BgRz0ODhAYqJc\nrqrCyVF78FQoYOtW/GPLOJ9wTg5ujx8nZ+Rz/OyrxdRPD8Gff9Ls/RXcyak479Om8T7A9OlyUNyy\npewOv3YtJeUlHIs+xuvtXifqXtR/P8gBEEIkCyHeQrZ4+Aa5s0kXsEA26jwEPC+EGCqEOPVEe/iM\n8K8sV508idaIEXhMn46enx8AxcXFDB36Jl26FNC/PwQHW2Fk9GSZg6p1+2XLlvHmx29itcwKkyUm\nlPQqYdbYWVxMvMjcoLnsHr2bIZ5D0NT4R1EsauD777/n5MmT/PTTT0RFRdGuXTskSUJbWxsdHR2M\njIxo1KiRKjPTs2dPhgwZonqvra1NYGAgHTp0qHX+RxQZf2Z4xuUqQ+TrhwoV/Lv2QFXOXR5Qi3nR\no6M+6umSJA0A3IQQzYBXge/qu25VVC1XWRQWYtukiTpJNYZKawDgTltnmhXp42jsyI24BMzMIKHo\nNmRCM8PhnMj4hTc7yJYBo31GY9jIkB6ePSgsLKS4uFjmphjD/v37a73hRmRE0OK7Fvyx9H20dKDL\nnA9h/nyIj4ekJLw+/ZSJAwfy0Q8/oDFvHp1ycvB4cQX6TeHCrXMc2HuAmN/DaAcYTJlKC7cWlJSU\nkJKaCpMn8x7AkSME79xJj06dsDA3f2CQA8Aff8Bzz9HBtAMZGhnYGtni6elJuFLquRYY6RhxccpF\n+rv1r/FZbGwsly6pWw64mLnUyOSY6ZoRFBT0wCCnSy3aYdVh18hOpexUV5ATmRlZI8gRQjB79mxc\nXFywFbYcjX5wySo0OZRWjWU7Nw1JAzsjOxJyEtTOrxCCpLwklTKxGrS04LvveBsoKi4mMzcXfv4Z\njhwBJyfw9YWiInjlFdkMNK9ad5VkAFFRctt6jx7w5Ze0/WYXN2IvclsTmDmTH+c/R4BHf1lFu21b\nHEdMIr0wnfySfOjdm2kA3t5yevLWLbnCsHw5ZyKDaWbRjDa2bchMz8Tc4gEW9fXAEzE4K+wdrla8\n/lH413WIREbCiBFI27YxMaDSSG3zZh2OHWvHb79p0qvXA9avBWUKuR20KhF4w68bWLV2Fc2myCqY\ngZ0C2frXVvo26UtcdhyHXzyMrZEt/dz6seXqlkdWZf07IC4uDnt7e7S0/nsE6L9LyUopCviMxABT\nkU051R6ManEgb1Ix9mmgVvV01IOq54DvK/blvCRJppIk2SCbiT5sXRWUQU56ejrtc3Mpd3Pjxpkz\nlQNiUWv7vutiTLOjJTiaOHLu9l4mTYLvVn8H2jC48Xz6lbyPl5X8G9TT1uPAuAO0aNyCuRZzSU9P\nl2+k+aCjo0NcXByHIw9zLuEcC7ovAORsQEZ+OhMOL6XMBexW/wxbd0CrVnLaafJkNilNLUeOpPMn\nn3ApIYEhFzcw8MfuZOqWkZz5O9O7dwdzcyTklvYbN27Qa/x4bs+YQe/Fizlx/jwDystJLC0lXUdH\nfmJfulTmhlRFebnsQfXBB3jvK2GLtAU3AzfcPd0JCwujY0d165Vbt24xceJEzp49q8aNqQohBFFR\nURQUFKCvL1tpV3UYl08eaGbKD1nNmjUjMTGxRpATHx9PmzZt2LJlC/n5+RgY1Cw1AVgoLDAyN6Ko\noKjOICciPQLDbHVGysGDB0lKSuKdd97h5PmTHIk8Ipf5YoK5mHiR4qRiqLLJ0JRQRniPUL13MHYg\nPjse4wxj1bLs4mx0tHRUjvB1Yf369Rw/fpzdu3fLpl7h4fL9on9/yMsDT0+al1VkcrKzSU24g3Wf\nIaBhKf9OJk2C6dMx0NTE/QtPBtqGczc0lG++82FNu0qCsaaGJk3NmhKREUErm1ZyynbGjModsbEB\nX18O/L6c/n790dTQxEJYoGdSX3nE2vE0Wsgb8HdHURHTO3TgyoQJEBBAYWExy5adY9IkuZvv8uVx\n9Or1aKWinTd3EvBDAEZzjXDv7Y7RYiNcv3JlypUpWI+0xqiREcM8h3Hq3ilea/sah148xO3Xb2Nr\nZAvAgGYD2DFyxzM42MfD9u3bWb9+PRkZGdy7d4/s7GyuXbtGWloaEyZMYPXq1Vy9epXnnnuOJk2a\nPPMAp1u3bpSXl6NQKP42WZz/Ak5SP0LxRGS19aeB2tTTq/tk1TXGrh7rqqDsUExLTsW8vBxbV1dV\nuQqAFFmUTtlGfdeolGZRWejmWpKYl4C5+WYOfH8A3MHBQeL+PfXfoL+DP/ra+iqTTicTJ4iDSZMn\nER8fz7GoYxyMOKgaH5ERwbhEC9ziLcECdP07y11M0dEyP2KBHAzFxsKyFRp4tujJqWvX2HEG0stL\n6DHYhN82b2bEwYNkZsqGvS1atJBLVkZGzAREUBDBhoZ0DwvD4uuvSR82DJYtk0sUn36qzgE5fx4a\nNwYXF8rzyrG1scXG0AZPT09u31aPG4uKihgzZgwXL14k9gGEWWdnZ7y8vLhWpWW5Rhu5PmjmaeLi\n4oIkSTg4ONQIUBISEnB0dKw4H3Vvz7DEEB0T+YuuLchRKBT8/vXvfDn+S7Us3qeffsqiRYtwdnYm\nNy0XHS0dhu8Yzsu7Xmb3d7tZMGYBVCQ0hBCEJoeqGaE6msjk46qZnPoKAV6/fp2goKDKcrqHBwwY\noHI657PPWJ8H9xPvQJMmpIg8gt+cT/6NG/KXPnOmrO4M9PQeQOTz8OKel9DS0KrBkWpm0Yw76XcI\nSwuDmdB8dXNG7hxZKVz5wQccSAgm0E5ez6zcFCnlAYToeuBfG+T8q8pVb7zBxNat8froI3JyYODA\nGD7/fAP29rKsQtOa5eEaWHNxDTtv7iT0fihDpwxl+p/TcTB24NC0Q9h2tuXEhBOsGbSG93u9z5Ep\nR9gyZAsz/Weyd+xevgh8XG/Wp48TJ06oSkSHDx9m8+bNNG/enDFjxvDqq69iYWGBg4MDpqamtG7d\nGisrK77//ntef/11fH192bt3b722M2/ePFasWME777xD3759OXnyJL6+vuzfv5+uXbvSrl07hBDk\n5ORgbW3NwYMHMTY2Jjs7G4VCwYkTJ9DQ0PjbZHCUeMblqs+BrpIkbZQkqYb+b0UGZT3QpWLs00B9\nI8gn+iIWLlzIwoUL0dJaSFRkFOYWFtja2akHOQK6N+muyubczY3B3bAJ+77WQ6MshkWL5uDztg/k\nyNzNuix9LC0tSUtLQ1PShHDw7tgDISSuxFwhPL2y7BMRGULTq3HEZJRDVWkWExN44QVo1IgJE6Bt\nW9i0CcLD23Hz5k3KimDg9EH0Hz1PNp/U0mL1atlyqXnzFty4UemxquxKcnZ2xtzamvSCApn8d+EC\n/P47vP46CCEbk33zjawThKyR08mjE16WXnh4eHDnjjoXffbs2Xh4eNCrVy/ClNbstcDJyYm2bduq\nlaxqIx5rl2pjair/5Ozt7bl3755KEBDkIMfBQVZ5j4mpsm416BbpIlWU+6sTj4UQvPTSSyTevo+d\nc0vWrLmIMia5cuUK/fv3V5XKxrccj4WeBeOyx6GToMOWn7dAkWxAHJcdh2EjQzULDkdjmXycnp6O\nvb0cZ9c3yLl9+zZFRUWcP3++9gEvvcTqAshvJJF18xKl2hosWP6Vmm6QEksClsBeaGndklWBq2pc\nv9zN3QlPD2fK3ilwGbYO38qd9Ducjj8NQLyXPYmmmhT6DWFhq1ZkH7xDeGR0je08EoQQ/7oXKjmf\n/98oLy8Xii1bhPDwEIrsbBEaWiQ8PYV4/XUhiovrN0dCdoKY8vkUYfepnei2uZsw/9xcmA83F0Hh\nQc925x8TcXFxwt/fX6xdu1ZkZmaKmTNnCuQb2TN5hYaGiqKiIqFQKERhYeH/+vD/a6j4G3oWf5sj\nkDk3BciZnZ+RGxhOVCzLBUY8xe11AA5Wef8eMKfamDXAmCrvw4DG9VlXVLveNG0qRBMbR3GndWuR\nkpIizM3N1c7p1+e/FkO2DRFCCOH+tbu4MGOiMNfPE5ptNMWCjxYIm+U2AmPEtWtCeHvX/t28/PLL\nYujQoWLnzp0CY0Sb0QeEhoaPsHzTVmh8pCFS81OFEEJ0/8xdbBzZUbi4uAhA5OYKMWCAEJmZ8jw3\nbghhaytEQYEQu3cL0a+fEB07dhSAfG1RKFTb7NpVCF1dIVatOiE6duyoOp5Tp04Jf39/IYQQx44d\nE927d6/c0exsITp2FGLyZHEUhAgIECI9XQghxLhx48QPP/wghBDi/Pnzom3btqo5Y2JihIWFhcjI\nyBAzZ84UK1asqPU8AOK1114Ta9euFRMmTFAtVygUQv9TfZFTlCOPG42YsXiGGDdunGqMhYWFSE5O\nVr23srISSUlJAhCrV6+u/cQLIWZ8OEPY9bITgNDT01M7RxERUcLAoLHgXQPh2fxt4ey8QBgbCwF9\nha+vrxBCiLS0NGFmZqZaZ9iwYWLbtm0iPz9foIk4F3tO/BH2h/D/OlBMmiRE8+ZCuLkJ8cnBb8TU\nvVPFqlWrxLBhwwQgfrn+ixi1c1Sd+6o8R+bm5mLChAli/vz5tY5RKBQCEI4rHcXJmJPCbqmd0NDQ\nEO+8806dc9aFDZc2CKulVsJ/vb9AksctPL5QvH3wbSGEEGsvrhUv7HpBiMREIVauFF0CWoiur3V9\nouvNQzM5FW3gX0mS9IckSW9IkqRVsXyYJEnPzKWvAU+ORSNGsGHmTPj1V15793s6dPicd96RH5ga\nNap7vdjYWJYfWc6M/TPwWe3D0XNHWd97PScmnODEhBPc2nKLXu6PSOB5TMh/M7Bx40aSkpJYtmwZ\nAwcOJCMjg+joaPbs2YOfn58qO+Pk5MT58+eZOnUqZmZmfP3114+8zTbVnHyHDRum9nR6+fJlysvL\nEULQokULdHR0kCQJXV3d6lM14BEhhPgV8EDO1JQCbYC2QBmwBPCoGPO0UB/19D+A8QCSJHUAsoQs\nePrIyuvGxpCZnYW5nR0WFhbk5uaqddFN8p3ErdRbbL+xndisWG7wMm30/0LcFnQc0lEmbebITS91\nZXK+/PJLfHx8mDBhAljpEZ0VSfOWdqQlZuCi24adx8NJToa7hQkkCXO6dZNLA1u3ylIqyj+Zdetk\n3qmeHvTsCadPw7x58iW/apYxJweuXIF58+DkyaZER1c+eUdFxaCj4wyAhYWFOvHY2BgOHoTYWM6D\n/H9zuSaTkpKCtbWsqmtvb6/GkYmKisLb2xszMzO8vLxqlLKqorZMjiRJ6tkcPaAIVSYHUCMfFxUV\nkZ2djVWF2eaDMjnlOeUU6Mht0Do6OmpdYYcPx1KmcMbErBGLP+mKt/dFZEpWFywt/QEwNzensLCQ\n/Px8AE6fjmDlymakpelDI/hi2y7e/Pwat4Nb4eMjyxFNngwbvnAkOkPO5Hh7y/pBUYlR2Bg8PJMj\nhJxhOnz4cK2fK7u1rBtZE5ocipnCDIVCwalTNXuKlNfruuBu4U5mUSbrB69X5VAHuQ9i7x05Q77/\n7n5Z+sDWVrZ/0DYhRyvnocfwINSnXPUtcAdYi6yQ8KskScZCiN3A9CfaegOeDYSAxYuZERLCmKDj\nLNrTnL17J3HgwFwmT645XKFQkJsri4sfjTpKv7f78e1v36KrpUvotFCidkcxwH8AIDvS1srWfwKU\nl5fTsmVL9uzZgyRJDBo0iKCgINatW4eGhgZubm5MnjwZW1tbZs+ezf79+7GwsMDV1ZWhQ4fW6J54\nEIKCguhfQXhctWoVKSkpqu4TIQQ3b97k0qVLFBQUUFZWhhCCXbt24ePjo3oy8PX1VWkLNeDpQwhx\nTwjxkRCitxDCq+LVWwjxsRDiwQIij76tWtXTJUmaKknS1Iox+4EoSZIikK+D0x+07oO2Z2paTl5R\nPqaOjmhoaMgeVlW4GXraenw/5Hte/fNVGhs25kCEL3oFi7DpYMOFzAtyWzhgZgalpRVWV/v3Q2Fh\nlW2Y8sknn8icEMsR+PaKxMVHH6vSxuTHeLFiSzhtOuSTQRHXs4tVQc6338KqVfIrJUVutpkyRTmn\nLLOip9enxjEdPw4dOsDUqXD4sC1ZWVkUVOidHD0aw8mTTbh3r5YgB8DYmJ1TpjAPVLwOUA9ybGxs\nyMjIUHFG4uPjVfyY+gQ5zZs3JyIigsIq58jZ1LmyjVwPFIUKtSDH0dFRFeQkJiZiZ2en+pt/UJBT\nmFVIbqNckMDOwY4fTv6g+iwoKA4bZxOaWTbFz8+PkJAQvL0FsJJz59rz119yAKYslwkhSEmJwMOj\nGX5+gIYXvwUdxcwzlK/nteStt+RS4uzZ0KapI2dvykGOsuU6/G54vcpVXl5edO7cmVu3bpGRkUFK\nSgpHjlTWL5W/T5NiE0KTQzEsNsTb25sbN26ogjElHuYY3smxE+cnn1fjE7WxbUN+aT57Tu7hWPQx\nAt0CVZ+V5paSJp7M1qE+7MkzQoh1Ff/fV9FV8IEkSUueaMv/YyjFAP+/CQKKsjLypk3DKCSEjUHq\nuwAAIABJREFUj/uN5NpsXQoL4eJFfezsal/n1fmvEnQ9CK0+WpSWlzL3vbmMbT4WE12Tp7ZfJ0+e\npGvXruzbt4/s7Gz8/f1xc3MjICAAExMTrl+/ztAKEat9+/axb1+lXEpkZKTaXMOGDZM7Aarh9OnT\n+Pn5oVlxsfzkk0/w9vbGysqKkJAQpk+fjqGhIb2qtZH1r9LloXwK0tN7Mkb//1cEBwc/My6bJEnH\neThHRlKOEUI8lXSiEOIAcKDasrXV3s+gFtS27oNgYpKNrpYemhW8CRsbG5KSklQu4iCL273W9jWu\nJV3n0BloVHCWzkMHcTjyMF5WXoQQgiTB5s0wMLCcWZlneX9jKhoTX1bbVlSUGeR0Rd/+T6RYDWyK\nTBgz1pOsojAizO5yPN2Kv27e4pNu3YCOFBTIsiWnT8PAgeDvLzsBKBEQALXQMDh0CPr2BSsrCAjQ\n4MwZZ6KiZHn/Eydi0dPzZd8+GD9eDnKEEGpcjRUrVtSYs2qQo6mpibW1tYq/VJUErAxyqs+phJOT\nEzo6OirysVKiwcW0Shu5PpQXlGPiVHm9q0o+rsrHgQcHOakpqRhYGZBjkEOpYSn/+fU/xOrFsqLv\nCi5disOmhQ7OZk2xt7dHU1OzYhtZLFniz/jxsqWTMouUnGyAJBmxZYsRN25AqzaFQCQZOlm0sV+g\n2qYkwerPnLBbFsXha2DjIQc2MVEx9OjUo859VcLb2xsdHR26du3KunXr2LhxI5qamoSGhrF3L1hY\nyLo/evl6hKaEopOvg3VTa0xMTLhw4QI9e/ZUzXXlypUHbktTQ5M2tuqZckmS6Gvfl+EBw2nxQQvZ\n9bwC+Vn5JD+hS1R9HkfLJUnykyTpy4oMThKygvEIZG2cfyT+vyoe/z5kCNP+OMuH/c6xeedA2ra1\n5cQJVAFOekE6C35YgFc3Lz4/9TkDfh7AX43/YsXnK9g7di+3Xr/Fa36v1QhwLl++TGaFz44S+/fv\n57PPPiM7O5vo6GgKCwv56KOPmDZtGocOHeLChQuMGDGC5cuX0717dyZPnszgwYN58cUXGTBgAJ9/\n/jlBQUHs3r0bLy8vYmJiyMrKYvXq1YSFhfH8888D0KFDB3bs2EFkZCQlJSXs2rULIQQKhUJVNhJC\nlqRv1KgRmpqaaGpqsnDhQkaNGkXPnj2ZPXs2hoYPEhJvQH3wjBWP05F1cup6pQPaQI+K1z8ORkYZ\n6GsYyu2ygK2trRr5uLCwkCNHjjDEcAhO5/pQVOTFWH1dmjs5y87Vlt6qsaNGweVBC/hVZxw/La9p\nCrl+PZBxmujsSAr0CtDO1cbDwoPw9HCG9A4iP86d4hJB06ZNgelMmwYaGnLZ6eJFOTNTFX36yDIq\n1XH4sOwMAHLmJzfXlVu3ogBb7t2LYfJkZ/78E3R1ddHS0qrx9F/9IUahUJCamqoqD4F6ySo+Pl4V\ndFhbWyOEqNPfSOnO3rZtWy5erFQicDZ1JiozSi6v6EFRflGd5arqQc6DuquSkpKws7EDU7gv3WeG\nxwxCk0MZs/NFEhJiMXcBVzNXJEnCz8+PgwflbrepUz3o2xfeeEPe9r1799i69S7W1s3Q0KhQny+P\nwTDbkIScBDwsPNS2a2NiwcCMQ6Tez+GL0C/AGRJiE1TdrA+Cl5cs7dS3b1/ef/99Jk2aRGJiIocO\nwYgRcPmy/NvSzNHkevJ1NPM0sbe3p2vXrvz1119qcylJ4A8rW1WHdZI1ilIFLvkuasvT09Oxsnwy\nxfuHZnKEEBslSeoGXEEmBCrTtOskSapdt7oB/xOItWs5fUaX/SKUEZkaXL7cm6ZNITU1lWkzp+E6\nyZUNlzfQ1a4rg94eRHJ+MmOaj2GE94hatRSEEPzyyy+0bNmS3r17Y2Jigq+vL++++y4bN25k06ZN\naGhosHbtWhISElAoFOjp6VFYWMiaCgM2BwcHdu3aha6uLps2bWL8+PG8/PLL9O7dm7lz57J+/XrG\njx9PoyokoWkVZqF79ux54PH+3SwPGvBkEEKMqOszSZKcgDnAICAN+Pu07D0C9PQy0MZQ5hwg37yr\n3uTnz5/Pn3/+iYmJCcnJ1kyd+gerQmawtqgR5aIcL6sqWoOpqTj8sZqvv5/By2NHMyb6Ho1cKjvY\nz58HMq8TlRlFI+1GlGaU4mHpQXhaOEnFmjjfu0dM9jC8vCRgEBMmyOs1by5bZ3WqZq3UoYMsoQJm\nqmWRkZCfDy0qqg/9+oGDgyvLl0cBEzEw+JUxY5oQGChX1JQlK+UDR1ZWVg2X6aysLAwMDNSuCcob\nP8hBTmCgXNKQJEmVzVFmfgBVaUvZadShQweCgoKYUaHLEuAaQP+f+zO3y1wQkJeTVyPIUXYPVQ2q\nlPtXWFhYa7Y3KSkJHwcfwtqEYaJhgl6BrF/kvNwbXTNzSoxNVEKA7dq149tvvwXkbNWKFbIGn4+P\nHGAFBRXTvLm6yrJ2ujbeVt5oa2rX2Pbkfp0J/tGC3S+tp++NvkRFRNWrXKXMXk+cOJH27dvToUMH\nPv74Y3buzMXR0Yg9e+Qgpzy7nHzDfBS5Cuya2dG6dWs1zmNeXp7qu4yLi1PLTj4MMedikKwlNO9X\nliyFEKSlpdHRsSP31ezsHg31IhYIIU4KIb4XQiiqLT8oSdJwSZKWSpLU8Jj8P4IQgrsbN/Lzu+f4\npqARBw/l0sh4BuEl+9l4eSPTjk3jkMEhCkoKOD/xPNtHbmfZkGWs7LcS83hzIm5HqNolZ86cybJl\ny+jWrRuDBw9mwoQJtGzZkqlTp/Lzzz8THBzM888/z71795g9ezapqak0a9aMkJAQtm7dSmBgINev\nX+fgwYPcuXOH0aNHs2DBAgoKChg5ciSrVq2iV69e7NixgyZNmtQIcBrQgKqQJKmZJEmbgAhkob33\ngCbiMUx5/5eYOnUq6enpaGuno6kwUmVyRowYwebNm1VPvj/++CN79+7l7NnzlJbu5fXX24KzMw7Z\n8udKU0pAZgiPHEnXUbZ42OWy8Z1b8vJduygIl20gKLmBYSNDbhbfJDslGzdzN2KyYriVdJ2M2GR+\n/30i8rOEt5LzC8iOL1UoMoCs8yOL/lZ6I23dKgc2ymcNSYIJE1yJiIgC5lJUFEerVk3w9YVjx2ry\ncqpncUC9VKWEkqcC6uUqqJ2Xoxyr1LPq1KkTZ6oIL7a2aU0vl168H/Q+FEB2djYmJpXZ66qcnOrb\nc3JyqjWbo1AoSElJwc3RDVpDX9++xMfHo6Olg2/WQooUN8nQyVC5pLdr147Q0FDV+oaGsuhvUJA9\nly4lEBt7ly5dmqltI/teNq+0foXaEBAA+fnpWBo2hXPghpv676UOKDM5xsbGdOzYEUmSsLOz488/\n77N1K4SEJAMSRemyPUZJZgnh4fa4uHTm3LlzlJXJorB37tyhWTN5f+tsR68FJSUlHD54mI1fbCTq\nRqWLeU5ODrq6uvg38a/3XLXhaSiatQX8gWbI2Z4G/JdxYvFGRn9wBoXJx7y0ch39f3elJLKEq0FX\nKb9dTqfmnbjy8RW2bd6G/0v+aGhoMHbsWECWmD99+jRJSUm0atWKmJgYTE1NeeWVV4iMjCQ8PBx7\ne3tVILJ3714MDAzw9a1UGFU+8bRp00Y1b/PmzQFYvny5atyOHZXifyNHjmTkyJHP9sQ04B8LSZKa\nA/OAkUAcMAvYJP6hHnipqans3bsXLS1tKDdRBTm9evVS6SKBfMORHxpkHTYPD8DZGceUPHQ0dXAx\nrUjnl5bCd9/JBBpg0QfFPDejFS/P/Rj9pQu5MmE9Pj6TCAkpoal5U26X3CY5KRltSRs7Izv23YrG\nyMiawMA2FQFK/Z6UP/4YDhxYTFgYZGTInZrV72deXq60aBHEyZOrMTIyxMDAgMGDZTHj2oIcTU1N\nysvL1c5VbUFO1XLVw4Kc6ho17u7u5OTkqEjEAIt6LsLrWy8olLMzDypXVeWd+Pj4sH37dj788EO1\nbWRmZmJoaIiLpQuUwpjuY5i/b748x4GxlOe8zLXCa7iauQLgV2GtUxVt28ILLziwceNRrK018fYe\nq/a5pYUlz9k8V2M9AAMD0NBI4/JlSxCQm5RLI826HyCzK8w3q57LyrnsgES6dHHH2jqJuDhXclLk\nLqespAK2HbDn1i1zHBycuHr1Kn5+foSFheHp6UloaCjnzp1j1KhRKBRQVvbgbt4TJ07g4eHB2GFj\nmTFlhipLlpaWhqWlJUv7LGUZy+qe4CF4Gi0i8UAPIURDgPMMoFAoKC4upqysjCNHjrB06VKOHz9O\nYmIi4RF3sWvmTc/176DfNonm777G7u3fYbvPFo1jGlyZc4Xs49kcW3cM/5b+REVFsW/fPoKCgmjS\npAlNmzbF1NSUVatWkZGRwZIlSzhy5Ajh4eHMmTOHdevW4eLiopZp6dKli1qA04AGPE1U8P9+A64h\nm3BOAtyFEGv+qQEOwPDhw9m1axco0ihXWMrKvsjllhkzZvDNN98AMLmi/XH7dhg+vGJlZ2c8Y/NZ\nO2htpcfb8eOyiqe7OwBtp7ShjVYo27cDmzZx4a9i2reXhzY1a0oLuxaYmZmRnJyMh5kbabfKeWHi\n+Ecu98r35bkMGQJjxsDGjeDsrD7G1dWV1FQ5k+Nc8eGgQXKQY17NvyoiIoIWLVqorV9bJqdquSo/\nP1/NtLE+QY6GhgYdO3bk7NmzqmUuZi683u51KKg9yLl37x5btmwhOjparVz17bffsmnTJn788Ue1\nbSQlJWFjY0MXpy5wEjq268iNGzdITCwjJjoTfT19tPS0sDeSS2hWVlY891zNgGXSJAfMzBJo1Oiu\nKjOihLe3N7du3aqxDlAhRVDC0aOyrVtOTo6qa7Y2KM/Z2bMajB8Pq1fLYtfyXHa0aiU3MtrYJAHD\nSLsvl6Lux2QzapQ9vr5QWNiDoKDjAKogByozOWvXytZUd+/K8woB1Xdpz549DBkyBF1dXTw9Pbl6\nVXaJUgY5T4qnkckJArZWKJGeFUIUPmyFvwP+Lt1ViYmJlJSU4OzsTHl5ORs3buTevXv4+flRXFzM\nzz//TFJSEpGRkVjaWpJTmEPKghS09RpRkJePhpEexpIWuRoXiPnNCNMsU+YsmENgYCB6enpcu3ZN\n1oVwdlY9wQC0atWqxr5UfVppQANqwzPurjoI9AWuA2OBneJRGYx/UwwaNIhp06ZhaeBIsWQL2pWc\nipdeeokPPvgAkIMhhUIOcg4oe7acnWn066+83LpK99SuXTIrVAkNDV5a4sPGvT2ZODSPC5MP079N\nGd8CzcybYaxjTLFTMXFxcbjk6EMYTH915mMezWYGDtyEiYkcvFSHi4uLSitHGeR4eMh6OwqFhZp2\nTGRkJO3bt1fd2ODh5SoHBwe14Kw224fayknKktVwVfQIi3otYuXYlWRrqZer9PX12bt3L8uXL+fG\njRtq/BI7OzsOHDhAz549cXd3x99fLqcog5wODh3gNBgZGeHk5MSmTbdo27aUzEw3vp/8vZoZ8e+/\n/14j0HR0dECS4klJyavhfK4McgYMGFDj+NLT0zE3N+fQIQnQxMfHhytXrqgkAqpDec42bJCNxC9e\nhA8/lA3Jk5LsGDhQzu6VliYDA0iIuw8CsjPSmTbNDn9/8PXtzaZNa5kz513CwsJUHbJXr16lpKSE\nX35pRJcu0K0bvPmmLEtw546sjg3eHD9+nF27dqmuKe3bt+fChQt07NhRFeQ86RXgaQQ5C4EsYDng\nJUnSVWR10j/F39SBHJ6dQacQgrKyMrS1tVEoFPz555+cP3+e7Oxszp8/T3h4ONra2jg5OWFiYkJ2\ndjbl5eWUl5cTFRWFqakpdnZ2XLp0CR0dHXR1dfHq7EWqUyrx9+MZ+fEoLp+NJexULrbR3bjy8zCs\nuvlz6NAhYmNjmThxIjpKkxygc+fOz+Q4G/DvxDM26FS6tdoj63N9I9WdahBCCOs6PvvbwcTEhC5d\nunDg6K8U8o7aZ0ZGRkyaNInly5ejp6fHyZOyLk1FxVdOlVRpW9YA2LMHzp1Tm2fQFFumLoSUYhMu\naHbgQ73rALzV8S3KFGVM2TSF+JgYIlYdw8hdF3vbOm22Hopaur5VMDQ0xMTEhKKiIlVwIEmyA8SZ\nMxY1Mjkvv/wy69atUy1LTk5W66wC9XJV9fKKs7Mz6enp5ObmYmQkZzGqZ3JADnLee+89tWV62nqQ\nCVnaWWpBDkDv3r3p3bu32rxKeHt7M3fuXNauXasW5DRurK4h5ufnx++/X6RdO1Pu3WtCK5uaD5fV\nYW1tTWZmJpaWljU6Qr28vNS4RVWRlpaGtbUlpaWQmelJQEAAR44cqTXIEULw008/AbIG46lTcmIw\nJkYWgNTUtEMpSSXr5HxJWZkBrYsCucYhunQxQ5Lgxx974Of3MmFhJWqZHFdXV44dC+X6dT+OHJE7\n8LZtg5UrwcTkDnPn/gjkMXPmmyxcuFC1Xrt27Th27JjqeKysrNQ8PB8HTyPIuQHsF0JMkyTJBNlX\npjvwFtXchP/pSE9P59y5c6SkpGBmZkZRURHXrl3j3r17+Pr6UlRUxOnTp0lISMDa2pqQkBB0dHRw\ndXVlxIgRjBkzBlNTUxWhKjs7W+VV5ObmhpubG/r6+iTmJrLl4BbWLF2D1ota6GvrM2/sPNrqjuM/\nbzUi+3IIq8qXMuXsZCRPuZWwf3VH3wY04J+Hjx9h7D8uwzN8+HAOHDiABo0pKVHnKXz++ecq/tov\nv8DYqlQMJyeIj5cftzU06AqyeZWLerutvr6sb/Pdd5COJe5R8k3MWMe4Yhon1n/4Idfji5n6y+Nm\nceoHV1dXkpOTVZkckIOcH3+0ID29MsuizORUxf3792ndurXaMnt7exIT5Ztu9SBHQ0MDd3d3wsPD\nVTyX2jI5SqJvUVFRDXVyTU3NOhXLqwc4SowYMYJFixZRWlqKtrY2YWFhamUtAF9fP3bsuMjgwe5o\najrVOk91aGpqYmtrq3bulOjWrRsff/wxCoWihiBpeno6FhYW2NrCnTu+9OnTh/fee49PPvmkxjx7\n9uwhJSUFaImhYaV/obOzrIW0YYMdx49fUrXzQxk2Nr5c/XwUVlbXVdmnNm3MsLNz56WXznD37l3c\nK8qn/v7+bNlyjv79/dDRgcGD5detW7fo1KkTr7zyCseOTWD06I947bXK/Wrfvj1LlsgSfKmpqVhY\nWLLjCX2cHznIkSRpEbBLycERQnwmSVI3SZJGCiF2AvsqXv8YVP3BREdHExERgbu7O1euXOHXX3/l\n/PnzFBcXk5WVhZ2dHaWlpbRo0QJdXV2Ki4spLy8nNjYWXV1d2rZtS7du3fD29qZdu3Y1Ivu6cC32\nGh0HdURnrA4RGRF0c+jG1I+mMsRvCN5W3syde5B3N5YQ2GQbm3U/xP7E0fo5azagAf8QCCEW/q/3\n4Vni+eef59UpUzDW0ictDTVxTuX1p7QUfv21GplXT09O7SQlgZ0dw6EKYUcdY8bIrw6euWicOqn2\nmRPw9Z07nNi2jc5DRj/VY6sOV1dXzp49q3aj7tABCgstiIm5DMiaQGlpaaruHmXwce/ePQYOHKg2\nn76+Pvr6+hQXF9cIJEAuWYWFhamCnPDw8BpjDAwM8PLy4tKlSzUy3NWzOPWBo6MjHh4eBAUF0aNH\nDzZs2KCmFAzQqJEfWlo/k5mpo9LsqQ8cHBxq8HFAPk4jIyMuXrxI+/btKSgo4PDhwwwZMkQV5Pj6\nwvbtbejcuTO3b98mMzMTM7PKtv+ioiLeeecd1q9fT0DAEao/H2toQLNmtvz8cyLp6ekYGxuTkZGB\nn58TMTFnMTZWzwCOGdObTZs2oa9vjYGBASA3lgwdOo21a18CKs/thQsXGDRoECtXruSLL3z49tuP\nePddUMaXXl5e3L9/n/T0dNLS0igvt+RJ3XIeJ5MzEtgAUOFjNQtZ5ryJJEnLgYPA10KIG3VP8ffB\nH3/8wZdffsnEiRO5evUqJ0+e5ObNmyoXag8PD95880369euHq6vrU5PzT8pL4s25b2LT14a/kv4i\nLiuOdt3aMav7LHo37a3SQSgpgVmz4KefTrNp8GVi9q2kfO/ehgCnAQ34h8HS0pJp/v4cCXckNZVa\nFciPHZP/tF1dq32gLFnZ2MgN3CNqlxTq21fOELXvaQibz8ju3mfOwLx5DL59G4dZs+g8+tkGOCAH\nOYAal0VDAzp2tCA8XC5XRUVF4ezsTHS0zFFROmhX7YCqCnt7ezIzM2vtBvLy8lIJ0eXm5pKcXLtK\nbqdOnTh9+nSNIKcq6fhRMHLkSHbs2EF8fDxt2rRRdZUqERHRmtLSm0RENKZjx471nreuIAdg6NCh\n7N69m/bt27Nu3Trmzp1Ldna2ytJB7gvxRUdHh86dO3Ps2DE1HtKqVato3bo1vXv3BrQIDKy5DTs7\nOxITE1U8o4yMDJo0cSQp6RyOjur3nr59e7Nq1WCgB0qLPz+/fpSV9WX//hm89FIlQTs0NLQK0fwW\nbdrATz+hshvS1NRk3Lhx9O/fHysrK3R0htC/v9zF99h4VEdPYHXFv/bI5ahk4ANkR15vZCO7o8Dw\nx3UNfdYvQEyZMkX4+fkJXV1d4ebmJkaNGiUWL14sDhw4IJKSksTTRlFpkZi/Yb4YtmGYcP3KVZgu\nMRXNJzUX7/3xngiODhal5aU11jl3Lku0bSvEc88pRObcJUJ4eAiRkPDU960BDXhU8IxcyP8/vqjq\nyjxrlujVLFYcOVL7OX3vPSE+/LCWEz56tBA//yzEH3+ICw9weRZCiOXLhTh7Vgjh4yM2ghCNGwvx\nww9ClJTUuQ4PmfNRx27evFkAIjs7W235ypVnhaFheyGEELt37xEeHgOFlpYQYC6uXbsmhBCicePG\n4t69ezXmDAwMFIDYu3dvjc+2bdsmhg8fLuLihDhz5qxo06ZNrfv566+/iv79+9c4HqVT+sNQfc64\nuDhhbm4u3N3dxfHjx2uM8/YWomnT5sLAwECcPXu2XnMKIcTZs2dFdHR0reNCQkKEu7u7KC4uFg4O\nDsLS0lKcOXNGLFq0SMydO1ekpAgBmUKhEGLlypXi1VdfVZvH29tbnDlzRmRlCQE5Ij+/5j7l5OQI\nfX19cfjwYdGrVy8BiKVLlwotLa0a7uN5eXlCW1tbBATMEp6eQkBfMW6cEM8/nyc8PDzEL7/8ohob\nEBAg9u/frzqeoCDZRf3ECSHKyuQxCoVCLFmyRGhoaAhv79/Evn1Pdr15nEzO6Qr38a7AVsAOWC6E\nKKr4/Bbwg7Ks9Rjz/1eQkJDA+PHjmTBhQp011ydFcnIyIfEh7E/ez46bOzC9YsqYQWNY9NwiPCw9\n0JBqzwopFLBkSS4LFnTii5UhGB4eTcgv1+lz7pxKX6MBDfhf4Fl2V/0rkJSElYUgJaX2jy9dgpm1\n0WWUmZzjx/kK+OkBm3hHyWvu2xe3mzfltplaSjzPEspMjrGxsdryvn0t+M9/0vHzg6ioSAwMmrJi\nBbzxhj3p6emUlpaSkZFRa5lfqV5cWybH09OTM2fCcHaGuXNv0KJFCy5fvlxjXM+ePZk4cSIlJSVq\n0hiPU65S7ou7uztlZWV0795d7bPoaEhLg8DAtvzww41HUgBWemzVhrZt21JYWMj8+fPx8PDA09OT\n06dPk56ejp2dHTJnO5foaFP69Omjpkp869YtsrJyKCry56uvAM6gr9+vxjaMjIzQ0NDgzp07qu/C\nycmJsrIy1feghIGBAR07dmTYME/u3oWwsIU0awaTJhkQGrqSxYsXM2bMGACuX7+uJhnQs6dsBTJr\nlnyuLl8Ga2uJOXPm0LlzIIGBHjxxA/TjRkfKF+AG7Ad8qi2f96RzP6sXj/DU8ihQKBQiNzdXxGbF\nisV/LRbWw6yF9QvW4pMTn4iYzJh6zREbK0T37kJ06SLEzRuFQrz7rjjbrJm4c+7cM9nnBjTgcUBD\nJufRrzfJyUI0bSpmDk8QX34pL7p+XYi4uMpzam4uRGJiLSf8u++E6NRJCFtboV3f61d5eb0zNI9y\nTazP2JycnFrHpaenC2NjU3HhghCjRk0TX321SuTmCgGDxfff7xRxcXHCzs6u1jkXLFggAJGWlqa2\nXKEQYsaMfCFJuiIwsFR07z5LLFu2rM799PPzEydOnFA7nlGjRj30mJRjq+PYsWPi/PnzNcbNmyfE\n668L8fXXXwttbW1RXl5e7zkfNm7mzJkCEIcPHxZbt24VQ4YMEePHjxebN2+uGPu72LlTvifZ2tqK\n27dvi7AwIVq3/ljo6LwhunYVYvBgIaBXndtzd3cXkyZNEm+99ZYAxJkzZwQgtm/fXmPs3bt3VVm7\nqvuZl5cnDAwMRF5enkhOThZmZmZCoVDUetwjRwqxcWPl+19/FaJvX7Vjf6y/vycmmAghIoD5wE5J\nkq5KkvSLJElHgHtPOvc/AUIIojOj+f7q9/Se2xu7ADt81/oSlRnF7i92k/RTEh90+4Ampg+P4pcu\nvYSn55sEBkLwcYH3zx/DkSN0OHeOZv5PJm3dgAY04H+IS5egXTsYMwarlrYo/STnz6/ajt0EXV2V\nrZU6nJ1lbs1rr1Fa320+Jf7g46Cu7LipqSn5+bl4eOQQHX0RN7emyF3SJfz1V3qdfByozOSYV/Wf\nAL78Ek6d0sfR0YauXWMID79RQ2CwKgICAlQq7VX363HRs2fPGh1ioM+6dbI2jL+/Py4uLk+Nzwnw\n4osvEhgYSEBAAJ07d+b06dOkpaVhYaF08L7ClSuy2OT06dP5z3/eZcAAwf37v7Jr13BOnoQ//gA4\nVuc27OzsuHz5slomR7m8Otzc3Gpk7QCVOv7p06dVWZy6VCEGD4a9eyvf799PDVL04+CpnHUhxCWg\nOfA6sBuYJoTY8jTm/rtBCEFYWhjrLq3juW+eQ89Dj06bOnEg4gBDhwzl9O+nSX03lXWD19HZqXO9\nFEULCuC11+C777z49ttxzJ0Lh19+kQ82bJBtf6v9UTegAQ34h+Gtt+CLL2DRIqysNVRW30J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1f34emnL+ArX2klyMZG/vatbzHqkUd4Yvly9u7Vq9DDFKkoSnKiU5KT3DoXLYL+/e+ksfEqVq+G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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fad1f61d050>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "length=1000\n",
      "steps=10\n",
      "\n",
      "\n",
      "plt.figure(figsize=(6.5,4))\n",
      "\n",
      "for alpha in [1.0]:\n",
      "    gaussianparamter2=[]\n",
      "    msd_all=[]\n",
      "    for i in range(steps):\n",
      "            print 2,i\n",
      "            c=analyse_tool.Analyse(D=2,particles=5000,length=length+1,alpha=alpha,dt=1.0,version=\"lowencpp\")\n",
      "            b=c.nongaussian_parameter()\n",
      "            gaussianparamter2.append(b)\n",
      "            msd,std=c.msd_ensemble()\n",
      "            msd_all.append(msd)\n",
      "    msd_all=np.array(msd_all)        \n",
      "    gaussianparamter2=np.array(gaussianparamter2)\n",
      "    #plt.errorbar(range(356), gaussianparamter3.mean(axis=0), yerr=gaussianparamter3.std(axis=0),errorevery=10,label=\"Choleski\")\n",
      "    plt.errorbar(range(length+1), gaussianparamter2.mean(axis=0), yerr=gaussianparamter2.std(axis=0),errorevery=10,label=\"$\\\\alpha $=$%.2f $\" %(c.alpha))\n",
      "    #plt.errorbar(range(length+1), gaussianparamter1.mean(axis=0), yerr=gaussianparamter1.std(axis=0),errorevery=10,label=\"Our\")\n",
      "\n",
      "plt.xlabel('t', fontsize=10)\n",
      "plt.ylabel('$\\mathmr(t)$', fontsize=10)\n",
      "plt.legend(loc=2,fontsize='small')\n",
      "plt.show()\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2 0\n",
        "2 1\n",
        "2 2\n",
        "2 3\n",
        "2 4\n",
        "2 5\n",
        "2 6\n",
        "2 7\n",
        "2 8\n",
        "2 9\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "analyse_tool.py:133: RuntimeWarning: invalid value encountered in divide\n",
        "  nongaussianparamter=(1/3.)*moment4/moment2poten2-1\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.errorbar(range(length+1), gaussianparamter2.mean(axis=0), yerr=gaussianparamter2.std(axis=0),errorevery=10,label=\"Lowen\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(msd_all.mean(axis=0))\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "length=1000\n",
      "steps=50\n",
      "gaussianparamter1=[]\n",
      "for i in range(steps):\n",
      "        print 1,i\n",
      "        c=analyse_tool.Analyse(D=2,particles=5000,length=length+1,alpha=0.5,dt=0.1,version=\"python\")\n",
      "        b=c.nongaussian_parameter()\n",
      "        gaussianparamter1.append(b)\n",
      "gaussianparamter1=np.array(gaussianparamter1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 0\n",
        "1 1\n",
        "1 2\n",
        "1 3\n",
        "1 4\n",
        "1 5\n",
        "1 6\n",
        "1 7\n",
        "1 8\n",
        "1 9\n",
        "1 10\n",
        "1 11\n",
        "1 12\n",
        "1 13\n",
        "1 14\n",
        "1 15\n",
        "1 16\n",
        "1 17\n",
        "1 18\n",
        "1 19\n",
        "1 20\n",
        "1 21\n",
        "1 22\n",
        "1 23\n",
        "1 24\n",
        "1 25\n",
        "1 26\n",
        "1 27\n",
        "1 28\n",
        "1 29\n",
        "1 30\n",
        "1 31\n",
        "1 32\n",
        "1 33\n",
        "1 34\n",
        "1 35\n",
        "1 36\n",
        "1 37\n",
        "1 38\n",
        "1 39\n",
        "1 40\n",
        "1 41\n",
        "1 42\n",
        "1 43\n",
        "1 44\n",
        "1 45\n",
        "1 46\n",
        "1 47\n",
        "1 48\n",
        "1 49\n"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "plt.figure(figsize=(6.5,4))\n",
      "plt.errorbar(range(356), gaussianparamter3.mean(axis=0), yerr=gaussianparamter3.std(axis=0),errorevery=10,label=\"Choleski\")\n",
      "#plt.errorbar(range(length+1), gaussianparamter2.mean(axis=0), yerr=gaussianparamter2.std(axis=0),errorevery=10,label=\"Lowen\")\n",
      "plt.errorbar(range(length+1), gaussianparamter1.mean(axis=0), yerr=gaussianparamter1.std(axis=0),errorevery=10,label=\"Our\")\n",
      "\n",
      "plt.xlabel('t', fontsize=10)\n",
      "plt.ylabel('$\\\\alpha_2(t)$ non-Gaussian parameter', fontsize=10)\n",
      "plt.legend(loc=2,fontsize='small')\n",
      "plt.savefig('Abschlussarbeit/data/nongaussianneu.png',dpi=300)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def gen_log_space(limit, n):\n",
      "    result = [1]\n",
      "    if n>1:  # just a check to avoid ZeroDivisionError\n",
      "        ratio = (float(limit)/result[-1]) ** (1.0/(n-len(result)))\n",
      "    while len(result)<n:\n",
      "        next_value = result[-1]*ratio\n",
      "        if next_value - result[-1] >= 1:\n",
      "            # safe zone. next_value will be a different integer\n",
      "            result.append(next_value)\n",
      "        else:\n",
      "            # problem! same integer. we need to find next_value by artificially incrementing previous value\n",
      "            result.append(result[-1]+1)\n",
      "            # recalculate the ratio so that the remaining values will scale correctly\n",
      "            ratio = (float(limit)/result[-1]) ** (1.0/(n-len(result)))\n",
      "    # round, re-adjust to 0 indexing (i.e. minus 1) and return np.uint64 array\n",
      "    return np.array(map(lambda x: round(x)-1, result), dtype=np.uint64)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = gen_log_space(2000, 50)\n",
      "y1 = gen_log_space(300, 20)\n",
      "\n",
      "print y1\n",
      "def gamma(D,dt):\n",
      "     return 2*D*dt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  0   1   2   3   4   6   8  11  14  19  25  34  44  58  77 101 133 174\n",
        " 228 299]\n"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# ensamble vs time average\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "plt.figure(figsize=(8,4))\n",
      "\n",
      "e=analyse_tool.Analyse(D=200,particles=1000,length=2000,alpha=0.5,dt=0.1,version='lowencpp')\n",
      "msd,std3=e.msd_ensemble()\n",
      "d=analyse_tool.Analyse(D=200,particles=1000,length=2000,alpha=0.5,dt=0.1,version='lowencpp')\n",
      "msd1,std1=d.msd_time()\n",
      "\n",
      "#msd_text='MSD=2 K_\\\\alpha t^\\\\alpha'\n",
      "print len(msd)\n",
      "print len(e.t[:])\n",
      "plt.plot(e.t[:], 100*msd1[:]/gamma(e.D,e.dt),color=\"b\",label=\"time av. Lowen\")\n",
      "plt.errorbar(d.t[y],100* msd[y]/gamma(e.D,e.dt) ,yerr=100*std3[y]/gamma(e.D,e.dt), color=\"b\",linestyle=\"\",markevery=y,label=\"ensemble av. Lowen\")\n",
      "plt.loglog(d.t,100*d.msdanalyt()/gamma(e.D,e.dt),color=\"k\")\n",
      "\n",
      "\n",
      "e=analyse_tool.Analyse(D=20,particles=1000,length=2000,alpha=0.5,dt=0.1,version='python')\n",
      "msd,std=e.msd_ensemble()\n",
      "d=analyse_tool.Analyse(D=20,particles=1000,length=2000,alpha=0.5,dt=0.1,version='python')\n",
      "msd1,std1=d.msd_time()\n",
      "\n",
      "msd_text='MSD=2 K_\\\\alpha t^\\\\alpha'\n",
      "\n",
      "plt.plot(e.t[:],10*msd1[:]/gamma(e.D,e.dt),color=\"g\",label=\"time av. Naive\")\n",
      "plt.errorbar(d.t[y], 10*msd[y]/gamma(e.D,e.dt),yerr=10*std[y]/gamma(e.D,e.dt), color=\"g\",linestyle=\"\",label=\"ensemble av. Naive\")\n",
      "plt.loglog(d.t,10*d.msdanalyt()/gamma(e.D,e.dt), color=\"k\")\n",
      "\n",
      "e=analyse_tool.Analyse(D=2,particles=1000,length=300,alpha=0.5,dt=0.1,version='nhoch3')\n",
      "msd,std=e.msd_ensemble()\n",
      "d=analyse_tool.Analyse(D=2,particles=1000,length=300,alpha=0.5,dt=0.1,version='nhoch3')\n",
      "msd1,std1=d.msd_time()\n",
      "\n",
      "#msd_text='MSD=2 K_\\\\alpha t^\\\\alpha'\n",
      "\n",
      "plt.plot(e.t[:], 1*msd1[:]/gamma(e.D,e.dt), color=\"r\",label=\"time av. Cholesky\")\n",
      "plt.errorbar(d.t[y1], 1*msd[y1]/gamma(e.D,e.dt) ,yerr=1*std[y1]/gamma(e.D,e.dt), color=\"r\",linestyle=\"\",label=\"ensemble av. Cholesky\")\n",
      "plt.loglog(d.t,1*d.msdanalyt()/gamma(e.D,e.dt),color=\"k\")\n",
      "\n",
      "\n",
      "e=analyse_tool.Analyse(D=100,particles=1000,length=2000,alpha=0.5,dt=0.1,version='hartecppverion2')\n",
      "msd,std=e.msd_ensemble()\n",
      "d=analyse_tool.Analyse(D=100,particles=1000,length=2000,alpha=0.5,dt=0.1,version='hartecppverion2')\n",
      "msd1,std1=d.msd_time()\n",
      "\n",
      "#msd_text='MSD=2 K_\\\\alpha t^\\\\alpha'k: \n",
      "\n",
      "plt.plot(e.t[:],0.1*msd1[:]/gamma(e.D,e.dt), color=\"c\",label=\"time av. Davis-Harte\")\n",
      "plt.errorbar(d.t[y], 0.1*msd[y]/gamma(e.D,e.dt) ,color=\"c\",linestyle=\"\",yerr=0.1*std3[y]/gamma(e.D,e.dt), label=\"ensemble av. Davis-Harte\")\n",
      "plt.loglog(d.t,0.1*d.msdanalyt()/gamma(e.D,e.dt),color=\"k\")\n",
      "\n",
      "\n",
      "plt.xlim((0,200000))\n",
      "\n",
      "#X=main.plot_ensemble_mean_of_time_msd()\n",
      "plt.xlabel('$t/ \\Delta t$', fontsize=14)\n",
      "plt.ylabel('$\\delta r^{2}(t)/ \\Gamma^2$', fontsize=14)\n",
      "lgd=plt.legend(  ncol=1,fontsize='small')\n",
      "\n",
      "plt.savefig('Abschlussarbeit/data/timevsensambenew.png',bbox_extra_artists=(lgd,), bbox_inches='tight',dpi=300)   # save the figure to file \n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2000\n",
        "2000\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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s9+GH6pdi9oA6f/2VyOrVYURF6Th6VEdqqh6wNKbONRie5z//aYeZmaWs8CuB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\nU9T/B2sOOSChSa2TAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1ff6471d10>"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {
      "collapsed": true
     },
     "source": [
      "Smoluchwoski and erban chapmann disribution functions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def radial_erban(Diffusion,bincenters,micro_reactionrate_forward,reactiondistance):\n",
      "    c_inf=1.0\n",
      "    a_2=c_inf*(np.sqrt(Diffusion/micro_reactionrate_forward)*np.tanh(reactiondistance*np.sqrt(micro_reactionrate_forward/Diffusion))-reactiondistance)\n",
      "    return c_inf+a_2/bincenters\n",
      "def index_cut(bincenters):\n",
      "    for i in range(len(bincenters)):\n",
      "        if bincenters[i] >= 1.:\n",
      "            return i\n",
      "def radial_erban_inside(bincenters,Diffusion,micro_reactionrate_forward,reactiondistance):\n",
      "    c_inf=1.0\n",
      "    a_3=c_inf*np.sqrt(Diffusion/micro_reactionrate_forward)*(2*np.cosh(reactiondistance*np.sqrt(micro_reactionrate_forward/Diffusion)))**(-1)\n",
      "    return ((2*a_3)/bincenters)*np.sinh(bincenters*np.sqrt(micro_reactionrate_forward/Diffusion))           "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Diffusion=1.0\n",
      "bincenters=np.linspace(0,10,1001)\n",
      "reactiondistance=1.0\n",
      "\n",
      "plt.figure(figsize=(6.5,4))\n",
      "plt.plot([1.0, 1.0], [-0.1,1.1], 'k--', lw=2)\n",
      "plt.ylim([-0.01,1.01])\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "plt.text(0.9, 0.1, '$\\\\sigma$',\n",
      "        verticalalignment='bottom', horizontalalignment='right', fontsize=15)\n",
      "\n",
      "\n",
      "micro_reactionrate_forward=0.0001\n",
      "\n",
      "plt.plot(bincenters[index_cut(bincenters):],radial_erban(Diffusion,bincenters,micro_reactionrate_forward,reactiondistance)[index_cut(bincenters):],c=\"g\", label=\"micro. reaction limit $\\\\lambda_{+} = 0$\")\n",
      "plt.plot(bincenters[:index_cut(bincenters)+1],radial_erban_inside(bincenters,Diffusion,micro_reactionrate_forward,reactiondistance)[:index_cut(bincenters)+1],c=\"g\")\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "micro_reactionrate_forward=1./3.\n",
      "\n",
      "\n",
      "plt.plot(bincenters[index_cut(bincenters):],radial_erban(Diffusion,bincenters,micro_reactionrate_forward,reactiondistance)[index_cut(bincenters):],c=\"r\", label=\"Erban-Chapman $\\\\lambda_{+}=\\\\frac{2D}{\\\\sigma^2}$\")\n",
      "plt.plot(bincenters[:index_cut(bincenters)+1],radial_erban_inside(bincenters,Diffusion,micro_reactionrate_forward,reactiondistance)[:index_cut(bincenters)+1],c=\"r\")\n",
      "plt.xlabel('$r / \\\\sigma$ ', fontsize=12)\n",
      "plt.ylabel('$\\\\rho(r)/c_A$', fontsize=12)\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "micro_reactionrate_forward=100000\n",
      "\n",
      "plt.plot(bincenters[index_cut(bincenters):],radial_erban(Diffusion,bincenters,micro_reactionrate_forward,reactiondistance)[index_cut(bincenters):],c=\"b\", label=\"diffusion limited $\\\\lambda_{+}= \\\\infty$\")\n",
      "plt.plot(bincenters[:index_cut(bincenters)+1],radial_erban_inside(bincenters,Diffusion,micro_reactionrate_forward,reactiondistance)[:index_cut(bincenters)+1],c=\"b\")\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "plt.legend(loc=4,fontsize=\"small\")\n",
      "plt.savefig('Abschlussarbeit/data/smoluchowskierban.png',dpi=300)  \n",
      "#else:\n",
      "#    plt.plot(bincenters[index_cut():],radial_erban(bincenters,radial_s.mean(axis=0)*self.boxsize**3/len(self.radial_time_range))[index_cut():],linestyle=\"--\")\n",
      "#    plt.plot(bincenters[:index_cut()+1],radial_erban_inside(bincenters,radial_s.mean(axis=0)*self.boxsize**3/len(self.radial_time_range))[:index_cut()+1],linestyle=\"--\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/mi/janekg89/miniconda2/lib/python2.7/site-packages/ipykernel/__main__.py:4: RuntimeWarning: divide by zero encountered in divide\n",
        "/home/mi/janekg89/miniconda2/lib/python2.7/site-packages/ipykernel/__main__.py:12: RuntimeWarning: divide by zero encountered in divide\n",
        "/home/mi/janekg89/miniconda2/lib/python2.7/site-packages/ipykernel/__main__.py:12: RuntimeWarning: invalid value encountered in multiply\n",
        "/home/mi/janekg89/miniconda2/lib/python2.7/site-packages/ipykernel/__main__.py:12: RuntimeWarning: overflow encountered in sinh\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RnZ1NamoqUPxnjxV1/QXIOXjnHbjiiqArEQmfyF98kbcj/xBs3749GRkZRZ5L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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1fff690250>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "[]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": null
    }
   ],
   "metadata": {}
  }
 ]
}