ploting_for master.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import main \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "import analyse_tool \n",
    "import numpy as np\n",
    "import simulation\n",
    "import test_cython.genereatefracincrements as ginc\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "e=analyse_tool.Analyse(D=2,particles=50,length=20,alpha=0.5,dt=0.1)\n",
    "b=analyse_tool.Analyse(D=2,particles=50,length=20,alpha=1,dt=0.11)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e5dc82f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# abbreviation for plotting functions in certain range\n",
    "def plot(f, xRange, **kw):\n",
    "     yRange = np.array(map(f,xRange))\n",
    "     plt.plot(xRange,yRange, **kw)\n",
    "\n",
    "plot(lambda x: x**(0.5), np.arange(0.,1.,0.01))\n",
    "plot(lambda x: x, np.arange(0.,1.,0.01))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "float() argument must be a string or a number",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-5ead8d1af035>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmsdanalyt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/home/mi/janekg89/miniconda2/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   3152\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3153\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3154\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3155\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3156\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwashold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/mi/janekg89/miniconda2/lib/python2.7/site-packages/matplotlib/__init__.pyc\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1810\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[0;32m   1811\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1812\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1813\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1814\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/mi/janekg89/miniconda2/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1423\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1424\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1425\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1426\u001b[0m             \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/mi/janekg89/miniconda2/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36madd_line\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m   1706\u001b[0m             \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1707\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1708\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1709\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1710\u001b[0m             \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'_line%d'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlines\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/mi/janekg89/miniconda2/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_update_line_limits\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m   1728\u001b[0m         \u001b[0mFigures\u001b[0m \u001b[0mout\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupdating\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataLim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1729\u001b[0m         \"\"\"\n\u001b[1;32m-> 1730\u001b[1;33m         \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_path\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   1731\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1732\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/mi/janekg89/miniconda2/lib/python2.7/site-packages/matplotlib/lines.pyc\u001b[0m in \u001b[0;36mget_path\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    923\u001b[0m         \"\"\"\n\u001b[0;32m    924\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalidy\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 925\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecache\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    926\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    927\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/mi/janekg89/miniconda2/lib/python2.7/site-packages/matplotlib/lines.pyc\u001b[0m in \u001b[0;36mrecache\u001b[1;34m(self, always)\u001b[0m\n\u001b[0;32m    619\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myconv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    620\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 621\u001b[1;33m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myconv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    622\u001b[0m             \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    623\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/mi/janekg89/miniconda2/lib/python2.7/site-packages/numpy/core/numeric.pyc\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m    472\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    473\u001b[0m     \"\"\"\n\u001b[1;32m--> 474\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    475\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    476\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: float() argument must be a string or a number"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e5e268810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(e.msdanalyt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.         -0.42500927  1.49209464 ...,  7.05964464  9.05336685\n",
      "  6.77006684]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZldiWvw2Lb1mcxFalHk4tA7M7qdRfqitXIeOAMrkmyyjGmGFddOgSMeUlkMKW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\nQQD4f+TOlTLbake9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f774ea8fb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "e=simulation.Felix_Method(D=1,particles=500,length=2000,alpha=0.5,dt=1,version=\"cpp\").compute_trajectory()\n",
    "plt.plot(e[0]/2000.,\"b\")\n",
    "d=simulation.Felix_Method(D=1,particles=500,length=2000,alpha=0.5,dt=1,version=\"python\").compute_trajectory()\n",
    "plt.plot(d[0],\"g\")\n",
    "print e[0]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000 2.0 1.0 0.5\n"
     ]
    }
   ],
   "source": [
    "e=simulation.Felix_Method(D=2,particles=500,length=2000,alpha=0.5,dt=1,version=\"cpp\")\n",
    "old=e.compute_trajectory()\n",
    "print e.n , e.D*1., e.dt*1.0 ,e.alpha\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'colors' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-7a45d09388b2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmsd\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmsd_ensemble\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrorbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmsd\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"$\\\\Delta t=%.2f $\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloglog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmsdanalyt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\":\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"$%s$  mit $D=%.2f$,$\\\\alpha=%.2f$\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsd_text\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mD\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malpha\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      5\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvert_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mmsd1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstd1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmsd_ensemble\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'colors' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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6PCJOKpWfDNxddtxjpTK1uYULYf/+yvuOO84AIHWreiWGZzTq64orrvjd9sjICCMjI3U6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\nJKmHGQQkqYf9fygRI+gFA52+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f774ea8f110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "e=analyse_tool.Analyse(D=2,particles=5000,length=2000,alpha=0.5,dt=1)\n",
    "msd,std=e.msd_ensemble()\n",
    "plt.errorbar(e.t*e.dt, msd, yerr=msd*0,label=\"$\\\\Delta t=%.2f $\" %(e.dt))\n",
    "plt.loglog(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1], label=\"$%s$  mit $D=%.2f$,$\\\\alpha=%.2f$\" %(msd_text,e.D,e.alpha))\n",
    "e.invert_time()\n",
    "msd1,std1=e.msd_ensemble()\n",
    "plt.errorbar(e.t*e.dt, msd1, yerr=msd1*0,label=\"$\\\\Delta t=%.2f$ time backwards\" %(e.dt) )\n",
    "plt.xlabel('t', fontsize=14)\n",
    "plt.ylabel('MSD', fontsize=14)\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'c' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-3a3db6c24d0c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mmsd_all\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mstd_all\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mii\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\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[0m\u001b[0;32m      4\u001b[0m     \u001b[0mmsd\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmsd_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mii\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mmsd_all\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'c' is not defined"
     ]
    }
   ],
   "source": [
    "msd_all=[]\n",
    "std_all=[]\n",
    "for ii in range(c.particles):\n",
    "    msd,std=c.msd_time(i=ii)\n",
    "    msd_all.append(msd)\n",
    "    std_all.append(std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "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",
    "msd_time=np.array(msd_all)\n",
    "std_time=np.array(std_all)\n",
    "msd_time_mean=msd_time.mean(axis=0)\n",
    "std_time_mean=std_time.std(axis=0)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "#c.plotting(msdtype=\"ensemble\", particlemsdtime=0,error=1, showlegend=None,scale=\"lin\")\n",
    "#c.plotting(msdtype=\"time\", particlemsdtime=0,error=0, showlegend=None,scale=\"lin\")\n",
    "msd_text='MSD=2 D t^\\\\alpha'\n",
    "t='t=\\\\Delta t \\\\cdot n'\n",
    "plt.loglog(c.t*c.dt,c.msdanalyt(),\":\",color=colors[1], label=\"$%s$  mit $D=%.2f$,$\\\\alpha=%.2f$\" %(msd_text,c.D,c.alpha))\n",
    "plt.errorbar(c.t*c.dt, msd_time_mean, yerr=msd_time.T.std(axis=1)/(np.sqrt(c.particles)*0),label=\"time + ensembe average of $D=%.2f$, $%s$=$%.2f \\\\cdot %d$, $ \\\\#=%d $, ,$\\\\alpha=%.2f$\" %(c.D,t,c.dt,c.n,c.particles,c.alpha))\n",
    "plt.xlabel('t', fontsize=14)\n",
    "plt.ylabel('MSD', fontsize=14)\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d=analyse_tool.Analyse(D=2,particles=40,length=3000,alpha=0.5,dt=0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "msd_all=[]\n",
    "std_all=[]\n",
    "for ii in range(d.particles):\n",
    "    msd,std=d.msd_time(i=ii)\n",
    "    msd_all.append(msd)\n",
    "    std_all.append(std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colors=['r','b','g','k','c','w','b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
    "msd_time1=np.array(msd_all)\n",
    "std_time1=np.array(std_all)\n",
    "msd_time_mean1=msd_time1.mean(axis=0)\n",
    "std_time_mean1=std_time1.std(axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#c.plotting(msdtype=\"ensemble\", particlemsdtime=0,error=1, showlegend=None,scale=\"lin\")\n",
    "#c.plotting(msdtype=\"time\", particlemsdtime=0,error=0, showlegend=None,scale=\"lin\")\n",
    "msd_text='MSD=2 D t^\\\\alpha'\n",
    "t='t=\\\\Delta t \\\\cdot n'\n",
    "#plt.loglog(d.t*d.dt,d.msdanalyt(),\":\",color=colors[1], label=\"$%s$  mit $D=%.2f$,$\\\\alpha=%.2f$\" %(msd_text,d.D,d.alpha))\n",
    "plt.errorbar(d.t*d.dt, msd_time_mean1, yerr=msd_time1.T.std(axis=1)/(np.sqrt(e.particles)*0),label=\"time + ensembe average of $D=%.2f$, $%s$=$%.2f \\\\cdot %d$, $ \\\\#=%d $, ,$\\\\alpha=%.2f$\" %(d.D,t,d.dt,d.n,d.particles,d.alpha))\n",
    "plt.xlabel('t', fontsize=14)\n",
    "plt.ylabel('MSD', fontsize=14)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "e=analyse_tool.Analyse(D=2,particles=40,length=3000,alpha=0.5,dt=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "msd_all=[]\n",
    "std_all=[]\n",
    "for ii in range(d.particles):\n",
    "    msd,std=e.msd_time(i=ii)\n",
    "    msd_all.append(msd)\n",
    "    std_all.append(std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colors=['r','b','g','k','c','w','b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']\n",
    "msd_time2=np.array(msd_all)\n",
    "std_time2=np.array(std_all)\n",
    "msd_time_mean2=msd_time2.mean(axis=0)\n",
    "std_time_mean2=std_time2.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#c.plotting(msdtype=\"ensemble\", particlemsdtime=0,error=1, showlegend=None,scale=\"lin\")\n",
    "#c.plotting(msdtype=\"time\", particlemsdtime=0,error=0, showlegend=None,scale=\"lin\")\n",
    "msd_text='MSD=2 D t^\\\\alpha'\n",
    "t='t=\\\\Delta t \\\\cdot n'\n",
    "#plt.loglog(d.t*d.dt,d.msdanalyt(),\":\",color=colors[1], label=\"$%s$  mit $D=%.2f$,$\\\\alpha=%.2f$\" %(msd_text,d.D,d.alpha))\n",
    "plt.errorbar(e.t*e.dt, msd_time_mean2, yerr=msd_time2.T.std(axis=1)/(np.sqrt(e.particles)*0),label=\"time + ensembe average of $D=%.2f$, $%s$=$%.2f \\\\cdot %d$, $ \\\\#=%d $, ,$\\\\alpha=%.2f$\" %(e.D,t,d.dt,e.n,e.particles,e.alpha))\n",
    "plt.xlabel('t', fontsize=14)\n",
    "plt.ylabel('MSD', fontsize=14)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in (np.logspace(0,1,num=15,base=2)-0.99):\n",
    "    e=analyse_tool.Analyse(D=2,particles=4000,length=3000,alpha=0.5,dt=i)\n",
    "    msd,std=e.msd_ensemble()\n",
    "    plt.errorbar(e.t*e.dt, msd, yerr=msd_time2.T.std(axis=1)/(np.sqrt(e.particles)*0),label=\"$\\\\Delta t$=$%.2f $\" %(e.dt))\n",
    "plt.plot(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1], label=\"$%s$  mit $D=%.2f$,$\\\\alpha=%.2f$\" %(msd_text,e.D,e.alpha))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.xlabel('t', fontsize=14)\n",
    "plt.ylabel('MSD', fontsize=14)\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zeit läuft rückwärts Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'colors' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-32d8e51ba5c4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmsd\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmsd_ensemble\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrorbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmsd\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"$\\\\Delta t=%.2f $\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloglog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmsdanalyt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\":\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"$%s$  mit $D=%.2f$,$\\\\alpha=%.2f$\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsd_text\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mD\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malpha\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      5\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvert_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mmsd1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstd1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmsd_ensemble\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'colors' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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hrMaKFfDKK9nrOxCYlV+1WqVarbbktRqemCbpC8CrwCeASkRMS1oDPBYRGyWNAhERe9P6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7778175b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "e=analyse_tool.Analyse(D=2,particles=5000,length=20000,alpha=0.5,dt=1)\n",
    "msd,std=e.msd_ensemble()\n",
    "plt.errorbar(e.t*e.dt, msd, yerr=msd*0,label=\"$\\\\Delta t=%.2f $\" %(e.dt))\n",
    "plt.loglog(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1], label=\"$%s$  mit $D=%.2f$,$\\\\alpha=%.2f$\" %(msd_text,e.D,e.alpha))\n",
    "e.invert_time()\n",
    "msd1,std1=e.msd_ensemble()\n",
    "plt.errorbar(e.t*e.dt, msd1, yerr=msd1*0,label=\"$\\\\Delta t=%.2f$ time backwards\" %(e.dt) )\n",
    "plt.xlabel('t', fontsize=14)\n",
    "plt.ylabel('MSD', fontsize=14)\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variiere alpha\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in (np.logspace(0,1,num=7,base=2.8)-0.99):\n",
    "    e=analyse_tool.Analyse(D=2,particles=4000,length=3000,alpha=i,dt=1)\n",
    "    msd,std=e.msd_ensemble()\n",
    "    plt.errorbar(e.t*e.dt, msd, yerr=msd_time2.T.std(axis=1)/(np.sqrt(e.particles)*0),label=\"$\\\\alpha$=$%.2f $\" %(e.alpha))\n",
    "    plt.loglog(e.t*e.dt,e.msdanalyt(),\":\",color=colors[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.xlabel('t', fontsize=14)\n",
    "plt.ylabel('MSD', fontsize=14)\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# variere dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(1,6):\n",
    "    e=analyse_tool.Analyse(D=2,particles=4000,length=2000*i,alpha=0.5,dt=1./i,x=1)\n",
    "    msd,std=e.msd_ensemble()\n",
    "    plt.errorbar(e.t*e.dt, msd, yerr=e.t*0,label=\"$dt=\\\\frac{1}{%.2f} $\" %(e.dt))\n",
    "plt.plot(e.t*e.dt,e.msdanalyt(),\":\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.xlabel('t', fontsize=14)\n",
    "plt.ylabel('MSD', fontsize=14)\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# variere vervielfachung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(1,6):\n",
    "    e=analyse_tool.Analyse(D=2,particles=4000,length=2000,alpha=0.5,dt=1,x=i)\n",
    "    msd,std=e.msd_ensemble()\n",
    "    plt.errorbar(e.t*e.dt, msd, yerr=e.t*0,label=\"Vervielfachung $x=%.2f $\" %(e.ki/e.n))\n",
    "plt.loglog(e.t*e.dt,e.msdanalyt(),\":\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.xlabel('t', fontsize=14)\n",
    "plt.ylabel('MSD', fontsize=14)\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# neuer test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "e=analyse_tool.Analyse(D=2,particles=400,length=200,alpha=0.5,dt=1,x=2,version=\"cpp\")\n",
    "msd,std=e.msd_ensemble()\n",
    "plt.errorbar(e.t*e.dt, msd, yerr=e.t*0,label=\"Vervielfachung $x=%.2f $\" %(e.ki/e.n))\n",
    "plt.plot(e.t*e.dt,e.msdanalyt(),\":\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.xlabel('t', fontsize=14)\n",
    "plt.ylabel('MSD', fontsize=14)\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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