ranRods/ranRods.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import cm # color stuff\n",
    "from scipy.optimize import curve_fit\n",
    "%matplotlib inline\n",
    "\n",
    "def pltDefaultPar():\n",
    "    #reset default matplotlib parameters for this notebook\n",
    "    plt.rcParams.update({'font.size': 20})\n",
    "    plt.rcParams['lines.linewidth'] = 1\n",
    "    plt.rcParams['lines.markersize'] = 6\n",
    "    \n",
    "def pltPrintPar():\n",
    "    plt.rcParams.update({'font.size': 15})\n",
    "    plt.rcParams['lines.linewidth'] = 2\n",
    "    plt.rcParams['lines.markersize'] = 8\n",
    "\n",
    "rootTP=\"/home/johann/Remote/imac\"\n",
    "rootMAC=\"/Users/jh\"\n",
    "root=rootMAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plotdataArr(dataArray,xlabel,lineArr,lineParam='',title='',linestyles='',setlog='',legendloc='upper center'):\n",
    "    # color stuff: http://stackoverflow.com/questions/4971269/how-to-pick-a-new-color-for-each-plotted-line-within-a-figure-in-matplotlib\n",
    "    pltPrintPar()\n",
    "    colors=cm.rainbow(np.linspace(0,1,len(dataArray)))\n",
    "    if linestyles=='': lslist = len(dataArray) * ['o-']\n",
    "    else: lslist = linestyles\n",
    "    plt.figure(figsize=(7,5))\n",
    "    for i, line in enumerate(dataArray):\n",
    "        ti = lineParam + ' = ' + str(lineArr[i])\n",
    "        xs = line[:,0]\n",
    "        dd0s = line[:,1]/6.\n",
    "        plt.plot(xs,dd0s,lslist[i],label=ti,c=colors[i])\n",
    "    plt.xlabel(r'$'+xlabel+'$')\n",
    "    plt.ylabel(r'$D/D_0$')\n",
    "    plt.ylim([0,1.3])\n",
    "    plt.title(title)\n",
    "    plt.legend(loc=legendloc, bbox_to_anchor=(0.5, 1.2),\n",
    "          ncol=3, fancybox=False, shadow=True)\n",
    "    if setlog=='y' or setlog==\"double\":\n",
    "        plt.yscale('log')\n",
    "    if setlog=='x' or setlog==\"double\":\n",
    "        plt.xscale('log')\n",
    "    plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare D over p for ranRod and SPSnoHI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "triggerArr = ['normal','ranRod','ranRod/nrods1']\n",
    "triggerNameArr = []\n",
    "U=0\n",
    "a=0# This if fixed here, for no HI\n",
    "datadir= root + \"/Documents/workspace-cpp\"\n",
    "afrac = '%g'%(float(a)/10)\n",
    "dataArr=[]\n",
    "for trigger in triggerArr:\n",
    "    if trigger == 'normal':\n",
    "        typedir='/SingleParticleSimulation/sim_dataOLD/noreset/dt0.0001/t200/d0/b10/'\n",
    "        MSD=''\n",
    "    else:\n",
    "        typedir='/SingleParticleSimulation/Release/sim_data/noreset/'+trigger+'/dt0.0005/t200/d0/b10'\n",
    "        MSD='MSD'\n",
    "    triggerNameArr.append(trigger.replace('/',''))\n",
    "    folder=datadir + typedir\n",
    "    data = []\n",
    "    for pdir in os.listdir(folder):\n",
    "        file=os.path.join(folder, pdir,'k1.000/u0/InstantValues/linear_fit_parameters'+str(MSD)+'.txt')\n",
    "        if os.path.isfile(file):\n",
    "            p=file.split('/p')[1].split('/k')[0] # assign value from dir to p\n",
    "            p='%g'%(float(p)/10)                 # make p value p/b\n",
    "            with open(file, 'r') as fitf:\n",
    "                m = (fitf.readline().split('m')[1]).split()[0]\n",
    "            data.append([float(p)+a/10.,float(m)]) #the [:-1] serves to cut the linebreak '\\n' from the string m.\n",
    "    dataArr.append(np.array(sorted(data)))\n",
    "u0data = (dataArr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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qVauyZMkSli9fzj333JPvz/yFLqcPJW30sFFs2LSR2WPfJsPpJsTtZEjnnoWS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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bbf9b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "u0data[1],u0data[2] = u0data[2],u0data[1]\n",
    "triggerNameArr = ['fixed rod positions', 'random # rods','fixed # rods']\n",
    "plotdataArr(u0data,r'p/b',lineArr=triggerNameArr,lineParam='geometry')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions for all heterogeneous hydrogel obstruction models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "286.86755537231954\n",
      "ranRod Trigger:  ranRod/nrods1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: divide by zero encountered in true_divide\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: invalid value encountered in multiply\n"
     ]
    },
    {
     "data": {
      "image/png": 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FUco/lWCztW7dGoAjR47ku8RUaTM2NtZOZ1bYYKeHbd++nYCAAJKSkoqurCiK\nohiMuk0nW506dahRowY3btzg/PnzZbKEU4cOHQgLC6Np06bF3uf27dsEBwdTpUoVPUamKIqiPC7V\ng80mhKBt27YAj7UKxqO2+SjJFcDLy4uGDRsCWffKnj59Wg+RKYqiKI9LJdhcclbT2bdvn4EjKVpG\nRgYRERH8+OOP6vYdRSlH+vbti6WlpaHDUMoBdYo4l7Zt22JsbMzJkye5c+cOVlZWhg6pQBqNBicn\nJ9zd3Z+qJaQU5XEUd+k1IQRxcXHY29uXegxCiEdaAk55eqkEm4u5uTktWrTg8OHDHDhwgG7dupV5\nDJcvX6ZGjRpUrly50HpCCF5++WXt86tXrxIfH689za0oFdH333+v83zHjh3MmzePYcOG0eWh6Uht\nbW31EsNPP/1UJgMllfJPJdiHODs7c/jwYfbt21fmCXb9+vUsWLCAfv36ae+TLY7bt28TGBiIj49P\nngQrpWTshLFM/mKy6ukqT73+/fvrPE9LS2PevHl06tQpzzZ9MTIy0lnsXam41HmMh3To0AHIug5b\n1AQQpc3e3p709HR++eUXrl27Vuz9rKys+Pjjj+nZs2eebSujVjL7t9msWreqNENVlKdCRkYGoaGh\ntGrVCktLS6pWrUqLFi145513dOqtX7+e1157DQcHB8zMzLCxsaFnz57s2bMnzzHVNVglh0qwD6lb\nty729vYkJSVx5EixlpQtNa1ataJLly6kpqaycOHCYu8nhKBly5ba57///jt79uxBSsm0pdO4636X\nqUumqsFQivKQcePGMWbMGFq0aEFYWBihoaF4eXkRExOjU+/bb78lOTmZwYMHM2vWLD744AMOHjyI\nm5sbBw4c0KkrhFBnixRAJdh8PffccwDs3r27zNv29/encuXK7N69u0SzSsXFxbFo0SLq1q3LyqiV\nHLE8AgKOWBxRvVjlkeSePawsnhtCZGQkHTt25Oeff2bYsGEMGzaMKVOmcPLkSZ16P/74I+vXr2f8\n+PEMGjSI4OBg/vzzTypXrsyUKVMMFL1S3qkEm4/nn38egD/++IOMjIwybbtGjRr069cPgMWLFz9y\nr7Nhw4aEh4dTt25dpi2dRrJ9MgDJDZJVL1ZRHmJtbU1cXFyRi26YmZlp/33v3j1u3rxJ5cqVadeu\nXb6niRUFVILNl729PXXr1uXu3bscPXq0zNv39vamV69ejB079pFPNQkhqF69uk7vNWuD6sUqj2bt\n2rVl+txypVQHAAAgAElEQVQQQkNDyczMxMXFhQYNGjBw4EBWrFiR54f1yZMn6dOnD1WrVsXKyooa\nNWpQs2ZNtm3bxq1btwwUvVLeqQSbDyGEthe7Y8eOMm/fxMSEYcOGlfg2gpxrrzm91xyqF6soutzc\n3IiLi2P58uX06tWLPXv28Prrr9OxY0ftfN+3bt2ic+fO7Nixg9GjR7Nq1Sqio6PZsmULnTp1Urfk\nKAVSCbYAOffM7dq1i9TUVANH82jy9F5zqF6souRhYWFB3759mTVrFsePHyc0NJSDBw9q76nduHEj\nN2/e5JtvvmHcuHF4e3vz4osv4uHhwZ07dwwcvVKeqftgC9CgQQMcHByIi4tj37592oFPT4Jd+3bh\nnOGMiPtfhk1NTeXcuXPUq1ePnXt30serjwEjVJTyISEhARsbG52ytm3bIqXk5s2bQNZ9rVLKPD3V\n1atXc+zYMSwsLMosXuXJohJsIdzd3YmLi+P33383eILN+QMvzg3s4RPC85SdOHGC8+fP68z+pCgV\nQWGXROzt7fH09MTZ2ZnatWtz6dIl5s2bR5UqVejbty+Q9T1QvXp1hg8fzt9//42dnR379u1j+fLl\ntGjRgosXL5bVS1GeMOoUcSG6du2KRqNh//79Bj0VdPnyZT7//HOioqJKfAxHR0ed5Pr7779rf6Er\nytOssIGCY8aM4fr168yYMYMRI0awcOFC3N3diY2N1a50VbNmTTZv3kzbtm2JiIhg9OjRXLhwgS1b\nttC8efN8j6/ug1UAREUb8CKEkI/ymoOCgjhw4ADDhg2jV69eeoysYPv27WPChAmYmpoya9asx55D\nNTY2lnnz5jFlyhS9zceqlC9CCDW4TamwivP5z65Tqr+MVA+2CC+++CIAmzZtMtgXlLOzM8899xz3\n79/n22+/fezjNW/enJCQEG1yVV+8iqIopU8l2CK4urpiZWXFhQsX8szuUpaGDBmCmZkZsbGxRd4U\nnyN2+3bG+/gQ5O7OeB8fYrdvB7Jurq9fvz6QNRl6UFAQp06d0lvsiqIoFZFKsEUwMTHhhRdeALJ6\nsYZSo0YN7Wogc+fO5cGDB4XWjwgI4HTv3gRFRhISE0NQZCSne/cmIiBAp15sbCympqY0btxYb7Er\niqJURCrBFoOnpyeQNenEvXv3DBZHr169aN68OT169Ch0QefY7duxWbCAt27fxiS7zAR46/ZtbBYs\nYE+uyTO6dOnCZ599ph2dfOHChTJfRUhRFOVppBJsMdSpU4fWrVuTmpqaZ5WNsmRkZMTkyZPp06cP\nJiYmBdZbFx6O3+3b+W7zu32bqOnT8xwX/jdaOS4urvSCVhRFqaBUgi2mV155BYB169YZdGq0wnqu\nOYwSEyko/ZoARgUk39TUVAYOHMgzzzxT8gAVRVEUQCXYYnNxcaFmzZr8+++/7Nu3z9DhFCqjalUK\nOsmbBmRYW+e7zcHBAQ8PD+3zH374oURL5imKoigqwRabkZERPXv2BMrHKiCF6TVyJD8VkER/srbG\n65NPijzG8ePH+f3333FwcCjt8BRFUSoElWAfQffu3TEzM+Pw4cPl4jqllJIdO3YwZ84cnXLXrl1J\nGDyYpdbW2p5sGrDU2pqEwYNxyV7IoDCOjo5MmzYNKysrIGtFkeTk5CL2UhRFUXKoBPsIqlSpop14\nIjIy0sDRQGJiIv/973/ZuHEjsbGxOts+DgvjmagoQry9CXJ3J8Tbm2eiovg4LKxYxxZCYJ3dC37w\n4AFffvmlQZbuUxRFeVKpqRIf0ZUrV3j33XeBrPtRa9WqVVqhlci6deuYN28e1atXZ9asWVSpUqXU\n27h+/Trr1q1j4MCBao7VJ5SaKlGpyNRUiU8IOzs7unXrRmZmJitXrjR0OPTo0YNmzZpx8+ZNFi9e\nrJc2bG1t8ff31ybX2NhYVqxYoZe2FEVRnhYqwZZA3759EUKwZcsWEhISDBqLkZERH3zwAcbGxvz6\n668cPXpUr+3dv3+fb775htatW+u1HUVRimfx4sVoNBq2Z0+FWpRffvmFtm3bYm5ujpGREdu3b3/k\nYyjFoxJsCdSvX59OnTqRnp5eLq7F2tvb07dvX+rVq1foBBSlwdTUlJkzZ9KsWTMgay5jNY+xosC2\nbdvQaDQ6D0tLS9q3b8+0adNIT0/XW9vFvXRz+vRp+vfvT9WqVZk1axZLly6lefPmj3SMx7F69Wo0\nGg1XrlwBYPny5RgZGRl0OVB9Uguul9Brr73G7t272bhxIz4+PlSvXt3g8fTt25dKlSrpva2ckcUA\nCxcu5NatW4wZM0bv7SrKk6B///688sorSCm5cuUKS5YsYfTo0Rw9epRFixYZNLaYmBgyMjKYMWMG\nbdq00ZYPGDCAN954Q+/fH7t376ZRo0bY2dkBsHPnTpycnHS+U54mqgdbQo0bN8bV1ZXU1NRycT3S\nxMSkTJJrblJKbGxseP/997VlGRkZZRqDUvoKWoXpSWsjt8zMTFJSUvTaRo727dvTv39/3nzzTQIC\nAvjjjz+oV68eS5cu5erVq2USQ0Hi4+MBqFatmk65EKJMvj92797N888/r32+c+dOnnvuOb23aygq\nwT6GN998EyEEmzZt0p7yqEiEEPTt2xcLCwsga4T1hx9+WORKP0r5VdxVmMpzGznXE7du3crEiRNp\n0qQJpqam2h/Cmzdvxs/Pj8aNG2Nubk61atXw9PTM9/qjm5sbjRo1Ij4+njfeeIPq1atTpUoVXn75\nZU6fPl2seMzNzXF1dQXg/PnzebZHRkby/PPPY2FhgaWlJZ07dy5wMptvv/2W5s2bY2pqStOmTZkx\nY0axR4drNBpCQkIAaNiwIRqNhkaNGgGwaNEinWuwGRkZdO7cGQsLizyXgObNm4dGoyE4OLjINtPT\n00lISCAhIYErV67w119/0apVKxISErh48SJHjhyhZcuW2jpP3Uh3KWWFemS95NIzffp06eXlJcPD\nw0v1uE+iBQsWyMjISEOHoeSjOJ/7P7Ztk0usraWEPI8l1tYydvv2x46jLNpYtGiRFELItm3bypYt\nW8opU6bIOXPmyNjYWCmllP3795fdu3eXISEhcsGCBXLChAnS3t5empiYyJ07d+ocy83NTdasWVM2\nadJEvvXWW/Kbb76Rn332maxcubJs0aKFzMzM1NaNiYmRQggZFhaWJ6Z27dpJjUYjL168qFM+a9Ys\nKYSQLVq0kKGhoTI0NFS2aNFCCiHkt99+q1M3PDxcCiFk+/bt5fTp0+WXX34p69evL9u3by81Go3c\ntm1boe/LsmXLpK+vr9RoNHLmzJly2bJlcs2aNdr37OFjXLhwQVarVk22a9dOpqamSimlPHr0qDQ3\nN5fdunXTee0FyXlPch4ajUZqNJp8n2s0GnnhwoUij1kSxfn8Z9cp3XxT2gcs74/STrDx8fHS29tb\nvvrqq3r7cJREcnKynDNnjjx79myZtZmRkaHzR7dmzRp57dq1MmtfKVhxPveB3t4yNZ/EJ0Gmggz0\n9n7sOMqijZwE6+joKO/fv59ne3Jycp6ya9euyRo1asiePXvqlLu5uUmNRiOnTZumUz516lSp0Whk\ndHS0tiwnmUycOFHeuHFDXr9+XR45ckSOGDFCajQa6evrq3OMW7duSQsLC/nMM8/Ie/fuacvv3r0r\nGzduLK2srOTt27ellFImJibKKlWqyJYtW8qUlBRt3cuXL0sLC4tiJVgppQwODs43keWXYKWUctWq\nVVIIIT/44AOZkpIinZycpI2Njfznn3+KbCvnNW7dulVu3bpVvv7669LOzk7+9ttvcuvWrdLX11c2\nbNhQ+3zr1q3ywYMHxTruozJUgi03p4iFEM2FEFuFEElCiMtCiBBRzGFtQghfIcSfQohkIcQNIcQG\nIYSZvmOGrPtiu3fvTmZmJt99911ZNFksq1atYsOGDcyYMUPv67tKKRkTMgYhhHYk4t69e1m7dq1e\nJr5Q9KOkqzCVtzZyjBgxgsqVK+cpNzP731dDUlISN2/eRAiBi4sLe/bsyVNfo9HwwQcf6JR5eHgg\npcz3NHFQUBC2trbUrFmT1q1bM2fOHEaOHMmPP/6oU2/z5s0kJSXx4Ycf6vydWFhY8OGHH3Lv3j22\nbNkCQHR0NMnJybz33nuYmppq69apU4c333yzmO/Io/Px8WH48OF8/fXXvPjiixw/fpwFCxZQt27d\nYu1ftWpVPDw88PDw4OLFi7z00ku4u7vj4eHB+fPnefnll7XPPTw8ynwcib6ViwQrhKgKbAHSgd5A\nCBCQ/d+i9h0CLAPWAy8Dg4HTlOEI6TfeeANzc3P279/P/v37y6rZQvXp0wc7Ozvi4uJYvny5Xtta\nGbWS2b/NZtW6Vdqy5s2b8/nnn2Nubg5kXZ+9deuWXuNQHk9JV2Eqb21A1viApk2b5rvt3Llz+Pn5\nUb16dSwtLalRowY1a9Zkw4YN+X5G69Spk+eL38bGBiDf++CHDRvGli1b2LhxI6GhodjY2LBixQqu\nX7+uUy8uLg4hBC1atMhzDCcnJ6SUnDt3ThuzEEJ7e1xu+e1fmqZPn07jxo35448/GDp0KK+++mqx\n9st9/TUuLo69e/fi6upKQkICZ86c4eDBg3To0EFb52lULhIsMBwwBXyllFullPPISq6fCCEsCtpJ\nCGEDTAfek1KGSCm3SynXSCk/klLeLZvQs36l9evXD8i6baU8jKQ1NTXlo48+QgjBihUrOHPmjF7a\nkVIybek07rrfZeqSqTmn4bGwsKBBgwZA1h9aaGhovr0DpfwojVWYykMbOXJ+3OWWlJREly5diI6O\nZuTIkaxcuZLo6Gi2bNmi7ZU+zMjIqMA28qvftGlTPDw88PT0ZNSoUaxdu5Z//vmHIUOGPN4LMpCD\nBw9y8eJFAI4ePVrs9bB37dqFra0ttra2NG7cmMzMTD744ANsbW21a04PHTpU29t/GpWXBPsysElK\nmZSr7CfAHOhWyH6vAxJYosfYisXLyws7OzsuXbrEr7/+auhwgKxfwV5eXmRmZhIREaGXU8Uro1Zy\nxPIICDhicUSnF5sjOTkZZ2dnPD09tWWpqamlHovyeEpjFaby0EZhtm7dSnx8PBEREXz++ef4+Pjw\n4osv4uHhwb179/TSZqdOnXjrrbeIjo7mt99+05Y3atQIKSXHjh3Ls8+xY8cQQmhH+ebUPXHiRL51\n9eXu3bu88cYb2Nra8tVXX7F7926CgoKKtW/btm3ZsmULW7Zs4dVXX6VRo0ba556enjg6Omqfb968\nWW+vwaBK+6JuSR7AVeCLfMrvAQGF7LcI+BN4F7gEpAKxQKdC9inyYndJ7d69W3p5ecn+/fvLxMRE\nvbXzKO7fvy/feecdGR4eLpOSkkr12JmZmdKlr4skCEkwkiCkS1+XIkcX7ty5UwYHB5dqLErhHuVz\nH7t9uwz09pZfuLvLQG/vUhnZW5ZtFDRgR0op161bJ4UQ8rvvvtMp37Rpk3Yka25ubm7SwcEhz3HO\nnz8vhRAyJCREW1bYKOIzZ85IY2Nj2bVrV21ZYmKidpDT3bt3teV37tyRTZo0kVZWVvLOnTtSyqzB\nQubm5tLJyUlnkNOlS5ekpaWl3gY5vf7669LY2FjGxMRIKaV87bXXdJ4XV8eOHeWwYcO0z52cnOSH\nH374SMd4HMX5/KOHQU7lZSanakBiPuW3srcVxA5wBAKBT4GbwGfARiFEUynl9UL2LXWurq60adOG\nQ4cOsWjRIj766KOybD5flStXJiwsTC+DjXL3XgGdXmwfrz757iOlJDo6mjfeeKPU41FKh0uXLnrv\nSeq7DZnPqVuAzp07Y2dnR0BAAHFxcdSrV4+DBw+ydOlSWrVqpbe5vBs3boyfnx8//PADMTExuLm5\nYW1tTWhoKO+//z4uLi4MHDgQKSWLFy/m3LlzzJs3D0tLSyDrMtTEiRP59NNP6dSpEwMGDCApKYm5\nc+fyzDPPcODAgceO8eH3bMGCBSxfvpzx48fTrVvWicRvv/2WvXv38n//938cOnSoWDPY3bt3j7/+\n+osPP/wQgBs3bnD8+HHtPblPs/JyirikBFAFGCSl/ElKGQ14A5nAe2UejBC8++67GBsbs3XrVr2e\nunkU+kiuMvvaa7K97iLsyQ2Sda7FPkwIQXBwMI6OjkDWqeKQkBASE/P7faUoJVPQDQjW1tZER0fj\n4uLC119/zahRozh+/DgbN26kffv2+e5X0LFyj5ovrCxHYGAgGo2GCRMmaMuGDx/OqlWrqFatGhMm\nTGDixIlUq1aNyMhIBg8erLP/J598wjfffMP9+/cZN26cdgrG3DOpPY7ccZ88eZKPPvqIzp0760wo\nYW1tzY8//si1a9cYNGhQsY67a9cuMjMz6dq1K5A1exOgff40KxfrwQohrgJfSyknPlR+DwiSUua7\nSrgQ4iegL2AupUzNVb4ZSJRSvpbPPjL3NQQ3Nzfc3NxK5XXk+OGHH/jpp5+wt7cnIiICY+PycqKg\n9Pyy9hfejnyb5AbJebaZnzdnie+SAnuxuUVFRXH06FHGjBmj1prVI7UerFKR5ff5j4mJISYmRvs8\nJCQEWcrrwZaXBLsN+EdK+WausnrARcBLSrm+gP2CgC/ISrAPcpVvAW5KKfvls4/U92tOTU3l/fff\n58qVKwwYMIC+ffvqtT1DGPnFSP668Fe+SVFKSfsG7QmfEF7kcTIyMrh//762l71jxw6qV6+Ok5NT\nqcdckakEq1Rkhlpwvbwk2DHAKKCBzB5JLIQYBQQDdlLKfIf3CSGeJWuQU08p5a/ZZdbAeSBUSjk5\nn330nmABDhw4QFBQECYmJkRERFC/fn29t1lccXFxLFq0iFGjRmmv8ZQHCQkJfPTRR0yaNAl7e3tD\nh/NUUQlWqcgMlWDLyzXYb4AHwGohxAtCiGFAEBCWO7kKIc4IIb7NeS6l3A+sBRYIIQYIIXpmP08F\nZpfpK3hIu3bteOmll0hLS2PGjBnl4t7YHPPnz+fAgQPMmjWrXH3pVq1alXHjxmmTa3JyMtHR0QaO\nSlEUpWTKRYKVUiYCL5AVz1qykytZPdjcNOSN+U0gMrv+cuA+4CGlLL0510po0KBB1KhRg1OnThW4\nOoYhfPDBB5iZmbF7925+//13Q4ejZWRkpDMrzffff5/vfX+KoihPgnJxirgsldUp4hz79+8nJCQE\nExMTZsyYQb169cqs7cJs3bqVGTNmYGZmxowZM7QLIJcnsbGxtGjRQrsY8+HDh2nWrFm+88sqhVOn\niJWKrKKfIn5qPfvss7zwwgukpaURFham94n3i8vDw4PnnnuOlJQUIiIiytUp7Byurq7a5BofH8+U\nKVO4c+eOgaNSFEUpHpVgy8DQoUOpVasWZ8+e5fvvvzd0OEDWr7URI0ZQt25dXnrpJTSa8v1RePDg\nAYMGDcLW1haAW7ducenSJQNHpSiKUjB1iriMnDhxgjFjxpCZmcnEiRNp06ZNmceQn4yMjEInMy+v\nwsPDqV69Om+//bahQ3kiqFPESkWmThE/5RwdHfHz8wOykkN5OdX5JCbXzMxMnRWMAE6fPq0SiKIo\n5YpKsGXotddeo3nz5ty8eZPw8PBiL/uk6NJoNPj7+2sXzo6Li2PChAkkJSUVsaeiKErZKXGCFUJo\nhBBDhRA/CiFihBC/Zf/7fSHE07UsfSkxMjLSTu6wf/9+VqxYYeiQ8pWSkmLoEB5JQkICgwYNwsIi\na+ngq1ev5lncWlEUpaw9Tg/2S7Im1Q8FBpO1aHo4WRPw/+fxQ3s62draEhAQgBCCH374gYMHDxo6\nJB2//vorQ4YM4cKFC4YOpdicnZ1xd3fXPp83b552QnFFURRDeZwEu19KuUBKeUBKeVZKeVJK+aeU\n8r/AH6UV4NOoffv2vP7660gpCQsL48aNG4YOSevkyZPcvXuX0NBQHjx4UGT92O3bGe/jQ5C7O+N9\nfIjdvr0MoixYWloatWrVolevXtqyAwcOlMvbkBT9OX/+PN7e3tSsWRONRqNd+SX3vw3Fzc1Nu5B6\nSQ0cODDPyP/8yhTDepz/Gy2EEKFCCD8hhJcQ4hUhxGtCiC8Bl9IK8Gn1+uuv07ZtW27fvs2kSZOK\nlczKwjvvvEPdunW5dOkS8+fPL7RuREAAp3v3JigykpCYGIIiIznduzcRAQFlFG1eJiYmDBs2DBMT\nEwCOHTvG119/TXp6usFiUsre22+/zY4dOxg7dizff/8977zzDlD4cnJlpTTaL2ipvJIk2AsXLhAS\nEsLhw4cfOy5FV4kTbPbSchuAZwBPoBfQHNgipRxVOuE9vXKux9aqVYszZ87w3//+t1yMgjU1NWX0\n6NGYmJiwadOmAk+1xm7fjs2CBbx1+zYm2WUmwFu3b2OzYAF7duwos5gLk5qayjvvvKOd/ens2bOc\nPn3awFEp+pSamsrOnTsZMGAAI0eOpH///ri4ZP3mT0lJYd68eQaOUD/mz59PcnLe5SOLcv78eUJC\nQsrd5aqnwSMnWCGEtRCiOoCUMkZKOUFK+b6UckT2v2NKPcqnlJWVFePHj8fMzIzt27ezcuVKQ4cE\ngIODg3ax5/nz55OampqnzrrwcPxu5z/ds9/t20RNn67XGIurXbt2dOzYEchaRm/u3LlqgopikFIy\nJmSMXn/06auNK1euIKWkWrVqebZVqlTpibw1rTiMjIyoVOnRx5eWhx/2T6tiJ9js1WpOAzeB60KI\n60KIBUKIFkXtqxSsQYMGjBw5EoClS5cSGxtr4Iiy9OjRgz59+jBx4sR8/2iNEhO1PdeHmQBGBSRf\nQ8rIyOC5556jW7duQNYXy/Lly0v0q/9ptzJqJbN/m82qdaueqDb8/f1p2LAhQgiCg4PRaDQYGRmx\nPXtswMPXYOfMmYNGo+Grr77SOU58fDy2trY4OTnpjKpPTU1l0qRJtGzZEjMzM6pVq0bv3r3z7f0l\nJiYydOhQbG1tsbCwwMPDg7/++uuRXs+DBw/49NNPqVu3Lubm5ri6urJ58+Z86+Z3Dfaff/5h0KBB\nNGzYEFNTU2rVqsXzzz/P0qVLAVi8eDEeHh4IIbT7azQaPDw8gKy/ka+++opu3bpRu3ZtKleuTIMG\nDRgxYgQ3b97UaevChQtoNBomTJjA+vXr6dixI2ZmZtSpU4fRo0fne1vi2bNn8ff3p379+lSuXJm6\ndevi7e3NgQMHdOrt27cPHx8fbG1tMTU1xdHRkUmTJpX/sRVSyiIfwBDgOFkjhV8GBgKTgb+BNCC4\nOMcpD4+sl1z+/Pzzz9LLy0v26dNHnjhxwtDhFCnQ21umgpT5PFJBBnp7GzrEIu3Zs0e+//77Mj09\n3dCh6N2jfO4zMzOlS18XSRDSpa+LzMzMLPV49NVGbGysnDFjhhRCyD59+shly5bJZcuWyWvXrkkp\npRRCSH9/f519+vTpI01MTOSuXbu0sbm5uUkzMzN55MgRbb20tDTp5uYmTU1N5dChQ+U333wjp0yZ\nIps0aSLNzc3l/v37dep26NBBajQaOXDgQDlnzhz5zjvvyGrVqskmTZpIBweHYr0eb29vKYSQ3t7e\ncvbs2TIgIEBaWFjIVq1aSY1Go1N34MCBOmXp6emyWbNm0srKSo4dO1YuXLhQhoeHS39/fzl06FAp\npZTnzp2TgYGBUggh3333Xe37tWXLFimllPfv35dVq1aVQ4YMkdOnT5dz586VQ4YMkZUqVZKtW7eW\naWlp2vbOnz8vhRCyY8eOslatWjIoKEh+8803skePHlIIISdPnqwT7969e6WVlZWsUqWK/Oijj+S8\nefPkf/7zH9mjRw/59ddfa+utW7dOVq5cWbZs2VL+5z//kfPmzZP+/v7SyMhI9uvXr1jvY3E+/9l1\nSjffFKsSxABVC9jWDtgCRJR2cPp4lNcEm5mZKSMiIqSXl5d888035eXLlw0dUqH+2LZNLrG2zjfB\nLrG2lrHbtxs6xCLFx8fLU6dOaZ/v2bNHRkVFGTAi/XmUz/2KNSukub+5JBhpPtBc/rL2l1KPR59t\n5HzRh4SE5NmWX4K9deuWbNiwoWzQoIG8deuWnDBhgtRoNHL27Nk69aZPny41Go3cvHmzTvndu3el\nvb29dHd315bNnTs33xhykn9xEuymTZukEEIOGjRIp3zNmjVSCFFkgj18+LAUQsipU6cW2k5MTIwU\nQsjFixfnu/3+/ft5yhYuXCg1Go1csWKFtiznfbewsJAXL17Uqd+yZUtZp04dnTInJydpZmYmjx49\nWmBs9+/fl3Z2dtLNzS3Pj7CIiAip0Wjktm3bCn19UhouwRb3FPE5mbVma3494APAS4CNEMLzUXrP\nyv8IIXjvvfdo3749d+7cITg4mFu3bhk6rAK5du1KwuDBLLW2Jmd9oDRgqbU1CYMH49KliyHDKxY7\nOzuaNm0KZP3QXLZsGTVr1tRur4gzbUkpmbZ0Gsn2WafNkxskM3XJ1Jwfp09MG4+iatWq/PDDD8TH\nx9OjRw8mTJjAq6++yvDhw3XqLVu2DEdHR9q1a0dCQoL2cf/+fV566SV27typvRtgzZo1GBsb88kn\nn+gc491339WuEFWUNWvWIIRg1CjdMaO9e/emWbNmRe5vbW0NwO+///5YE6/kDBDMzMzk9u3bJCQk\n4ObmhpSSPXv25Knv4+ND/fr1dcrc3d25cuWK9nLMwYMH+fvvvxk0aBBOTk4Ftr1582auXr3KwIED\nuXnzps77/vLLLyOlJDo6usSvTd+Km2ALvaCWnf2Hk3XqWCkhY2NjPvvsM5o0acKVK1eYOHFiuZpV\nSUrJb7/9xr179wD4OCyMZ6KiCPH2JsjdnRBvb56JiuLjsLAyi6e0BskIIQgICKBDhw5A1vXaTz/9\nlH///fexj/0kWRm1kiOWR7KmiwEQcMTiSKleJy2LNh5Vp06dGD16NHv27KFmzZosWLAgT53jx49z\n4sQJbG1tdR41a9bku+++IyMjQ3tP+7lz56hdu7Z2drEclSpVKvY9sOfOnUOj0fDMM8/k2da8efMi\n9+zBZtkAACAASURBVLe3t2f8+PFER0dTu3ZtnJ2d+eyzz9i3b1+x2s+xfPlyXF1dtdecbW1tady4\nMUKIfDsBDg4OecpsbGyArFnXAO1I/rZt2xba9vHjx4Gsa+sPv+/NmzdHCMHVq1cf6fWUJeNi1ivy\nxi0p5T0hRPntcj0hzMzM+Pzzzxk9ejRnzpwhNDSUwMBAjI2L+79Kf5YvX86yZcvo2LEj48aNQ6PR\n4NKli8F6qzmDZDq070Afrz6PfTx7e3vtv48ePYqRkRG1a9cGID09nbt37+Y7MvVpoe1ZOukO+srp\nYfr28n3sezjLoo2SSEtLY9OmTQghuHnzJhcvXszz/1pKSatWrQgPDy/wR13OcorlxYQJExg0aBDr\n169nx44dLFiwgKlTp/LZZ58xefLkIvdftWoVfn5+uLi4MHPmTOrXr4+pqSkZGRl4enrme5ansFHa\nj/pjWEqJEIJp06YVuAJZnTp1HumYZam439pvCyGsgJ3ALinlyQLqlY/ZEp5w1apVIzg4mNGjR7N/\n/37CwsIYNWqUwW8v6NatG5GRkfz555+sWrWKvn37GiyWnC/qu+539fLF3KZNGxwdHbXHjImJYffu\n3XzxxRel1kZ5k6dnmSNXD/Nxf8iURRslMWbMGPbv309oaChTpkzBz8+Pv/76S7ugBEDTpk25fv26\nzrScBWnUqBGbN2/m3r17Or3Y1NRUzp07R/Xq1Yt1jMzMTE6dOpWnx/r3338X+7U1bNiQ9957j/fe\ne4/U1FS6d+9OaGgoAQEB1KhRo9C/m++//x4zMzNiYmK0p4oha8a3x5HTKy/q3tumTZsipcTc3Fw7\nsvlJUtxTxJcBUyAIOC6EuCqEWC2EGCWEcBVC5CRqdUNVKalbty7BwcGYm5uza9cuIiIiDD4k3c7O\nTntN6fvvv3/kWw5KU+4van2dXsz9hRIXF4e3t7f2+aFDh8r1qamS2LVvF84ZznSL65bn4ZzpzM69\njz+/c1m08ag2btxIREQEAwcOJCAggO+++46TJ0/y/vvv69QbMGAAV65cIayASyDXrl3T/vvVV18l\nPT09T93Zs2cXe6nKV199FSklU6dO1SmPjIzk1KlTRe5/586dPDOYVapUCUdHRwDt6d2cHwAP33YD\nWb1RIUSe756JEyc+1g/aNm3a4OTkxMKFCwv9seDp6UnNmjX5z3/+k+/p6Pv372svWZVLxRkJBXye\n69/2QH9gFnAISAeSyBpp/Hdpj8Iq7QfldBRxQY4fPy5fe+016eXlJWfOnCkzMjIMHZJctmyZ9PLy\nkn5+fgYZ7Zz7Fg+C0evtJPlJTU2VgwYNkidPniyT9krDk/a5fxyPMor433//lba2tvL/27vz8KiK\nrIHDv0pCICGQhAQIEMIumzDsqyiIA4jAuKIzijMCOqB+7CgosrkjmwiCIsMoOgMqwgiCgMoiCJgg\nKIvsIYSEELIS0lm76/ujk2uWDgToTifd532efpJ7u253dYk5XXWrTrVs2VKnp6cb58eNG6eVUnr1\n6tXGuZycHN2/f3/t4eGhBw4cqOfOnas//PBDPW3aNN29e3d99913G2Wzs7N1x44dtYeHh37yySf1\n0qVL9TPPPKNr1KhxQ8t0hgwZoj08PPSQIUP0+++/rydMmFDqZTrr16/XtWrV0qNHj9YLFy7UH330\nkR41apT28vLSPXr0MMplZGTo6tWr62bNmully5bp1atX6x9++EFrrfXq1au1h4eH7tKli168eLFe\nsGCBvuuuu3SXLl2KteW12n3mzJnaw8NDR0VFGefCw8O1v7+/rlq1qh4zZoz+8MMP9Zw5c/R9991X\naJnOli1btK+vr65Zs6Z+8cUX9fLly/U777yjhw8frgMDA8v1LGJ7BCx/rGtjXwcu2ruCdv/AFfAP\nzeHDh/VDDz2kBw8erJcuXVpmgaQkZrNZv/rqq/q5557TsbGxZf7+BZd45D8ctZzElrS0NL1u3Trj\nODMzU8+ZM6fQmsDypiL+u79Z586d0x4eHnr27NnFnvPw8DCWvVgsFn3PPfdoX19f/dtvvxUql52d\nrTt06KADAgL0uXPnjPNms1m/9957ukuXLtrPz0/7+fnp2267TT/xxBPFlu8kJyfrkSNH6uDgYO3n\n56fvvvtufeDAAd27d2/duHHjUn2WzMxMPWnSJF2nTh3t6+uru3btqrdt26b/8Y9/aE9Pz0Jli56L\njIzUo0eP1q1atdL+/v7az89Pt2rVSs+cOVNfuXKl0LWbN2/WHTt21D4+PtrDw6PQkqOPPvrIWFJT\nt25dPWrUKJ2cnFyoLa/X7rYCrNZanzx5Ug8bNkzXqVNHV65cWderV08/8MAD+uDBg4XKHT16VA8b\nNkyHhobqypUr65CQEN2zZ0/9+uuv6+Tk5Ou2o7MCrLK+rn0opZZqrUdfv6TzKKW0PT9zWTl06BCv\nvvoqOTk5DB48mJEjRzo1abnJZEIpVegeVVnQWtN9aHf2t95f+D6ehq5Hu7L3871l3i6bNm3i4MGD\nvPzyy4A1+46np2e5mJiWTylFRfx3L4Q9lObff14Zu/7xsPfeRovs/HoiT7t27Zg6dSpeXl5s2LCB\n999/36n3ZH19fcs8uELpJsmUtTvuuMPI3Qzw9ddfs3LlyjKvhxCifLnuV2yl1CvAdq31dWcfaK1/\nt0uthE2dOnXipZde4q233mLLli1kZmYyduzYctVTcrT8STIqsvgXTa01u8N3l/ks1OrVqxdKHnDo\n0CGefvpp4/jAgQM0bdrUWPgvhHAP1x0iVkr5Aw8APbEm+l+ntS4fGelvQkUdIi7o8OHDvPbaa2Rk\nZNC1a1djezln01qTlpZW6kw1rkprbQxTm0wmnn76aRYuXOjUNZIyRCzcWbkdItZap2qt/621fhp4\nG7hdKfWRUupNpVQne1ZGlE6bNm2YPXs2VatWZf/+/bz22mtO37A9KyuLefPm8eKLL5bvafNloOA9\n4IyMDIYOHWoE1+TkZF555RW3TMMohLu5oXuwWuskrfVHWuuRwHygU96Wda8ppa6d80rYVfPmzXnj\njTfw9/fn4MGDvPLKK6VeX+cIWmvOnz9PTEwMb731VrH1d+4qKCiIv/zlL8bxtm3bCAkJMbYVS05O\ntrn+UAhR8dllFrFSKgR4GOgARANfaK2P3PILO4ArDBEXdOHCBaZPn05CQgJ169ZlxowZRnq/shYf\nH8+kSZNISUmhX79+PPfcc06d6VweZWdnk5mZaQyjL1++HG9vb/7+97879H1liFi4s3I7RFwaWus4\nrfVirfVw4EPgHqXUSqXUFHu8vihZaGgo77zzDo0aNSI2NpYXXnjhltOY3axatWrx8ssv4+3tzdat\nW/nf//7nlHqUZ97e3oXuUWdkZDBw4EDj+OOPPyYyMtIZVRNC2NkNB1illL9SqsREmlrrGK31Qq31\nU8DyW6qdKJWgoCDefPNNOnToQGpqKi+//DL79jlnHlrz5s0ZN24cALt375ah4usYM2ZMofuz3377\nbaHJUOfPn5eepxAVVKmHiJVSTwKvAPl7LSUBXwPztNalzzztZK42RFxQbm4uy5YtY+vWrSilGDly\nJIMGDXLKMO1PP/1Ex44dC+XzFdeWm5vLuXPnaNq0KWAd/n/ppZf417/+dctLsWSIWLgzZw0RlyrA\nKqVGAhOxJpKIBEKA5sBfgGbA61rrmfasmKO4coAF62SjL774gk8//RSAP//5z4waNapcLOMRN+bn\nn38mOjqahx6yrus9duwYR44cYejQoTf8WhJghTsr7wF2B3C/1jrFxnPtgXeAI1rrcfasnCO4eoDN\nt2vXLhYtWkR2djYtWrRgypQppdoiS5Rfc+fO5bbbbmPIkCEAREdHU7169VIlsGjYsCFRUVGOrqIQ\n5VKDBg04d+7cNcs4M8D+K28CU0nPK+AT4FOt9RY71s/u3CXAApw5c4Y33niDy5cvU6NGDaZOnUrz\n5s2dVp/81I7O3te2ojp//jxBQUFUrVoVgGnTpjFgwADuuOMOJ9dMiIrPmbOIU6/1ZF7EGg3841Yr\nJOynSZMmzJs3j9atW5OUlMTUqVP5/vvvnVKXrKws3nzzTZYtWyZDlTcpLCzMCK65ubn4+/vTpUsX\nwHpr4OWXX+by5cvOrKIQooDSBtjrRnWt9VWg+I64wqkCAgJ49dVXGThwILm5ubz77rssWbKkzDM/\nRUdHc+jQIbZs2cKaNWvK9L1dkZeXF5MnT8bb2xuwjlYkJiYSHBwMQE5ODlu3bpUvM0I4UWkD7N+V\nUv9SSg1XSl1rjNG5+fqETV5eXowaNYrnn3+eSpUqsWXLFiZPnkxMTEyZ1aFp06ZMmjQJDw8P/vOf\n/7B169Yye2930KRJE958801jxnh4eDg7duwwjrOyssjOznZmFYVwO6UNsDFAFWAG8LtS6pJSap1S\napJSqptSKn8NgXxdLsf69evHO++8Q926dTl37hwTJkxg586dZfb+3bp145lnngHg/fffJzw8vMze\n29UppQgMDDSOq1WrxiOPPGIcb926lWXLljmjakK4rdIG2DVa679prRsADYHxQCwwDNgNpObNNB7g\niEoK+2ncuDHz58+nV69eZGRkMG/evDIdMh44cCBDhw7FYrGwZcsWGcJ0kDZt2tC+fXvj+OzZs/Ts\n2dM4Xrt2LREREc6omhBu45ZzEedtZ9cd6AUM11o7JxFuKbnTLOJr0VqzZcsWli9fTk5ODg0aNGDC\nhAk0atSozN67b9++sj63DOVvo2exWBg5ciQzZsygQYMGAOzdu5fbb7+datWqObmWQjiH05bplPrF\nlFqqtR5ttxd0AAmwhZ09e5Y5c+YQGxuLl5cXTzzxBH/5y19kKY0L01pz9uxZmjRpAljzIQ8fPpwP\nPvjAyJOcmJhIUFCQM6spRJkqt8n+C1hk59cTDta4cWMWLlzIgAEDyM3N5d///jfTpk3j0qVLzq6a\ncBCllBFcwbop/NChQ43geunSJcaOHWusWxZC3By79mArAunBliwiIoJFixaRkpKCj48P//znP+nT\np0+Z5TLOysrCbDbj6+t73bL7du1i44IFeKakYA4IYND48XS7884yqKXrCw8P57fffmPEiBGANUXj\n5s2bmThxopNrJoTjlPsh4luhlGoJLAa6ASnAR8DM0kbDvGxS4Vj3pB2ktd5UQjkJsNdw5coVFi9e\nbOzG07VrV0aNGuXw4UKTycTrr7+OxWJh5syZ19wkYOHEiQStWMFjqalUAnKA1f7+JI4Ywbh58xxa\nT3e0YsUKqlatymOPPQbAL7/8QmZmJj169HByzYSwn4owRHxTlFIBwHdALjAEmIV1c4FZN/AyTwP1\nkKVCt6R69epMnTqVsWPH4uPjw/79+3n++ecdnrTg6tWrxMTEcPToUd566y1ycnJsltu3axdBK1Yw\nLC+4AlQChqWmErRiBft//NFhdXRXTzzxhJH/GGDz5s1kZGQYx7/++itxcXHOqJoQ5Vq5CLBY0yxW\nAR7UWn+vtf4Qa3CdoJTyu97FeQH6NeAlSpF1SlybUoq+ffuyZMkSOnfuTHp6OosXL2batGnExsY6\n5D1r1arF7NmzqV69OgcOHGD+/Pk27wFuXLCAx1JtZ+58LDWVDfPnO6R+7qxy5cqFhu379+9P165d\njeOVK1eSkJBgHJ8/f77EL0hCuJPyEmAHAFu01ukFzq0GfIG7SnH9a8CPwA8OqJvbCg4OZtq0aUya\nNInq1atz+PBhxowZw7p16xwyASYsLIxZs2bh6+vLnj17WLJkCRaLpVAZz5QUSlrYUwnwLCH4Cvvp\n1KkTfn7W771ms5m2bdvSsmVL4/jll18mKSnJKC8ZpIS7Ki8BtgVwvOAJrXU0YMp7rkRKqbZYNxmY\n5KjKuTOlFHfeeSfvv/8+vXv3Jjs7m5UrVzJx4kSOHz9+/Re4QU2aNGH69Ol4e3tz9erVYoHcHBBA\nSX2jHMBciq3b7ElrzZRZU9w2YYanpyfDhw83lnUlJSXRpk0bateuDViH/ocPHy49WuGWykuADcQ6\nsamo5LznrmUR8J7WOtLutRKG6tWrM2HCBGbMmEHNmjU5e/YsL7zwAu+99x6pdu41tmrVijlz5vDi\niy8WS0QxaPx4VpcQRFf7+zN4wgS71uV61m5Yy/s/vM9XG78q0/ctr2rWrMkLL7xgHJ84cYJWrVoZ\n/x0jIyOZPXu2s6onRJkqLwH2piilHgNuwzpELMpAx44dWbJkCY888gheXl5s27aN0aNHs3nzZrsO\nGzdu3Nhmsotud95J4ogRrPL3N3qyOcCqvFnEXXv1slsdrkdrzdxVc0nrk8Y7n7zjtr3Ya+nYsSMv\nvviicfzLL78YvVuAgwcPyu5KwmV5Xb9ImUgGbHVLAilhC7y8DQbmAG8DXnkpG/Nfo6pSyi9vC71i\nZs6cafzeu3dvevfufdMVd0dVqlRh2LBh9OnThw8//JBDhw6xdOlStm3bxj//+U+Hb+o+bt489t9/\nP7Pmz8czNRVzXs+1LIMrWHuvh6sdBgWH/Q7z1caveGjwQ2Vah4qg4BelIUOGkJmZaRzv3buXOnX+\nyK66Z88eKlWqZOxzK4Sj7Nixgx07djj0PcrFOlil1E7ggtb68QLnQoHzwGCt9Tc2rvHHGnw1hWcO\n5x+f1lrfZuM6WQdrR1prfvrpJ1asWGHMJL3rrrt48sknqVmzpl3fy2QysX37dgYOHFhmyS9KorWm\n+9Du7G+93/qvTUPXo13Z+/lep9etIomLi8PHxwf/vGH/GTNm0L9/f2ON7fbt22nUqBENGzZ0Yi2F\nO3DZdbDAZqC/UqpqgXOPYZ3kVNJ+aleB3kCfvJ+9865RwBTg8RKuE3aklKJnz54sWbKEhx56CC8v\nL3bu3Mno0aNZtWoVJpPJLu+jteb111/ngw8+YOXKlU4fji3YewUK9WJF6YWEhBjBFaw93Hbt2hnH\nn3/+eaFbD9999x3JyTYHtYQod8pLgF2GdbP2dUqpvkqpZ7DuPTuv4DCvUuq0Umo5gNbarLXeVfAB\n7M8rekRrLZuNliEfHx/+/ve/8/7779OrVy+ys7P54osvGDVqFFu2bLnl+7NKKQYPHoyXlxfr1693\napDNv/dqCiv85cHUwCT3Ym9Rx44djTW3FouF++67z9jhKTc3lxUrVhQacv7uu+/KbKtFIW5UuQiw\nWusUoC/W+nxNXnAFZhYp6sH16yx/3ZwoJCSEyZMnM2fOHJo3b05KSgpLlixh3LhxhIeH31Lw6dat\nGy+88ILTg2yx3ms+6cXalYeHB4MGDcLDw/q/fFZWFo899pixKUFSUhL/+te/8PKyTiUxm83s2LFD\nvuCIcqNc3IMtS3IPtuxordm9ezcff/wx8fHxALRs2ZJhw4Zx++233/Tr7tu3jzlz5pCbm8v48ePp\n06ePvapcKuOnj+eXqF9s3mvVWtOhQQcWzF5QpnVyR3FxcURERDBo0CAAjh8/ztKlS3n33XcBa17t\nEydO0LlzZ2dWU1QQLp3sv6xIgC172dnZbNq0iS+//JIrV64A0L59e4YNG0bTpk1v6jXDw8P58ccf\nGTt2rOxdKwDrmtuoqCj69esHwM6dO9m9ezcvv/wyYA3ICQkJt/TlTrguCbB2IAHWeUwmE19//TXr\n1q0zksX36NGDxx9/nPr16zu5dsLVREREkJubS7du3QDrhKkrV64wcuRIAM6ePYvWutDeuMJ9SYC1\nAwmwznflyhXWrl3LN998Q3Z2NkopevXqxdChQwkLC3N29YSL+uGHHwgJCaFVq1YALF68mIYNGxpD\nzAcPHiQwMFCWBLkpCbB2IAG2/EhMTGTNmjV899135ObmAtYe7aOPPmrMHL1RV65cwcvLq1Sbtgv3\ntn79ejp16kRoaCgA06dPZ9CgQUaSi+3bt9O8eXPq1q3rzGqKMiIB1g4kwJY/ly9fZu3atWzdutUI\ntF27duXRRx+9oXu0JpOJadOmobVm+vTpBAZeL421EH/48ssvGTBggLFT0D//+U+mTp1q9GjXrl1L\n7969CQoKcmIthaO4cqIJ4cZq1qzJqFGjWL58OUOGDMHb25v9+/czYcIEZs2axZEjR0q19OLq1atc\nvXqVM2fO8OKLL3Lx4sUyqL1wFQ8//LARXC0WC/369TNuWeTk5PD5559TpUoVo/zixYvtlkhFuCbp\nwYpyJzk5mfXr17N582Yjb22zZs148MEH6dat2zVnDScnJzN79mzOnDmDv78/M2fOlEks4pZlZmay\nZ88e+vbtC1hvb4wZM4ZPP/0UpRTZ2dnMmjWL2bNny6z2CkqGiO1AAmzFceXKFTZt2sTGjRuN5T0h\nISHcf//99O3bl8qVK9u8zmQy8eabb/Lrr7/i4+PD/PnzqVevXllWXbg4k8nEqVOn+NOf/gTAsWPH\n+Oijj5g/fz5gXRI0d+5c5s6dC1iTYGitjaQYovyRAGsHEmArnqysLL7//nvWr19PXFwcYN2f9r77\n7mPAgAE277Xm5OSwcOFCvLy8GDt2rJENSAhHyMzMJCEhwZgwtXPnTvbu3cuUKVMA+O2331i9ejVv\nvPEGAOnp6WRlZVGjRg2n1VkUJgHWDiTAVlxms5m9e/fy1Vdfcfr0aQC8vLzo1asXgwcPLjYhymKx\nYLFYpNcgypzZbMZkMlGtWjUANm7cSHx8PMOHDwdg27Zt/Pbbb0ycOBGA2NhYMjIy5HaGE0mAtQMJ\nsBWf1pojR47w9ddf8/PPPxsToFq0aMHgwYPp3r27BFVR7pjNZuP+7P/+9z98fHyMrFOfffYZFouF\nYcOGAXDo0CGUUsYQtHA8CbB2IAHWtcTFxfHNN9/w3XffkZ6eDkBQUBD33nsvf/7zn20OHyckJODr\n6ytrZUW58e233xIaGmqkcVywYAGtWrWif//+gLUHXKdOHTp27OjMaro0CbB2IAHWNWVkZLB9+3Y2\nbtzIhQsXAPD09KRr164MGDCAtm3b4uHhwdWrV3nxxRfx8vJi+vTpsqZRlEu7d++mWbNm1K5dG4CX\nXnqJRx55hPbt2wPWAHzXXXfRoUMHwDqr2d/fX0ZuboEEWDuQAOvatNYcOnSITZs2ER4ejsViAayz\nj/v370/btm2ZN28esbGxBAcH88orr9x01ighysqxY8do2LChMeryzDPPMH36dGNS1cSJExk5ciQt\nW7YErJthtGjRwrgHLK5PAqwdSIB1H4mJiWzbto2tW7eSkJAAWCdFdejQgUuXLhEVFUWVKlWYMGGC\nkRBeiIrgypUr+Pn54eHhgdaacePG8dZbb+Hj44PWmieffJIFCxYQHBwMwMcff8yDDz4oAfcaJMDa\ngQRY92M2m/nll1/YsmULERERRq/Wx8fH2NVn/vz5N711nhDlSXZ2Nh999BGjR49GKUVmZibDhg3j\nv//9L15eXlgsFiZOnMjbb7+Nt7c3YE1XGhwcbHOPY3chAdYOJMC6t4SEBLZt28a2bduMXi1Au3bt\n6NOnDz169CgxgUVR+3btYuOCBXimpGAOCGDQ+PF0u/NOR1VdiJuSkZHBgQMHuOOOOwCIiYlhxowZ\nfPTRRwCkpqYyatQo/vOf/xhZqX788Ucja5W7kABrBxJgBfzRq/3+++/Zv38/ZrMZsPZqe/ToQd++\nfWnVqlWJCSoWTpxI0IoVPJaaSiUgB1jt70/iiBGMmzev7D6IEDcoNzeXhIQEQkJCADhz5gxr1qzh\npZdeMo4XLlzIe++9B/yxGceoUaMA6/pypZTL9XYlwNqBBFhRVFpaGj/++CM//PADJ0+eNM7Xrl2b\nPn360KdPH+rUqWOc37drF6eGDGFYamqx11rl789tGzbQtVevMqm7EPYWFRXF0aNHGThwIAD79u1j\n69atTJ8+HYAjR47w+eefM3v2bMCa/zshIYFmzZo5rc72IAHWDiTAimu5cOECP/zwA9999x0pKSnG\n+ebNm9OrVy969uzJgpEjmbF+PZVsXJ8DzLr/fl5bt67M6iyEIyUmJpKSkmJkmdq8eTORkZE8++yz\ngHXf3IiICCZPngzA77//TlRUFAMGDACsM/srQm/XEQFWFk0JUUBoaCh//etf2bNnDykpKdSqVYuU\nlBROnDjBiRMnWLFiBdUPHbIZXAEqAZ42erZCVFRBQUGF1osPGDDA2LcZwNvb21iPC3D48GEj6QvA\npk2buHTpkpEmMjY2Fq21W2zAIRnQhSiiUqVKDBs2jCpVqhAfH0/t2rUZMWKEsVXexexsckq4Ngcw\n+/uXZXWFKFNKKSpV+uMrZs+ePQtNiOrYsSN9+vQxjqOjo6lVq5ZxvG3bNnbv3m0c79q1i/DwcOM4\nJ6ek/7sqHgmwQthwxx13MHfuXOrVq0d0dDT//e9/6du3L6tWreL+F15gZQkzjVf5+XHP6NFlXFsh\nyo8mTZrQsGFD4/jpp582Uj6CdSes/IQYYE2Kkb8dJcDy5cvZvHmzcXzkyBFiY2MdW2kHkQArRAnC\nwsKYN28ePXr0wGQyYTKZqFq1Ks+PH4/puef42N/f6MnmAEu9vPi4Vi0WLVvGzJkz2bJlC8nJyc78\nCEI4naenZ6Ee7wMPPEDbtm2N4379+hXa1CA+Pr7QpMJ169Zx/vx543jVqlUcPXrUOE5MTCy3vV6Z\n5CTEdWit+fXXX2nXrl2h8/t//JEN8+fjmZqKyceHOr16kZiWxtGjR41kFkopWrZsSffu3enevXuh\noTIhRHFaa7TWxhK51atX06dPHyMv87hx43juueeMWctTpkzhr3/9qxGkN27cSOfOnY3ypZ1kJbOI\n7UACrHC01NRU9u/fz759+zh06FChCSFNmjShW7dudOvWjbCwsAoxu1KI8uT06dOEhYUZWaimTJnC\nCy+8YGxe//TTTzNz5kxjEtW4ceOYMGECYWFhAPz000/86U9/omrVqoVeVwKsHUiAFfZ07NgxmjZt\navzPXpTJZCIiIoK9e/dy4MABMjMzjedq1apFp06d6Ny5M23atCnxNYQQpbdhwwbuvfdevLy8uqDm\nMgAAH4hJREFU0Frz6KOP8vHHH+Pj4wPA448/zpIlSwgICABg+vTpTJw4kYCAAAmwt0oCrLCXyMhI\nJk2aRP369Zk8efJ1lx1kZWXx66+/snfvXiIiIkgtsJyncuXKtGvXjs6dO9OxY0fZRk8IO8nOzja+\nvObk5LB8+XIjT3NWVhaPP/44a9asyQ/IEmBvhQRYYS9nzpzh7bffJi4ujsqVKzNixAj69+9fqmFf\ni8XCqVOnCA8PJzw8nMjIyELPN2nShM6dO9O5c2eaNGlSYspGIcTNM5vNREdH07BhQxkitgcJsMKe\n0tPTWbZsGTt37gSgc+fO/N///Z8x/FRaCQkJREREEBERwaFDh8jOzjaeq169Ou3ataN9+/a0a9dO\nerdCOIAEWDuQACscYefOnSxbtgyz2cy7775baJnBjcrKyuLw4cOEh4cTERHB5cuXCz3foEEDOnTo\nQLt27WjdurXcuxXCDiTA2oEEWOEoly9fJioqik6dOtntNbXWxMTEcPDgQQ4ePMjhw4fJysoynvf2\n9ub222+nffv2tG/fnvr168vMZCFuggRYO5AAKyqynJwcjh07xqFDh/jll1+K3bsNDAykbdu2tGnT\nhrZt21K7dm0JuEKUggRYO5AAK8qa1prNmzdz9913U6VKFbu+dnJyMocOHTIeRTNH1apVywi2bdu2\nlfu3QpRAAqwdSIAVZW3btm289957hISEMGbMGG6//XaHvI/WmgsXLvDbb7/x22+/cfjwYa5evVqo\nTL169YyA27p1awIDAx1SFyEqGgmwdiABVpS1M2fOsHDhQqKiogAYPHiwsVuPI1ksFiIjI41ge/To\nUTIyMgqVqVevHq1ataJ169a0atVKhpSF25IAawcSYIUz5OTk8MUXX/DFF19gNpupXbs2s2fPvqXZ\nxjcqNzeX06dPGwH3+PHjhSZMgXXvz/xge/vttxMaGiprcIVbkABrBxJghTOdOXOGRYsWobVm/vz5\neHl5Oa0uubm5nD17lqNHj3L06FF+//130tLSCpWpVq0aLVu2pHXr1jRv3vyaaSGFqMgkwNqBBFjh\nbLm5uaSkpBAcHOzsqhRisViIjo7m6NGjHDt2jKNHj5KYmFiojJeXF40bN6Z58+a0aNGC5s2bU7Nm\nTRlWFhWeBFg7kAAryrOCeVOdTWvNpUuXOHr0KMePH+f48eOcP3+eov//1KhRo1DAlV6uqIgkwNqB\nBFhRXl29epUxY8Zw11138eijjzp8EtTNMJlMnDx5kuPHj3PixAlOnDhRbKZy0V5us2bNZPKUKPck\nwNqBBFhRXm3fvp0FCxYAEBwczPDhw+nZs+cNB6Z9u3axccECPFNSMAcEMGj8eLrdeacjqozFYiE2\nNtbo4Z44ccJmL7datWo0bdqUpk2b0qxZM5o2bUpQUJAEXVFuuHSAVUq1BBYD3YAU4CNg5rWioVKq\nE/A8cCcQApwH/gO8rbXOKuEaCbCi3Dpx4gQffPABp0+fBqBt27aMGjWK0NDQUl2/cOJEglas4NHU\nVKZXgdmZsMbfn8QRIxg3b54jq25IT0/n5MmTnDhxgpMnT3Lq1KlCW/PlCwwMLBRwmzZtesObJAhh\nLy4bYJVSAcBR4AgwB2gCzAfma62nX+O6d4DOwCfAaaAt8BqwTWv9SAnXSIAV5ZrZbGbbtm2sWrWK\ntLQ03n77bVq2bHnd6/bt2sWpIUMYlprKl5Vg+O2w8gg8lAOr/P25bcMGuvbqVQafoDCtNQkJCZw6\ndYpTp05x+vRpTp8+TXp6erGyNWvWNIJukyZNaNy4Mf7+/mVeZ+F+XDnATgUmAWFa6/S8c5OBGUCI\n1vpqCdfV0FonFTn3NLAMaKi1jrZxjQRYUSFcuXKFiIgI7r777lKVn/bAA8xYvx4voHso7B8BXVfA\n3guQC8y6/35eW7fOoXUuLa01Fy9eNALuqVOnOHv2LJmZmcXKBgUF0ahRIxo3bmw85J6usDdHBFjn\nLcIrbACwJT+45lkNvA3cBXxj66KiwTXPwbyfdYFiAVaIiqJ69eolBtecnBy8vLwKBRnPlBQqAV9W\ngsM9AGX9+dU6ay/W08YwrbMopahbty5169blrrvuAqw995iYGKOne/bsWc6dO0diYiKJiYlEREQY\n1/v6+hYLuqGhoVSqVMlZH0mIYspLgG0BfF/whNY6WillynvOZoAtQQ/AApyxX/WEKF+WL19OXFwc\nw4cPp2HDhgCYAwLIBubWBlPeiLKpJbzzEwy+AOZyPtTq6elJWFgYYWFh9O3bF7AG3bi4OCIjIzlz\n5gyRkZGcPXuWlJQUI0FGPi8vL8LCwmjUqBGNGjWiQYMGNGzYUIaYhdOUlyHibGCS1npRkfPRwMda\n62mlfJ0Q4Fdgo9Z6RAllZIhYVGgmk4mnn36atLQ0PDw8uOeee3jsscc4fewYnw25l3/1N2Fq9Ud5\n32MwYosvj2/41in3YB0hOTmZs2fPGo/IyEhiY2Ntlg0ICKBBgwbGo2HDhtSvX79cLoMSzuPK92Bv\nOcAqpSph7QXXATpprW2Oh0mAFa7gypUrrFmzhk2bNmE2m/H29mbQoEHMWDKV8/fFQcE/ExoabAoh\ncn+sS9+3NJlMnDt3jrNnzxIVFWU8im5wANY/prVr16Zhw4aFAm+dOnXw9PR0Qu2Fs7lygL0ELNZa\nv1rk/FVghtb6uusLlFKrgb5AD631qWuUkwArXMaFCxf47LPP2LNnDzXr1WR11mpMDUzFyvme8+WT\nBz/hocEPOaGWzqO1Jj4+vlDAjYqK4sKFC5jN5mLlK1WqRGhoKGFhYYSGhlK/fn3CwsIICQlxat5o\n4XiuHGB3Ahe01o8XOBeKdV3rYK31Ne/BKqXeBUYC92it916nrJ4xY4Zx3Lt3b3r37n0LtRfC+U6f\nPs3cD+bye/zvNnupWms6NOjAgtkLnFC78icnJ4eYmBiioqI4d+4cUVFRnD9/nvj4eJvlPT09qVu3\nLvXr16d+/fpGEK5bty6VK1cu49oLe9ixYwc7duwwjmfNmuWyAXYK1mU6DQos05kEzOQay3Tyyk0F\nZgOPaK3Xl+K9pAcr3MrPP/9M+/btZYZtKZhMJs6fP090dDQXLlwgOjr6moE3f6g5P/DmB9969erh\n5+dXxrUXt8KVe7D5iSaOYl2a0wSYhzXRxIwC5U4D27XWT+cd/w34FFgJfFjkZc9orRNsvJcEWOE2\nTp06xcSJEwkKCuLhhx/mz3/+syTivwmZmZnExMQQHR1d6HHx4kUsFovNa/z9/Y2lSKGhodStW5d6\n9eoREhIi/w3KIZcNsABKqRZYUyV2x5oqcTkwq2A0VEqdxRpgR+QdrwSeLOEln9Jaf2LjfSTACrdx\n7Ngxli5dSlRUFGDd+ebhhx+mX79+8kfeDnJycrh48WKhoBsTE0NsbKzNpBlg/UNeq1YtI+Dm/6xX\nrx7BwcGywb2TuHSALSsSYIW7sVgs7Nu3jzVr1hAZGQnAE088wdChQ51cM9eltSYpKckItjExMcbv\ncXFxJfZ6vb29qVOnjhF469SpQ506dQgJCaFGjRoSfB1IAqwdSIAV7spisfDzzz/zv//9j5deeolq\n1ao5u0puKTc3l7i4uGKBNzY2lqQkW8nprLy9valdu7YRcENCQozfa9WqJffYb5EEWDuQACuEbflJ\n+WvWrOnsqrgtk8lUKPDGxcURFxfHxYsXbe5IlM/Dw4Pg4OBigTf/d19f3zL8FBWTBFg7kAArhG0R\nERG89tpr9OrVi/vvv58mTZo4u0qiAJPJVCjgFvw9ISGhxGFnsOa1DgkJoXbt2tSqVcv4mf+Q+/ES\nYO1CAqwQtq1du5ZVq1YZf6hbt27NkCFD6NKli2Q3KudycnKIj4+3GXwvXbpEdnb2Na+vUaNGoYCb\nH4Rr165NzZo13WL4WQKsHUiAFaJk8fHxbNiwgW3btmEyWTNCjR8/nj59+ji5ZuJmWSwWkpOTuXjx\nIpcvX+bSpUtcunSJ+Ph44uPjuXz58jV7v/BHAM4PurVq1aJmzZrGwxWSbUiAtQMJsEJcn8lk4ocf\nfmDnzp28/vrrdh9C3LdrFxsXLMAzJQVzQACDxo+n25132vU9ROmYzWaSkpKKBd74+HguXbp03eFn\nsA5BBwcHU7NmTeNnwd8DAwPL/SiIBFg7kAArxK3LysriwIEDdOnS5YZz9C6cOJGgFSt4LDWVSkAO\nsNrfn8QRIxg377ppx0UZy83NJTExsVDQzQ/ECQkJJCYmkpube83X8PDwICgoqFjgLfh71apVnboZ\nhQRYO5AAK8St++6771i0aBE1atSgX79+9OvXj+Dg4Otet2/XLk4NGcIwGzNiV/n7c9uGDS6zpZ67\nsFgspKSkkJCQwOXLl7l8+bLxe/7PlJSU676Oj48PNWrUICgoiKCgoEK/5z8CAgIc1hOWAGsHEmCF\nuHV79uzhs88+48KFC4C1h9KlSxeGDh1K06ZNS7xu2gMPMGP9emxNmckBZt1/P6+tW+eYSgunycnJ\nKRZ0i/60ta1gUR4eHgQGBtoMwAWPfXx8briOjgiwsv+SEOKG9ezZkx49enDkyBE2bdrEvn372Ldv\nH/3797/mdZ4pKTaDK0AlwPMaaz1FxVWpUiUjK5UtWmvS09NJSkoiMTGxxEdqaqrx+7X4+vraDMKB\ngYEEBgZSo0YNAgMDHT47WgKsEOKmKKVo06YNbdq0ISkpiV27dtGuXTubZXNzc/Hy8sIcEEAOlNiD\nNfv7O7LKopxSSuHn54efnx9hYWEllsvJySE5ObnEAJwfoE0mEyaTiejo6Gu+r5+fnxFwHUGGiIUQ\nDpWamsqzzz5L9+7dqVGtGrljx/Kk3IMVDqK1Ji0tzQi4+ROxkpKSSE5ONh4pKSmYzWbjug0bNsgQ\nsRCiYvnll19IS0tj69atAGSHhpKdk8PfTaZis4gluIpbpZSievXqVK9enUaNGpVYzmKxkJaWZgTe\nDRs22L8u7tabkx6sEGUvOjqaHTt2sHPnTuLj47mSmEjdq1dpFhSE2d+fwRMmSHAVTiWziO1AAqwQ\nzmOxWPj999/ZsWMH99xzD82bNy9WJicnxy1S84nyRQKsHUiAFaJ8mzJlChaLxZipLLv7iLIgAdYO\nJMAKUX6lp6fz1FNPkZmZaZxr3rw5PXv25L777pOerXAYCbB2IAFWiPItIyODAwcOsHv3biIiIsjO\nziYoKIgVK1bg4eHh7OoJFyUB1g4kwApRcWRmZnLgwAEyMzPp27dvsecTEhKIioqiTZs2sqepuCUS\nYO1AAqwQrmPdunWsXLmSKlWq0K5dO7p06UKnTp0ICAiw6/vI7j+uTwKsHUiAFcJ1fP/993z99ddE\nRkYa55RSjB49mgEDBtjlPWT3H/cgAdYOJMAK4XouX75MeHg44eHh/Prrr7zxxhu0aNGiWLmrV6/i\n5+dX6teV3X/chwRYO5AAK4Rry8zMpFKlSja3NXv++efJzs6mXbt2tG/fnrZt21K1atUSX0t2/3Ef\nspuOEEJcR5UqVWyeT09PJzk5mbS0NL799lu+/fZbPDw8aNy4MW+++SaVK1cudo3s/iNuhQRYIYRb\nqFq1Kp988glnzpzh4MGDHDx4kJMnT5KRkWEzuJrNZrL8/GT3H3HTZIhYCOG2MjMzSUhIIDQ0tNhz\nx48fZ/SIETwWEcE/s7OLPS/3YF2LI4aIZdW2EMJtValSxWZwBbh48SKBtWvzn9BQlnp5kZN3Pgdr\ncJXdf8T1SA9WCCFKkJWVxYkTJ/j6yy85+PnnkJxM9QYNGDVvXrHgeurUKRITE2nevDmBgYFOqrG4\nWTKL2A4kwAohbpbZbCY3N9fmPdvFixcbe94GBwfTtGlTmjZtSs+ePalXr15ZV9UgSTJKR2YRCyGE\nE3l6etpc/gPQsGFD2rRpw+nTp0lISCAhIYF9+/ZRv359mwE2NzcXLy/H/gnOT5Ixo2CSjO3b2SdJ\nMsqE9GCFEMKOzGYzsbGxnD59mtOnT/Pggw8SFBRUrNyUKVNISkqiYcOGNGrUyPhZu3ZtlLr1jpQk\nybgxMkRsBxJghRDOZrFYGDZsGGlpacWeW7p0qV2GlCVJxo2RIWIhhHABHh4efPzxx8TExBAZGUlk\nZCTnzp3j4sWLhISEFCuvtWbs2LHUrFmT+vXrExYWRv369QkNDcXHx8fme0iSDOeTACuEEE7g5eVF\ngwYNaNCgAb17975m2cTERM6dO8e5c+cIDw83zleuXJk1a9bY3CfXHBAgSTKcTAKsEEKUc4GBgSxe\nvJjo6Gjjcf78eapUqWIzuMbHx3Pw6lVWenvzjI0kGav9/Rk8YUJZVN2uKtqMaAmwQghRznl6ehIW\nFkZYWFih8yXNJ4mJicHTx4fPQkMxnz/PyNzcYlvtFZzglJ6eTlxcHCEhIdfc/MCZKuKMaJnkJIQQ\nLkZrTVJSErGxsfzw7beE//e/eKakYAkI4Pl33y02ezg8PJxXX30VAD8/P0JCQqhVqxZ/+tOfuPfe\ne53xEQopixnRMovYDiTACiFEYT///DOffPIJcXFxZBcYUu7Tpw/jx48vVv7IkSNs27aN4OBg41Gz\nZk1q1aqFr6+v3etXFjOiZRaxEEIIu+vSpQtdunRBa01KSgqXLl3i0qVLNtfvgjUt5Pbt24udHzBg\nAM8++2yx87GxsVy6dMkIxiXNfC5JRZ0RLQFWCCEEYO3FBQYGEhgYSIsWLUos17lzZ6pWrWpkrEpM\nTCQhIYG6devaLP/jjz/y2WefGcc+Pj7UqFGDwYMHM3DgwGLlr169Cli3GFRKVdgZ0RJghRBC3JDQ\n0NASdyGyJTg4mDZt2hgBOSMjg5iYmELD0QWtXbuWtWvX4u3tTUBAANlVqrCyShWeycwsVna1vz/3\njhmD1touGbDsSQKsEEIIh+rbty99+/YFrBOw0tPTSUpKolq1ajbLWywWfHx8yMjIID4+HoDP6tbF\n69IlhqWnF5sRbTp5krmLFuHv74+/vz/Vq1cnICCAfv360aZNm2Kvn5GRgZeXF5UqlTTwbB8SYIUQ\nQpQZpRR+fn74+fmVWOapp57iqaeeIiMjg+TkZJKSkkhOTuZKQgKzPvkEz9RUzHlrebv26sXChQsx\nm80kJSWRlJRkvE6HDh1svv6HH37I999/j4+Pz3XrcivKzSxipVRLYDHQDUgBPgJmXm/Kr1KqOvAu\n8BesG8hvBMZorZNKKC+ziIUQwsXk5OSQmppa6NGyZUubqSfnz5/Prl27sFgsxrkNGza45jIdpVQA\ncBQ4AswBmgDzgfla6+nXuXYL0BSYCOi86+O01neVUF4CrBBCuDmtNRkZGaSlpZGWlkazZs1cNsBO\nBSYBYVrr9Lxzk4EZQIjW+moJ13UH9gC9tNZ78s51BvYD92itf7BxjQRYIcoBrTVTZ0/lzelvlrvJ\nKcL9OGIdbPEkls4xANiSH1zzrAZ8AZs90QLXxeUHVwCtdTgQCTg//Ug5tWPHDmdXwemkDZzfBms3\nrOX9H97nq41fOa0Ozm6D8kDawHHKS4BtARwveEJrHQ2Y8p4r9XV5fr/OdW5N/oeSNgDntoHWmrmr\n5pLWJ413PnmnxJy6jib/DqQNHKm8BNhArBObikrOe87e1wkhnGjthrUcrnYYFBz2O+zUXqwQjlJe\nAqwQwk3k915NYSYATA1MTu3FCuEo5WWS0yVgsdb61SLnrwIztNY29yJSSq0BgrXWfYuc3whorfVg\nG9c4/wMLIYQod1w12f9xitwzVUqFYp3kZOsea8HrRto43wKwubWCvRtQCCGEsKW8DBFvBvorpQru\n9PsY1klOO69zXYhSqkf+CaVUJ6AxsMkRFRVCCCFKo7wMEecnmjgKvI010cQ8rIkmZhQodxrYrrV+\nusC5b7EmmpiMNdHEW1iX7vQusw8ghBBCFFEuerBa6xSgL9b6fI01wcQ8YGaRoh4Ur/NQrL3cFcC/\ngXDgQcfVVgghhLi+chFgAbTWx7XW92itq2qt62mti+Uh1lo31lqPKHLuitZ6hNa6htY6QGs9rGge\nYqVUS6XU90qpdKVUjFJqlnKR1DFKqSZKqQ+UUr8qpXKVUsWyV+WVe0kpdV4pZVJK7VRK/clGmQrZ\nTkqpoUqpjUqpWKVUmlIqQin1mI1yrtwGDyml9iilEpRSGUqp40qpl5VSlYqUc9k2KEopVVcpdVUp\nZVZK+RZ5ziXbQSn1d6WUpcjDrJR6pkg5l/z8+ZRSnkqpKUqpk0qpTKVUtFKq2GRZh7eD1tqlH0AA\nEANswdpLfga4Csx2dt3s9PmGAFHAGqxD7D/YKDMVSAdGA3cD3wCXgVqu0E7AT8CnwMNAb6z5qC3A\nc27UBs8As7FuenEX1lsmJmCRu7SBjTb5DxALmAFfd2gH4O95n/dOoEuBR7A7fP4C9f8UuIB1Emwv\n4G/Aa0XKOLwdnN4QZdDQU4FEoGqBc5PzGsnP2fWz82f9giIBFqiMNRnHywXO+QLxBf+RVOR2AmrY\nOPcZcMZd2qCEdnkNSHLHNsgLMAnABAoEWFdvB/4IsL4lPO/Snz+vngOALKD5NcqUSTuUmyFiB7rZ\nPMeuogdQDWvwBUBrbQI2UDhfc4VtJ217a8KDQN2833vi4m1QgiTAO+93t2kDpZQHsAiYhfWPY0Eu\n///DdbjD538Ka0fjxDXKlEk7uEOAvdk8x66iBdZvtKeKnC+ar9nV2qkHcDLv9+a4SRsopTyUUj5K\nqTuA/wOW5j3lNm2AdcjPG3jfxnPu8P+DAs4qpXLy7sUXvP/qDp+/K3BSKfWeUio1797pWqVUnQJl\nyqQdykuiCUdy93zFgcBVnTe2UUAy4KuU8tJa5+JC7aSU6ov1XuQ/8k65UxukYx3+Aus9yBfyfneL\nNlBKBWG9F/03rbXZxlwUV2+Hi8A04GfAE2s+gWVKKR+t9bu4/ucHCMHaiz2EdZVJdeAd4Cuge16Z\nMmkHdwiwwo0opRpivf+6Tmu9yrm1cYruWIevumBd7rYUGOXUGpWt14GftNZbnF0RZ9BabwW2Fji1\nRSnlA7wMvOucWpW5/G9VQ7R1CShKqThgp1Kqt9Z6R1lVxB0CbDLgb+N8YN5zri4Z8FOq2E7zgYAp\n71tafrkK3U5KqUCs2b0igScKPOU2baC1PpT3609KqUTg30qpObhBGyilWmHtufRSSuV/hvzscAFK\nKQtu0A42fAk8opRqgHt8/mSsExwL9jx3A9lAa2AHZdQO7nAP9mbzHLuK41iHipoWOV/03kKFbqe8\nb+nfYP2sg7TWmQWedos2sOEXrN/mG+IebdAMa6dhH9Y/fsnAYqxtEI114tPxvDKu3A5FFQwg7vDv\n4Hf+6MUWpPijLcqkHdwhwN5snmNX8ROQBjySfyJv0f1gCudrrrDtpJTyxPotvQkwQGtddOaoy7dB\nCe7A+gflLO7RBj8CfbCuhc5/vI21De7Feh/uJ+AKrt0ORT0CJGqto3CPfwcbgTZKqRoFzt2F9YuV\nMcJDWbSDs9csOfrBHwuFt/LHQuE0YJaz62anz+cDPIQ1ycJPwOG844eAKnllpmBdt/Usfyyojgdq\nukI7AR9iTSzxPNYZhAUfldykDTYDE7EuK/gz1iUqacBnBcq4dBuU0C7F1oW6cjtgXXYyEegP3Aes\nyvv8z7rD58+rezXgXN7fw0FYk0ycB74tUs7h7eD0xiijBm8BfId1hmUM1hzHytn1stNna5AXXMw2\nHmEFyk3N+0eWjvUeRFtXaSes91xtfX53aoNZwG9Ye2dJQETeHw7PIuVctg1KaBebiRdctR2wJhf5\nPS9wpGPNzf43G+Vc8vMXqHtjrD3ZNKxroVcA/mXdDuViNx0hhBDC1bjDPVghhBCizEmAFUIIIRxA\nAqwQQgjhABJghRBCCAeQACuEEEI4gARYIYQQwgEkwAohhBAOIAFWCCGEcAB32E1HCLeilGoKjAUq\nYc1kkwOswZoMP1lr/b0TqyeE25AAK4QLUUqNw5qH+h9a6zMFzs8BRgN1nFU3IdyNBFghXIRS6iVg\nKNBTa51e5OlVQG+t9dWyr5kQ7kkCrBAuQCnVDWvC/y42givARcr/NmNCuBRJ9i+EC1BKbQUsWusB\nJTzvDdTR1j1BhRBlQGYRC1HBKaUCse5n+UVJZbTW2TcaXJVS05VSH99q/YRwVzJELETF1wRQwMGS\nCiilqmmt027wdU8Al26lYkK4M+nBClHxZeb9vGLrSaWUF/DETbxuD2DPzVZKCHcnPVghKr7fsfY0\nWwCnbTz/DPCFUqozcC+QAERhnW38klKqL3AbkAvEaa035F3XWmt9RCl1G9AXyNBa/1sp9RbwgdY6\n0rEfS4iKTXqwQlRwWmszMBmYoZSqXvA5pdQo4LDW+jJQFUjCmmziG6CxUupOYLLWeinwEfBS3nU+\ngCnvZWoDcYBP3nEnCa5CXJ/0YIVwAVrrz5RSicC/lFIxWIeLs4FP84Oh1nqHUmo08H95lzUHHgU+\nzTsOBfKX+HQBwvOu+1Ep9RnwolLKF2t2KCHEdUiAFcJFaK2/Bb69TrH6Wuv4vGHf48BVIL83+iSw\nNO/3rsBepVTfvNSKIVrrC0qpPsBeB1RfCJcjAVYIN5GXo1grpYYAnYBRQDDwqFIqFOv917V5xU8D\nHflj6c8+pdRDwLPAtLKtuRAVkySaEMJNKKWewpqM4obWtuYF1qpYg+0XWutBjqifEK5GJjkJ4QaU\nUg2AkUD9m7j8Itah5Oex9nqFEKUgPVghhBDCAaQHK4QQQjiABFghhBDCASTACiGEEA4gAVYIIYRw\nAAmwQgghhANIgBVCCCEcQAKsEEII4QASYIUQQggHkAArhBBCOMD/A2FuSqn4KZsNAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d249ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Vcyl = lambda a, b: np.pi*b*(a/2.)**2\n",
    "def phi(a,b=10): return (3*Vcyl(a,b) - 8 * np.sqrt(2) * (a/2.)**3)/b**3\n",
    "def alpha(p, a):\n",
    "    return phi(a,b=1)*((a+p)/a)**2\n",
    "\n",
    "def Ogston(p, a):\n",
    "    return np.exp( -(a+p)/a * np.sqrt(phi(a,b=1)))\n",
    "\n",
    "def Johansson(p, a):\n",
    "    return np.exp(-0.84 * alpha( p, a)**1.09)\n",
    "\n",
    "def Tsai(p, a):\n",
    "    return (1 + 2/3 *alpha(p, a))**-1\n",
    "\n",
    "\n",
    "# For Alexa in Dextran values in nanometers\n",
    "m_a=162*1.66\n",
    "aexp=0.39\n",
    "pexp=1.9\n",
    "aop = aexp/pexp\n",
    "\n",
    "def Cpoly(a,aexp=0.4):\n",
    "    \"m_a is the monomer weight, a the monomer diameter in relative units\"\n",
    "    Vmonomer_exp = Vcyl(aexp,aexp)\n",
    "    # nmonomerPerCell = Vpolymer/Vmonomer with phi = Vpolymer/Vcell\n",
    "    return m_a * phi(a,b=1) / Vmonomer_exp\n",
    "atest=0.05\n",
    "\n",
    "\n",
    "\n",
    "def CpolyFrompNoHI(p_s):\n",
    "    return (p_s**3 *m_a / (aexp*(pexp+aexp)**3)) * (3 * (pexp+aexp)/p_s - 2 * aexp)\n",
    "print(CpolyFrompNoHI(0.9))#WORKS!\n",
    "\n",
    "#Extract simulation data \n",
    "idata=2\n",
    "ranRodData = u0data[idata]\n",
    "print(\"ranRod Trigger: \",triggerArr[idata])\n",
    "psRR = ranRodData[:,0]\n",
    "dd0sRR = ranRodData[:,1]/6.\n",
    "SPSData = u0data[0]\n",
    "psSPS = SPSData[:,0]\n",
    "dd0sSPS = SPSData[:,1]/6.\n",
    "\n",
    "avals = np.arange(0.0001,0.27,0.001)\n",
    "Cvals = Cpoly(avals)\n",
    "\n",
    "#Plot it all together\n",
    "pltPrintPar()\n",
    "plt.figure(figsize=(7,5))\n",
    "colors=cm.rainbow(np.linspace(0,1,5))\n",
    "lst=['-','--',':']\n",
    "functions=[Ogston,Johansson,Tsai]\n",
    "for i,f in enumerate(functions):\n",
    "    #plt.plot(phivals, f(avals/aop , avals),label=f.__name__)\n",
    "    dd0vals =  f(avals/aop , avals)\n",
    "    plt.plot(Cvals,dd0vals,label=f.__name__,linestyle=lst[i],c='0.3' )\n",
    "plt.plot(CpolyFrompNoHI(psRR),dd0sRR,'ro',label='ranRod fix #')\n",
    "plt.plot(CpolyFrompNoHI(psSPS),dd0sSPS,'g^',label='fixed distance')\n",
    "plt.xlabel(r'$C_{poly}$')\n",
    "#plt.xlabel(r'$\\phi$')\n",
    "plt.ylabel(r'$D/D_0$')\n",
    "plt.xlim([0,600])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.87505777  2.15750749]\n"
     ]
    },
    {
     "data": {
      "image/png": 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/fixZsoQlS5bQqlWrXPXq1KnDkiVLEBGCgoKy6y1evNhNkStvoZd/3cQYQ2al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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bc380f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sexp(x,a,n):\n",
    "    return np.exp( - a * x ** n )\n",
    "\n",
    "fit, pcov =curve_fit(sexp, psRR, dd0sRR)\n",
    "print(fit)\n",
    "xs = np.arange(0,2,0.005)\n",
    "ysI = sexp(xs, *fit)\n",
    "pltPrintPar()\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(psRR,dd0sRR,'ro',label='ran Rod fix #')\n",
    "plt.plot(psSPS,dd0sSPS,'g^',label='fixed distance')\n",
    "plt.plot(xs,ysI,'k--',label='fit')\n",
    "plt.xlabel(r'$p/b$')\n",
    "plt.ylabel(r'$D/D_0$')\n",
    "plt.ylim([0,1.3])\n",
    "plt.legend()\n",
    "plt.savefig('DoverpRanRodFit.png')\n",
    "plt.show()\n",
    "pltDefaultPar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Same plot for random number of rods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.57204531  1.79292655]\n"
     ]
    },
    {
     "data": {
      "image/png": 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CnzP7/MTJ9ZO1WlXljqN5fh1tmz9/Pvfeey+vvfYajzzyiD5ao4pMZ1Rys7nj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\nsZCWpKoyh8lQ636Icjfhw+R3Mc1NKlMsJJdioUZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c5efd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extract simulation data \n",
    "idata=1\n",
    "ranRodData = u0data[idata]\n",
    "print(\"ranRod Trigger: \",triggerArr[idata])\n",
    "psRR = ranRodData[:,0]\n",
    "dd0sRR = ranRodData[:,1]/6.\n",
    "def sexp(x,a,n):\n",
    "    return np.exp( - a * x ** n )\n",
    "\n",
    "fit, pcov =curve_fit(sexp, psRR, dd0sRR)\n",
    "print(fit)\n",
    "xs = np.arange(0,2,0.005)\n",
    "ysI = sexp(xs, *fit)\n",
    "pltPrintPar()\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(psRR,dd0sRR,'ro',label='ran Rod #')\n",
    "plt.plot(psSPS,dd0sSPS,'g^',label='fixed distance')\n",
    "plt.plot(xs,ysI,'k--',label='fit')\n",
    "plt.xlabel(r'$p/b$')\n",
    "plt.ylabel(r'$D/D_0$')\n",
    "plt.ylim([0,1.3])\n",
    "plt.legend()\n",
    "plt.savefig('DoverpRanRodFit.png')\n",
    "plt.show()\n",
    "pltDefaultPar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare to analytic theories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: divide by zero encountered in true_divide\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: invalid value encountered in multiply\n"
     ]
    },
    {
     "data": {
      "image/png": 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/B/nh7+9frM+LwrJly6RGo5HLly83ebx+/frSw8PjseWkpKQYpV2/fl1qtVrZ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\njIuzmZlZw/wXgSz+gB0FRWMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bdf98d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extract simulation data \n",
    "ranRodDataFix = u0data[2]\n",
    "psRRFix = ranRodDataFix[:,0]\n",
    "dd0sRRFix = ranRodDataFix[:,1]/6.\n",
    "ranRodDataRan = u0data[1]\n",
    "psRRRan = ranRodDataRan[:,0]\n",
    "dd0sRRRan = ranRodDataRan[:,1]/6.\n",
    "psSPS = SPSData[:,0]\n",
    "dd0sSPS = SPSData[:,1]/6.\n",
    "\n",
    "\n",
    "#Plot it all together\n",
    "pltPrintPar()\n",
    "plt.figure(figsize=(7,5))\n",
    "colors=cm.rainbow(np.linspace(0,1,5))\n",
    "lst=['-','--',':']\n",
    "functions=[Ogston,Johansson,Tsai]\n",
    "for i,f in enumerate(functions):\n",
    "    #plt.plot(phivals, f(avals/aop , avals),label=f.__name__)\n",
    "    dd0vals =  f(avals/aop , avals)\n",
    "    plt.plot(Cvals,dd0vals,label=f.__name__,linestyle=lst[i],c='0.3' )\n",
    "plt.plot(CpolyFrompNoHI(psRRFix),dd0sRRFix,'ro',label='ranRod fix #')\n",
    "plt.plot(CpolyFrompNoHI(psRRRan),dd0sRRRan,'bo',label='ranRod random #')\n",
    "plt.plot(CpolyFrompNoHI(psSPS),dd0sSPS,'g^',label='fixed distance')\n",
    "plt.xlabel(r'$C_{poly}$')\n",
    "plt.xlim([0,760])\n",
    "plt.ylabel(r'$D/D_0$')\n",
    "plt.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare D over U_0 for ranRod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XRNRDc/DWMR6ifklAIFzKmgfflPTaj3gGAsOdv+ZG9pOHlY60pGv5RwUAP/5o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\nefANCbMuYqIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b59d278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trigger ='ranRod'\n",
    "triggerNameArr = []\n",
    "p=0\n",
    "ks=['0.500','1.000','2.000']\n",
    "a=0# This if fixed here, for no HI\n",
    "datadir= root + \"/Documents/workspace-cpp/SingleParticleSimulation/Release/sim_data/noreset/\"\n",
    "dataArr=[]\n",
    "for k in ks:\n",
    "    MSD='MSD'\n",
    "    triggerNameArr.append(trigger.replace('/',''))\n",
    "    folder=datadir +trigger+'/dt0.0005/t200/d0/b10/p'+str(p)+'/k'+str(k)+'/'\n",
    "    data = []\n",
    "    for udir in os.listdir(folder):\n",
    "        file=os.path.join(folder, udir,'InstantValues/linear_fit_parametersMSD.txt')\n",
    "        if os.path.isfile(file):\n",
    "            u=file.split('/u')[1].split('/I')[0] # assign value from dir to \n",
    "            with open(file, 'r') as fitf:\n",
    "                m = (fitf.readline().split('m')[1]).split()[0]\n",
    "            data.append([float(u),float(m)]) #the [:-1] serves to cut the linebreak '\\n' from the string m.\n",
    "    dataArr.append(np.array(sorted(data)))\n",
    "ksdata = (dataArr)\n",
    "plotdataArr(ksdata,r'U_0/k_B T',lineArr=ks,lineParam='$k$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get noRan Data and compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p=0\n",
    "ks=['0.500','1.000','2.000']\n",
    "a=0# This if fixed here, for no HI\n",
    "datadir= root + \"/Documents/workspace-cpp/SingleParticleSimulation/sim_dataOLD/noreset/\"\n",
    "dataArr=[]\n",
    "for k in ks:\n",
    "    MSD='MSD'\n",
    "    triggerNameArr.append(trigger.replace('/',''))\n",
    "    folder=datadir +'/dt0.0001/t200/d0/b10/p'+str(p)+'/k'+str(k)+'/'\n",
    "    data = []\n",
    "    for udir in os.listdir(folder):\n",
    "        file=os.path.join(folder, udir,'InstantValues/linear_fit_parameters.txt')\n",
    "        if os.path.isfile(file):\n",
    "            u=file.split('/u')[1].split('/I')[0] # assign value from dir to \n",
    "            with open(file, 'r') as fitf:\n",
    "                m = (fitf.readline().split('m')[1]).split()[0]\n",
    "            data.append([float(u),float(m)]) #the [:-1] serves to cut the linebreak '\\n' from the string m.\n",
    "    dataArr.append(np.array(sorted(data)))\n",
    "ksdataOld = (dataArr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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SnkgyEJYkSZIkSZKeSDIQliRJkiRJkp5IMhCWJEmSJEmSnkgyEJYkSZIkSZKeSDIQliRJ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U2QdwnCb4HqZdhmMH812Oe0/h+rI63TmjVrZtt9Wkj+KoDUB5e9tm1hyBBYulRvp/bpp+6OqFhG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\ngYH6protW/RWa6+8ovcjPnIEX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b32de80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotdataArr(ksdata,,lineArr=ks,lineParam='$k$')\n",
    "\n",
    "combinaksData =ksdata + ksdataOld\n",
    "print(len(ksComb), len(combinaksData))\n",
    "pltPrintPar()\n",
    "colors=cm.rainbow(np.linspace(0,1,len(ksdata)))\n",
    "plt.figure(figsize=(7,5))\n",
    "lslist= ['*-','+--','x-.']\n",
    "for i, line in enumerate(ksdataOld):\n",
    "    ti = 'fixed dist $k = ' + str(ks[i]) +'$'\n",
    "    xs = line[:,0]\n",
    "    dd0s = line[:,1]/6.\n",
    "    plt.plot(xs,dd0s,lslist[i],label=ti,c='0.3') # '0.75' is shade of grey\n",
    "lslist=3 * ['o-'] \n",
    "for i, line in enumerate(ksdata):\n",
    "    ti = 'ran Rods $k = ' + str(ks[i]) +'$'\n",
    "    xs = line[:,0]\n",
    "    dd0s = line[:,1]/6.\n",
    "    plt.plot(xs,dd0s,lslist[i],label=ti,c=colors[i])\n",
    "plt.xlabel(r'$U_0/k_B T$')\n",
    "plt.ylabel(r'$D/D_0$')\n",
    "plt.ylim([0,1.3])\n",
    "plt.xlim([-10,20])\n",
    "#plt.legend(loc=2, bbox_to_anchor=(0.5, 1.2), ncol=3, fancybox=True, shadow=True)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Pore size distributions\n",
    "which pore size distributions do I pick up with my usual methods?\n",
    "\n",
    "## THE SECOND ONE LOOKS ALRIGHT\n",
    "It's square integrable and has an integral of 1:\n",
    "$$\\int_0^\\infty x * \\exp(-x^2/2) = [-\\exp(-x^2/2)]_0^\\infty = 1$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HP4Qe/8SJcPnlGtwVSUdTNiVW7roLzjsvbMd87rlw883QsmXUUYnkl+bpS0l7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\n9EVESsj/B1vWell7d/AYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1240e56a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12485c9e8>]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HPyYdkUjxypr0zaw98DSwHjgBGAUMTW3r0wb4b2AV8HxjA1TTjkD897/++tgf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\nPIKTJNy4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124a06c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def gauss(x,x0):\n",
    "    return np.exp(-(x-x0)**2)\n",
    "\n",
    "xs = np.arange(0,6,0.01)\n",
    "plt.plot(xs,gauss(xs,1))\n",
    "plt.show()\n",
    "\n",
    "def linxNorm(x):\n",
    "    return x*np.exp(-x**2/2)\n",
    "plt.plot(xs,linxNorm(xs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bbR+x/TwwBCyVNACcaXtnabe51qe+rfuAq7rIN06oX1A9Dnbsn+HhF3qYW0R0quOiYfsQ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\niIiIxlI0IiKisf8PRbpwfxsyIsoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1239627f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrodsPoiss = [np.random.gamma(20,0.5) for x in range(10**6)]\n",
    "plt.hist(nrodsPoiss,bins=range(40))\n",
    "plt.show()\n",
    "bsP=[]\n",
    "nzeros=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.90000000e+01,   3.57000000e+02,   1.51400000e+03,\n",
       "          3.44700000e+03,   5.53800000e+03,   7.73600000e+03,\n",
       "          8.96900000e+03,   9.59100000e+03,   9.55200000e+03,\n",
       "          9.03800000e+03,   8.03000000e+03,   7.24100000e+03,\n",
       "          6.24300000e+03,   5.13800000e+03,   4.19300000e+03,\n",
       "          3.22300000e+03,   2.65400000e+03,   1.94100000e+03,\n",
       "          1.50000000e+03,   1.21300000e+03,   8.15000000e+02,\n",
       "          5.72000000e+02,   4.43000000e+02,   2.89000000e+02,\n",
       "          1.97000000e+02,   1.74000000e+02,   1.21000000e+02,\n",
       "          7.90000000e+01,   5.50000000e+01,   3.70000000e+01,\n",
       "          2.50000000e+01,   2.20000000e+01,   1.30000000e+01,\n",
       "          6.00000000e+00,   4.00000000e+00,   5.00000000e+00,\n",
       "          2.00000000e+00,   3.00000000e+00,   1.00000000e+00]),\n",
       " array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34, 35, 36, 37, 38, 39]),\n",
       " <a list of 39 Patch objects>)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x125ea6320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TEST boost::gamma_distribution\n",
    "gammafile = \"/Users/jh/Documents/workspace-cpp/RanbSPS/Release/tmp_gamma.txt\"\n",
    "bs = []\n",
    "with open(gammafile, 'r') as f:\n",
    "    bs= f.readlines()\n",
    "bs = [float(b.strip('\\n')) for b in bs]\n",
    "bs = np.asarray(bs)\n",
    "plt.hist(bs,bins=range(40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x12225fdd8>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7I2xv53M32rE1tM25kzRN0hGSXksaf75phPDK5662YyaM/flcI+33snFo13gZ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\nMjEzs8qcTMzMrLL/DyV8oEQg/z78AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1225122e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tries = 8\n",
    "probrod = 1./tries\n",
    "nrods = []\n",
    "posArrprev=[0]\n",
    "d=[]\n",
    "for k in range(10**5):\n",
    "    count=0\n",
    "    posArr = []\n",
    "    posArr.append(posArrprev[-1]-1)\n",
    "    for i in range(tries):\n",
    "        if (np.random.uniform() < probrod):\n",
    "            count+=1\n",
    "            posArr.append(np.random.uniform())\n",
    "    posArr=sorted(posArr)\n",
    "    posArrprev=posArr\n",
    "    for i in range(len(posArr[1:])):\n",
    "        d.append(posArr[i+1]-posArr[i])\n",
    "    nrods.append(count)\n",
    "#plt.hist(nrods,bins=[0, 1, 2, 3, 4, 5, 6, 7,8])\n",
    "bins=np.arange(0.1,3,0.1)\n",
    "plt.hist(d,bins=bins)\n",
    "plt.xlabel(\"pore opening d\")\n",
    "# plt.show()\n",
    "# bs=[]\n",
    "# nzeros=0\n",
    "# for n in nrods:\n",
    "#     if n== 0: nzeros+=1\n",
    "#     elif nzeros!=0: \n",
    "#         bs.append(nzeros)\n",
    "#         nzeros=0\n",
    "#     else: bs.append(1./n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9296811156210055, 0.5463770984051464, 0.32532154872746266, -1, 0.2390177177590299]\n",
      "[-1, 0.2390177177590299, 0.32532154872746266, 0.5463770984051464, 0.9296811156210055]\n"
     ]
    }
   ],
   "source": [
    "te = []\n",
    "for i in range(5):\n",
    "    te.append(np.random.uniform())\n",
    "te[3]=-1\n",
    "print(te)\n",
    "print(sorted(te))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Can I manage to pick up the same distribution that I was sourcing from?\n",
    "Use Poisson distribution\n",
    "$$P(m) = \\frac{\\lambda^m \\exp(-\\lambda)}{m!}$$\n",
    "this is np.random.poisson(lam=1.0, size=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SNQwtSVI1DC1JUjUMLUlSNQwtSVI1DC3peWcFSXpd1q1bP/SdkgCIX8u9PElK349dUsPX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\nUjUMLUlSNf4/NTYkfNU5Am8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1068e0b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrodsPoiss = [np.random.poisson(4) for x in range(10**7)]\n",
    "plt.hist(nrodsPoiss,bins=[0, 1, 2, 3, 4, 5, 6, 7,8,9,10,11,12,13,14,15,16])\n",
    "plt.show()\n",
    "bsP=[]\n",
    "nzeros=0\n",
    "for n in nrodsPoiss:\n",
    "    if n== 0: nzeros+=1\n",
    "    elif nzeros!=0: \n",
    "        bsP.append(nzeros)\n",
    "        nzeros=0\n",
    "    else: bsP.append(1./n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  2.10920000e+06,   3.45354800e+06,   1.91730600e+06,\n",
       "          1.43872500e+06,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   8.94726000e+05,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   3.21400000e+03,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   7.60000000e+01,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   1.00000000e+00]),\n",
       " array([ 0.05263158,  0.18421053,  0.31578947,  0.44736842,  0.57894737,\n",
       "         0.71052632,  0.84210526,  0.97368421,  1.10526316,  1.23684211,\n",
       "         1.36842105,  1.5       ,  1.63157895,  1.76315789,  1.89473684,\n",
       "         2.02631579,  2.15789474,  2.28947368,  2.42105263,  2.55263158,\n",
       "         2.68421053,  2.81578947,  2.94736842,  3.07894737,  3.21052632,\n",
       "         3.34210526,  3.47368421,  3.60526316,  3.73684211,  3.86842105,  4.        ]),\n",
       " <a list of 30 Patch objects>)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tuto9lq00Y2MpaRbJv54XAUsjIs9qrraP5RT72UwR35fnped8cYz9ZfnenKif\nzczkeP5v4E9I7q0Nb+vS810M/NcxjpveeLb7OYAyb8AC4FWSG50XAH8G7Cd5TVQ27v8CPQ1t/ytt\n/zTwpyT/cqiXqZ8kDzCvI7mBfgHJDdZB4Psz1M85wBXAvyR5WPIX6ecrgGPLMJZT7WcBY3kPyfMv\nXwL+acN2dInGctL9bPdYpufcDKwkWa79SZLl3fuB+8f6+1PQeE66n0WMZ5N+f4GG57xaPZ5t+Q+p\n0kYyZd1KsrrnVaCL9B2QmZgXgHsb2j4I3EvyDEY/8F2aPLBZZD+BfwXsIpnWv0uyfHnN8A+VGejj\n8MOSQ022j5RoLCfdzwLG8sUx+le2sZx0P9s9luk5u4Gfk6zG2wc8AfxHYNZYf38KGs9J97OI8WzS\n72bFq6Xj6RfzmplZ5fiel5mZVY6Ll5mZVY6Ll5mZVY6Ll5mZVY6Ll5mZVY6Ll5mZVY6Ll5mZVY6L\nl5mZVY6Ll5mZVc7/Bz5yfBbGzpNeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d5cb5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bsP,bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test position shifting of ranRods\n",
    "data obtained by printRodVec() function output, copied from terminal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = [\n",
    "    [[0,1] ,[0,17.5536,14.5519],[0,19.2135,16.2913],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,16.3634],[0,5.98765,19.7305],[0,14.3178,16.7654],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,-1] ,[0,17.5536,14.5519],[0,19.2135,16.2913],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,16.3634],[0,5.98765,19.7305],[0,14.3178,16.7654],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,1] ,[0,17.5536,14.5519],[0,19.2135,16.2913],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,16.3634],[0,5.98765,19.7305],[0,14.3178,16.7654],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,-1] ,[0,17.5536,14.5519],[0,19.2135,16.2913],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,16.3634],[0,5.98765,19.7305],[0,14.3178,16.7654],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,1] ,[0,17.5536,14.5519],[0,19.2135,16.2913],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,16.3634],[0,5.98765,19.7305],[0,14.3178,16.7654],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,-1] ,[0,17.5536,14.5519],[0,19.2135,16.2913],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,16.3634],[0,5.98765,19.7305],[0,14.3178,16.7654],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,1] ,[0,17.5536,14.5519],[0,19.2135,16.2913],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,16.3634],[0,5.98765,19.7305],[0,14.3178,16.7654],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[2,-1] ,[0,17.5536,-7.06849],[0,19.2135,-3.31735],[0,12.4903,2.46071],[0,5.0386,9.0335],[0,4.61076,-8.87373],[0,5.98765,-3.07538],[0,14.3178,-1.66017],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[2,1] ,[0,17.5536,17.8838],[0,19.2135,13.9608],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,12.5631],[0,5.98765,18.2601],[0,14.3178,18.8949],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,-1] ,[0,17.5536,17.8838],[0,19.2135,13.9608],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,12.5631],[0,5.98765,18.2601],[0,14.3178,18.8949],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,1] ,[0,17.5536,17.8838],[0,19.2135,13.9608],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,12.5631],[0,5.98765,18.2601],[0,14.3178,18.8949],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,-1] ,[0,17.5536,17.8838],[0,19.2135,13.9608],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,12.5631],[0,5.98765,18.2601],[0,14.3178,18.8949],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,1] ,[0,17.5536,17.8838],[0,19.2135,13.9608],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,12.5631],[0,5.98765,18.2601],[0,14.3178,18.8949],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,-1] ,[0,17.5536,17.8838],[0,19.2135,13.9608],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,12.5631],[0,5.98765,18.2601],[0,14.3178,18.8949],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[2,-1] ,[0,17.5536,-8.09765],[0,19.2135,-1.14375],[0,12.4903,2.46071],[0,5.0386,9.0335],[0,4.61076,-4.28119],[0,5.98765,-4.71684],[0,14.3178,-0.0902639],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[2,1] ,[0,17.5536,16.6799],[0,19.2135,10.6748],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,10.734],[0,5.98765,17.0538],[0,14.3178,17.569],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[2,-1] ,[0,17.5536,-4.72065],[0,19.2135,-0.884411],[0,12.4903,2.46071],[0,5.0386,9.0335],[0,4.61076,-9.87775],[0,5.98765,-2.11346],[0,14.3178,-2.76274],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[0,1] ,[0,17.5536,-4.72065],[0,19.2135,-0.884411],[0,12.4903,2.46071],[0,5.0386,9.0335],[0,4.61076,-9.87775],[0,5.98765,-2.11346],[0,14.3178,-2.76274],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[0,-1] ,[0,17.5536,-4.72065],[0,19.2135,-0.884411],[0,12.4903,2.46071],[0,5.0386,9.0335],[0,4.61076,-9.87775],[0,5.98765,-2.11346],[0,14.3178,-2.76274],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[2,1] ,[0,17.5536,18.8964],[0,19.2135,13.8635],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,13.8445],[0,5.98765,14.2374],[0,14.3178,17.2286],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,1] ,[0,17.5536,18.8964],[0,19.2135,13.8635],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,13.8445],[0,5.98765,14.2374],[0,14.3178,17.2286],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,-1] ,[0,17.5536,18.8964],[0,19.2135,13.8635],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,13.8445],[0,5.98765,14.2374],[0,14.3178,17.2286],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[2,-1] ,[0,17.5536,-2.30332],[0,19.2135,-7.03338],[0,12.4903,2.46071],[0,5.0386,9.0335],[0,4.61076,-7.47563],[0,5.98765,-7.72904],[0,14.3178,-5.56459],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[2,1] ,[0,17.5536,12.6133],[0,19.2135,17.0976],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,11.3394],[0,5.98765,10.7561],[0,14.3178,15.8702],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,1] ,[0,17.5536,12.6133],[0,19.2135,17.0976],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,11.3394],[0,5.98765,10.7561],[0,14.3178,15.8702],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,-1] ,[0,17.5536,12.6133],[0,19.2135,17.0976],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,11.3394],[0,5.98765,10.7561],[0,14.3178,15.8702],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[2,-1] ,[0,17.5536,-7.88341],[0,19.2135,-9.29957],[0,12.4903,2.46071],[0,5.0386,9.0335],[0,4.61076,-5.14611],[0,5.98765,-5.6972],[0,14.3178,-4.73573],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[2,1] ,[0,17.5536,16.7513],[0,19.2135,17.2186],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,10.3537],[0,5.98765,12.9444],[0,14.3178,10.6922],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,1] ,[0,17.5536,16.7513],[0,19.2135,17.2186],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,10.3537],[0,5.98765,12.9444],[0,14.3178,10.6922],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,-1] ,[0,17.5536,16.7513],[0,19.2135,17.2186],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,10.3537],[0,5.98765,12.9444],[0,14.3178,10.6922],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,1] ,[0,17.5536,16.7513],[0,19.2135,17.2186],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,10.3537],[0,5.98765,12.9444],[0,14.3178,10.6922],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,-1] ,[0,17.5536,16.7513],[0,19.2135,17.2186],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,10.3537],[0,5.98765,12.9444],[0,14.3178,10.6922],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,1] ,[0,17.5536,16.7513],[0,19.2135,17.2186],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,10.3537],[0,5.98765,12.9444],[0,14.3178,10.6922],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,-1] ,[0,17.5536,16.7513],[0,19.2135,17.2186],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,10.3537],[0,5.98765,12.9444],[0,14.3178,10.6922],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,1] ,[0,17.5536,16.7513],[0,19.2135,17.2186],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,10.3537],[0,5.98765,12.9444],[0,14.3178,10.6922],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,-1] ,[0,17.5536,16.7513],[0,19.2135,17.2186],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,10.3537],[0,5.98765,12.9444],[0,14.3178,10.6922],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[0,1] ,[0,17.5536,16.7513],[0,19.2135,17.2186],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,10.3537],[0,5.98765,12.9444],[0,14.3178,10.6922],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[2,-1] ,[0,17.5536,-9.83167],[0,19.2135,-3.85335],[0,12.4903,2.46071],[0,5.0386,9.0335],[0,4.61076,-3.23918],[0,5.98765,-4.81447],[0,14.3178,-7.05761],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[0,-1] ,[0,17.5536,-9.83167],[0,19.2135,-3.85335],[0,12.4903,2.46071],[0,5.0386,9.0335],[0,4.61076,-3.23918],[0,5.98765,-4.81447],[0,14.3178,-7.05761],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[2,1] ,[0,17.5536,15.201],[0,19.2135,16.4838],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,15.2495],[0,5.98765,15.7068],[0,14.3178,13.0242],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[2,-1] ,[0,17.5536,-3.56043],[0,19.2135,-2.6421],[0,12.4903,2.46071],[0,5.0386,9.0335],[0,4.61076,-7.97212],[0,5.98765,-4.39498],[0,14.3178,-5.5449],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[2,1] ,[0,17.5536,11.43],[0,19.2135,13.8048],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,11.463],[0,5.98765,13.7145],[0,14.3178,16.2598],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[2,-1] ,[0,17.5536,-4.20246],[0,19.2135,-5.5926],[0,12.4903,2.46071],[0,5.0386,9.0335],[0,4.61076,-7.11877],[0,5.98765,-3.77231],[0,14.3178,-8.32121],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[2,1] ,[0,17.5536,16.6708],[0,19.2135,14.7999],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,15.9347],[0,5.98765,10.6155],[0,14.3178,14.6497],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[2,-1] ,[0,17.5536,-7.72679],[0,19.2135,-0.219519],[0,12.4903,2.46071],[0,5.0386,9.0335],[0,4.61076,-1.26659],[0,5.98765,-8.87014],[0,14.3178,-2.949],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[2,1] ,[0,17.5536,18.7752],[0,19.2135,12.3974],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,15.1226],[0,5.98765,11.7797],[0,14.3178,13.8957],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[2,-1] ,[0,17.5536,-0.909343],[0,19.2135,-3.39543],[0,12.4903,2.46071],[0,5.0386,9.0335],[0,4.61076,-8.48194],[0,5.98765,-6.57403],[0,14.3178,-0.563963],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[2,1] ,[0,17.5536,19.0213],[0,19.2135,12.75],[0,12.4903,-7.53929],[0,5.0386,-0.966501],[0,4.61076,19.2715],[0,5.98765,15.81],[0,14.3178,14.4671],[0,-4.96658,-7.16735],[0,-7.9321,-3.96433]],\n",
    "[[2,-1] ,[0,17.5536,-2.5792],[0,19.2135,-1.99245],[0,12.4903,2.46071],[0,5.0386,9.0335],[0,4.61076,-9.50458],[0,5.98765,-9.28507],[0,14.3178,-7.92592],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[1,-1] ,[0,-8.67502,-2.5792],[0,-3.47459,-1.99245],[0,-9.83747,2.46071],[0,15.0386,9.0335],[0,14.6108,-9.50458],[0,15.9877,-9.28507],[0,-8.20457,-7.92592],[0,5.03342,2.83265],[0,2.0679,6.03567]],\n",
    "[[1,1] ,[0,14.9442,-2.5792],[0,19.5572,-1.99245],[0,10.4239,2.46071],[0,5.0386,9.0335],[0,4.61076,-9.50458],[0,5.98765,-9.28507],[0,17.3684,-7.92592],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[1,-1] ,[0,-0.855232,-2.5792],[0,-3.59433,-1.99245],[0,-9.43163,2.46071],[0,15.0386,9.0335],[0,14.6108,-9.50458],[0,15.9877,-9.28507],[0,-7.04523,-7.92592],[0,5.03342,2.83265],[0,2.0679,6.03567]],\n",
    "[[1,1] ,[0,16.4255,-2.5792],[0,11.5895,-1.99245],[0,14.8879,2.46071],[0,5.0386,9.0335],[0,4.61076,-9.50458],[0,5.98765,-9.28507],[0,16.7299,-7.92592],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[1,-1] ,[0,-1.10965,-2.5792],[0,-7.23816,-1.99245],[0,-9.44881,2.46071],[0,15.0386,9.0335],[0,14.6108,-9.50458],[0,15.9877,-9.28507],[0,-9.54438,-7.92592],[0,5.03342,2.83265],[0,2.0679,6.03567]],\n",
    "[[1,1] ,[0,11.0236,-2.5792],[0,12.7848,-1.99245],[0,18.6695,2.46071],[0,5.0386,9.0335],[0,4.61076,-9.50458],[0,5.98765,-9.28507],[0,18.0019,-7.92592],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[1,-1] ,[0,-0.2943,-2.5792],[0,-3.59211,-1.99245],[0,-4.26221,2.46071],[0,15.0386,9.0335],[0,14.6108,-9.50458],[0,15.9877,-9.28507],[0,-5.99729,-7.92592],[0,5.03342,2.83265],[0,2.0679,6.03567]],\n",
    "[[1,1] ,[0,11.7893,-2.5792],[0,15.6756,-1.99245],[0,12.9437,2.46071],[0,5.0386,9.0335],[0,4.61076,-9.50458],[0,5.98765,-9.28507],[0,10.4926,-7.92592],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[1,-1] ,[0,-4.02359,-2.5792],[0,-2.57171,-1.99245],[0,-6.6748,2.46071],[0,15.0386,9.0335],[0,14.6108,-9.50458],[0,15.9877,-9.28507],[0,-4.38877,-7.92592],[0,5.03342,2.83265],[0,2.0679,6.03567]],\n",
    "[[1,1] ,[0,13.3802,-2.5792],[0,19.8281,-1.99245],[0,18.1618,2.46071],[0,5.0386,9.0335],[0,4.61076,-9.50458],[0,5.98765,-9.28507],[0,14.2589,-7.92592],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[1,-1] ,[0,-8.37462,-2.5792],[0,-3.22208,-1.99245],[0,-7.13753,2.46071],[0,15.0386,9.0335],[0,14.6108,-9.50458],[0,15.9877,-9.28507],[0,-7.66328,-7.92592],[0,5.03342,2.83265],[0,2.0679,6.03567]],\n",
    "[[1,1] ,[0,16.9961,-2.5792],[0,11.2909,-1.99245],[0,13.2235,2.46071],[0,5.0386,9.0335],[0,4.61076,-9.50458],[0,5.98765,-9.28507],[0,15.8771,-7.92592],[0,-4.96658,2.83265],[0,-7.9321,6.03567]],\n",
    "[[1,-1] ,[0,-3.58536,-2.5792],[0,-1.30896,-1.99245],[0,-2.48585,2.46071],[0,15.0386,9.0335],[0,14.6108,-9.50458],[0,15.9877,-9.28507],[0,-6.44978,-7.92592],[0,5.03342,2.83265],[0,2.0679,6.03567]],\n",
    "[[0,1] ,[0,-3.58536,-2.5792],[0,-1.30896,-1.99245],[0,-2.48585,2.46071],[0,15.0386,9.0335],[0,14.6108,-9.50458],[0,15.9877,-9.28507],[0,-6.44978,-7.92592],[0,5.03342,2.83265],[0,2.0679,6.03567]],\n",
    "[[0,-1] ,[0,-3.58536,-2.5792],[0,-1.30896,-1.99245],[0,-2.48585,2.46071],[0,15.0386,9.0335],[0,14.6108,-9.50458],[0,15.9877,-9.28507],[0,-6.44978,-7.92592],[0,5.03342,2.83265],[0,2.0679,6.03567]],\n",
    "[[0,1] ,[0,-3.58536,-2.5792],[0,-1.30896,-1.99245],[0,-2.48585,2.46071],[0,15.0386,9.0335],[0,14.6108,-9.50458],[0,15.9877,-9.28507],[0,-6.44978,-7.92592],[0,5.03342,2.83265],[0,2.0679,6.03567]],\n",
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    "[[2,1] ,[0,-3.58536,-2.5792],[0,-1.30896,-1.99245],[0,-2.48585,2.46071],[0,15.0386,9.0335],[0,14.6108,-9.50458],[0,15.9877,-9.28507],[0,-6.44978,-7.92592],[0,5.03342,2.83265],[0,2.0679,6.03567]],\n",
    "[[2,-1] ,[0,-3.58536,7.4208],[0,-1.30896,8.00755],[0,-2.48585,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-6.44978,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[2,1] ,[0,-3.58536,-2.5792],[0,-1.30896,-1.99245],[0,-2.48585,2.46071],[0,15.0386,9.0335],[0,14.6108,-9.50458],[0,15.9877,-9.28507],[0,-6.44978,-7.92592],[0,5.03342,2.83265],[0,2.0679,6.03567]],\n",
    "[[2,-1] ,[0,-3.58536,7.4208],[0,-1.30896,8.00755],[0,-2.48585,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-6.44978,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,10.0569,7.4208],[0,10.3666,8.00755],[0,15.3351,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,18.9755,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-7.42743,7.4208],[0,-5.61501,8.00755],[0,-5.67481,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-2.12252,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,12.3812,7.4208],[0,19.2878,8.00755],[0,11.174,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,10.0765,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-6.18095,7.4208],[0,-4.10015,8.00755],[0,-9.87288,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-6.62967,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,17.3535,7.4208],[0,15.1618,8.00755],[0,18.3804,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,13.3829,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-9.0983,7.4208],[0,-6.8765,8.00755],[0,-1.91964,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-9.59996,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,16.3227,7.4208],[0,15.0876,8.00755],[0,19.294,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,15.7885,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-2.59913,7.4208],[0,-4.9087,8.00755],[0,-4.31041,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-9.39303,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,16.7847,7.4208],[0,10.0947,8.00755],[0,15.8307,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,14.5957,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-2.37932,7.4208],[0,-1.13518,8.00755],[0,-7.41998,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-3.15709,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,12.6012,7.4208],[0,12.8433,8.00755],[0,17.2604,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,18.5224,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-8.0529,7.4208],[0,-1.35002,8.00755],[0,-5.39185,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-8.08522,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,17.7868,7.4208],[0,11.5847,8.00755],[0,12.313,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,18.0162,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-0.816015,7.4208],[0,-0.328064,8.00755],[0,-0.734412,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-8.04746,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,19.1668,7.4208],[0,18.7604,8.00755],[0,11.4411,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,10.0694,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-9.02046,7.4208],[0,-8.26794,8.00755],[0,-4.11423,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-6.73348,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,16.6757,7.4208],[0,15.6209,8.00755],[0,19.4883,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,18.0632,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-8.66273,7.4208],[0,-3.78072,8.00755],[0,-4.72159,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-7.63277,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,16.0992,7.4208],[0,14.937,8.00755],[0,18.164,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,11.3854,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-4.46016,7.4208],[0,-0.285726,8.00755],[0,-6.21346,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-7.76053,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,14.2511,7.4208],[0,11.2044,8.00755],[0,18.5924,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,10.8083,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-0.495505,7.4208],[0,-3.39792,8.00755],[0,-2.91413,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-1.83639,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,18.313,7.4208],[0,14.1378,8.00755],[0,16.1147,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,10.0817,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-9.57944,7.4208],[0,-9.19393,8.00755],[0,-6.85079,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-6.57476,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,16.0753,7.4208],[0,15.8552,8.00755],[0,13.2335,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,19.1033,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-1.12142,7.4208],[0,-8.53701,8.00755],[0,-8.21456,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-7.89067,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,14.8607,7.4208],[0,11.5581,8.00755],[0,14.7874,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,17.4909,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-2.16308,7.4208],[0,-3.0388,8.00755],[0,-0.670731,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-4.15286,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,18.6076,7.4208],[0,11.5766,8.00755],[0,10.5344,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,13.9347,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-7.76828,7.4208],[0,-2.37792,8.00755],[0,-2.28592,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-7.40578,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,17.9227,7.4208],[0,18.3784,8.00755],[0,12.4166,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,14.1868,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-4.01956,7.4208],[0,-0.487758,8.00755],[0,-4.11193,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-0.141399,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,16.0844,7.4208],[0,12.1086,8.00755],[0,19.1311,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,19.5449,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-7.31057,7.4208],[0,-7.62012,8.00755],[0,-6.79396,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-9.23181,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,14.7003,7.4208],[0,11.2803,8.00755],[0,17.2454,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,15.4228,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-4.00138,7.4208],[0,-0.327408,8.00755],[0,-2.43129,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-1.15612,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,14.3059,7.4208],[0,15.1504,8.00755],[0,11.0033,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,15.1738,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-8.25175,7.4208],[0,-1.16516,8.00755],[0,-5.29679,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-4.49231,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,19.2005,7.4208],[0,16.4139,8.00755],[0,18.0542,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,13.5471,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-2.74301,7.4208],[0,-6.45891,8.00755],[0,-0.86153,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-3.18915,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,18.8107,7.4208],[0,17.116,8.00755],[0,19.9991,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,17.1851,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-7.60377,7.4208],[0,-3.34804,8.00755],[0,-9.34944,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-6.46816,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,15.1776,7.4208],[0,12.5979,8.00755],[0,10.5538,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,19.7928,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-4.8655,7.4208],[0,-9.86769,8.00755],[0,-5.18803,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-8.96571,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,18.6739,7.4208],[0,17.9224,8.00755],[0,11.0311,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,18.9131,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-2.51175,7.4208],[0,-8.46828,8.00755],[0,-3.9015,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-1.1841,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,14.5702,7.4208],[0,11.2928,8.00755],[0,17.9466,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,10.4252,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-4.79258,7.4208],[0,-7.31105,8.00755],[0,-7.17946,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-6.03215,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,16.9856,7.4208],[0,14.5905,8.00755],[0,16.3133,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,13.1753,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-9.03561,7.4208],[0,-8.57416,8.00755],[0,-1.67301,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-0.927066,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,16.8815,7.4208],[0,11.4796,8.00755],[0,11.9455,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,12.8149,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-0.485404,7.4208],[0,-3.29791,8.00755],[0,-7.57475,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-2.64369,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,13.2518,7.4208],[0,17.412,8.00755],[0,16.7154,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,16.8649,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-2.53311,7.4208],[0,-4.619,8.00755],[0,-9.48917,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-6.94518,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,15.4555,7.4208],[0,17.8913,8.00755],[0,10.6733,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,15.5603,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]],\n",
    "[[1,-1] ,[0,-4.8897,7.4208],[0,-6.2985,8.00755],[0,-5.30187,12.4607],[0,15.0386,19.0335],[0,14.6108,0.495421],[0,15.9877,0.714931],[0,-1.00699,2.07408],[0,5.03342,12.8326],[0,2.0679,16.0357]],\n",
    "[[1,1] ,[0,19.8424,7.4208],[0,16.6043,8.00755],[0,16.6217,12.4607],[0,5.0386,19.0335],[0,4.61076,0.495421],[0,5.98765,0.714931],[0,17.6583,2.07408],[0,-4.96658,12.8326],[0,-7.9321,16.0357]]\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.        17.5536    14.5519  ]\n",
      " [  0.        19.2135    16.2913  ]\n",
      " [  0.        12.4903    -7.53929 ]\n",
      " [  0.         5.0386    -0.966501]\n",
      " [  0.         4.61076   16.3634  ]\n",
      " [  0.         5.98765   19.7305  ]\n",
      " [  0.        14.3178    16.7654  ]\n",
      " [  0.        -4.96658   -7.16735 ]\n",
      " [  0.        -7.9321    -3.96433 ]]\n"
     ]
    }
   ],
   "source": [
    "axis = [x[0][0] for x in data]\n",
    "exitmark = [x[0][1] for x in data]\n",
    "positions = np.asarray([x[1:] for x in data])\n",
    "print(positions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0effb4d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f01073c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0efe7ee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0effce470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f022ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f011b978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f01efa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0efaa9cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f0047908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0efd35dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f0047e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0efaaf198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0efd34438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0effc3c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VfTfJduBqhqF2eVUNOi5Lkjalse2GOhZ2Q0nS0k1aN5QkaUwYFpKkVoaFJKmVYSFJamVY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\ngbau/fFs/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f0124400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(60,75):\n",
    "    ys = positions[i,:,1]\n",
    "    zs = positions[i,:,2]\n",
    "    plt.scatter(ys, zs)\n",
    "    plt.scatter(positions[i+1,:,1], positions[i+1,:,2],c='r')\n",
    "    plt.title('axis: '+str(axis[i])+'   exitmark: '+str(exitmark[i]))\n",
    "    plt.xlabel('y')\n",
    "    plt.ylabel('z')\n",
    "    plt.xlim([-10,20])\n",
    "    plt.ylim([-10,20])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc0f020a588>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qumZ9fZ26rrsuRVIBnBqvY2trZzh58gEWFysuXhyzunqaEyfu67osSQeEc57OmbquGQyW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\nwS5Jhfn/VxDI7aGWOTwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0efa8c4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [[0,-3.47478,5.95625],[0,-2.00696,8.21978],[0,11.4951,4.11067],[0,14.3885,-0.456447],[0,-2.43914,-6.49816],[0,-6.69911,1.95724],[0,15.9702,2.22784],[0,19.0883,-0.0222026],[0,-0.0184129,-4.45041]]\n",
    "data = np.array(data)\n",
    "plt.scatter(data[:,1],data[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataMoreRan = [\n",
    "    [[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,-0.833875,12.9025],[0,-6.73522,12.6424],[0,0.625328,14.2353]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,1.3502,10.7357],[0,-0.702474,14.5147],[0,-3.77078,12.4025],[0,-6.23867,19.3871],[0,0.71116,17.6119]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,12.7195,-5.7193],[0,1.35086,-3.93772],[0,5.18134,-3.81986]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,5.85031,12.276],[0,18.0489,12.9921],[0,16.1542,11.7154],[0,-9.8026,17.3382],[0,-7.47845,16.9403]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,17.0789,-5.72255],[0,-3.42368,-2.71693],[0,14.3468,-1.87695],[0,-3.55651,-5.08898]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,-5.12678,16.7994],[0,4.66272,15.8985]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,-9.29264,-5.01078],[0,-6.22915,-7.05887],[0,5.09122,-8.28036],[0,-0.382473,-5.79482],[0,-9.84026,-4.47026]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,12.5048,18.4957]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,-4.78059,-1.17354],[0,-2.55441,-1.96206]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,1.61125,12.2773],[0,-2.28283,14.0311],[0,9.61954,12.3577]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,19.6538,-1.1555],[0,4.24622,-5.70739]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,8.20089,10.0746],[0,1.26174,11.6795],[0,11.9481,16.4708],[0,13.0402,10.265]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,-5.3799,-8.135],[0,-9.52233,-4.79585],[0,-2.78691,-7.79535],[0,-2.63922,-3.69468],[0,12.0885,-6.40265]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,-5.93865,11.3408],[0,7.57853,17.3994],[0,-8.69558,18.617]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,4.69669,-2.83988],[0,15.7082,-8.05681]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,19.162,11.2063],[0,18.8937,17.2805]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,18.3531,-8.57485],[0,19.8681,-8.4114],[0,14.9246,-9.94398],[0,17.2598,-7.99119]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,-4.03204,13.6819],[0,-3.9507,19.9335],[0,1.34273,10.4686],[0,2.58248,16.413]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,13.0239,-0.696414],[0,-2.60812,-8.99336],[0,-4.67634,-2.17395],[0,10.2982,-2.00519],[0,19.9888,-6.37685],[0,-3.79755,-4.13481]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,-1.53608,17.372],[0,9.26949,11.243],[0,5.8562,13.5806],[0,-9.74077,19.3317]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,-7.65569,-4.8425],[0,10.1097,-2.45089]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,14.0538,11.1919],[0,1.80364,19.1517],[0,-3.51428,15.9478],[0,18.1966,11.1654],[0,14.2877,16.8681],[0,4.61291,17.036]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,10.1865,-5.36853],[0,-5.50257,-1.73531],[0,17.4876,-4.33709]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,11.0569,14.0357],[0,4.17405,15.9435],[0,0.895474,16.0928],[0,0.663764,16.0009]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,9.4231,-1.44878],[0,-3.20285,-8.04225],[0,-1.74683,-0.686116],[0,11.1103,-4.2465]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,-6.61516,16.4415],[0,5.20512,13.5427],[0,-8.79445,14.5786],[0,13.1643,19.5108],[0,-9.55655,17.8774]],\n",
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    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,10.4734,19.2511],[0,11.3144,13.9586],[0,5.31048,16.8485]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,4.87263,-9.65066],[0,-0.0229091,-4.43521]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,1.99603,15.2463]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,-6.24328,-5.13978],[0,11.6496,-0.865752],[0,-9.92423,-9.65169]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,15.1608,14.5958],[0,0.378629,12.3983]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,12.9494,-1.43609],[0,8.78003,-1.95256]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,2.88879,15.7362],[0,-9.92691,10.4327]],\n",
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    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337]],\n",
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    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,-8.18323,11.2951]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,4.43902,-7.66512],[0,-4.52811,-3.8745]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,9.94624,19.8578],[0,9.62253,14.6915],[0,1.23737,16.6293],[0,9.44653,12.6319]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,-8.49117,-1.03299],[0,10.4952,-1.12689]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,5.54217,13.1667],[0,-5.8977,15.5428],[0,18.1004,13.2917],[0,11.5229,19.5093],[0,-4.77723,13.9423]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,3.58031,-9.62095],[0,16.857,-3.02737],[0,5.58315,-0.765597],[0,0.522401,-1.69684],[0,-1.91692,-9.05505]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,19.4435,16.9699],[0,8.33118,19.5996],[0,-4.82432,17.3932]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,-3.68157,-8.03467],[0,8.25834,-4.55992],[0,13.1743,-2.08086]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,0.263941,18.0299],[0,2.21593,15.6],[0,-6.58563,17.6831]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,12.6094,-4.19872],[0,-3.1117,-8.16032],[0,2.20564,-5.79298]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,17.5207,12.3975],[0,6.29175,12.184],[0,-2.33101,15.923],[0,9.23176,10.5069],[0,9.55069,19.029]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,-7.74911,-4.45827]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,5.38028,17.0698],[0,15.4419,19.1186],[0,18.7966,19.5759],[0,-5.63199,15.1436],[0,8.82495,18.1333]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,2.8847,-7.40259],[0,-2.99607,-6.58507],[0,18.6167,-7.11781],[0,3.40871,-8.31133],[0,15.4802,-2.37036]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,-5.3182,19.59]],\n",
    "[[2,-1] ,[0,10.3355,18.9658],[0,2.05898,0.771884],[0,2.4621,2.21453],[0,18.3463,7.799],[0,11.5232,14.6597],[0,17.7632,6.5389],[0,16.5134,7.75385],[0,5.29287,7.62309],[0,10.8578,13.7838],[0,3.42192,4.77555],[0,17.6791,8.35312],[0,11.2254,1.38838],[0,14.9589,5.89663],[0,19.1101,-9.29422],[0,-3.78988,-7.58317],[0,3.78814,-9.95952],[0,12.5679,-3.8318],[0,-9.95433,-3.65463]],\n",
    "[[2,1] ,[0,10.3355,8.96575],[0,2.05898,-9.22812],[0,2.4621,-7.78547],[0,18.3463,-2.201],[0,11.5232,4.65965],[0,17.7632,-3.4611],[0,16.5134,-2.24615],[0,5.29287,-2.37691],[0,10.8578,3.78382],[0,3.42192,-5.22445],[0,17.6791,-1.64688],[0,11.2254,-8.61162],[0,14.9589,-4.10337],[0,5.53999,15.601],[0,-6.72395,13.1247],[0,1.27831,15.1816],[0,16.0265,12.6774],[0,8.9467,11.0477],[0,12.9579,16.2232]]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n"
     ]
    }
   ],
   "source": [
    "axis = [x[0][0] for x in dataMoreRan]\n",
    "exitmark = [x[0][1] for x in dataMoreRan]\n",
    "positions = ([x[1:] for x in dataMoreRan])\n",
    "print(len(positions[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ePHTWzGyMpT1L+1ScLMzMMiDrc3LcDWVmlrJRj5xzN5SZ2Rgahzk5ThZmZinLyiztU3Gy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f014fa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f004e5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NOifH51mYmbWon6ZncYHbzKxF3VBkTpOThZkZ3VFkTlOmk4WkiqS/l3SfpLvTjsfMelc3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\nWZiZWVNOFmZm1pSThZmZNeVkYWZmTf1/ar4RSblZkHQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f0186f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0eff48a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LXtujmtr9fEQctl2SdLvthyPirvYbHDhw4NjparWqarXah7IwjLKeYz9KRmGoJ08WFha0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\nAACkIiwAAKkICwBAKsICAJCKsAAApPr/YCR2sAhsCk0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f00f0e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AfQpJt9k+ZPsXt+HxsMPl+UBuQJZ2bfJ2PyzpatsXRcTB5LLXDKmmbj8VEUdtFyXdYfvB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f8d3b2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0efab3828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0efa820b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Lng8DLs87cgOytG2dt/tJSdfY3hUR1yWXvapHNbX7+Yg4bLss6U7bByPinvYb7Nu37/jp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\nB7biN1gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f0124198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0eff84ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0f0124ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0eff30cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0eff8fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0eff170f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0efe4b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(25,40):\n",
    "    ys = [x[1] for x in positions[i]]\n",
    "    zs = [x[2] for x in positions[i]]\n",
    "    ysn = [x[1] for x in positions[i+1]]\n",
    "    zsn = [x[2] for x in positions[i+1]]\n",
    "    plt.scatter(ys, zs)\n",
    "    plt.scatter(ysn, zsn,c='r')\n",
    "    plt.xlabel('y')\n",
    "    plt.ylabel('z')\n",
    "    plt.title('axis: '+str(axis[i-1])+'   exitmark: '+str(exitmark[i-1]))\n",
    "    plt.xlim([-10,20])\n",
    "    plt.ylim([-10,20])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": true,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}