dir_calcs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'b3um_I1000mA_t50us_fs10kHz.txt'</li>\n",
       "\t<li>'b3um_I1000mA_t50us_fs12kHz.txt'</li>\n",
       "\t<li>'b3um_I1000mA_t50us_fs15kHz.txt'</li>\n",
       "\t<li>'b3um_I1000mA_t50us_fs17kHz.txt'</li>\n",
       "\t<li>'b3um_I1000mA_t50us_fs5kHz.txt'</li>\n",
       "\t<li>'b3um_I1000mA_t50us_fs7kHz.txt'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'b3um\\_I1000mA\\_t50us\\_fs10kHz.txt'\n",
       "\\item 'b3um\\_I1000mA\\_t50us\\_fs12kHz.txt'\n",
       "\\item 'b3um\\_I1000mA\\_t50us\\_fs15kHz.txt'\n",
       "\\item 'b3um\\_I1000mA\\_t50us\\_fs17kHz.txt'\n",
       "\\item 'b3um\\_I1000mA\\_t50us\\_fs5kHz.txt'\n",
       "\\item 'b3um\\_I1000mA\\_t50us\\_fs7kHz.txt'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'b3um_I1000mA_t50us_fs10kHz.txt'\n",
       "2. 'b3um_I1000mA_t50us_fs12kHz.txt'\n",
       "3. 'b3um_I1000mA_t50us_fs15kHz.txt'\n",
       "4. 'b3um_I1000mA_t50us_fs17kHz.txt'\n",
       "5. 'b3um_I1000mA_t50us_fs5kHz.txt'\n",
       "6. 'b3um_I1000mA_t50us_fs7kHz.txt'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"b3um_I1000mA_t50us_fs10kHz.txt\" \"b3um_I1000mA_t50us_fs12kHz.txt\"\n",
       "[3] \"b3um_I1000mA_t50us_fs15kHz.txt\" \"b3um_I1000mA_t50us_fs17kHz.txt\"\n",
       "[5] \"b3um_I1000mA_t50us_fs5kHz.txt\"  \"b3um_I1000mA_t50us_fs7kHz.txt\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddir = '../opt tweez data/02_12_2014/2_12_2014_part3/'\n",
    "files = list.files(ddir)\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'b3um_I1000mA_t50us_fs5kHz.txt'</li>\n",
       "\t<li>'b3um_I1000mA_t50us_fs7kHz.txt'</li>\n",
       "\t<li>'b3um_I1000mA_t50us_fs10kHz.txt'</li>\n",
       "\t<li>'b3um_I1000mA_t50us_fs12kHz.txt'</li>\n",
       "\t<li>'b3um_I1000mA_t50us_fs15kHz.txt'</li>\n",
       "\t<li>'b3um_I1000mA_t50us_fs17kHz.txt'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'b3um\\_I1000mA\\_t50us\\_fs5kHz.txt'\n",
       "\\item 'b3um\\_I1000mA\\_t50us\\_fs7kHz.txt'\n",
       "\\item 'b3um\\_I1000mA\\_t50us\\_fs10kHz.txt'\n",
       "\\item 'b3um\\_I1000mA\\_t50us\\_fs12kHz.txt'\n",
       "\\item 'b3um\\_I1000mA\\_t50us\\_fs15kHz.txt'\n",
       "\\item 'b3um\\_I1000mA\\_t50us\\_fs17kHz.txt'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'b3um_I1000mA_t50us_fs5kHz.txt'\n",
       "2. 'b3um_I1000mA_t50us_fs7kHz.txt'\n",
       "3. 'b3um_I1000mA_t50us_fs10kHz.txt'\n",
       "4. 'b3um_I1000mA_t50us_fs12kHz.txt'\n",
       "5. 'b3um_I1000mA_t50us_fs15kHz.txt'\n",
       "6. 'b3um_I1000mA_t50us_fs17kHz.txt'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"b3um_I1000mA_t50us_fs5kHz.txt\"  \"b3um_I1000mA_t50us_fs7kHz.txt\" \n",
       "[3] \"b3um_I1000mA_t50us_fs10kHz.txt\" \"b3um_I1000mA_t50us_fs12kHz.txt\"\n",
       "[5] \"b3um_I1000mA_t50us_fs15kHz.txt\" \"b3um_I1000mA_t50us_fs17kHz.txt\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear part of the acf to include (indices)\n",
    "linpart = 1:80\n",
    "\n",
    "# Files to include (indices in array files)\n",
    "#fidx = 1:length(files) # All\n",
    "fidx = c(5, 6, 1, 2, 3, 4)\n",
    "\n",
    "files[fidx]\n",
    "\n",
    "# Frequencies of measurements\n",
    "fps = c(1/10000, 1/12000, 1/15000, 1/17000, 1/5000, 1/7000); # 02_12_2014/2_12_2014_part3/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nfiles = length(fidx)\n",
    "\n",
    "varx = vector(,nfiles)\n",
    "vary = vector(,nfiles)\n",
    "meanx = vector(,nfiles)\n",
    "meany = vector(,nfiles)\n",
    "kx = vector(,nfiles)\n",
    "ky = vector(,nfiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Loop through the files and calculate everything for each\n",
    "for(k in 1:nfiles){\n",
    "    tmp = read.table(paste(ddir,files[fidx[k]],sep=''),sep =';',dec = ',')\n",
    "    coords = ts(tmp[,1:2])\n",
    "    #coords = ts(tmp[,2:3])\n",
    "    \n",
    "    # Means and variances for x, y\n",
    "    varx[k] = var(coords[,1])\n",
    "    vary[k] = var(coords[,2])\n",
    "    meanx[k] = mean(coords[,1])\n",
    "    meany[k] = mean(coords[,2])\n",
    "    \n",
    "    # Autocorrelations for stiffness estimation\n",
    "    acfx = acf(coords[,1], lag.max = 1000, plot=FALSE)\n",
    "    acfy = acf(coords[,2], lag.max = 1000, plot=FALSE)\n",
    "    \n",
    "    logacfx = log(acfx$acf[linpart])\n",
    "    logacfy = log(acfy$acf[linpart])\n",
    "\n",
    "    # Calculate units for the acf \n",
    "    tau = seq(0, (length(linpart)-1)*fps[k], fps[k])\n",
    "    \n",
    "    # Linear fit \n",
    "    fitx = lm(logacfx ~ tau)\n",
    "    fity = lm(logacfy ~ tau)\n",
    "    \n",
    "    stiffnessx[k] = -fitx$coefficients[2]\n",
    "    stiffnessy[k] = -fity$coefficients[2]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JRb1tDqWkLqQvQnLwvOsfOEVScefWlX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\nUPWXFGcTE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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ldPgCeE5NBTzWeqJ8ATQnLogl+qF8AXQnKo+1nqBfCFkNyZ0+Yh9QT4Qkju/Dv5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\nMEBIgAFCAgz8P1yQN/GMZ2PAAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8TsMtUF5Py9prvyncIbsnYM7K2dwIceZ2onyMtvLc7WjkCRbOl2mXYCH4Y4faFH\nJuor3ynunLT694WDVtglbkWaX03/xUduX3kTtSPk4Rbtle8Udy6j+LXPjius783Jfw4XvVc8\nWzuCH61v1U4Q3PiaTuwWyIV9VlwdjbMGBvTVThCYAyvfKVxqbsVex2kn8OWZmvO1IwR1Wks3\nbkxAkaxofr92Al/mVn9VO0JAr5WM1I6wFC/tbJjkfa8dwZ8dT9VOEMxvLc7SjrCMO4sNlcWt\nSA81jsh9w69rq50gmD26OLDyneLO8ndlcSvSSVH5Jf6bqPzoXOrWGmO1IyzHG7I2dL1CO4Ff\n6w7VThDA+Fp3aEdYgSJZMK/0Le0Ifp3k+M4SlZVv0tudV8wUyYJ3SiLzP+fF6pGJmjy92a/a\nEVZSK1Ihrdpd1UU7gW/za72gHcGv14tdukc8RbJgjxO1E/i3a1Syzmzh1LnqvLSzoFmE7uJ1\nY2vtBD7t0XmBdoTKKJJ5P3gTtCP4N9H7WjuCL7fVcOuGvLyPZN4DTbUTBNHhv9oJ/Pi61u3a\nEVbFmQ3mnbCHdoIgTuulncCH8k12c2flO4Vz7czb5CrtBEG8Vub6Tpb/OqOpQyvfKZz9bdzc\n0khtql1e52ntCDm5tfKdQpGMe7N0rnaEQHZ3fivLmS3O0I5QBS/tjLs8YjeVvK2FY79+VLGn\nWyvfKSw2GNfnZO0EwUzx/qcdIbvbHVv5TmH527REk4e1IwS0gduLI1/Xuk07Qhq8IWvad94k\n7QgBndVdO0E25V1cW/lOoUim3d9MO0FQb5b+qR0hizOdW/lOoUimHRe5ezwsrD9CO0Jm75S8\nqB0hLYpk2kbXaCcIbJ8jtRNkNGut07UjpEeRDJtT8p52hMDubu7ibyFLTOy2saO3nqFIhr1R\nFrk9F5PTij7TjpDeQ/V6/aKdIQOKZNil3bQT5GHjS7UTpDNnQOlgZ+8yRZEM2y1iWy6mnLuV\ndoI0RrdvE/x71RqKZFai0WPaEfLwXon6rSRXlxharZ/Lq/IUyayvvcnaEfKwuJFrZ2PM2K3u\nA9oZsqJIZt3TUjtBXg44VDvBql5vvtkP2hmyo0hmHbOvdoK83N9ksXaESsoHlQx0ZY/vTCiS\nWRtEYgeEKmYUj9KOsNI3XZq9op0hJ4pk1OxihxeastnsIu0EKwyvvbOrbx5VQpGMeq26e5eg\n+XKRK29//XVQjaGunmdRGUUy6uIttRPkaVSxG+dYj2rb8UvtDL5QJKN2cfQUy5wqmrpws87F\nQ8r6R2TDC4pkUqKhwxckZHfIAdoJkskp3es/qp3BL4pk0jhvqnaEfD3UcJF2hKcabRmdi4sp\nkkl3RfPt2CX+KHlfN8D8gaWDXXozKweKZNJR+2snyN9W56lOP27D1pG6kIsimbT+DdoJ8nfp\nJoqTJ4bV3OcPxfmDo0gG/Vn8sXaE/H1WNE1t7t/61ozSTaGXoEgGvVzd0eui/Ui0uEtr6jda\ndh6rNXe+KJJBF26tnSCMI/bRmXfR4JKB0ft/QBTJoB2dustpUI/XUznj+set1nBzw63sKJI5\nFfWf1I4Qxl9lbynMOrxOr+kK04ZGkcz5ytP7dV1C97OtTzn7UIf3N8mKIpkzbG3tBOFctYHt\nGT9tt45D10EFQpHMOeJA7QThjPF+tDpfYmi1/pH9d6dI5nS4UTtBSK2t3jn8113qPWRzPlkU\nyZjfiz7VjhDSsTZvx/5s480nWJxOGkUy5sUarm/YkcvTta1d37tgYPHAhbYmM4EiGXP+dtoJ\nwvq7+muWZvp64+a2pjKEIhmzwznaCULb4TQ78wyvtZdzW7sGRJFMWVzvGe0IoV3bwcYsfx4Q\njf1NsqJIpnzpRWATqRzGexb2N317zU6O30bdD4pkym3raCcQ0PYm0zMsGlzSf57pSSygSKb0\nP1g7gYATdzM8wU/bNnnO8BR2UCRT2t2snUDACzXM/rR4omHPaJ+PuAJFMmRm0efaEQTMr2ny\nkoaI7W+SFUUy5Pla6ttZSdjlJHNjf9Zh7Q/MjW4ZRTLk3B7aCUQMNbZkkhhabb9Zpga3jyIZ\n0uM/2glETPS+MTPwjN51h5kZWQdFMmNRnXgsRiXbXWdk2JEtNvveyMBaKJIZn3tu3M0htIE7\nGhh04WD3b8EXEEUy45Z22gmEvFJtjviY33Zp+rL4oMookhmH9NdOIKS8zrPSQw6vs9PP0mOq\no0hmtL1NO4GUvsfJjjf74OpDorm/SVYUyYgZXjTuM+fDrWuKnpn9cduOX0iO5wqKZMQztWPx\nduwSP3pfyQ0WoVvwBUSRjMlz4ywAAA8YSURBVDinp3YCOesPERtqSvf6j4gN5haKZMR252sn\nkHNmD6mRnm60xUSpsVxDkUxYWCuK21dn8EbpnyLjLBhYOijS+5tkRZFMGF00UzuCnH/qPiEx\nzLgN13pXYhxHUSQTbrSy14Etex0VfozEsFp7R+sWfAFRJBMOPFw7gaRhzUMvgM/cPXK34AuI\nIpmw9h3aCSRNDX2N4putNhBcQ3cSRTLgZy8G2+JUstHloZ6+aHDJgPkySdxFkQx4qm5cLqBe\n6j/bhHn2j1uv8YJUEndRJAPOMnHpgaJ3S0LsgzqiwQ6RvAVfQBTJgG0u1E4ga1GDvM9HmNM/\nqrfgC4giyVtYM25X2/TL96KQ0e3afCSaxFkUSd7HRXF7x+TeJnn9VEkMrdZP5qwI91EkeTd0\n1E4g7ZeiT/J41q+71ntQPIqrKJK8/Y/UTiBu08HBn/Na824WtuB3BUWSt9ad2gnEXbh50Gcs\nuQVfzPY3yYoiiZvujdOOIO6j4oCbIn2zSbNXzURxFEUSN6Je/NZ7FzceHuj44bX3jPot+AKi\nSOJO31k7gQEHHxjg4L8OjMEt+AKiSOK2HKydwIAHG/rfhGLUOuuPMRjFTRRJWnn1OP5yMLPE\n740jltyCL577m2RFkaR9VBSjeyystIXPXSgmb1f/MbNJ3ESRpF23gXYCIy7u4uuwJxtuP9Vw\nEjdRJGn7HaOdwIhPi3ycwx2nW/AFRJGkrXmPdgIjKprl/t81tnPr9y1EcRJFEjbZ+1o7ghmH\n75fjgMTQ6vvG8tdDXyiSsMcaxO/t2JTH6mXflO63PnHf3yQriiTs1F21Exgyq/TtbA+PbLHp\nd5aSOIkiCdv8Eu0Epmx7TubHYngLvoAokqwF1UZqRzDlis4ZH/q2a9OXLCZxEUWS9X7xX9oR\nTPnS+ynDI8Pr7Bi/W/AFRJFkXbOhdgJjEq3Sb3s5+5CywtjfJCuKJGvvAdoJzDl6z3Sf/WTd\n9WJ5C76AKJKslvdpJzDnydrlVT6XGFrWP6r/VqIokqgfvW+1I5gzu+pCytQe9R7WiOIeiiTq\nkcZxvqCt5xmrfeKZxptPUEniHook6pQ+2glMumbVfcaW7G8S31vwBUSRRG0W7r4NjhvnVf75\nM36jWN+CLyCKJGl+2ZvaEYxa++aVHw+P+S34AqJIkt4tma0dwajjey//aOYeNQr5FNWqKJKk\nIRtrJzDruZrzln7wVqtO8bqXWmgUSdKeJ2gnMGtujdR9NpbsbzJPOYprKJKk5g9oJzBs51P+\n/eOnbZo8rx3EORRJ0AQv7rvG37BO6hZ807RzuIciCXqwSZzfjl3iW2/0gAK5BV9AFEnQSX21\nExjXrsa6+dwqKf4okqCuV2gnMO7ao+ZoR3ATRZIzN/uuBogziiTn7dIopoYIiiTnyq7aCaCG\nIsnZ/STtBFBDkcQk1nhIOwLUUCQx33sTtSNADUUSM7ypdgLooUhijt9LOwH0UCQxG1+tnQB6\nKJKUv0vf044APRRJyhtlXKJTwCiSlMs2004ARRRJSu9TtBNAEUUSkmjyiHYEKKJIQr7NeNMT\nFAKKJOS+FtoJoIkiCRmwj3YCaKJIQjpfq50AmiiSjNklH2hHgCaKJOP1svnaEaCJIsm4ZAvt\nBFBFkWTsepp2AqiiSCISjR7XjgBVFEnEeG+KdgSookgi7m6pnQC6KJKIo/fTTgBdFElEp+u0\nE0AXRZLwZ/Eo7QjQRZEkvFq9XDsCdFEkCYO30k4AZRRJwk5naCeAMookoKL+E9oRoIwiCRjr\nTdWOAGUUScCdrbUTQBtFEnDkAdoJoI0iCeg4VDsBtFGk8GYVcaPvgkeRwnupxj/aEaCNIoV3\nwTbaCaCOIoXX62ztBFBHkUKrqPe0dgSoo0ih/c+brh0B6ihSaLe30U4AfRQptMMO0k4AfRQp\ntPY3aSeAPooU1u9Fo7UjQB9FCuuFmrwdC5UieUtkPSJKRTqvu3YCOMBukVL98bycTYpSkXoO\n0k4AB9gvkre8TVmOi1CRFtd5VjsCHKBRpBUfZhShIn3h/aIdAQ6gSCHd2lY7AVxAkUI69FDt\nBHCB7SItW2fIsdoQoSKte6t2ArhAr0jZjotOkWZ4X2hHgAt4QzacZ2sv0o4AF1CkcAZtr50A\nTtApUo4TGyJUpO7naieAE1R+R8p5akNkirSo9vPaEeCEqBXpxZucuoHK50W/aUeAEyy/tPPz\nJlIyW5Gea7L28Ir855d2SzvtBHCD7d+RllYoxO9Ifw+pt/6IEAFkHXyYdgK4wfpig1fp7IaM\nsv6ONHNQ9S3fDRNBUJvbtRPADfZX7ZY0KeSq3U8Dinf8X6gQQn71xmhHgBu0LuzLfkTOVbux\nfYv7TQoZQ8DTvB2LpVTeRwpfpGRy5KbVBswIGySss3tpJ4AjontmQ2JE+7qD5hiPktW2F+jO\nD2doLDYIvLRLWTisRathmq+tFtZ6SXF2uMT6+0gZ34/9qWXDFWr5PLNh3pD6641I5B8npE+K\nZqrNDbdYP7OhykfLLXpmxAqXeH63uPp9UI1ub+adJ6Sh62nNDNco7CJU5cOqPvRdpGRyyoCS\nHZWuCTrgCJ154Z7oFymZHN+vuN+EvDOF0HqYxqxwkTsv7SoLVqRk8qPuZQPsb+Yz3fvK+pxw\nlDuLDZUFLVIyOXKj2oNm558rL0/WW2x5RjjLzeXv4EVKVoxo02SI3UssztzJ6nRwmZtvyOZR\npH//pwxbo/Uwmz8jtrrI4mRwW4yKlEzOGVxzA3uXWJTXeMXaXHBdrIqUTE4bULrN+7JZMhpV\n9IelmeA+tSKJrtpV8m2/or7f5fvkQK7vZGUaRELsipRMftKzdICN+4z3O9rCJIiImL20W2pk\nl1qD/pTKktGadxufApERyyIlK0as22jIAqEwGUzzxpudAFESm/eRVrNwWLM1za6FP97Aod2M\noC0uZzZUNXfJdkMGL7E4bRdzYyNy4nGuXXpLtht6R2Cc9La42NjQiJ44nP2d2ZLthgzt81Ne\n/TUzAyOS4l2kZHJsP0PbDX1YbH5dENER55d2S32wjZHthq7tLD8moiu+iw0rJEa0ryO/3dA+\nx0qPiCiL6/L3KhYOa7HGUOHthlrdKzseoi2eb8hWMW9Igw6ia+E/ed8IjobIK5AiJZN/yG43\n9Chvx6KyginS0u2GPpcabGBvqZEQCwVUpGTy637F/X6QGarbZTLjICYKqkjJ5Cih7YbmV3tD\nYBTER4EVael2Q3+FHuW9YttbFsFtBVekZMWIdRqH3m7o6o1EsiA2Cq9IS7Ybahp2u6G9jhPK\ngpgoxCItuaNz3XDbDTW/XyoK4qEwi5Tabmjr/LcbmuR9L5gFMVCoRUptN7Rjvnt3P9RY76ZM\ncFLhFimZ/HSHfLcbOqmvcBREXSEXKZkc2TW/7Ya6XiEeBdFW2EVKJpZsNzQ/6LPmlb5lIgwi\nrMCLtOQSi+ZrBr2j8zsl/m5xi8JR8EVKbTfUMdglFld1MZUFUUWRkqnthrYIst3QHicai4KI\nokgpkweUBNhuqNmDBqMgkijSMuP6Ffeb6O/QHzyVWz/DZRRphQ+29bnd0ANNTUdB5FCkSp7v\n4Gu7oRP2MB8FEUORKls0rOUaQ3JOvclVNrIgUijSquYNadBmePa18Lml71oKg+igSKv7Y1DN\nblmvI3+rdK6tLIgMilTV1OzbDV2+qb0oiAqKlE7W7Yb6nGwzCqKBIqU3qkem7YYSTR62nAUR\nQJEyGblx+u2GvvOM3CYG0UaRMkptN1T1js73N1PIAtdRpCzSbjd03N4qWeA2ipTV30Pqdlpt\nu6GNrtGJAqdRpBx+G1Rt6/cq/X1OSf6bDyG+KFJO362y3dDIsnmKWeAqiuTDpzsU9/tx2ceX\ndtNMAldRJF9Wbje026nKUeAkiuRPYkS71HZDiUaPaUeBiyiSX0u3G/rGm6wdBC6iSP7NGVy3\n09EttFPASRQpiBmnVNtPOwOcRJGCmfyzdgI4iSIBAigSIIAiAQIoEiCAIgECKBIggCIBAigS\nIIAiAQIoEiCAIgECKBIggCIBAigSIIAiAQIoEiCAIgECKBIggCIBAigSIIAiAQLcLNJoD4iY\n0YG/zc0XKTnmswx27f6gqu7MX9jz75rpO3NM8O9yC0XK6IgjFCdnfuaXnJ8iMT/zC6BIzM/8\nAigS8zO/AIrE/MwvgCIxP/MLoEjMz/wCKBLzM78AisT8zC+AIjE/8wvQLNKAAYqTMz/zS86v\nWaRZsxQnZ37ml5xfs0hAbFAkQABFAgRQJEAARQIEUCRAAEUCBFAkQABFAgRQJEAARQIEUCRA\nAEUCBFAkQABFAgRQJEAARQIEqBbJU55df37l/z+mOP+y+6eozS/976/8lVSbXf8fUj1AKoLm\n3LH6+hdwkVb+WYjzJ5V/JGv/MF4aIA5FSjrwxSzoIv07dwEXSXr+wi6S9i8p2j+S1V/aqU1P\nkWTnL9hfURwpkupPxJgsNiTVi6T/80j7pZX+/IovrWWLXMBF0u6RagT9X9EqxdCbmcUGicm1\nv4uSil8Bz9N+bbUshu7MFElgbu3XdSv/VJleuUjaPxIpUgzmXjG/G23Wm1r3d6QkRRKZW33Z\nqJBfWamv2kl//SmSagK92ZdF0Jxb/92HuCx/A3FBkQABFAkQQJEAARQJEECRAAEUCRBAkQAB\nFAkQQJEAARQJEECRAAEUCRBAkQABFAkQQJEAARQJEECRAAEUCRBAkQABFAkQQJEAARQJEECR\nAAEUCRBAkQABFAkQQJEAARQJEECRAAEUCRBAkQABFAkQQJEAARQpGtLfpVH93plYjn+JaKBI\njuNfIhookuP4l4gGiuQ4/iWiIVWZf/9YcU/71AdLP17+ucp/wjK+6tGwrEhLpftw6QfL/oB1\nfNWjYWVTkpX/8Fb5ObTyr7CNL3s0rGxP5bqsVqSVL/xgG1/3aMhWpBWv7fh5pIcvfDTk+olU\n+TAo4AsfDf6KxEs7NXzdo2G1Iq2y2LDKCgRN0sGXPRqqFmn1lXCWv1XxVY+G1YvEG7KO4asO\nCKBIgACKBAigSIAAigQIoEiAAIoECKBIgACKBAigSIAAigQIoEiAAIoECKBIgACKBAigSIAA\nigQIoEiAAIoECKBIgACKBAigSIAAigQIoEiAAIoECKBIgACKBAigSIAAigQIoEiAAIoECPg/\nw+5cWHYDD6cAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(meanx,type='l')\n",
    "plot(meany,type='l')\n",
    "\n",
    "plot(varx,type='l')\n",
    "plot(vary,type='l')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vbb5TbtCeLcCWnB0CFr7E5IkfbrAdoLxHFpB/tejN61HTcbYF9tl9u1J0jj9jcU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\ngJAAAYQECPh/Sc8i1TMm424AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(stiffnessx, type='l')\n",
    "plot(stiffnessy, type='l')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>0.0552754878675683</li>\n",
       "\t<li>0.0534529075489834</li>\n",
       "\t<li>0.0562474172592477</li>\n",
       "\t<li>0.0564044199968204</li>\n",
       "\t<li>0.0583520994289822</li>\n",
       "\t<li>0.0502747971843618</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 0.0552754878675683\n",
       "\\item 0.0534529075489834\n",
       "\\item 0.0562474172592477\n",
       "\\item 0.0564044199968204\n",
       "\\item 0.0583520994289822\n",
       "\\item 0.0502747971843618\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 0.0552754878675683\n",
       "2. 0.0534529075489834\n",
       "3. 0.0562474172592477\n",
       "4. 0.0564044199968204\n",
       "5. 0.0583520994289822\n",
       "6. 0.0502747971843618\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 0.05527549 0.05345291 0.05624742 0.05640442 0.05835210 0.05027480"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xRq+Bx3grBu68ydIGpL46dzRwiRPfoSh7/tGnYFd4KwGj3JnSB6D6f9zB2BYIFI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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kb = 1.38064852 * 10^(-23)\n",
    "T = 293\n",
    "varx\n",
    "plot((kb*T)/(2*varx), type='l')\n",
    "plot((kb*T)/(2*vary), type = 'l')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.3.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}