emma.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[tutorials](https://gist.github.com/fabian-paul/b14793bea0bf0014c1c1a2e48e24e61f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import pyemma\n",
    "matplotlib.rcParams.update({'font.size': 20})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# CVs, distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gerry/gp120/env/lib/python2.7/site-packages/ipykernel/__main__.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "cvs = pd.read_csv('108/meta_0718_lab108/CVS', sep=r'\\s*', header=None, comment='#')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>distance1</th>\n",
       "      <th>distance2</th>\n",
       "      <th>bias</th>\n",
       "      <th>work</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.075519</td>\n",
       "      <td>1.619646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.177365</td>\n",
       "      <td>1.732320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.200224</td>\n",
       "      <td>1.804025</td>\n",
       "      <td>1.130038</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.283586</td>\n",
       "      <td>1.830151</td>\n",
       "      <td>1.502643</td>\n",
       "      <td>2.926362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.203514</td>\n",
       "      <td>1.827165</td>\n",
       "      <td>3.330090</td>\n",
       "      <td>4.329245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time  distance1  distance2      bias      work\n",
       "0   0.0   2.075519   1.619646  0.000000  0.000000\n",
       "1   1.0   2.177365   1.732320  0.000000  0.000000\n",
       "2   2.0   2.200224   1.804025  1.130038  1.500000\n",
       "3   3.0   2.283586   1.830151  1.502643  2.926362\n",
       "4   4.0   2.203514   1.827165  3.330090  4.329245"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#! FIELDS time d_v12_b d_b_v3 meta.bias meta.work\n",
    "# bias: the instantaneous value of the bias potential\n",
    "# work: accumulator for work\n",
    "cvs.columns = ['time', 'distance1', 'distance2', 'bias', 'work']\n",
    "cvs[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f22db2a0950>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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QyuYxvwHwaNQjqiYs3HQAo16Yi2vfts6pKTpebrkOAMaNtg5BPlCSXr4pI5/9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\noWihgeQu/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2319656510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(cvs.distance1)\n",
    "xlabel('frame (steps)')\n",
    "ylabel('distance1 (nm)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f22db3076d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tCmONjBIOnYtcnfjXNbfEUr/zePPLXzM6RLvodEVUvedpuE/vlzhBZt8/PjON\nR6Y0mj5nLFqdtty/l1+N3pIHfrpbqLFGyyLq3eltYEQ+BCkFtu6bqY5nMJUe00NlCDNEVEvFWzOW\n+i4Po8xyQRS7+8CLn82Zb6iUaq/lgkGXPOu7fMma7KsTf7N4TYJSP2f/IYFjA4pRAPDQ5Ll8t8xR\nYvvd+AY/vvPd0DJsuUnH0GONlkNUBXMJMEpETsqHMMWmKbcyb2RVmNlJrgIICuWD8eZq7Ddsk4zj\nszE3+vlbmpl+SeC9S/bNyX72veH1hPM0K02eyt6DewWum/Ltcg67bVKDsvK2R85Er06pYdGGURVx\n/JHARGC8iJwJTAH8DOmqqlc1VbhCsr6mnruaEMM/e0njjzq5RIcfuYqOKpQJafUGr4+gUfgTdx3A\nf336smcjVmWF0KlNFas9/ognP57HnoOjl+IpRZJNh9lm1vvhvQ7SzaCD6unFWbm+lh/d+U7O5DJa\nNlGv8HE4tcgE2As4x13m9yor0iWPhcE7IwmjPHIWplwgBeNNoHt5emMG+WWHbc2saw5JlSuLqYeq\ncvEhwxKWPe5Tc6tcSa651jZHxScBbnzxq4a/m/rw8sFs/8CRUT69j1oiD34wp8GUaKQnqoIZE/JV\ndi2TO7dtmi9j14HdG/6uTKM8hvR2bNW5UjCl4KMQEa49eruEZdkoPiXVR1DIUjj5Jjm8O5cK5sVp\njUo/rPn1ooOHRvqOeGfXOLsN6h4wsvlSUxfj949+1jDLW7muljUb6xj/1ize+Goxqpq3grSqmlIo\ntdSJZCJT1dfzJUixaeqN7LDtN214yg/zA89VDmcx9ct1x2zXcJP8wYh+XOTp855O8QWtq49pirP7\n4IDyJuXIoxnK4tTWx2jlY8LaUFvPMf8IX/stU5DJU2ftQW29hqp87W2lkPxQNHLzlqdg4qbIpWtr\nmPT1En5y73t0blvFKo8JeXj/rjz56z1y+r0vTVvIT/89GYDjd+nPNUdnToUoBcoy4z4fNDXqyfvb\nm7Ms2Mka78Ve7Ez+8W/N4tf/TS09EsSOPt08j92pf8PfyQl86eT6eK7/jS2myh1JSX2jtihv/8s3\ni9ewwc1fmbdifdqx8QrSyXyxYDVTvw/vcM9kItt+s67stHk3jt9lQMZ9+bVSaPie5lhmISQ1dTF+\ncq9TF27VhsRZRTYtK/x4adpCzn7gI655bnqDcgF44P3CVdJoKqZgXJrqLPcqjKBS5l6i6pfLDtva\nd3k20/EZFpxKAAAgAElEQVQNtfWMe3oaEz6bz8a64OQ9L4dtv2nKMu8NpqqyguuPHd7wOabw9jdL\n2O/G1/lq4WpuePHLBll/et/klH2Bo5SuPHKbhGVh/y+FLvoZhrUb69j3htd9K0a/fsHolGV3vj6T\n92Yu5dUvFlF90YSGY2obMddpaJ9w4fZhy8vESQ5FTqdfaupi/P6RT1mwMneFUUuBsNfZhjRJsWH5\n6b8n89Qn33Pn6+VbQDSbYpd9ReRvIjJDRNaLSL3Pq7wMheRAwXjOZJhyHFG/buma1CRLyM60N/Sy\nxr4hZwbc7JNZtjZznsYPd9qs4e/nP5/PuKemMmPRGg646Q1umziDucudJ/jktghx6mOaEu4a5gc9\n9fuVbHHJs7z6ZeGKcIYhXv3gLbeYZXvPDX3zHh1Sxs9bsZ4f3/Uup413zFLx0Peo5V2GR+iw+svR\nW4Qe2797+4TPQb7GDbX1TPxiEQ9O/o7Ln/w89P7LgbD3iXU10RWMqnLNs9OZuXhNxtluuRC12GU/\nYDLwc2AtTl2yOcDXQD1OdNknwJu5FTP/NPUJ2DuDCZNoGXUG8/zUBb7Lm/rg/ubX4Sr53v5quIKM\ncS57ciorInajjGnqTSvM/2WyG/X0agGrPIfhiwWOWWv5ulq+WLAqcpHIOp9S/AAP/Xz34O+86qBI\n3/Ht0mBzrh+njqpu+PvAAP+Y9wGmuTHl23ClmZ76OHr049zl67nzjZmcNv4DTrg7XJLrupq6nMyW\n8kXUGczlQB/gIFWN20P+papDgUHAC0A74OjciVgYmpqw6NUpA5Ke9LzEWwJEVTCtAlpgRo0ii6pI\n6+pjVF80IdI2cZJL22Q65jUb61Ls+rOWZL4Bxp8qS80jMMmjvA+6+U1qItZzi/+rrnELWgIM6tmB\nXQZ2D1QyuYxM88Mbqty/W+J17o2kbG6oKpNnL+PUf32QeTAw7ulpPP5RtF5H8clRfUzTlgXysvXl\nL7DfjaUbexVVwRwIPK+qLyevUNW5wLE4CubKHMhWULympmd+sycTz9sn0vbeyLG90mRLx79GIt4O\nW1X5j49q2ntvln/JmSBqclhiOii67oRdG53NybM/vyTOZBrOaYn1jmmqOPGHkZc8IcgzXYW7S45u\n5rsNipbbcsA2jbOW5P9Vc1Ew81eu53vXRDVj0Rqe+Ggej0yZyw/viJaAeu6D6Ru5JZPtw2fc9FyK\nRFUwfYCpns/1OAoFAFVdA7yEk/FfVnif7Pt3b8+gXtFqK4U1kcW/JepFVOWZwXhrpkWdkfjpo3if\nkf9791vuc/uLpBufjh4dUlsKxAmabQ3xOI+zia4rPfe+w3szl2Ucc8txqQ3A4hQibqEp0YzJviFv\nlYBf/GdK1vstNrtfM5FR104EYL8bX+ecBz/mgkc+zfv3Pvnx9wChTMsLk7rKLvYphFsKRFUwqwDv\nHWQ50C9pzEog+BG+RAlbaiMIr+8gXaKlZmnOaeVpFHXF4Y0RZelmMI9Omct/3v02YZmfaN8sXsPs\nJWu57InPueKpqQnrJnw2P5KcA3umOq/jBPkgurZvvKSyOffxc5qPFtfZ8syn3zM5hL3+yB2Sfz6N\nZIoQTE5uzYZsTtk7F4/lHyfumGLOzOZ/ZzRy40tONYbVGzLHSO169SsJnwvVtiMqURXMt0B/z+dP\ngLEi0h5ARCqAA4Cya7Tufbpul4Ud2/tDDXCXAI2RRVFt5Vtu0smz/8YvS/eUe97Dn3DpE4lRPF3b\npc4w2req4qlPvk9Ypqo88+n3XPxYtOZS447YJnDdm187rXz3SqotNqBHoy0/nXIOotFEFnnTvPFE\nDkrcZGpN/OOd+6ddH4bt+4WPOIvTt0s7Dt6ub8pybzWBOK9+uSgwArLUaWpGfr5NhlO/b6wMUUKX\nfgJRFcwrwBgRiddVuQ/YFHhbRP4KvAVsAzyYOxELQ30TZzAJOSFpNEz8Cb9T22h1Rk/effPG7/KI\nF2Qiey0gZNcv3Pi/732bcoG++uUizrr/o8D9b9rFv9PkJp2Dq+o+/7kTCdezY+KY1q5ppWObqrTK\nOYi47bqUzATJyXdezt0vsZx+8vmIs1WfTmmPKRc+p+3S9I6JykafaKbaeuXYO99heYgw92z5aM5y\ndvjjizn/jhcCIjfDkosHgHSs8VxjpeZ/jBP153wvcB3QE0BV/wPcAmwLnAfsiqNc/pxDGQtCLqPI\n0t0kbz9hR+4/c9cEs1AYvPZu78UUZCILinaJZx97ufONmSn5NNPnr04rT5D86XxCcV9PTJW+Xdry\nyeUH8M9TRzK0Tyc27dKWq4/eLksTmfOePAsrJu/PCva/JFeH/vHOm/mOi6mGiqKL839n7BJ6bDLd\n2rfituNHMPvaQ7PeR/uAxM2Zi9cy4qqXst5vJv7+2jesWFeb8/9/uoeEMAT1cEqmtj7GDS9+Gbyf\ni/xLO7765eKGv0vVOhlJwajq16p6narO9yw7F+gL7A70VdUTVLXs0nebWovMG4KazszTpV0rRm0Z\nvfyJd4YkwCZuQmKYKLI5IUIeb37564TP/5w0K+3420/w7zvXPY2TP24erI8p7VpV0qV9K8YO7U1V\nZQVvX7wvRwzfNCsTWbJOm/DpfJaUqFmmT+e27LR5t4RlJ+1W7Tv226XrOPuBjxKW3X3yyITPX/3p\n4Ia/00UvpuOrPx3MlEv35/DhqdUaohC1MkCuiP80kv2HTeXCJjr2H/0w2FNw76RZnHjPu6gqg//w\nHLdNDM4z69e1ne/yO15vLKv05cL0D4TFIielYlR1saq+p6qpRtgyYVYTy/WvWt/4tJMPZ6f3xlsh\nwiO/GAWEe0ra+6+vRo42C8q2H+xGfAVF2bWpCr7JfDrXsRmrBvtLgupb1ceU6osmpES5QaOJDGDV\nhlp+ff+HjPxTSiR9SbBgVeqzV21AKPhvHvgoZfzQPp0SPrfOQU+Z1lUVCef91fNHc3lSaaIdQlQH\nKFaQxcaI+UXpCNO2Y9+hm/DwL3ZvMHenK2x576RZ1NXHUvw5Vz0zjbdmLOXyJ8MpxQ8v2z/t+r4B\nJutiEzWTv15ELssw5g/lWCpm3NPTmrS997eVD3uoV2lViDSY4R7JUKE3TrKC+e+Zu3Lzj4NDZP3Y\nZ0gvHvnlKF46d++047boFRxJtnDVBmKqgUo4aAYTnyFe/ez0lHXe3+4NLwSbGkqVsPXgILhJ2T5D\nche4ObBnB7ZyFdmoLXrw4rl7hzK/5bKBWhRe85iKmkJNXYx9bwiXtLhzdXd+M3ZLIH1i9VXPTGPL\nPzzHvZNmcfcbM1m9ITEE+f+SojyDSGcZKGWiXhFCuICFErUI5o/krOZck2Aik+hPi8mmtGF9Owc2\nkAqKnjl3/yF0adeKwb07+a6P8+K5wUmqqzfUElMNlD9I8cSHZzIJ3vdOuB9sKVHtU5csCL9y/rOv\nPZT7Ts/e/+JHH/eJeI8tezKkdyc6heiXtP/WvdOuz0eflFwWOb3gkWiJkUfvuBmzrz2UbiFu/n+a\nMJ0/Pzud7ca9mK14PHXWHg1KLZldBpZmM7h8PHJ0A8rOB9NUokaFRcX7ZC8S3QyX/EPs3qF1YIJi\nsj8mThgzCWSSTaiPBc/yvIr0qBH9GuzP6e5NpdB0rSlUVVaEdq53zPN1FmeLXh2Z9Psx/HKf8MUw\nfzSyf6BvDmBxHvxi785MNBGvWFfDi1MXMH1+Y3uDLxasYlqGdgcbausbEh0zkam6xSHbpe9hFKZw\nrB/bb9aV3QMqL3Rt17SGifki49UqIsn2kGqfZQCVwADgRKDs7BQDe3Zg1pK1nL7HwMyDfch3b4yE\nKDWRtDkf5z74ccoyvyCGoKz7WyemKph9h26SWcgQiDhPskGt4b1mFu8sJ+5n8VM0zanrZRBb9e7E\nCxlMk7lms4izchEJjCSD6OWR/Fi6ZiMb6mK8PG0hG2rrG0x5cXb4Y2O0WlxpH3SzU3t31jWHBD7Y\nRGnNfYRPMMTVR23HJY87OWMXHDiUZz8LDnHeMUJE3a3HJyrsoAChUk1yDfM49BqN1TgUOMV9+SFA\nDCdkuaw4effNufLpaYFT0GLjfWqqEAn0VUz7fpXvj8XvKb+qsoK7Tx6Z0MwI/G/i9566c0SJ/YnF\nNK2JzOu0rqxonHnFZfJTJX5mo2IztE8nvliQu8ieyw/37wdUTjTVNRmLKTv/+eWEqMFBafx9H81Z\nziuehml1MU2oiAFOhOU/35rFeJ/gkSD8lNQJuw7goG37oKr0CMhrygY/ZeZHOSuYP+L8rgWnmvJr\ngJ8nrB5YCryqql/4rC9p4tE6JZqvlGDiqkjjgznkVv9OCUFlWvYMETKdzuwRlf1veoN9hvQKfJJs\nXelVMNLgc0k3R8mU8V4MOrTJrSkrKBmz1Eg3S7nxpa+4+qjsytvEYsqgS55NWT5zcXCe0FF/T2wz\n7deS+jcPfMgnc1eSibP3Hcytrzgz+44B/9tiOeKPHtGvJB+yIISCUdVx8b9F5BTgCVW9NZ9CFYN4\n17jaErxZgVOeI45IdJNckJ+iXetKZl97KCvW1SSYF7yM2Sq6eeyfp47k9PH+zcxe/yo46sf7QxHx\nKJikdy91PlmyxXxQ2FBbH7pvSFhK9Qk1Cve/NydrBbNifbTeQn5sqI2RnB8c9Huv7tE+oTPtTpt3\n4+Xf7c3vHvqEAzIEM+SKIHP9Ydv35ZlPG2sE3hgxGrSQRE20HNgclYuXUp3BeE1HIhI5czeTnyJd\nZYFsbm5jh/bmz0dtG3k773dVijSYQ+IzOD9Z/GZnxSx86U2ASya57bCXdOGuZaNg8iTmoQEz8yj4\nFW6dNt/f+X/LcY2z9ncuHss+Q3qx5SadeOqsPfPub43TvYO/475srgWi58FUxgtbJi0fKyK3iMg1\nIpKdl7xEKKWKvEFUiPheZGOufy1wm/qYNsxifrqX/7/ot/sOTvh89I792G9Y76zzG07cdfPMg9Kw\nfF1NQ8TNcreEud8TZ51PVE8x/4vp2uX+eGRwfarxpwX7uZJ9B6VKWzfR9vDhm/rmzkTJ+fEyf2XT\nA1MveyJ8+2Zv2+lszJO5MGn+KOBaueywrRtyzd652L+MTKkQ9c5xPbBMRBoq5InIcTg9YH4D/B54\nX0RCV3kTkR4icqaIPC4iM0RkvYisFJFJInKGW6G5YJRDQpOfD6Y+lr5uVSzWWO327jf9y8Ccs1+i\ngjls+77cc8rIJiWOPn3Wnjz3272y2jZuBli5vpZbXv4qcFzUVsT5xusve/qsPRPWjR0WbG4c1Ktj\nYPJr6xK1sSez26DuXHroMP505LbsNbhXSr+bk+59H4BFqzek9DQpNAtCKq3kvjdhOHvf7IKFfrBD\no1N/k87+2fk9O7bhlfNGM/vaQxNM56VI1Kt2bxwnvtcrdgWwAjgZuBDoCvwuwj6PBe7GKZT5HnAz\n8ChOAc17gIekVEuFFglBUhTMlxkilupVWbsxfYGF5NOci2aW223WhWGeBmnZsKG2Pq0N3s/JX1XE\nJ35vPkVyteItMjSy+8GIgP4wZfILEBHO3GsQXdo75p0jd+jHZt0ab4LxIqC7/PmVlJ4mhcCbD7No\ndXoFc/wuznNyNrefk3evZuqVBzZ8/kWGfKJ4h9IwCa3lRFQF0x9oqMomIoOArYDbVPU/qno98Bxw\nUIR9fgUcAWymqieq6sWqejowFPgOOAY4OqKckRk7dBO27de0G2GhEEk1mVzxVPrp/wezlnHew9Ey\nlaO2Y84Vhw/ftOHHDU4Jj421wdquLqYpJonaeqefTT6yxzORXGjzUJ/eKen4zxm7pizrFrH6dimR\nXNfMyyvTC1u+8M43vuGtGUsAOOL2t1LXn7RTQ0O/a47evknVpb1m7PMPGJJmpFMEF6BtK+eWnK4z\nbDkRVcF0xulqGWcPnAjS5z3LpgL+9cd9UNWJqvq0qsaSli8A7nA/jo4oZ2TqY5pVJd9iUFEhCU9V\nNXUx5mXoy/3Ex9EbYI1MqvpbKG47fgTXHL19w+f1tfWs9/QaSa5KUFcfS1G49THlrPs/4u1vwpVM\nzxXrffwvUcO89xzckzP2TPSTlWoYahjSlZAJW+wxE4//alSocU9+/D0n3vNe4IPHgdv04bQsk62T\n8TYVrErz/ztg694NE9Terlns1FHVOZGh2ES9aucD3rO/H7Ae8Dbg7gjkqthl3C6S9+KZMdWCRYc0\nlWQxN9TVs9bnxvbL0Y3T8rA32imX7gfAfsN65zRhrCnU1scSouiGXPpcwvr6mAaaxM5+4COqL5rg\nGwiQDx6Z8l3KsmxMLLmoklwqpDv+XM0wRwyI9jD0aYjcl1xw50k78er5owGnllgy95+5K3edPJJf\nj9mSHh1ac8yOm/H+Jfvy6zGlmfAdlahX8bvAESJymIjsB/wQmKiqXgP5QKDJ/WJFpArHrwOJM6S8\nUE4zmORkNsFxhCcTFLE0pHewH6BHxzbMvvZQ7jllZOCYbMhkg07HF/NXJ+QeJM9gamNKVUUF1T1S\nw3zjbQf8FHAhueuknSI1BDtmx9BGgLJj7vLG/JLvcxAd9vN9BkXe5si/pZrHmnKNBnHgNn0ayvpv\nv1liLb9endo0lH4Z3r8rUy7bn24dWrNJ57Zl87CbiajpxlcDRwJPup9jeLpXikhbYC/gkRzIdi2O\no/9ZVX3Bb4CI/Az4GcCAAQOa9GX1seAS8qVGsph+QVS9O7dpsOsmc9WR0fNTmsovR2+RNj8kHel8\nR6s31PLK9IX069qOu0/emdEBodpBPVdyyca6euau8DdVHrBN+gKIyXgjl9Llx5QjL01L9LuoatpZ\nzqdzV/guf/LXe3Dk395KG/odllaVwkUHD23yfjLx+4OG0q19K4b17cymAY3EmhNREy0/w4n2usl9\njVJVbw/eEcBE4IGmCCUiZ+PUM/sCOCmNPHep6khVHdmrV9P6YZSTgkn+MfqZGWrqYnRu14pu7VOV\nTDp7cL5IPrdhbMxBlWO9nP/wJ6yrqW/oMxNEIQIWfvvAxw0VIZpKK4+JLNsw71JldlI4/YMfpJoV\nvXwwu7EqwqxrDmn4e3j/rsy+9tCG5nf/OHFHhvXtnNVM5IrDt4m8TTb8cvQWHLfLAIb370qvTqVh\ngs4nke80qvqZqp7vvj5IWveOqh6lqlm3ExSRs4BbgGnAGFUNbm6eQ+rTNMEKy1sXjeU1196aT5If\n9vxmMHGF+VRSHgbAyvXZlQtvCsnmx6OCwnE9nLpHdcYx8dyfDXUxNuvWPqXjY5yV65peaiQTL+cw\nIsrbJjfXdc2KgTfKL7lnz0WPfZZ2W2878nQznYO368tzv92Liw4eyl+O2T5h3QUHbhW43Tn7DeYn\nuzUtKdjwp6Q8iSJyDnAb8DmOcgmueZ1jYrHgCr9h6de1HdU9wzePypb40/hVRzpPXX4O7L3cDod+\nWfh+/pp8k3xqh4foLZPuv/HtUkexLFrthATX1DmBAM+f41/SPpsouqiUVrpnaXHBgenDdOPMWLSa\nN79ezNaXP99Qs+6656PXzg1q+e3HjhEDBIzwpH00EpG4k/1xVV3t+ZwRVf13FEFE5Pc4fpePgf1V\ndUmU7ZtKLmYw+aZD68oEZ3XcEZjck3zUFj24za2l1M6nP0dlRfFNZE1lyZqNbN6jAytCzky+Wpi5\n13o2xGLKH574jD6d2+U85+aZ3+wZ6EcrN8aE7Ce0341vNPx9yj/fD8xDydQienhSgmu6bPx0PWyM\nppFp7j0e58HsXWC153M6xB0TWsGIyGU4bQGmAAcUyizm5btl62nfurRNEe1cBROPIovPuLy9Yn6+\nzyAuPHBog/LxywxuVQRFmusab1Hv5cmO5Vwx/u3ZPPB+eh9Ctmzbr0vmQWVCpgjNKd8uZyefvCu/\n8kdf/ukgqjI8JA3xmEp3ru7GKaOqueY5/5mQ3/cauSHTHfV0HGURL0N6Wq4FcFsA/BGnn8ybwNk+\ndtbZqjo+19/tZeX62oYyFuVC/EfrbcF61pgtM84WilGOorJC+Msx23Pho5+G3iadvf2eN2cxsrp7\nLkRrEnOWrfNdvt+wTfjhTs031DgqmXKqJn29xPdG7y3gev9PnQoHbaoyzzi8puEeHdokJD16ad+6\nskm19oz0pFUwyTd1Vb0vDzLEEzcrgXMCxryOM3syPMR/F8fe8U7DsjCmqJHVxXli+9HO/bnw0U85\nbPtwpVPSmZyWr3OUaoX4BzkUm7P3HZyS99DSmXrlgWxzhW/GATe9/BW/TSq2mkyUcO1ObVtx3v5D\nuOGlrxp+J+fuN4SbXv6KW47bgd/+7+MGmYz8UXQnv6qOU1XJ8BpdbDlLgTt+shMHbN27oU6RnzLx\nM0W9eeGYhM9BT3OF4PMrDwysGJzMkjXBjtp4Xotff5X4k24y3yzOrR8mFlOmfu+fEb68AFFr5Uam\naLiHJqc3NUadaZy+50B2HdidCw9y8lvOGrslk34/huGu4u/fvZ3NXvJMaTsdCsyB2xSmU122jKzu\nnmAW8lMmfkqnf/f2XH/scFpVSsZqvvkmqN2sH+meWON9YUZvtUmKAz8of2bfG15vUvHCZO5+c2ZC\njoaXYhTZLHcufCS9+TSqKujQpooHf757w+fKCmGzbu3ZUFtP9w6tuezQ4CKcRm7IFEWWbdaYqmru\n6y7kkdZVFQUJMc4lfg9fQc7UcvQHbLNpcHXreBSRX6n+Qj2VBjmNoby6DhaDmVcfwqBLno20Ta7+\nrW1bVfLhZfvnZmdGWjI9TlaQGjXWGogb0euBJUBPHB8KOAEBhc/kayJ19TFaFSF8tyn4msia0Y2t\nW4fW9Ovajnk+5VcGuHXHitVSIFPxzHKpa1csKiqELu1aRcrJSq7BZ5Q+ae+oqlqtqgPjL2A4TiHL\nd4ExQFtV7Qu0BcbiNAybC2wftM9SpD6mxLT8SqL7Pb03NzbU+hepvPxJp/9NXSz9jT6ejJpr/Fo3\nezHbvj9en9kbF4zx7X1jNB+i+mD+jNOxcltVbZilqGo98JqIjAE+c8ednTMp88gLUxfwuwediJJi\ndkHMhre/ScxFjdrYqhwIKlu/wW1AFs9vSU5I/Oiy/Ymp0qNjGy7LUc8RL5kUW7ldS4Ximd/s2dDi\nukv7VoGJpAdu05sXpibmLpnOLj+iPrIfBTzpVS5eVHUDTqXlvHegzBV/fHpaQ3b8A+/PKbI00Th1\nVGJjpBEDml9Y7Pykcu5vXTQWgDPdhlwLVzmlYl45b5+Ecd06tM5rP5vklgHJtCtipF4p07ZVZUKg\nR7vW/regA7ZOrT7duswsDEZ0BdMDyJSl18odVxZ4n0R/ulf0vhLFpFPbxAloc2pSFUQ8RDtZeSS3\nTM43NRl8MJt0bv6VcnNBUFSj32ylWzNpI9ySiHpH+gb4oYj41rAQkW44TchyU7O8AHjvE8P6Bkct\nlSLJCqVvl+bfXyIe2PD5vPAdCf9wyLCcy/Hi1PSlZzbp1Dbn39kcEREO8umVc9j2mxZBGiPXRFUw\ndwCbAu+LyMkiUi0i7dz3U3Cc/H2Av+Va0HzhLYJXbgFYySaDfUMWFCxn4tFZEz6bn2GkZ5s8/GMv\nfeLzwHXH7dz0BlgtiTtO2imhK+WXfzqoRczGWwJRG47djlNOfzDwL5wZzRr3/Z/AlsDtqvr3HMuZ\nN1pVNd58lqzZWERJotMq6UfYnEKUg8jmGDP5S6KwbG0N1RdNaPjctlUFz/ymsedOdY/2XHtMWQVR\nlgRn7NnoT4zXGpvo+tUO2Lo3D/9id9/tjNImcia/qv5WRP6HUwhzBNAFWAl8CIxX1bdzK2J+8YYm\nz13u3+62VElXgrwlEA9hzlTSPpemzwsfSWzf/I+f7MS2/brw6vmjGXP9a9wUsgyOkUivjm34+d6D\nOHrHxoTgQb06MuXS/ejeobWFfZcpWZWKUdV3gHcyDiwDvMmVuXzSLQTeirEtof1qMvEEzEzJensO\n7kmfzm1ZsGoDMxat8a1fFuf7FesZde1ErjtmO36884CU9S9PX5TwOW6yG9izQ07L0LQ0RISLfXxl\n+YwENPJPizd0evMV6spMwYgI2/Zzns6bYw4MwIm7pt7k4xx266TQ+xna1+kPst+Nr7O+xj95E2Dq\n96sA+P2jn7Fo9YbAcXFa+izSMNLR4hWM10R2eBlGrnw+z7khPvbh3CJLkh+qewTXh1sfkOXvx2tf\nLm74O11VZe8sdterX8m430wVgg2jJdPiFYw3Equcs69XbagrtghlQ7r6Zee6VR3Av2tm/+6JoeDD\n+ze/5FbDyBUtXsEcsUPjrKXcfDAtgfoQxSx3GxSts2W6f3PQrOj9WctYV1PHlm5i4Mu/24dvrj4k\n0vcaRkujxc/vh3u6DharMq8RTNsQ+RC/2CdzZwhvVWa/B4mHPviOLQKc/4tWbeBHd77DQdv0oS6m\n7Diga9pAAcMwHFr8DKZr+8YQ1ygtWY3CcPyuA7ji8PSNoT6YvSzjfg70ZIt7HyRirrK58NFPOeYf\nqRH2c5evYxfXFzN1/kpq6mJlV3XbMIpFi/+l9O7cWNLDYu1LjzZVlZy2x8C0Y+KRX+no0bGxjlW8\nzcEe107M2PRqz+tebfg7FoPZS9eyZqP5uwwjDC1ewbQqY8e+4eDXOjrdmM/mrQAa82i+Xrg61PfM\nW7Gehas2hlJohmGYgmk2s5ZHf9n8S2mcEJATM8zNcUnH/lv3bvj76me/YGNdozP/1H990HThDMNI\nocU7+QF+NXoL9tiyZ7HFaBIj+ncrtgh5xZslv/1mXfh0bmM1ZW95kSCSnfJbXfp8w99+LZnT8cOd\nMn+fYRg2gwHgwoOGlr2CaQmFLuM89stRCZ8L3YjKzKqGEQ5TMEbZUZWkUAqdIJuP8v+G0RwxBWOU\nPWHDhuN12zLx873TdzatqrCfjWGEwX4pZU7fLtY5sVXIG/79P90t1LiLDh6adr3NYAwjHObkL3Oe\n+PUefBUyzLa54m0al47ObdP3jYkjInTv0Jpla2t815uCMYxw2AymzOnduS17De5VbDEKzlsXjW34\nOx69idkAABDvSURBVOoNP52T/ma3Ydjpe1QHjjH1YhjhMAVjlCX9urbj/87YhQO27h0pimzKpfvx\nyRUH8JPdnJyazbo1Vkeefe2h/GBEPwCO3KFf4D5q6mNZSm0YLQtTMEbZstfgXtx18shIybI9Orah\nfesqdhzg5A3tXO1fibl/mrp0/3prdiQ5DaOlYj4Yo0Vy0LZ9eP7zBVxw4FaMHboJ/bq1y7yRYRiR\nMAVjtEjat67irpNHArBpV3/lMqhXB2YuXsvE8/ahZ6c2/OHxz3n6k+8LKaZhlDWmYAwjgInnjU74\nvMvA7qZgDCMC5oMxjJB0bus8jx23c/8iS2IY5YHNYAwjJIds15dp81fxq9FbFlsUwygLTMEYRkha\nVVZw8cHDii2GYZQNZiIzDMMw8oIpGMMwDCMvmIIxDMMw8oIpGMMwDCMvmIIxDMMw8oIpGMMwDCMv\nmIIxDMMw8oIpGMMwDCMviKoWW4acICKLgW+z3LwnsCSH4jR37HxFw85XNOx8Racp52xzVc1L18Jm\no2CagohMVtWRxZajXLDzFQ07X9Gw8xWdUj1nZiIzDMMw8oIpGMMwDCMvmIJxuKvYApQZdr6iYecr\nGna+olOS58x8MIZhGEZesBmMYRiGkRdMwRiGYRh5ocUqGBHZTET+KSLfi8hGEZktIjeLSLdiy9ZU\nRKSHiJwpIo+LyAwRWS8iK0VkkoicISIVSeOrRUTTvP6X5rtOEZH3RWSN+x2vichhacZXisi5IvKp\nK9cyEXlWREbl8hxExf3/Bx3/goBtRrmyL3OP5VMROUdEKtN8z2HuOVrpnrP3ROSUDLJFOsf5RkRO\nzXC9qIjUe8a3mOtLRH4oIreJyJsisso9vv9k2KYkr6NcnMsW6YMRkS2At4FNgCeBL4BdgDHAl8Ae\nqrq0eBI2DRH5BfAPYD7wKjAH6A0cDXQBHgWOVfefLyLVwCzgE+AJn11+rqqP+HzP9cB5wFzgEaA1\ncBzQHfiNqt6eNF6Ah4Af4pznp92xPwbaAseo6pPZH3n2iMhsoCtws8/qNap6fdL4I3HO4wbgQWAZ\ncDiwFfCIqh7r8x1nAbcBS91tanDOxWbADap6vs82kc5xIRCRHYAfBKzeCxgLTFDVw9zx1bSQ60tE\nPgaGA2tcuYcC/1XVnwSML8nrKGfnUlVb3At4AVD3xHqX3+guv6PYMjbx+Ma6F2lF0vI+OMpG3Qsk\nvrzaXTY+wneMcreZAXRL2tdS9wdTnbTN8e42bwFtPct3BjYCi4BORTpns4HZIcd2dmXdCIz0LG+L\n8+CiwHFJ21S752Sp97wA3dxzqMDuTT3HxX4B77gyH9ESry+ch9TBgACjXXn+U27XUa7OZdEvyEK/\ngC3cEzeL1BtwJ5wnj7VAh2LLmqfjv8Q9/ts8y7K5Afzb3eY0n3V/dNddmbT8DXf5mCj7K9B5mU14\nBXO6K+t9PuvGuuteD3NO0u0vm3Nc5GtrO1emuUBlS7++yKxgSvY6ytW5bIk+mDHu+4uqGvOuUNXV\nOBq7PbBboQUrELXue53Puk1F5Ocicon7vn2a/Yx135/3Wfdc0hhEpC3Ok9Q64M0w2xSBNiLyE/f4\nfysiYwLs4OmO/Q2cYxwlIm1CbhN07NlsU0x+5r7fq6r1Putb+vWVTEleRzk9l8V+6in0C/grjvY9\nL2D97e76XxZb1jwcexXwmXt8B3qWV7vL/F6vAgOS9tPBXbc64Ht6uusXepZt4y77LGCbke7694p0\nbmYHHP9MYJ+ksR+463YK2Nfn7vphnmWL3WU9ArZZ465vn+05LvK11Q5YjvPg0j9pXYu8vsg8gynJ\n6yiX57IlzmC6uO8rA9bHl3ctgCyF5lpgW+BZVX3Bs3wdcBWwE44ttxuwD86PfzTwioh08IzP5hyW\n+nn/F7Avjp+qA465506cm+NzIjLcMzafx98l6b1Uz1cyP8KR5XlV/S5pnV1f/pTqdZSzc9kSFUyL\nRETOxoki+QI4ybtOVRep6uWq+qGqrnBfbwAHAO8BWwJnFlzoAqKqV6rqRFVdqKrrVPVzVf0FTuBH\nO2BccSUseeLmsTuTV9j11XJpiQomWcMnE1++ogCyFAQ3rPEWYBqO025ZmO1UtQ64x/24t2dVNuew\nXM/7He57oY5/ZdJ7yZ8vEdkGx2Y/F3g27HZ2fZXsdZSzc9kSFcyX7vuQgPWD3fevCiBL3hGRc3Bi\n5j/HUS6+SYNpWOy+N5gwVHUtMA/oKCJ9fbbxO4ffAPXAIBGpCrlNKZBy/KS5htxjG4jji5gZcpu+\n7v7nquo6yPocF4tMzv10tOTrq1Svo5ydy5aoYF513w+Q1Iz2TsAeODbjdwstWK4Rkd8DNwEf4yiX\nRVnsJh5NNzNp+UT3/SCfbQ5OGoOqbsCJ7W+Pk4yXcZsSwe/40x373jjH+Laqbgy5TdCxZ7NNQXEj\njk7CuSHdm8UuWvL1VZLXUU7PZaEiKkrpRTNPtHSP5TL3WCYD3TOM3ZGknCB3+b44SVgKjEpal6/k\nrc5FOFfD8Ml7co/la1fmSzzLO+M8eUdJkBtIM0y0xFEuCjxt11fK8Y0mc6JlSV5HuTqXRbswi/nC\nSbZc6J7AJ4BrcLSx4kxBfUMAy+UFnOIeSx3ODGacz+tUz/jXcKbRD7vjbwJeoTGU9NKA77nBXf+d\nu83fcPqCK3CWz3hxv0OB6cBfcJ5617iyHlmk8zUOWA1MAP4OXIdTTmO9K+sEoHXSNj9wZV6D40f4\nC04AhbrHKD7f8xt3/RL3XN3knjsFrs/FOS7CuXvTleXwNGNazPXlXhfj3dfzrizfeJZd7zO+5K6j\nXJ3Lol6cRf5h9McJTZ2PU8vnW5w6VN2KLVsOjm2c58cb9HrNM/4M4BmcXJA1OE8oc3DqHO2V4btO\nxYnnX4tzk34dOCzN+CrgXJx8nPU4uRPPkvQEW+DztQ/wgPvDXoGTjLoYeAk42e9H7m63hyv7cvdY\nPnOPrTLNdx3unqPV7jn7ADgll+e4gOdtmOemle6YW8z1FeK3N7tcrqNcnMsWWezSMAzDyD8t0clv\nGIZhFABTMIZhGEZeMAVjGIZh5AVTMIZhGEZeMAVjGIZh5AVTMIZhGEZeMAVjGIZh5AVTMEbeEZGz\nRWSaiKwXEXULcLZIROQ09xzsUmxZ8o2IHO0e677FlsUoDpZoaeQVETkOJ0v+I5wacBtxmlKVfTHR\nqIhIR5zaZh+o6hFZ7mM8Timggao6O3fS5R4REZxaeFXACE1qUW40f/xKMRtGLjks/q6q3xdVkuJz\nNk7HzGuLLUghUFUVketwSsIcB9xfZJGMAmMmMiPfbArQ0pWLiFQCvwC+UtW3iy1PAXkKp77br4ot\niFF4TMEYeUFExomIAmPczxp/ecaoiLwmIn1E5B4RmSci9SJyqrt+iIhcKyKTRWSxiGwUkW9F5C4R\n2cznO0e7+xwnIiNF5HkRWSkiy0XkURHp744bJCL/c/e5XkReFZHhAcfRXkQuFpGPRWStiKwRkXdE\n5PiIp2R/nAKrDwV8z14i8rSIzHWPc4GIvCsiV3jPF455DGCW55zOTtpXdxG5RkSmu8e3UkReEZED\nfL73VHcfp4rIoSLytnucy0XkEREZ7LNNbxG5XkS+dMeucP8eLyKDvGPV6S3yBLCHiAyNeM6MMsdM\nZEa+eM19PxXYHLgyYFx3nOZua4DHgBhOKwWAo3Ge+l/F6Y9RA2yD07/9cBEZqarzfPa5M/B7nGqx\ndwPbufvaVkSOBCbhVE7+tyvb0cBLIjJIVdfEdyIiXXHaOIwAPgT+ifNQdiBwv4hso6qXhjwf+7nv\nk5JXiMhBOC0BVuE88c9zz8swnCf/+Lm7Eqe8+3CcFtjxlrUrPPvaHOfcV+OU0n8ep8vhYcDzIvJz\nVb3bR76jcRpJPe5uvwNwDDBGREap6pfu/tvj9AjZAqfa9NM4pd03B47EaXOQ3DzsLZzrYD+c8260\nFIpd8ttezfuFc7PSgHXxEub/Bqp81vcD2vgsPwCng+I/kpaP9uzzxKR197rLlwF/SFoXb87226Tl\n493lFyYtb4tz444BO4Q8D++6+0rpNQQ86q4b7rOuZ4BM1WnOd4zURlVdcTqbrgd6e5af6jlnhyVt\n81t3+SueZYe7y27y+e7WQCef5cPdbR4q9vVor8K+zERmFJsa4HxVrUteoarzNLFdbHz5i8BUnJmE\nH5NU9b9Jy+5z31eS6mT/t/u+Q3yBiPQAfgJMVtW/JH3/BpwZkgAnBMiQzACgVlWXphmzPnmBqi4J\nuX9cM98+wKOq+r+k/awArsBRjsf4bD5RVZ9JWnY7TrOsse7MKJOsNaq62mffC9z3AZmPwmhOmInM\nKDazVXWR3wo3zPVEnKfs4ThtYSs9Q2oC9jnZZ1k8yOBjVa1PWhc3s3n9Oju736UiMs5nf63c92EB\nMiTTA6dhkx//xTFRvSciD+KYBN9S1bkh9x1nd/e9S4DMvdx3P5lfT16gqvUiMgnHHDYCpynf6zjn\n6yIR2RGnAdVb+J/XOMvc955hDsJoPpiCMYrNgjTrbgTOwek6+gLOjS3+5Hwqjt3fj5U+y+qC1qlq\nnaPLGpQGOAoBHEWzcxoZO6ZZ52U9zuwhBVV9TEQOA84DTgd+DiAiU4CLVfWlkN8Rl3l/9xWEn8wL\nfZZB4/+niyvrKhHZDccfdASNs8glIvJ34E+qWpu0j3bue8qsx2jemIIxio1vpq+IbIKTN/I5TovW\n1Unro0ZxRSWuiG5S1d/lYH+LgMEi0srnBoyqTgAmiEgHYFccp/wvgWdEZISqTosg829V9daI8vUO\nWN4nad+4M6sz3Bnm1sBY4NfA5ThBEJcl7SOu+HxnqkbzxXwwRqkyCOf6fNFHuWzmrs8n7+M4y/fK\n0f4+dd+3SjdIVdeq6kRXqV2N4zg/2DMkboaqTNnYCSSA7GTeJ3mBm7uzp/vxIx9ZVVWnquptNM6Y\nfuCz73h48sdZyGWUMaZgjFJltvu+p3ujAxrKrdxNnmffrl/ov8BIEbnMK4NHli1EZGDIXb7mvu/m\ns5+9RcTveOKzinWeZfEggRSHuapOxglNPlpETvcTQkS2c2eHyYx1zXRezsLxv7yqqt+6228jIn6z\nHT9Z48SP+VU/mYzmi5nIjJJEVReIyP9wSox8LCIv4vgB9gc24DwN75BmF7ngLGAw8EfgJNfhvRCn\nOsEwHN/M8cCsEPt6ErgZx2dxT9K6W4F+IvIWjmKtAXbCMT19C3gjwl4BLgDuFpFHgdXAClW93V1/\nAk7uzr0icjbwHk6ezGbA9sC2OMEAyeaqp4HHReRxYAbOuT0Yx0HvzcLfH/iriLwDfOXuZzOcHJgY\n8FefYz/AlWFi4NkxmifFjpO2V/N+kTkP5rU027YH/oxzw9sAfAf8Dcemn7JfGvNgxvnsq9pdNz6K\nLDgmqrNwEj1X4hTrnINzoz8Hn7yWNMfzuHsc3ZKW/winIOjXOAmnq3B8T38Gevns53fAdFcWxYnE\n867vBFwCTHH3tx5HCU4AfgZ08Iw91d3HqTh+n3eAtTgK4VFgSNK+h+EEX0wGFrsyzMZJsBzlI+sQ\nd/83F/tatFfhX1ZN2TAKhIiMwgnp/Z2q3lRsecApFQP8CzhNVcfnYf834CjoYaqanOFvNHPMB2MY\nBUKdIpcPA793S640a0SkL04k3G2mXFompmAMo7CcD9wBhA0OKGeqgeuAPxVZDqNImJPfMAqIqs4B\nxhVbjkKgqu/g+HSMFor5YAzDMIy8YCYywzAMIy+YgjEMwzDygikYwzAMIy+YgjEMwzDygikYwzAM\nIy+YgjEMwzDywv8DYPqsAhA7WvIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22db078c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(cvs.distance2)\n",
    "xlabel('frame (steps)')\n",
    "ylabel('distance2 (nm)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f22db22fb90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ROMqsJdAfuB+owBkGHRBVN/LkJ/JjjpR7ZmZ0o/xPFpHJy5fnL+ROsRE7jLZp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\n/v1Z/QgdZSXC6AF164u8+vZUGaX9zAl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73P0/z7esWbj2MuBr9/qOiiqvcMu8Pu8APWPa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22db2238d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(cvs.bias)\n",
    "xlabel('frame (steps)')\n",
    "ylabel('bias (kj/mol)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HILL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gerry/gp120/env/lib/python2.7/site-packages/ipykernel/__main__.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "hills = pd.read_csv('108/meta_0718_lab108/HILLS_CVS', sep=r'\\s*', header=None, comment='#')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>distance1</th>\n",
       "      <th>distance2</th>\n",
       "      <th>sigma1</th>\n",
       "      <th>sigma2</th>\n",
       "      <th>height</th>\n",
       "      <th>biasf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.177365</td>\n",
       "      <td>1.732320</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.200224</td>\n",
       "      <td>1.804025</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.584847</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2.283586</td>\n",
       "      <td>1.830151</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.558759</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2.203514</td>\n",
       "      <td>1.827165</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.436897</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2.122232</td>\n",
       "      <td>1.776587</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.436893</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time  distance1  distance2  sigma1  sigma2    height  biasf\n",
       "0     1   2.177365   1.732320     0.1     0.1  1.666667     10\n",
       "1     2   2.200224   1.804025     0.1     0.1  1.584847     10\n",
       "2     3   2.283586   1.830151     0.1     0.1  1.558759     10\n",
       "3     4   2.203514   1.827165     0.1     0.1  1.436897     10\n",
       "4     5   2.122232   1.776587     0.1     0.1  1.436893     10"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#! FIELDS time d_v12_b d_b_v3 sigma_d_v12_b sigma_d_b_v3 height biasf\n",
    "hills.columns = ['time', 'distance1', 'distance2', 'sigma1', 'sigma2', 'height', 'biasf']\n",
    "hills[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f22db0a3910>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Avn37+qoMel0ik3RB8AN5DgW/sCtYl6vfBOBNAF9AMRvXUwVgD4A5zLw//uo5IjSCamSS\nH0pPdr0McWLvl6X6CYrVT0hHIiQDsRIU/MKWYDHz26G/iehSAF8w8zsJq5U7VqrfZmtUXdVvszWu\nwBA1wlKHWNUaI4ML+3XA+3M3JrFWqcWH8zZiW0GZ6/PFq4ggBA/HG4eZeUjso3xhmvo9jIiytJaC\nRNQQwEAoU5W/+FE5PVYWf/ocCo+wanIGdGkWJVif/7rF9vX7dWqKueutHcgyc8oG67vrs/iWUCWU\nizmi5YJfBMI1kxOIKJeIuqv7rsIw81oAkwHkAbhed9pYAPUBvMvMSbNenHlnjba3bVzX9nl6MQsZ\nXcTqKJZtO2D/GraPzEwSazKf2kjLCH7hyjUTEQ0G8G8obo+awFj4mJntWiGOAjBK/dla/R5ARBPU\nv3cz8+3q320BLAewAYo4abkOimumZ4noZPW4flD2aK0CcI+d+nhFuyb1wn/XyY1soiqL//XRVoKk\nnlOTQ/FuhZNex5IiG140lm87gD1F5Tihq/h4FIRk4FiwiGgkFKOLbAAboawdxesj5ygAl+rSOqsf\nQBGn2xEDZl5LRH0BPAhgOIDTAWwD8AyAscy8L856Oua7mwehUd1cXPj63Ij0r5dsxXN/O9rwHP1I\nKmx0oUmPd6ZO1mjiZ8QzMwAA+eNG+lyT5CJhRgS/cDPCGgOgAsBIZvbENw4zj1HLtXNsPiw8bTDz\nJtRYNfpO99bmLph6PfA9bjq5K/5xYueIdH13UFf1YKE1uoh3ZclOl8MsgQyFaESvBL9ws4bVC8DH\nXolVptC5ebQLw6KDlXhk0vKodP0b7D8HK4JWHTHCUpRk2BGtXNXHzghrV9HB8N+z1+zGxj0lrq4l\nCILgBW4EqwhAYuOTpyHjzz/K9bl1cpUR1sSlW8NpIUMMt7YBdt6SL39rfvjvv78+Fyc+Ps3iaEEQ\nhMTiRrCmABjgdUXSnYZ1ctG3o70YkmZrWDsO1Ix4GtRWZnOb1s91VR87Orez0P0+JiF9kSlBwS/c\nCNadALoQ0b2Uqpt0AsiAzs1M84wCNw7o0gyP/LkXHjizp6vrVdsYmhl1TFslPL0gCD4R0+iCiN40\nSP4Dyt6mK4hoMYzdHTEzXxln/dIKK3nX5s1cszsiL8vgRCLChf06uq7L0i3RPoLr5GahrKImMsue\n4nIUlFagUd2aUdzx46ZmnFVcLPaXlGPHgYM4vLV5UM50QlwzCX5hx0rwMou8PETvhQrBAESwNFjt\nnbISM4MBVkIwEsZlWw9gQBfz0Z/f7CwsQ8uGdXytw4hnZmBbQZkIuSAkGDuC1SnhtcgUXAqPfkpw\nZO82HlQmGqPqJUss3TL2q2V44cI/+VqHeHwWpiKyhiX4RUzBYuYNyahIJuC279cvFZ7aw50puxuC\n3jfJ9FTykRYX/CLlfAlmIkEf5fiJvO0LQubgxjWTnXC81QAOMLN9b6xpxoheraPStAOlRnVzUVBa\n4arsRNlmitFn6rJpbwle+nktHjyrJ3KyE/seKq6ZBL9w82TnA1gf47MBwD4i2kJEzxFRxngHnTV6\nKM45ph0eP7dPVJ7W6MLKG3i3Vg0SUrdY2HH4KigErdO+9ZPF+GDuRizckHh3mcG6cyGTcONL8B0A\nHQEMhmLOvhjADgCtoDixbQzgJwDFAI6EEurjDCI6jpl3eVDnQNO2cV08YSBWQOTIyEqwogM4kuVv\nIfl8PH+T31WIIPQ8iZgI6YybEdajAPoAGAegPTMPZea/MfNQAO0BPKbm3wbF2/pYKAJ3lzdVTg8s\nBSvGuSJX/vPt79v9rkIEizYqWyG/XrI1xpHxE7DBpZBBuBGscQCWMPPd+mCIzFzMzKMB/AZgHDNX\nM/NYKKOwM+OvbvpQWV1tmqd3TKsXKP0A65kLnPspPMOmaXzQOye/6vfzqmBOFuwrKU/CVQL+UAhp\nixvBOhFKkEQrZkOZMgzxC4B2Lq6VVmiFRj/AithUHKM/0G9APvuoto7rUisnC0e0MQ99kq7UVR0J\nx0PQ1q+0BLhqghA3bgSrNmqiApvRRj0uRBHiD/KY8sQdJThUjswJusaLLQJBFoVk1C3I9y+kN24E\nawmA84mol1EmEfUGcB6UacAQeQCCOYeSROwKjb4/0J/nlV5lYr+T5YFiZWK7acn0+xf8w42V4IMA\nJgGYT0TvAZiFGivBEwBcCCAXwEMAQER1AQwD8LUXFc4EYgVXdDPCqp2ThYOV5utmmYKRv0Sv2F10\nEM0b1I59oCAIrnA8wmLm76GIUhkU57ZvAPhG/b5CTb9EPQ4AagE4H8B9XlQ4E4g2uogyu3Bc5nUn\nHRZHjYKLUyODa0/qEvc1zdawSsurHJWTN3oiHv5mWdz10ZIMV1UyJSj4hast8cz8ERQT9osBjAfw\nJoCnAVwCoAMzf6A5toCZv2fm/Pirm9rY3T8Vq0NwM0hoog/0yPaMBxLVAS7auA8FJe48fWhZu6sI\nn/+6GXmjJ2K/DfEadVTbuL2qe9kir89c72FpiROTkvKaJWjx3yj4hZspQQAAMxcBeF/9CDbo16kp\nptswh47eOKz77VF9junYBCu2F3pUmn2YGX95cTZ6t2uEr244Ie7y3pqVDwDYsKcEjevVsjzWixlB\nM1FIZ2OYF6et9bsKgiDOb5PJtYO7YPq/h6Bdk7qWx8Ua+bjxdGF0xgNn9sTIIxMTqsQOv22ODiKZ\nCgR5hJGompVX1ax/ypSg4Bd2Ig5fov75OTMXan7HhJnfcV2zNCQri9ChWb2YC//J8HTBUPZi9WjT\nEBOXbvOgRP9w0oF60namI6w0HmIJQgCwMyU4AUr/9guAQs1vK0g9RgTLAKN+TZuWiDUss5MsPEQB\nAP7+2txARdK1Mmyw1S4J1JRMkSsZYQl+YUewroAiPqHX8MsTV53MQN+xrd5RiBmrd4d/J3PKSdv5\nHN2hMX5VfdIl65pOqdKdvLuoHLuLkuGOqIYgr2ElZeNwgKdEhfTGTsThCbrfbyesNhlC/p6SiN+3\n/29JxO8dBw5G/I4yunDRMZqdojWhjzXaSgfsehtpVDfXNC/YHbb3dZuzdg9enb7O83IFwSlidJGC\neOXiCQDOOabGxWO1iWIF2XeeU7TGA1ZY3bPpCCtNJwX/9tovEb/T6HEQUgxXgkVEg4noGyLaSUQV\nRFRl8Ml434EJw80IS3fOoK5KTM32Tevhm38ppuVmIU/SqYMyE2U9VvdslhXskZdzlm87gLdmebtP\nTBDiwfE+LCIaCeALANkANgJYCXFs64j2Teti095S1+fH+x6/5IFhEVNeITEzcwmVTt2w3enUWO6x\njGAG7v58KWat2Y2f/z3E8fle4OXLxYhnZnhXmCB4gJsR1hgAFQCGM3MeMw9i5iFGH2+rmj50at4g\nrvPjNZ+2Wp+xQ97oiThQFu2louhgJfJGT8R7v2ywPD+ePjXe6cnQloLbTu1meVxxeZXptYoPmr+f\nfTB3Izbo1iiTSTJeLtJpxC2kFm4EqxeAj5l5steVyRTiHSG5smq3OKt903oAgGsGd8Zblx8blW/U\ncW/dHz1C3F5QBgB402N3Q3ZxsoZUt1bsuFgb9xoLzztz8g3TM6UfT7epTyF1cOOaqQjAXq8rksmY\n/fcnMn6bdTPAqrAwNjikTq7lXiuj+lmKg8P6LdywD73bNUJuduz3p3hHl6HT7XhtN1vTMzs3nYxT\nAPPn7+P5m9C7XePkV0jIeNyMsKYAGOB1RTKJvcWR+4bMrc7M0qNzHj+nt+U1P56/yU7VDElkP/zH\n1gL89aXZePz7lYm7iAF2wmKZ3Xa2F1EgE4SbtTczcrOMu4f3525MO3EWUgM3gnUngC5EdC+JLxpX\n2G01s+Y1Sj6lRyvLsqqZcdPJXe1dWIf9KSC7Fng1x4U2/f62OfEblrXYCeRYbhI/zGwkmG59eG62\neRv9tDLj47EKPmDHl+CbBsl/ABgL4AoiWgzAqLdhZr4yzvqlJf8Y1Bn/+vDXmMflNauHtbuKba3N\n2Okrbzm1G26JYWxgWLbNacnQcbFqu08TVqSqWhGFX9bFP8vsZG3FzrtWndzY61zpjNW0qZHRjSAk\nGjtrWJdZ5OWpHyMYSoBHQUcH1cghFh9e3R+LN+5HrRx/93fPWbcnKs2qu48lBnd/vjT8d3mlP8OS\nChvRl3MCPPVnhpejvEILa0hB8AM7gtUp4bXIMPT9uX5NK0TLhnUwrGdrAMDpR7bGpKXbTctM5LrK\nim3RMbO+WLwF/z6te0RaqK9cs7PIsrylmrAildX2PE+EsArS6MS11K6igzGPMd2XZppu//qJIgBV\nEISEYceXoPWmGiFuthiYiOsZ99feloIV794qK4zWcl6YtjZKsOyy/UBZ+O/KKmdd7KOTVpjmaQWm\nWf1a2GPwIhCaXrUj72YCWGziMV7MvQUhsYgvwRRB+/ae7Df58T+uinlMVTXjjOdmOi7bjvGDlvYW\n06nadvHCHKjSYCsAM+Oln9xF391eUGYZHsULxHpPSGdEsHzATZ9S2+d1rFh8smCTqVWdFd1bNwQA\n1LOxkRcAmta38KIe0bDGitW8QS0AsOV9/PlpawyuYX58rH/X/o9OwQWvzgEATFm+I+b13ZAsvRID\nYcEPgt0Lpilu9spoLdbcTD0luiPbX+LOaixUryb1asVdh2qTEZZWDHNUk/S/HdchZnnLtx2ISrNq\nRjtNvERdv7vy7QU2jnaOHQ8egpCqiGD5QJtGdZN+zUSvr3z/h/n6mhWhetmdyrI6TPsioH3/Nzqn\nc4v6Eb+n3jY46phVO6KNR4I+5TbsCOv9eIKQyohg+UDrRnWSfs1E97OLN7nb+BuqlxdbkyMEK8aM\nldaqcuxZPdG5RbRD4tB0pZvr+0WypupkQlDwAxGsFOKEw5q7PtdJV5rM5YmwYNms4M4D5uboEUYX\nmi7VaHSp3RR7wXHtAQCXDOgYccw1gztbXkPPzR8tNs9MIAWlsolXyAxEsIQo7jm9R9KuFZ4StCmp\nb1oEFNSOcGKZzmsd24bEq2vLBrpjzOtrxNItBaZ5ieTSN+f5cl1BSDaBESwiakdEbxLRViI6SET5\nRPQ0ETVxUMZPRMQWn+TPxQUEJ2svDWq7ceLvDqcjLCtCGqQ3R6802FB1TMeax8rMBdE+g31cXtTT\n63Uw7XRsstbY/J/8FDKR5PVMFhBRFwCzAbQE8CWAFQCOA3ATgOFENJCZo/0DmTPWJD2lfc0s3LAP\ngHWoEDOcdDB+dEYMJXz9V0u24sw+h7ry3BHqrKtsdNq92jYK/x2+lE64Hpm0HP84MXJa0BvBir8M\n07ITV7Qg+E4gBAvAi1DE6kZmfi6USERPAbgFwCMA/mm3MGYe43UFg0BphbLptLAstu42rJMTeZyD\nniyZtgPaEdYH8zbi3i9+x4GyClwyIM91WcUHnW3OtTJUmJ+/F8fmNQ3/NgveGBiS9G+3cvsBoM+h\nybmYIKj4PiWojq6GAcgH8IIu+wEAxQAuJqL6yHBO6dESAFDXxIu4NhyEfoQS1DfvmjUhxh411Mju\nwth+/owIrWHtLY4+f9KNg/DLXSdbnm8kW+e+PCfi9waTKMSxKKuoEdGg/ls44YVp7rx9JJrN+0pM\nfXMKqY/vggVgiPo9mZkj5rqYuRDALAD1APS3WyARnU9Eo4noViIaQUS1vauun1hPk9166uHhv/Xr\nMk7WNqziIHmNUbXcduhFqnfxerWiJw6OOPQQT7YTuF0jinStlQ6SFUxO+O80HPvIj35XQ0gQQRCs\nUC9r5rButfrtJJDTRwAeBfAkgEkANhLROe6qlxjiMR03j4Rb87d+CciJJ/NRR7e1feyqHYX4+2u/\nmOZXVFWbhpoHau6lmmvaxG1/ftdnSyPKTASerGHFX4RF2SKGVs+bkNoEQbBCq99mNsGh9MY2yvoS\nwJkA2gGoC6A7FOFqDOBjIhpudiIRXU1EC4howa5diY+mahZ+3IpYHbrWfZN+hDXqKPvrDbnZWbZC\nyJdVVGHY+OmYvdbcHqbrPd/i8gnzTfM5PI0X/zROiepY1u0IJtRkfTtGGqZuK6jxpu92c7BWSBJq\ndCF9tZDGBEGwPIOZxzPzN8y8hZnLmHklM98N4DYo9/qoxbmvMnNfZu7bokWLhNfVzQgr1ilDuytr\nXE+c2ydcMrBOAAAgAElEQVRKsM52MGoCgO9vPjHmMR/P32SrrOmrzF8AjPpXt26ewmXG2Wl3bRW5\nH+vvr831rGxARkGC4JYgCFZoBNXIJD+U7s73j8LrUEzajyKiaH87PhBfwEXjDq9dk3rIHzcS5xzT\nLmqE5LSj7doqdjN5MfUSqlftnKywIK+OEQDSbpkhLh+YZ+s8MnklWL+7GNXqvbq9ZT/DwwhCuhAE\nwVqpfputUXVVv2MHZTKBmcsAhMLmBsLaMNvFECs0arLT4R1xqJn+22f0COsAjd70uyHnt54UppYY\nWVhDhxuhjepyzxeh9TG3U4LW5QuCEJsgCNY09XsYEUXURx0NDQRQAsB8ZT8GRHQ4gCZQRGu323K8\npPCg8z3MTjrLsWf3jDrbKV5GMT5QFu3vLjuLwp13eVW1Zz4M9YJQz6ZgWV3/w3mbwMyuxUa7rpZI\nJ7mihUI647tgMfNaAJMB5AG4Xpc9FsqI6F1mLg4lElF3Iop4/SeiTkTUVHc+iKgFgLfUnx8xc8p6\nu6ivdrx1TPZhadEHfHTTR1rph9NRy4inZ0SlVXOkBE+Yne+oTDP0t9rGoTm7WVsx2xcbveFHiSbS\nsJeCpb+OjN6EdCYoni6ug+Ka6VkiOhnAcgD9oOzRWgXgHt3xy9VvbZ86GMDLRDQTwDoAewF0AHA6\nlHWwBQDuSNQNOCWLrNdDHjund1TaA2f2RNeWDTG4W+KNQgDrEUeDOtaPDjNHeJDYsr/U4Bjgt801\nxqG7i7zZ8HmwMtLThd2QG6GjzEay1cy217CqGdBuZ/vLi7PDf3/+6xZ7hdhgyWZ/HO4Kgh/4PsIC\nwqOsvgAmQBGq2wB0AfAMgP42/QguhLL/qhWAv6plDAewFMCNAAYyczyGG57SLYZRQyiUu5ZGdXNx\n7UldkOXGz57jM6w7+lijBLsd+0PfLHNSJVs8MnF5xG+7rdU3TzFnH3FkGxzR5pCofIZ9k3m9QYpW\nsNfEaVSiRf/vIBaIQjoTCMECAGbexMyXM3MbZq7FzB2Z+WZm3mdwLDEz6dKWMvNlzHwkMzdj5lxm\nbsrMg5j5OWYOlL+WnBjeJMws1tzi9ZRgLP+7fnpz2KRzn2Smu//585E455h24d+HtWyI/HEjMeTw\nlqidG/1fo9pkDcsoyq9VQMs9LvecbdlfijFf/REWQ2bG54siR2uZNCX4+5YCXP7WPJRXOncGLaQm\ngRGsTCPHxcZhJ3ghd1YjLGa2vMaK7YUWubE55amfLfNn3DHENE8/ujMT/7/364Anzu1jmJebHf3v\nw2w8gslrHm14ajUCnfjbNtM8K279eDEmzM4Pe+3/aslWvPvLhsg6uirZmJG923hYmvfc/r8lmLZy\nF1bvjO9ZE1IHESyfyIlrH5ad8nVGFy66Mn0VG9ersRrcU1yOBy2m8x746g/H19MSa9rMallKLxZu\nrA+N/n263/cdJi2N3tRsFD8sEZaAc9fvjfi9dX9Z1DFePlVutl4kE3KwzUNID0SwfCLWxuF41yIa\n1Ys0SW/buK7jMvT9lZMOLNH+3KxHf7pjXZTvZGO3dlrRrA7xsrMwWpz++92KqLSAa4ynWN3q+t3F\nFrlCqiKC5RO1cpLX9PnjRqJhHed7qqI6fgedYVU1R0X+9RIC0LKhsRN+/eimcb1oA5ZYOBkBG61H\nejnC2rK/FMc9MiX8OxHrg5/M34Sr3jb3+ZhqrPXQsEUIDkExa884aufE3kvlN/p+sUHtHNum50u3\nFOCwe76NeRyRS4MQAnbq4madpQYUDJX356PbYniv1hjQpZnj8js0rWf7WL3fRsC9CycjthdEbwnw\nmjs+/S0qLdYLyuQ/tmNYz9YJqlFsrOpXUuEsiKeQGsgIyyfqGFihec3s0UOx4N5TXJ+v73NjuWpy\ndQ2XHTuBotbYJi7dhrdmrQ+PbmrnZOE0lx3qP07sbPtYY8Gyf2OxRkwFpdFeQvYFIEjh1e8u9PX6\nVtELxHIwPRHB8olrTuxiOS3oxazPoY3ronkD97Er9d4y6hoERkwk2wui12206A1LqqoZY79eFhZa\nuxuGjahlYCVohtHsoZNpu1je6Z/4PtqNptFG7EzFaL3Xy71uQnAQwfKJI9s1wvIHTcNzBYLTj4w0\na0723ir9yGJ/Sc2oolZOlqlhRKie8Rhi2vU/CBgL4xUTFth+y99bHD2C0qIfrX00fxPenLneuC4e\n2gkG3X4jdK9Gj+XLP69Ncm2EZCCC5SMJtmyPm+wswpl9agI/Jtt6WD/I0Y4qmtavZWoYEVo/isdi\nzshU3Qyzf8f8PfYs1SqrnU1fff7rFnxm4t4p0dslgkSFatQjVu2ZgwiWjzgxzfaLjhrjg36dmkZF\n400k2bE2V5s0Xyh6sdHaktec1rOV6b/jsPHTbZWxbpe1sJU6MCCoUyv4xjxeEe/mdCH1EMEKGMly\nbGuX849tH/67Xq0c/N+1xyft2vp9X1EiHkPUkyFYbRrVjXvqrEcb72KKxjttq3drpSeZ2zGsmLWm\nJkqQn27AhOQSjKdPCBNfJGLvOeijtVW83qsSrVePndMbo0d0j1sY7/x0qWV+MvvjN0zWxkK0PsRZ\nqJZEceHrc8N/h5wSJ3LfnxAMRLACRp92jQEArR3GcEoUfpoHn/DfafhysftQHIkeYZ3Xtz3q5GZ7\nLowv/rQGf2ytCRuSyICPenYVHbTMD6onjbdn59va9yekNiJYAeOGoYfh25sGoVfb+EPce4Hf4Sre\nnJWPc16ajYHjpkblaWvWvXX0tFq8fet5faNdLhnhdSf+2HcrMfLZmeHfyRxhabcSGF321B6Rnum/\n+W1rgmsUG2bg00XRLzZlsnk47RDB8plQZ9ewdg5euvBPyM4i9DCIxRQk8prZ9wIRi1jRgKurGQs2\n7MOW/aWWHffQ7i2j0tzEDdMy5qyeto7z0knsboMRjrNNyPFdX3stI28dg3RrrDd88GvYyMUvzNaw\nZq7ebZgupC4iWD6zbOxwjOjVGgvuOwUjjgx2OIcQTes7981nRMuGtcOmyWYUH6w0zWtUV/GPWCc3\nC39sPRCVH6+O1LO5UTrHwSZjM/JGT8TCDXvR9+Efo/KcCFa5g3WcWWt240BZRcS0r1ak7BozOLFi\n9ILS8sjrmTlavuqdBcmojpBERLB8pm6tbLx00TGB9S1o1GfFszak3UuVk0WoqLLuFNdpvG7rpydP\nVQMn3nxKN8P4VYlcwzKKSGzGrkLrdaEQ01bsMkx3shnYbgTnOWv34MLX56L3mMm44NU54XStSNmV\nyaoY/4ZeM3lZpGeQapM4ZUL6IYIlWGJk/FFp4dn1pMOtzfKXbq4xJsjJzoo5wtJi9sKfm52F64Z0\niUqvm5u4l4DXL+1r67gnJ6/EsY9Ej5qMMOt0nfg1LCwzH5Fq2XGgZq1q0caa6MgRozmbGlDhcONz\nvOj3vSXTKEXwFxEswZKQL0Ktbz2r8O+9VStHO+RkESodvJ3rj7zyhE44os0hGHXUoWHrSi1N6jkP\nqWIXu4O356ausV2m/j1gg+opY8Jsa1NzN5iJY+j9Yev+UkOnu0a8PmOdV9XC/pJynPfyHGy18JWo\nb/rPFm3xdaP9gvy9mKcLrikkBhEsISYz7hiCOXcNDf/O1cV/6taqQc2PGD2HtqPPySZHay76N+n2\nTeth0k2D0KxBbcP9ayN7HxqV5hV6x7teUK1TrMGP/4TCsgps2uvM0W2+jeCFZv9MBaq/xuPHTcXM\nNZFGC8N7tka/Tk2jzlmyqSAqzS2fLdqCefl78ep0cxHUvyxMWrrNs+u74ZyX5+C8V+bEPlCIGxEs\nISbtm9ZDM43X9/P6to/I13Z+TiwcV+1w5lHb6Vu0F9Z7//vnADx0dk888udeEen1EuAC6RWDTvqL\nxc7Nxs94biY+nr8R2yziaJnN6m618JD/8sXHoE5uNu4YfnhEupNp3Vjo/8nGfbsCPy7bEXmMboxl\nZfSxekek+6almwvwxPcr46uk4BsiWIJjDtfteWrTuG747yEG5uUh9haX45o4Yig5dsHjgc3FsXlN\ncfGAPFzYr2NEul6wEuVj8b4vfnd8TtHBStz56VIMeHQqDlYad+bxuDP6x6DINTU7grVye2GEyf62\nglLc/+XvUeeG/slC9Xv557VR1n7lVdH3tN5kVLlgw76I32e9MBPPT1sj7pxSFBEswTHDdUERrx1c\nY/BgNahZsS3a9DzEp9cOiHldM/PlEPrNw4n0XK5f+H/hwj85LuOTawagf+foKTY3GE3VATB9QYin\nu9ZbZGotPQ+UVUSEgfnHOwvQ9Z5JOO3p6Rjy+E/h9DOenYl35myI2isVald9/Z75cXX471s+XhJV\np5JyY2HWbx4O6ZToVWoigiU4Rr8hN0ezppVrsbZjpTcN6+Ti3SuPw5uXmVvffW4SUiPEWUdFrlkl\n0y9jq0Pq4OFRvfDaJfasBwHguE5N8cs6bxbrXzOxWvxl3R7D9Fji7wStJWnvMZNx1IM/hH//sGxH\nWNAKNXvq9qibjUP1eGPmekxZXjP1pxeU8T9GB7G0g9l9imVhaiKCJTimmcXGYSvvEhe9Mdc0jwAM\n6toCx3Q0H3HEMtm+5sRI0/ZkOxK+qH/H8N6wZJI/biQamsTvMtvDpd98Gw/n69Y0AWDG6l14zcJw\nIkTIufJD3yzDlW8vCI/Q55gIrdNIwg9PXB4hhCHi1eu9xeXIGz0xEK6pMgkRLMEx8YSeN2OtGhOq\ntkX4ilibQ/UC5aXLJAC4/4wjPC3PS8z+TUorqgxDhsQ7vvjHoE7hv6uZcfU7C3D5W/PCaRe/MQ+P\nTFoesxx98MrVqiHOmp1FUY6XZ63Zjeved74G+tH8TVFp8Y6wQsYcb8/Oj6scwRkiWELchIQiPsMD\npQOpk5uNBfeeYniEU1PyeH0J6vnz0W09LQ8AOjT1zi+jGYMemxZeW9pisb/Jinl3nxzx+/ohh4X/\nnp+/D5OX7cC0lcaeOvRoDUH0wvHuLxvCf+8rifRReOHrcx1blgLGRiFezQja9WIieIMIluCK9Y+e\nHv776PaNcdPJXfGiQ8MDrXsjrRul5g1q46aTu0YdH3obb97AG1+GTmlSvxZ+H3salj14mmdlXtiv\ng2dlWRFaWwp5vXdqJddSFwercb2af4NPF212VNYZGk/0yXCSEbpV7WbkeEdYobPz91gHvBS8RQRL\ncIV2CoqIcMup3aI6tVicprE2zNFtRjYSrElLFR9ydX0MA9+gdk5Mp7iDuja3XV6892K2dmXFjNW7\n8PsW882+/7ExlQe4d321WrMOZSUcXttFHK8JUROvYBk5WxYSjwiW4Bs3DK2ZVtKbSltN513SP880\n7/VL+uL2Yd3w5fUD466fW969sp/tY0Me513jYtbz4jfmWW5ItvIyoeWXu06OfVAMrNZD+z86xVWZ\nTevXwjMXHBX+bSRN8Rpd6DczC8lBBEuIi3ZN6kalPXVen5jnHdHmEGRnEd667FjUyc3C8V3sj0r+\nZLFWdsoRrXDD0K7o096+T0M/OSMO91GDujaPmIbt6GGcMjuQi95DP3qrlePMAbId9haXR7Tr9FW7\ncNdnv0UcU1Juz0mwGVUGI7SCEnu+FwX3iGAJrlnzyAhM//eQqPS//Kkdpt1+kuW5D5ypWNwN6d4S\nKx4a4dAEPfh7aOwaU2RnEYiAkb1jx0I74bDm6Nyifvj3u1f2w6CuNd7xf4rR5m65uH9Hw3Q3Vpj6\n0Vu7JnXj2hOmXUvVon+ePpwXaSn4+HfxuWcycna77YA7gxbBPiJYgmtysrNMp+46Na9vmF5zrnsL\nvlTY83nOMe0s87UOhNc/OhJPnht7VPreVf1wWAvF0bCRICZiu0HXlg3w0KhehnlehG+Jx0XSpQM6\nur7nzxdbb0J3QyLjrwkKIlhCwmhjEEsrhFHART23ndrNMH25hYunoDDwMOspzvo6Y4k6Njv/tuoU\n7CUDjEc93/zrBEdGH7FYbbFRNyuL8ODZPeMqf8mmAtcGEGPPNhZSOyTipcdvr/GZgAiWkDDmWCzK\n23k7P+lwY0e6TSw8bQSFY2LsSTPy/TfvHqW9rIwZhqht0q9TM8P8Xm0b4d0r+5mKvddc3L8jZo0e\nGvtAEx78ZlncBhBBYfoqe/vQBPeIYAkJpeehxuFG7IwozDxbnN4r9npP0NGuPYVo2bAO8seNNIzy\nHOLEbi2w9j+n48h2jSzL/5fBtoBEQERo27guPrq6v+sy3IywWjasbZoX62UhxLpdzjchA8CnC433\nnZVVJDfyciYigiUklJM14UaaakZGtgRL7cd66zpnrz1YJIpJNw7C9UMi/RteeYLi0ijW5ueXLzLf\nhJ1sH4l26N+5malxRiz0Lpjs0K1VjWf+p88/KiLvzcuOtVXG0Cd/RtFB59aCz0xZbZi+bNsBzFlr\n7ANR8AYRLCGhaAM/fviP/uF9R3VyYz96vdo2wiUDOuL5vzkP3REEjjj0EPz7tO4RaXef3gOvXHxM\nxKZpI4b3aoPvbz4x/PtGFyOmCZcfi64tG1geY8c60S4PjeoV0zrUiL4P/+j4HK19w6ij26J905rt\nFU72tp31/Ew89cMqR0EdrWwrnptqLGaCN4hgCQlF63qodaM6+PTa43HH8MPRsE7sTiU7i/Dg2b3Q\nIcn7ixLF1Sd2RnYW4bSerW1Zt3Vr1QDXntQFU28bjFtdrEmddHhLTL7lRMtjnr3gaAw8zHg9zA2d\nmtfHyoeHo9Uh5lN2XqAX8Bl3KOtoLSymCo1Yt6sYz05ZjeenrTHM37CnOGp/1QYLd0xiKJhYRLCE\nhJKTnYUl9w/DVzcMRKO6uTisZQNcd9JhsU804aoTOsU+KGC8f5Xi+eKifs6mzIgIdw7vjs4trEdJ\nscqYeWf0XrkQ2VmEZy84Gred2g2n9fQmNErtnGzMvdvYgbFXNKwT7ZJqyf3DMOvOGgMQq3UuI4xM\n7Ac//hP6PDgZT01eie0FZZ5vcvaK1TsKkTd6IvJGT/S7KglFBEtIOI3q5aJ3u/g8T3xx/UDceHJX\n3BvgEB9mDDysOfLHjfRtpNiuST2seWQEWjasjU+vPT4qv1mD2vjXyV3xysX2g0/a4bubBwEAaqlb\nGM6NsTfNCW0aRXtYaVQvF7U04Wk+uWYA7hxeMyX725hhlmV2umsS8kZPRGVVNW75eDG63D0pnPfs\n1DXo/+gU3Pv575ZlzFrjfg1rT9FB/LHV3MejFaeOn+76uqkExbNxL13p27cvL1iwwO9qCEJCmLpi\nB66YsACHtWyAH28dHJH32+b9OOv5WeHfAzo3w4dxWAACQHU14+Xpa3Fx/444csxkAMDfjuuAD+dt\ndFVe/riRjo4vr6wGg1E7JxurdxTisrfmW4ZZ6d66IVZsL3RVNwD47Lrj0b11Q9TJycbizfsx8bdt\nuHdkD8Np4GkrdiI7i/DDsh3h0CqfXjsALRvWQXsHoWe0I6t1/zndtmHStoJSDHh0Kh48uycuGZBn\n+3pmENFCZvb2zUdbvghWNCJYQrpTUVVtuHmbmXH9B4twyynd8NvmAgzp3jLCujNeNu4pwUs/r8V9\nZ/QAMzD+h1V4feZ6AAibxw96bFrEOfPvOQXHPqIYZhzfpRk++Ed8Agoo93n1uwvxQxxObOvXykax\nzcjNi+47FUVllXhz1nrcdXp3bN1fhm37S/H3182jcF93UhfUysnCzadYr19WVXPEaPDhUb0woEsz\ndGnRAAcrq1BUVhlh/KTl3/9bgv+pZvpLxwyztbZshQiWD4hgCULyOOnxacjfUxIeOWlHC8d3aYb3\nruyHzndPQvumdcPGFV4x/odVWLWjEJv2leD3LfY9qPz7tMNx8YCOuP79RXjyvD447hF3nuXtsOrh\nEeGpzrW7ilC/Vk7EXr0V2w9g+NMzos779b5TcfRDPwBQfHdePjB6/femj37Fl6rn/lN6tMTrl9rb\nEmCGCJYPiGAJgn/sLynHpW/Ow8sXH2O4VpUoNu8rwS0fL8b8/H0xj+3R5hB8e9Og8G9mRqe7Jlmc\nER9L7h+GhRv34ooJSr+0/tHTMeKZGWjRsDaWbyvE7iJvIh87nW7VkzGCRUTtADwIYDiAZgC2AfgC\nwFhmjv0E1ZTTFMD9AEYBaANgD4DvANzPzLZCo4pgCUJms3V/aUTARz33n3EErtBZrJZVVKH7fd8l\numoJ5V9DD8Ntww53fX6iBSsQVoJE1AXAQgCXA5gHYDyAdQBuAjCHiGxtFFGPm6Oet1YtZ55a7kIi\n6ux97QVBSDcObVwX+eNGYt1/TsdDZ/eM8v2oFysAqJ2ThWsGd8YX1w9E/riRUf4cbxjifjtHsnhu\n6pq4wr0kmkCMsIjoewDDANzIzM9p0p8CcAuAV5j5nzbKeQXA1QCeYubbNOk3AngGwPfMPDxWOTLC\nEgQhEfyxtQBz1+1F/87NcPqzM/Dl9QPRp31jVFRV48Gvl2FbQRl+XO7OEGTGHUOwakchrnw7vr7r\nioGdcP+Z7raPpP2UoDq6WgMgH0AXZq7W5DWEMjVIAFoyc7FFOQ0A7ARQDaANMxdq8rKgjNg6qtew\njAEugiUIQhBYvaMQF7z6C3KyCTsOKOtU71/VDxcaWBeu+8/pIELca2kNa+dg6djTXJ2baMGK3i6e\nfELb8CdrxQoAmLmQiGZBGX31B2BlitMfQF21nIhNFMxcrY7irlavZylYgiAIQaBrq4ZYeN+pUelW\nxhH6vKpqRkFpBXKzCQ3r5KKiqhrrdxcjJ4vQoHYOWjSsjeLyKtz3xe+YuWY3Xr8kYXoTN0EQrNAK\n3yqT/NVQBKsbrAXLTjlQyxEEQcgIsrMoYi9dbnZWhLd7AGhQOwfjdV7vg0gQjC5CsSPMfJKE0mP5\n9omrHCK6mogWENGCXbskEJsgCELQCIJgBQJmfpWZ+zJz3xYtooPrCYIgCP4SBMEKjXzMQqiG0vcn\nqRxBEAQhgARBsEKR08zWlkKBb8zWprwuRxAEQQggQRCskKfLYar5eRjVrH0ggBIAv8Qo5xcApQAG\nqudpy8mCYrihvZ4gCIKQQvguWMy8FsBkAHkArtdljwVQH8C72j1YRNSdiCJijzNzEYB31ePH6Mq5\nQS3/+1h7sARBEIRgEgSzdgC4DsBsAM8S0ckAlgPoB2XP1CoA9+iOX65+64O+3A3gJAC3EtFRUNwy\n9QBwNpRNxXpBFARBEFIE30dYQHiU1RfABChCdRuALlDcKfVnZlthPNXjBgB4FsBhajn9ALwF4Bj1\nOoIgCEIK4rtrpiBCRLsAbHB5enMAuz2sTiYgbeYMaS9nSHs5I5726sjMCdsXJILlMUS0IJG+tNIR\naTNnSHs5Q9rLGUFur0BMCQqCIAhCLESwBEEQhJRABMt7XvW7AimItJkzpL2cIe3ljMC2l6xhCYIg\nCCmBjLAEQRCElEAESxAEQUgJRLA8gIjaEdGbRLSViA4SUT4RPU1ETfyuW7wQUTMiuoqIPieiNURU\nSkQFRDSTiK408P+YR0Rs8fnI4lqXEtE8IipSr/ETEZ1hcXw2Ed1CRL+p9dpLRJOI6Hgv28Ap6r+/\n2f1vNznneLXue9V7+Y2IbiaibIvrnKG2UYHaZnOJ6NIYdXPUxsmAiC6L8cwwEVVpjs+IZ4yIziGi\n54hoBhEdUO/tvRjnBPI58qodZQ0rToioCxS3Ui0BfAlgBYDjoLiVWglgoF1PHUGEiP4J4CUA26A4\nDt4IoBWAv0AJ2fIpgHNZfZCIKA/AegBLAHxhUOTvzPx/Btd5Aopnks0A/g9ALQAXAGgK4F/M/Lzu\neALwCYBzoLTz1+qx5wOoA+CvzPyl+zt3DxHlQwkU+rRBdhEzP6E7/mwo7VgG4GMAewGcCSWK9v8x\n87kG17gBwHMA9qjnlENpi3YAnmTm2w3OcdTGyYIUN2qjTLIHARgKYCIzn6Een4cMeMaIaDGAPgCK\n1Dp3B/A+M19kcnwgnyNP25GZ5RPHB8D3AFj9h9KmP6Wmv+x3HeO8v6HqQ5+lS28NRbxYfeBC6Xlq\n2gQH1zhePWcNgCa6svZA+Q+Ypzvnb+o5swDU0aQfC+AgFN+RDX1qs3wA+TaPPUSt60EAfTXpdaC8\nCDGAC3Tn5KltskfbLgCaqG3IAAbE28ZB+ACYo9b7rEx7xqC89HaF4jP1JLUu76Xac+RlO/r+QKby\nB4q/Q4bytqfv0BtCeTMqBlDf77om6P7vVu//OU2am87kHfWcyw3yHlTzxurSp6vpQ5yUl6R2yYd9\nwbpCrevbBnlD1byf7bSJVXlu2tjvD4Aj1XptBpCdyc8YYgtWYJ8jL9tR1rDiY4j6PZmZq7UZzFwI\n5Y2iHoD+ya5YkqhQvysN8g4lomuI6G71u7dFOUPV7+8M8r7VHQMiqgPlTa8EwAw75/hAbSK6SL3/\nm4hoiMk6gtW9T4dyj8cTUW2b55jdu5tz/OZq9fsNZq4yyM/0Z0xLIJ8jz9vR77eoVP4AeBzK28Ft\nJvnPq/nX+l3XBNx7DoCl6v2dpknPU9OMPtMAdNCVU1/NKzS5TnM1f4cmraeattTknL5q/lyf2ibf\n5P7XARisO3a+mneMSVm/q/k9NGm71LRmJucUqfn13Lax3x8AdQHsg/Iy1F6Xl3HPGGKPsAL5HHnd\njjLCio9G6neBSX4ovXES6pJsxgHoBWASM3+vSS8B8BCAY6DMhTcBMBhKR3ISgClEVF9zvJs2DHq7\nvwXgZCjrfPWhTG29AqWj/ZaI+miOTeT9N9J9B7W9jDgPSn2+Y+ZNujx5xqIJ6nPkaTuKYAmOIaIb\noVgJrQBwsTaPmXcy8/3MvIiZ96uf6QCGAZgLJU7ZVUmvdBJh5rHMPJWZdzBzCTP/zsz/hGKIUxfR\nEbGFaELTga/oM+QZy1xEsOJD/waiJ5S+Pwl1SQqqGewzAJZBWUTda+c8Zq4E8Lr680RNlps2TNV2\nf1n9Ttb9F+i+U6K9iKgnlHWPzQAm2T0vw5+xoD5HnrajCFZ8rFS/u5nkd1W/VyWhLgmHiG6Gsmfj\nd9KHGW0AAAqkSURBVChiZbgJ1oJd6nd4uoaZiwFsAdCAiNoYnGPUhmsBVAHoTEQ5Ns8JAlH3D4tn\nSL23TlDWcdbZPKeNWv5mZi4BXLexn8QytrAiU5+xoD5HnrajCFZ8TFO/h1G0x4eGAAZCmW//JdkV\n8xoiuhPAeACLoYjVThfFhKwl1+nSp6rfww3OGaE7BsxcBmVvST0oG0tjnhMQjO7f6t5PhHKPs5n5\noM1zzO7dzTlJR7UquxhKJ/eGiyIy9RkL5HPkeTsmw8IlnT9I843D6r3cp97LAgBNYxz7J+j2pKnp\nJ0PZVMgAjtflJWoz4iE+tFUPGOy7U+9ltVrnuzXph0AZFTjZ8NkJabpxGIpYMYCv5RmLqMtJiL1x\nOJDPkZft6OvDmQ4fKJuHd6j/IF8AeBTK2wJDGXIbmoymygfApeq9VEIZYY0x+FymOf4nKNMG/1OP\nHw9gCmrMju81uc6Tav4m9ZwXAOxW024wOJ7UazCA5QAeg/JGXqTW9Wyf2msMgEIAEwG8COC/UNzX\nlKp1nQiglu6cUWqdi6CswTwGxaCF1Xskg+v8S83frbbVeLXtGMATXrSxT+03Q63PmRbHZMQzpj4X\nE9TPd2o91mrSnjA4PnDPkZft6PsDmg4fAO2hmDJvg+KLawMUP3JN/K6bB/c2RtMRmH1+0hx/JYBv\noOxFKoLyBrURip+yQTGudRmU/STFUDr9nwGcYXF8DoBboOwHK4Wyb2cSdG/XSW6vwQA+VDuK/VA2\nV+8C8AOAS4w6DfW8gWrd96n3slS9t2yLa52ptlGh2mbzAVzqZRsnue16aDpCq/vOiGfMxv+9/FR5\njrxqR3F+KwiCIKQEYnQhCIIgpAQiWIIgCEJKIIIlCIIgpAQiWIIgCEJKIIIlCIIgpAQiWIIgCEJK\nIIIlCIIgpAQiWEJKQUQ3EtEyIiolIlYd8mYkRHS52gbH+V2XRENEf1Hv9WS/6yL4h2wcFlIGIroA\niheJX6H4cDwIJcBfyjsXdgoRNYDim3A+M5/lsowJUFxvdWLmfO9q5z1ERFB8WeYAOJqZq32ukuAD\nRu7eBSGonBH6ZuatvtbEf26EEtF4nN8VSQbMzET0Xyjuly4A8IHPVRJ8QKYEhVTiUADIdLEiomwA\n/wSwipln+12fJPIVFP+M1/ldEcEfRLCEwENEY4iIAQxRf3PoozmGiegnImpNRK8T0RYiqiKiy9T8\nbkQ0jogWENEuIjpIRBuI6FUiamdwzZPUMscQUV8i+o6ICohoHxF9SkTt1eM6E9FHapmlRDSNiPqY\n3Ec9IrqLiBYTUTERFRHRHCL6m8MmORWKw+VPTK4ziIi+JqLN6n1uJ6JfiOgBbXtBmQ4EgPWaNs3X\nldWUiB4louXq/RUQ0RQiGmZw3cvUMi4jopFENFu9z31E9H9E1NXgnFZE9AQRrVSP3a/+PYGIOmuP\nZSW20hcABhJRd4dtJqQBMiUopAI/qd+XAegIYKzJcU2hBMssAvAZgGoooV8A4C9QRiXToMQHKgfQ\nE8BVAM4kor7MvMWgzGMB3AnFG/VrAI5Uy+pFRGcDmAnFM/s7at3+AuAHIurMzEWhQoioMZSwM0cD\nWATgTSgvjKcB+ICIejLzvTbb4xT1e6Y+g4iGQwlhcgDKiGSL2i49oIxMQm03Fko4ij4AnkFNiPL9\nmrI6Qmn7PChhP76DEoX2DADfEdE1zPyaQf3+AiUw3+fq+UcB+CuAIUR0PDOvVMuvByVGUhco3uy/\nhhKKoiOAs6GEZdEHYpwF5Tk4BUq7C5mE3yEF5CMfux8onR+b5IVCLrwDIMcgvy2A2gbpw6BEt31J\nl36SpswLdXlvqOl7AdyjywsFu7xJlz5BTb9Dl14HihBUAzjKZjv8opYVFWsNwKdqXh+DvOYmdcqz\naO9qRAf+awwl8nQpgFaa9Ms0bXaG7pyb1PQpmrQz1bTxBteuBaChQXof9ZxP/H4e5ZP8j0wJCulE\nOYDbmblSn8HMWzgyPHgofTKAP6CMdIyYyczv69LeVr8LEG308I76fVQogYiaAbgIwAJmfkx3/TIo\nIzgC8HeTOujpAKCCmfdYHFOqT2Dm3TbLhzqtORjAp8z8ka6c/QAegCK2fzU4fSozf6NLex5K8MGh\n6sgtVl3LmbnQoOzt6neH2HchpBsyJSikE/nMvNMoQzWLvhDKKKAPlDDg2ZpDyk3KXGCQFjL6WMzM\nVbq80LSidl3sWPVaTERjDMrLVb97mNRBTzMoAfCMeB/KlNxcIvoYyhToLGbebLPsEAPU70YmdW6h\nfhvV+Wd9AjNXEdFMKNN/R0MJcvozlPYaTUR/ghLQbxaM2zXEXvW7uZ2bENILESwhndhukfcUgJuh\nRIX+HkpHGXqzvwzKuokRBQZplWZ5zFypaGNYhABFYABFuI61qGMDizwtpVBGN1Ew82dEdAaA2wBc\nAeAaACCihQDuYuYfbF4jVOdT1Y8ZRnXeYZAG1Pz7NFLreoCI+kNZTzsLNaPc3UT0IoCHmblCV0Zd\n9TtqVCakPyJYQjphuAueiFpC2bf0O5SQ3IW6fKdWek4JCdt4Zr7Vg/J2AuhKRLkGHTqYeSKAiURU\nH0A/KEYS1wL4hoiOZuZlDup8EzM/67B+rUzSW+vKhjryu1IdAR8BYCiA6wHcD8Uo5T5dGSEhNRxJ\nC+mNrGEJmUBnKM/6ZAOxaqfmJ5J5UIwXBnlU3m/q9+FWBzFzMTNPVUXyP1AMGUZoDglNu2VHnawY\ndgDu6jxYn6DuHTtB/fmrQV2Zmf9g5udQM6IbZVB2yJx9sYt6CSmOCJaQCeSr3yeoHSeAsHuj15Dg\nmQZ1Xe19AH2J6D5tHTR16UJEnWwW+ZP63d+gnBOJyOh+QqOeEk1ayGgjyoCBmRdAMWX/CxFdYVQJ\nIjpSHb3qGapOS2q5Acr61TRm3qCe35OIjEZjRnUNEbrnaUZ1EtIbmRIU0h5m3k5EH0Fx6bOYiCZD\nWUc5FUAZlLf1oyyK8IIbAHQF8CCAi1UDhB1QvHf0gLK29TcA622U9SWAp6Gs+byuy3sWQFsimgVF\nqMsBHANlqm0DAK3F3xQA/wbwGhF9CqAQwH5mfl7N/zuUvWNvENGNAOZC2afVDkBvAL2gGGfop+e+\nBvA5EX0OYA2Uth0BxWBC66XiVACPE9EcAKvUctpB2YNVDeBxg3sfptZhqmnrCOmL33b18pGP3Q9i\n78P6yeLcegAegdKBlgHYBOAFKGsiUeWiZh/WGIOy8tS8CU7qAmVK7gYoG5cLoDjv3QhFOG6Gwb4q\ni/v5XL2PJrr086A4CF4NZQP1AShrd48AaGFQzq0Alqt1YSiWltr8hgDuBrBQLa8UiqhOBHA1gPqa\nYy9Ty7gMyrrZHADFUATmUwDddGX3gGIMswDALrUO+VA2DB9vUNduavlP+/0sysefj3hrF4QUhIiO\nh2ICfiszj/e7PoDimgnAWwAuZ+YJCSj/SSiC34OZ9R4whAxA1rAEIQVhxent/wDcqbo4SmuIqA0U\nS8fnRKwyFxEsQUhdbgfwMgC7xhqpTB6A/wJ42Od6CD4iRheCkKIw80YAY/yuRzJg5jlQ1sSEDEbW\nsARBEISUQKYEBUEQhJRABEsQBEFICUSwBEEQhJRABEsQBEFICUSwBEEQhJRABEsQBEFICf4fjx2Q\nDvB7DsUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22db14bf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(hills.height)\n",
    "xlabel('frame (steps)')\n",
    "ylabel('hight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29-08-17 10:28:15 pyemma.coordinates.clustering.kmeans.KmeansClustering[4] INFO     Cluster centers converged after 10 steps.\n"
     ]
    }
   ],
   "source": [
    "import pyemma.coordinates as coor\n",
    "ttrajs = coor.cluster_kmeans(cvs.bias,k=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29-08-17 10:28:22 pyemma.coordinates.clustering.kmeans.KmeansClustering[5] INFO     Cluster centers converged after 8 steps.\n"
     ]
    }
   ],
   "source": [
    "dtrajs = coor.cluster_kmeans(hills.height,k=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bias = [np.hstack((cvs.time.reshape(10001,1),cvs.bias.reshape(10001,1)))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Argument dtraj is not a discrete trajectory - only list of integers or int-ndarrays are allowed. Check type of KmeansClustering(fixed_seed=140568611, init_strategy='kmeans++', max_iter=10,\n         metric='euclidean', n_clusters=100, n_jobs=4,\n         oom_strategy='memmap', skip=0, stride=1, tolerance=1e-05)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-46-fe8323aceab7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m tram = pyemma.thermo.tram(ttrajs=ttrajs, dtrajs=dtrajs, bias=bias, lag=10, \n\u001b[1;32m----> 2\u001b[1;33m                           maxerr=1.E-12, direct_space=True, connectivity='reversible_pathways')\n\u001b[0m",
      "\u001b[1;32m/home/gerry/gp120/env/local/lib/python2.7/site-packages/pyemma/thermo/api.pyc\u001b[0m in \u001b[0;36mtram\u001b[1;34m(ttrajs, dtrajs, bias, lag, unbiased_state, count_mode, connectivity, maxiter, maxerr, save_convergence_info, dt_traj, connectivity_factor, nn, direct_space, N_dtram_accelerations, callback, init, init_maxiter, init_maxerr, equilibrium, overcounting_factor)\u001b[0m\n\u001b[0;32m    662\u001b[0m     \"\"\"\n\u001b[0;32m    663\u001b[0m     \u001b[1;31m# prepare trajectories\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 664\u001b[1;33m     \u001b[0mttrajs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_types\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_dtraj_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mttrajs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    665\u001b[0m     \u001b[0mdtrajs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_types\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_dtraj_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtrajs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    666\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mttrajs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtrajs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/gerry/gp120/env/local/lib/python2.7/site-packages/pyemma/util/types.pyc\u001b[0m in \u001b[0;36mensure_dtraj_list\u001b[1;34m(dtrajs)\u001b[0m\n\u001b[0;32m    177\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mdtrajs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    178\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 179\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mensure_dtraj\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtrajs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    181\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mensure_int_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mI\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequire_order\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/gerry/gp120/env/local/lib/python2.7/site-packages/pyemma/util/types.pyc\u001b[0m in \u001b[0;36mensure_dtraj\u001b[1;34m(dtraj)\u001b[0m\n\u001b[0;32m    162\u001b[0m         raise TypeError('Argument dtraj is not a discrete trajectory - '\n\u001b[0;32m    163\u001b[0m                         \u001b[1;34m'only list of integers or int-ndarrays are allowed. '\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 164\u001b[1;33m                         'Check type of %s' % repr(dtraj))\n\u001b[0m\u001b[0;32m    165\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    166\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mensure_dtraj_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtrajs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Argument dtraj is not a discrete trajectory - only list of integers or int-ndarrays are allowed. Check type of KmeansClustering(fixed_seed=140568611, init_strategy='kmeans++', max_iter=10,\n         metric='euclidean', n_clusters=100, n_jobs=4,\n         oom_strategy='memmap', skip=0, stride=1, tolerance=1e-05)"
     ]
    }
   ],
   "source": [
    "tram = pyemma.thermo.tram(ttrajs=ttrajs, dtrajs=dtrajs, bias=bias, lag=10, \n",
    "                          maxerr=1.E-12, direct_space=True, connectivity='reversible_pathways')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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