{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example for a Pandas Analysis\n",
    "## Analyzing the Velocity Vectors in a GROMACS output File\n",
    "\n",
    "At the end of a Molecular Dynamics (MD) simulation, GROMACS mdrun will write the system at the final simulation step into a file in the .gro format.\n",
    "\n",
    "Here we want to plot the velocity distribution of all paricles in the system at this simulation step to see if the system behaves according to a [Maxwell-Boltzmann distribution](https://en.wikipedia.org/wiki/Maxwell%E2%80%93Boltzmann_distribution).\n",
    "\n",
    "The format specifications can be found at:  \n",
    "<http://manual.gromacs.org/documentation/2016.3/user-guide/file-formats.html#gro>.  \n",
    "The first line contains a comment, the second line the number of atoms in the system. The last line contains the definition of the simulation box (box vectors). None of these lines are relevant for this analysis.\n",
    "\n",
    "Instead we are interested in the column with the element symbols and the columns with the x, y and z components of the velocity vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ostueker/Carpentry/CMSC6950-2017/lectures/pandas_data\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pylab as plt\n",
    "%matplotlib inline\n",
    "%cd pandas_data/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first and last lines of the first data file: \n",
    "(Yes, we can run Unix commands from within the Jupyter Notebook!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chloroethane\r\n",
      " 8000\r\n",
      "    1LIG      C    1   1.767   1.718   4.553 -0.3691  0.0876  0.2868\r\n",
      "    1LIG      H    2   1.874   1.726   4.572  0.2073 -0.9631 -2.1583\r\n",
      "    1LIG      H    3   1.731   1.817   4.580 -0.7323  0.1380 -0.3705\r\n",
      "    1LIG      H    4   1.728   1.643   4.623 -2.0876  0.3128 -0.4014\r\n",
      "    1LIG      C    5   1.734   1.699   4.405 -0.0319 -0.8964  0.3304\r\n",
      "    1LIG      H    6   1.777   1.606   4.367 -0.4904 -0.2839 -1.7862\r\n",
      "    1LIG      H    7   1.775   1.783   4.349 -2.4901  0.9136  1.0744\r\n",
      "    1LIG     CL    8   1.557   1.698   4.385  0.0449 -0.2186 -0.4189\r\n",
      "    2LIG      C    9   2.280   0.416   3.435 -0.3748 -0.2371  0.2111\r\n",
      "    2LIG      H   10   2.376   0.465   3.453  0.6476 -1.9921 -0.1844\r\n",
      "    2LIG      H   11   2.221   0.430   3.525  0.0786 -0.6578  0.5836\r\n",
      "    2LIG      H   12   2.309   0.311   3.437 -3.1216 -1.0492  2.4335\r\n",
      "    2LIG      C   13   2.222   0.461   3.301 -0.8851  0.5726  0.6990\r\n"
     ]
    }
   ],
   "source": [
    "!head -n15 nvt_berendsen.gro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1000LIG      C 7997   0.152   2.024   2.110  0.3288  0.4162 -0.2861\r\n",
      " 1000LIG      H 7998   0.081   1.954   2.155  0.7683 -1.1081 -1.8281\r\n",
      " 1000LIG      H 7999   0.141   2.021   2.002 -0.6068  3.3358 -0.3694\r\n",
      " 1000LIG     CL 8000   0.089   2.180   2.168  0.0204  0.2227 -0.0991\r\n",
      "   4.92200   4.92200   4.92200\r\n"
     ]
    }
   ],
   "source": [
    "!tail -n5 nvt_berendsen.gro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define some column headers and read the file directly into a pandas data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>res_id</th>\n",
       "      <th>elem</th>\n",
       "      <th>atomno</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>vx</th>\n",
       "      <th>vy</th>\n",
       "      <th>vz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7995</th>\n",
       "      <td>1000LIG</td>\n",
       "      <td>H</td>\n",
       "      <td>7996.0</td>\n",
       "      <td>0.360</td>\n",
       "      <td>2.076</td>\n",
       "      <td>2.128</td>\n",
       "      <td>0.3706</td>\n",
       "      <td>-0.2737</td>\n",
       "      <td>-1.3683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7996</th>\n",
       "      <td>1000LIG</td>\n",
       "      <td>C</td>\n",
       "      <td>7997.0</td>\n",
       "      <td>0.152</td>\n",
       "      <td>2.024</td>\n",
       "      <td>2.110</td>\n",
       "      <td>0.3288</td>\n",
       "      <td>0.4162</td>\n",
       "      <td>-0.2861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7997</th>\n",
       "      <td>1000LIG</td>\n",
       "      <td>H</td>\n",
       "      <td>7998.0</td>\n",
       "      <td>0.081</td>\n",
       "      <td>1.954</td>\n",
       "      <td>2.155</td>\n",
       "      <td>0.7683</td>\n",
       "      <td>-1.1081</td>\n",
       "      <td>-1.8281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7998</th>\n",
       "      <td>1000LIG</td>\n",
       "      <td>H</td>\n",
       "      <td>7999.0</td>\n",
       "      <td>0.141</td>\n",
       "      <td>2.021</td>\n",
       "      <td>2.002</td>\n",
       "      <td>-0.6068</td>\n",
       "      <td>3.3358</td>\n",
       "      <td>-0.3694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7999</th>\n",
       "      <td>1000LIG</td>\n",
       "      <td>CL</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0.089</td>\n",
       "      <td>2.180</td>\n",
       "      <td>2.168</td>\n",
       "      <td>0.0204</td>\n",
       "      <td>0.2227</td>\n",
       "      <td>-0.0991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       res_id elem  atomno      x      y      z      vx      vy      vz\n",
       "7995  1000LIG    H  7996.0  0.360  2.076  2.128  0.3706 -0.2737 -1.3683\n",
       "7996  1000LIG    C  7997.0  0.152  2.024  2.110  0.3288  0.4162 -0.2861\n",
       "7997  1000LIG    H  7998.0  0.081  1.954  2.155  0.7683 -1.1081 -1.8281\n",
       "7998  1000LIG    H  7999.0  0.141  2.021  2.002 -0.6068  3.3358 -0.3694\n",
       "7999  1000LIG   CL  8000.0  0.089  2.180  2.168  0.0204  0.2227 -0.0991"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gro_header=['res_id', 'elem', 'atomno', 'x', 'y', 'z', 'vx', 'vy', 'vz']\n",
    "\n",
    "df = pd.read_table('nvt_berendsen.gro', sep='\\s+', skiprows=2, names=gro_header)\n",
    "\n",
    "df.iloc[-1]=np.NaN      # last line contains the definition of the simulation box.\n",
    "df.dropna(inplace=True)\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>res_id</th>\n",
       "      <th>elem</th>\n",
       "      <th>atomno</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>vx</th>\n",
       "      <th>vy</th>\n",
       "      <th>vz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1LIG</td>\n",
       "      <td>C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.767</td>\n",
       "      <td>1.718</td>\n",
       "      <td>4.553</td>\n",
       "      <td>-0.3691</td>\n",
       "      <td>0.0876</td>\n",
       "      <td>0.2868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1LIG</td>\n",
       "      <td>H</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.874</td>\n",
       "      <td>1.726</td>\n",
       "      <td>4.572</td>\n",
       "      <td>0.2073</td>\n",
       "      <td>-0.9631</td>\n",
       "      <td>-2.1583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1LIG</td>\n",
       "      <td>H</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.731</td>\n",
       "      <td>1.817</td>\n",
       "      <td>4.580</td>\n",
       "      <td>-0.7323</td>\n",
       "      <td>0.1380</td>\n",
       "      <td>-0.3705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1LIG</td>\n",
       "      <td>H</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.728</td>\n",
       "      <td>1.643</td>\n",
       "      <td>4.623</td>\n",
       "      <td>-2.0876</td>\n",
       "      <td>0.3128</td>\n",
       "      <td>-0.4014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1LIG</td>\n",
       "      <td>C</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.734</td>\n",
       "      <td>1.699</td>\n",
       "      <td>4.405</td>\n",
       "      <td>-0.0319</td>\n",
       "      <td>-0.8964</td>\n",
       "      <td>0.3304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  res_id elem  atomno      x      y      z      vx      vy      vz\n",
       "0   1LIG    C     1.0  1.767  1.718  4.553 -0.3691  0.0876  0.2868\n",
       "1   1LIG    H     2.0  1.874  1.726  4.572  0.2073 -0.9631 -2.1583\n",
       "2   1LIG    H     3.0  1.731  1.817  4.580 -0.7323  0.1380 -0.3705\n",
       "3   1LIG    H     4.0  1.728  1.643  4.623 -2.0876  0.3128 -0.4014\n",
       "4   1LIG    C     5.0  1.734  1.699  4.405 -0.0319 -0.8964  0.3304"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the length of the velocity vector as:\n",
    "\n",
    "$ |\\overrightarrow{v}| = \\sqrt{ v_x^2 + v_y^2 + v_z^2 }$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['vel'] = (df.vx **2 + df.vy **2 + df.vz **2) **0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>res_id</th>\n",
       "      <th>elem</th>\n",
       "      <th>atomno</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>vx</th>\n",
       "      <th>vy</th>\n",
       "      <th>vz</th>\n",
       "      <th>vel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1LIG</td>\n",
       "      <td>C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.767</td>\n",
       "      <td>1.718</td>\n",
       "      <td>4.553</td>\n",
       "      <td>-0.3691</td>\n",
       "      <td>0.0876</td>\n",
       "      <td>0.2868</td>\n",
       "      <td>0.475566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1LIG</td>\n",
       "      <td>H</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.874</td>\n",
       "      <td>1.726</td>\n",
       "      <td>4.572</td>\n",
       "      <td>0.2073</td>\n",
       "      <td>-0.9631</td>\n",
       "      <td>-2.1583</td>\n",
       "      <td>2.372508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1LIG</td>\n",
       "      <td>H</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.731</td>\n",
       "      <td>1.817</td>\n",
       "      <td>4.580</td>\n",
       "      <td>-0.7323</td>\n",
       "      <td>0.1380</td>\n",
       "      <td>-0.3705</td>\n",
       "      <td>0.832212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1LIG</td>\n",
       "      <td>H</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.728</td>\n",
       "      <td>1.643</td>\n",
       "      <td>4.623</td>\n",
       "      <td>-2.0876</td>\n",
       "      <td>0.3128</td>\n",
       "      <td>-0.4014</td>\n",
       "      <td>2.148730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1LIG</td>\n",
       "      <td>C</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.734</td>\n",
       "      <td>1.699</td>\n",
       "      <td>4.405</td>\n",
       "      <td>-0.0319</td>\n",
       "      <td>-0.8964</td>\n",
       "      <td>0.3304</td>\n",
       "      <td>0.955884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  res_id elem  atomno      x      y      z      vx      vy      vz       vel\n",
       "0   1LIG    C     1.0  1.767  1.718  4.553 -0.3691  0.0876  0.2868  0.475566\n",
       "1   1LIG    H     2.0  1.874  1.726  4.572  0.2073 -0.9631 -2.1583  2.372508\n",
       "2   1LIG    H     3.0  1.731  1.817  4.580 -0.7323  0.1380 -0.3705  0.832212\n",
       "3   1LIG    H     4.0  1.728  1.643  4.623 -2.0876  0.3128 -0.4014  2.148730\n",
       "4   1LIG    C     5.0  1.734  1.699  4.405 -0.0319 -0.8964  0.3304  0.955884"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the velocity distribution (histogram):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f40822bcdd8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NY5AR/R3A54HjSZ5v2n4XeBg4lOQB4BXgXoCqOpHkEHCS+RU7D1bVOyOvXJI0kL5BX1V/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\nOs6gl6SOM+glqeMMeknqOINekjru/wPOeA/BOj5opwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f408350d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['vel'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate some basic statistics on the data columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atomno</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>vx</th>\n",
       "      <th>vy</th>\n",
       "      <th>vz</th>\n",
       "      <th>vel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8000.00000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4000.50000</td>\n",
       "      <td>2.463283</td>\n",
       "      <td>2.438690</td>\n",
       "      <td>2.454125</td>\n",
       "      <td>-0.012186</td>\n",
       "      <td>-0.004563</td>\n",
       "      <td>0.029142</td>\n",
       "      <td>1.467645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2309.54541</td>\n",
       "      <td>1.424087</td>\n",
       "      <td>1.413223</td>\n",
       "      <td>1.424752</td>\n",
       "      <td>1.054439</td>\n",
       "      <td>1.059265</td>\n",
       "      <td>1.064502</td>\n",
       "      <td>1.101733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>-0.204000</td>\n",
       "      <td>-0.233000</td>\n",
       "      <td>-0.224000</td>\n",
       "      <td>-5.189200</td>\n",
       "      <td>-4.971500</td>\n",
       "      <td>-5.588700</td>\n",
       "      <td>0.026528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2000.75000</td>\n",
       "      <td>1.225750</td>\n",
       "      <td>1.224000</td>\n",
       "      <td>1.235500</td>\n",
       "      <td>-0.455975</td>\n",
       "      <td>-0.460625</td>\n",
       "      <td>-0.454800</td>\n",
       "      <td>0.556672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4000.50000</td>\n",
       "      <td>2.446500</td>\n",
       "      <td>2.429000</td>\n",
       "      <td>2.434500</td>\n",
       "      <td>-0.014400</td>\n",
       "      <td>0.003250</td>\n",
       "      <td>0.013050</td>\n",
       "      <td>1.132518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6000.25000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>3.671250</td>\n",
       "      <td>3.690250</td>\n",
       "      <td>0.422125</td>\n",
       "      <td>0.447350</td>\n",
       "      <td>0.483900</td>\n",
       "      <td>2.192343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8000.00000</td>\n",
       "      <td>5.159000</td>\n",
       "      <td>5.149000</td>\n",
       "      <td>5.128000</td>\n",
       "      <td>5.687500</td>\n",
       "      <td>4.931800</td>\n",
       "      <td>5.115400</td>\n",
       "      <td>6.360739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           atomno            x            y            z           vx  \\\n",
       "count  8000.00000  8000.000000  8000.000000  8000.000000  8000.000000   \n",
       "mean   4000.50000     2.463283     2.438690     2.454125    -0.012186   \n",
       "std    2309.54541     1.424087     1.413223     1.424752     1.054439   \n",
       "min       1.00000    -0.204000    -0.233000    -0.224000    -5.189200   \n",
       "25%    2000.75000     1.225750     1.224000     1.235500    -0.455975   \n",
       "50%    4000.50000     2.446500     2.429000     2.434500    -0.014400   \n",
       "75%    6000.25000     3.700000     3.671250     3.690250     0.422125   \n",
       "max    8000.00000     5.159000     5.149000     5.128000     5.687500   \n",
       "\n",
       "                vy           vz          vel  \n",
       "count  8000.000000  8000.000000  8000.000000  \n",
       "mean     -0.004563     0.029142     1.467645  \n",
       "std       1.059265     1.064502     1.101733  \n",
       "min      -4.971500    -5.588700     0.026528  \n",
       "25%      -0.460625    -0.454800     0.556672  \n",
       "50%       0.003250     0.013050     1.132518  \n",
       "75%       0.447350     0.483900     2.192343  \n",
       "max       4.931800     5.115400     6.360739  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group by Chemical Element (groupby() function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gb = df[ ['elem', 'vel' ] ].groupby('elem')\n",
    "#.plot(kind='hist', bins=50, by='elem')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atomno</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>vx</th>\n",
       "      <th>vy</th>\n",
       "      <th>vz</th>\n",
       "      <th>vel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elem</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>3999.0</td>\n",
       "      <td>2.463380</td>\n",
       "      <td>2.438597</td>\n",
       "      <td>2.454332</td>\n",
       "      <td>-0.006034</td>\n",
       "      <td>-0.007916</td>\n",
       "      <td>0.002183</td>\n",
       "      <td>0.583025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL</th>\n",
       "      <td>4004.0</td>\n",
       "      <td>2.459352</td>\n",
       "      <td>2.438708</td>\n",
       "      <td>2.452138</td>\n",
       "      <td>0.006705</td>\n",
       "      <td>0.006123</td>\n",
       "      <td>-0.008224</td>\n",
       "      <td>0.406433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>4000.4</td>\n",
       "      <td>2.464030</td>\n",
       "      <td>2.438724</td>\n",
       "      <td>2.454439</td>\n",
       "      <td>-0.018426</td>\n",
       "      <td>-0.005359</td>\n",
       "      <td>0.047398</td>\n",
       "      <td>2.033736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      atomno         x         y         z        vx        vy        vz  \\\n",
       "elem                                                                       \n",
       "C     3999.0  2.463380  2.438597  2.454332 -0.006034 -0.007916  0.002183   \n",
       "CL    4004.0  2.459352  2.438708  2.452138  0.006705  0.006123 -0.008224   \n",
       "H     4000.4  2.464030  2.438724  2.454439 -0.018426 -0.005359  0.047398   \n",
       "\n",
       "           vel  \n",
       "elem            \n",
       "C     0.583025  \n",
       "CL    0.406433  \n",
       "H     2.033736  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['elem']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "elem       \n",
       "C     count    2000.000000\n",
       "      mean        0.583025\n",
       "      std         0.257527\n",
       "      min         0.026859\n",
       "      25%         0.393001\n",
       "      50%         0.556925\n",
       "      75%         0.746270\n",
       "      max         1.856181\n",
       "CL    count    1000.000000\n",
       "      mean        0.406433\n",
       "      std         0.173878\n",
       "      min         0.026528\n",
       "      25%         0.274518\n",
       "      50%         0.389485\n",
       "      75%         0.516921\n",
       "      max         1.098677\n",
       "H     count    5000.000000\n",
       "      mean        2.033736\n",
       "      std         1.025087\n",
       "      min         0.122229\n",
       "      25%         1.242298\n",
       "      50%         1.903030\n",
       "      75%         2.678381\n",
       "      max         6.360739\n",
       "Name: vel, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['elem'])['vel'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "elem\n",
       "C     2000\n",
       "CL    1000\n",
       "H     5000\n",
       "Name: vel, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['elem'])['vel'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "elem\n",
       "C     0.583025\n",
       "CL    0.406433\n",
       "H     2.033736\n",
       "Name: vel, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['elem'])['vel'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "elem\n",
       "C     0.556925\n",
       "CL    0.389485\n",
       "H     1.903030\n",
       "Name: vel, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['elem'])['vel'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "elem\n",
       "C     0.257527\n",
       "CL    0.173878\n",
       "H     1.025087\n",
       "Name: vel, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['elem'])['vel'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "elem\n",
       "C     Axes(0.125,0.125;0.775x0.755)\n",
       "CL    Axes(0.125,0.125;0.775x0.755)\n",
       "H     Axes(0.125,0.125;0.775x0.755)\n",
       "Name: vel, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pRj978sfelESSes0evSQVzqCXpMIVN3Rz9Ebgkt6q3RWz7XglbTns0UtS4Yrr0Tfdridf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\nQjOXoTx7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4086bf2198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['elem'])['vel'].hist(bins=50, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Panda's `.plot()` function offers more options than the `.hist()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "elem\n",
       "C     Axes(0.125,0.125;0.775x0.755)\n",
       "CL    Axes(0.125,0.125;0.775x0.755)\n",
       "H     Axes(0.125,0.125;0.775x0.755)\n",
       "Name: vel, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAD8CAYAAABthzNFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f408279ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['elem'])['vel'].plot(kind='hist', bins=50, alpha=0.7, legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subplots with Matplotlib\n",
    "\n",
    "Pyplot Tutorial [Working with multiple figures and axes](http://matplotlib.org/users/pyplot_tutorial.html#working-with-multiple-figures-and-axes)\n",
    "\n",
    "We create one figure with Matplotlib, which we divide in three subplots.\n",
    "The `suptitle()` command sets a super-title for the whole figure.\n",
    "\n",
    "The `subplot()` command specifies `numrows`, `numcols`, `fignum` where `fignum` ranges from `1` to `numrows*numcols`. The commas in the subplot command are optional if `numrows*numcols<10`. So `subplot(211)` is identical to `subplot(2, 1, 1)`.\n",
    "\n",
    "The titles for the subplot panels could also be passed to `DataFrame.plot( ..., title='Subplot Title')` \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f407e68e048>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2qm5NcveyqqokczrBAlYCLF26dL5CnbPp+ciecmksXZ9kt6rakGQ3YGPbvh7Y\ns2e9Pdq2e6mqNcAagOXLlw+c2yQtHvb8SZo4SXagKfw+UVWfbZuvb0+s2JYTrKpaXlXLlyxZMpzg\nJU2a04Aj2sdHAKf2tK9Icr8kewF7A+eOID5JY8TiT9JESdPFdzxwaVW9s2eRJ1iSRirJSTTXE++T\n5JokRwKrgWcnuRz43fY5VXUxcApwCfAF4Kiq2jyayCWNC4d9Spo0TwdeClyU5MK27U00J1SntCdb\nVwGHQXOClWTqBOsuPMGSNCRVdfgsiw6cZf3jgOOGF9HW9Q4vd2i51H0Wf5ImSlWdDWSWxZ09wZIk\nSdpWDvuUJEmSpAlg8SdJkiRJE8DiT5IkSZImgMWfJEmSJE0Aiz9JkiRJmgDe7VOSJEl3652+YS7b\nOeWD1F32/EmSJEnSBLD4kyRJkqQJYPEnSZIkSRPA4k+SJEmSJoDFnyRJkiRNAIs/SZIkSZoAFn+S\nJEmSNAH6Kv6S/OqwA5GkuTA/Seoic5OkLuq35+8DSc5N8r+SPLSfDZKckGRjku/1tB2bZH2SC9uv\n5/UsOzrJFUkuS/KcAV+HpMk1cH6SpAVgbpLUOX0Vf1X1W8CLgT2B85N8Msmzt7LZicBBM7S/q6r2\na78+D5BkX2AF8Ph2mw8k2a7P1yBpgs0xP0nSUJmbJHVR39f8VdXlwJuBNwLPBN6b5PtJ/tss658F\n3NTn7g8BTq6qO6rqSuAKYP9+Y5M02QbNT5K0EMxNkrqm32v+fi3Ju4BLgd8BXlBVj2sfv2vAY746\nyXfbYaE7tW27A1f3rHNN2yZJWzTP+UmS5oW5SVIXbd/nen8P/APwpqr6yVRjVV2b5M0DHO+DwF8B\n1X5/B/CKAbYnyUpgJcDSpUsH2VTS4jRf+WnRW7bq9Hu1rVt98AgikSaCuUlS5/Rb/B0M/KSqNgMk\nuQ9w/6r6cVV9rN+DVdX1U4+TfBj4XPt0Pc2Y+Cl7tG0z7WMNsAZg+fLl1e+xJS1a85KfRmWmgkzS\nojDWuUnS4tTvNX9fBh7Q8/yBbdtAkuzW8/RFwNSdQE8DViS5X5K9gL2Bcwfdv6SJNC/5SZLmmblJ\nUuf02/N3/6q6fepJVd2e5IFb2iDJScABwC5JrgGOAQ5Ish/NsM91wJ+0+7s4ySnAJcBdwFFTn5R1\nlZ/WS50xcH6SpAVgbpLUOf0Wfz9K8uSqugAgya8DP9nSBlV1+AzNx29h/eOA4/qMR5KmDJyfJGkB\nmJskdU5lceGZAAAVcElEQVS/xd9rgX9Mci0Q4BHAHwwtKknqn/lJUheZmyR1Tl/FX1V9K8mvAPu0\nTZdV1c+GF5Yk9cf8JKmLzE2Suqjfnj+ApwDL2m2enISq+uhQopL6NNu1l7Pdvn5L12p6y/uxZn6S\n1EXmJkmd0lfxl+RjwGOAC4GpG7EUYAKTNFLmJ0ldNN+5KcmfAX/c7uMi4OU0dxD9FE2BuQ44rKpu\n3pa4JS1u/fb8LQf2rSrn1ZPUNeYnSV00b7kpye7An7b7+0l7h/QVwL7AmVW1OskqYBXwxm09nqTF\nq995/r5Hc6GyJHWN+UlSF813btoeeECS7Wl6/K4FDgHWtsvXAofO4/EkLUL99vztAlyS5FzgjqnG\nqnrhUKKSpP6ZnyR10bzlpqpan+TtwA9ppov4UlV9KcmuVbWhXe06YNd5iFvSItZv8XfsMIOQpG1w\n7KgDkKQZHDtfO0qyE00v317ALTRTSLykd52qqiQzDjFNshJYCbB06dL5CmtWvTdX82ZqUrf0O9XD\n15I8Cti7qr6c5IHAdsMNTZK2zvwkqYvmOTf9LnBlVW0CSPJZ4DeB65PsVlUbkuwGbJwlljXAGoDl\ny5d7fbQ0wfq65i/JK4FPAx9qm3YH/nlYQUlSv8xPkrponnPTD4GnJXlgkgAHApcCpwFHtOscAZw6\n94glTYJ+b/hyFPB04FaAqrocePiwgpKkAZifJHXRvOWmqjqHppC8gGaah/vQ9OStBp6d5HKa3sHV\n2x62pMWs32v+7qiqO5sPm6C905TDBtRZW5rMXYuO+UlSF81rbqqqY4Bjph+DphdQkvrSb8/f15K8\nieYWw88G/hH4l+GFJUl9Mz9J6iJzk6TO6bf4WwVsohlq8CfA54E3DysoSRrAwPkpyQlJNib5Xk/b\nsUnWJ7mw/Xpez7Kjk1yR5LIkzxnS65C0uHjuJKlz+r3b58+BD7dfktQZc8xPJwLvAz46rf1dVfX2\n3oYk+wIrgMcDjwS+nOSxVbV5zkFLWvQ8d5LURX0Vf0muZIZx6lX16HmPSJIGMJf8VFVnJVnW5yEO\nAU6uqjuAK5NcAewPfGPwaCVNCs+dJHVRvzd8Wd7z+P7A7wM7z384kjSw+cxPr07yR8B5wOur6maa\n27N/s2eda9o2SdoSz50kdU5f1/xV1Y09X+ur6t3AwUOOTZK2ah7z0weBRwP7ARuAdwy6gyQrk5yX\n5LxNmzbNIQRJi4XnTpK6qN9hn0/ueXofmk+z+u01HGtOGSB123zlp6q6vmefHwY+1z5dD+zZs+oe\nbdtM+1hDM/cWy5cvd7oJaYJN8rmTpO7qNwn1fgJ+F7AOOGzeo5Gkwc1LfkqyW1VtaJ++CJi6E+hp\nwCeTvJPmhi97A+fOOVpJk8JzJ0md0+/dPp817EAkaS7mkp+SnAQcAOyS5BqaiZMPSLIfzQ0a1tHc\nmp2qujjJKcAlNCdwR3mnT0lb47mTpC7qd9jn67a0vKreOT/hSKMz2xDfdau9RKPL5pKfqurwGVY9\nfgv7OA44bvDoJE0qz50kddEgd/t8Cs3wJ4AX0Ax7unwYQUnSAMxPkrrI3CSpc/ot/vYAnlxVtwEk\nORY4vapeMqzAJKlP5idJXWRuktQ5fU31AOwK3Nnz/M62TZJGzfwkqYvMTZI6p9+ev48C5yb5p/b5\nocDa4YQkSQMxP0nqInOTpM7p926fxyX5V+C32qaXV9W3hxeWJPXH/CSpi8xNkrqo32GfAA8Ebq2q\n9wDXJNlrSDFJ0qDMT5K6yNwkqVP6Kv6SHAO8ETi6bdoB+PiwgpKkfpmfJHWRuUlSF/Xb8/ci4IXA\njwCq6lrgwcMKSpIGYH6S1EXmJkmd02/xd2dVFVAASR40vJAkaSDmJ0ldZG6S1Dn9Fn+nJPkQ8LAk\nrwS+DHx4eGFJUt/MT5K6yNwkqXP6vdvn25M8G7gV2Ad4S1WdMdTIJKkP5idJXWRuktRFWy3+kmwH\nfLmqngWYtCR1hvlJUheZmyR11VaHfVbVZuDnSR66APFIUt/MT5K6yNwkqav6GvYJ3A5clOQM2rtW\nAVTVnw4lKknqn/lJUheZmyR1Tr/F32fbL0nqGvOTpC4yNwHLVp0OwLrVB484EkmwleIvydKq+mFV\nrV2ogCSpH+YnSV1kbpLUZVu75u+fpx4k+cyQY5GkQZifJHWRuUlSZ22t+EvP40cPMxBJGpD5SVIX\nDSU3JXlYkk8n+X6SS5P8RpKdk5yR5PL2+07zdTxJi9PWir+a5bEkjZr5SVIXDSs3vQf4QlX9CvBE\n4FJgFXBmVe0NnNk+l6RZbe2GL09McivNp1gPaB/TPq+qeshQo5Ok2ZmfJHXRvOemdsqI3wZeRrOT\nO4E7kxwCHNCuthb4KvDGbQle0uK2xeKvqrZbqECkrpq6U1m/vKPZwjA/SeqiIeWmvYBNwEeSPBE4\nH3gNsGtVbWjXuQ7YdQjHlrSIbHWS97lKckKSjUm+19M269j0JEcnuSLJZUmeM6y4JEmSxsz2wJOB\nD1bVk2jmDfyFIZ5VVcwyzDTJyiTnJTlv06ZNQw9WUncNrfgDTgQOmtY249j0JPsCK4DHt9t8IImf\n6kuSJME1wDVVdU77/NM0xeD1SXYDaL9vnGnjqlpTVcuravmSJUsWJGBJ3TS04q+qzgJumtZ8CM2Y\ndNrvh/a0n1xVd1TVlcAVwP7Dik2SJGlcVNV1wNVJ9mmbDgQuAU4DjmjbjgBOHUF4ksbI1m74Mt9m\nG5u+O/DNnvWuadskSZIErwY+keS+wA+Al9N8iH9KkiOBq4DDRhifpDGw0MXf3aqqkgx8C+QkK4GV\nAEuXLp33uCRJkrqmqi4Els+w6MCFjkXS+BrmNX8zmW1s+npgz5719mjb7sVx65IkSZI0uIUu/mYb\nm34asCLJ/ZLsBewNnLvAsUmSJEnSojW0YZ9JTqKZeHSXJNcAxwCrmWFselVdnOQUmouX7wKOqqrN\nw4pNkiRJv2jQeW0ljZ+hFX9Vdfgsi2Ycm15VxwHHDSseSZIkSZpkCz3sU5IkSZI0AhZ/kiRJkjQB\nRjbVgyRp9KZf47Nu9cEjikSSJA2bPX+SJEmSNAEs/iRJkiRpAlj8SZIkSdIEsPiTNHGSnJBkY5Lv\n9bTtnOSMJJe333fqWXZ0kiuSXJbkOaOJWpIkadtY/EmaRCcCB01rWwWcWVV7A2e2z0myL7ACeHy7\nzQeSbLdwoUqSJM0Piz9JE6eqzgJumtZ8CLC2fbwWOLSn/eSquqOqrgSuAPZfkEAlSZLmkcWfJDV2\nraoN7ePrgF3bx7sDV/esd03bJkmSNFYs/iRpmqoqoAbdLsnKJOclOW/Tpk1DiEySJGnuLP4kqXF9\nkt0A2u8b2/b1wJ496+3Rtt1LVa2pquVVtXzJkiVDDVaSJGlQFn+S1DgNOKJ9fARwak/7iiT3S7IX\nsDdw7gjikyRJ2ibbjzoASVpoSU4CDgB2SXINcAywGjglyZHAVcBhAFV1cZJTgEuAu4CjqmrzSAKX\nJEnaBhZ/0ogtW3X6jO3rVh+8wJFMjqo6fJZFB86y/nHAccOLSJIkafgc9ilJkiRJE8DiT5IkSZIm\ngMWfJEmSJE0Ar/lrzXbdlSRJkiQtBhZ/0gLxAwZJkiSNksM+JUmSJGkCWPxJkiRJ0gRw2Kck6W4z\nDU92zklJkhYHe/4kSZLGQJLtknw7yefa5zsnOSPJ5e33nUYdo6Rus+dPmmfe2EWSNCSvAS4FHtI+\nXwWcWVWrk6xqn79xVMFJ6j57/iRJkjouyR7AwcA/9DQfAqxtH68FDl3ouCSNF4s/SZKk7ns38Abg\n5z1tu1bVhvbxdcCuCx6VpLHisE+po7Y0fNQbcEjS5EjyfGBjVZ2f5ICZ1qmqSlKzbL8SWAmwdOnS\nocW5Jb3/0/wfJo2OPX+SJEnd9nTghUnWAScDv5Pk48D1SXYDaL9vnGnjqlpTVcuravmSJUsWKmZJ\nHWTxJ0mS1GFVdXRV7VFVy4AVwL9V1UuA04Aj2tWOAE4dUYiSxoTDPiVpAXk3WEnzaDVwSpIjgauA\nw0Ycj6SOs/iTJEkaE1X1VeCr7eMbgQNHGY+k8eKwT0mSJEmaABZ/kiRJkjQBLP4kSZIkaQJY/EmS\nJEnSBLD4kyRJkqQJYPEnSZIkSRPA4k+SJEmSJoDFnyRJkiRNAIs/SZIkSZoA2486AEmSJE2OZatO\nv/vxutUHjzASafLY8ydJkiRJE2AkPX9J1gG3AZuBu6pqeZKdgU8By4B1wGFVdfMo4pMkSZKkxWaU\nPX/Pqqr9qmp5+3wVcGZV7Q2c2T6XJEmSJM2DLg37PARY2z5eCxw6wlgkSZI0ZMtWnf4L1wBKGq5R\n3fClgC8n2Qx8qKrWALtW1YZ2+XXAriOKTRpbs/0D9YJ6SZIkjar4e0ZVrU/ycOCMJN/vXVhVlaRm\n2jDJSmAlwNKlS4cfqSRJkiQtAiMZ9llV69vvG4F/AvYHrk+yG0D7feMs266pquVVtXzJkiULFbIk\nSZIkjbUFL/6SPCjJg6ceA78HfA84DTiiXe0I4NSFjk2SJEmSFqtRDPvcFfinJFPH/2RVfSHJt4BT\nkhwJXAUcNt8H9oJiSVvjVDSSJonnRtJkWfDir6p+ADxxhvYbgQMXOh5pHPnPeuieVVU39Dyfmopm\ndZJV7fM3jiY0SZKkuenSVA+S1FVORSNJksaexZ8k/aKpqWjOb+8uDE5FI0mSFoFRTfUgSV3lVDSS\nJGlRsudPkno4FY0kSVqsLP4kqeVUNJIkaTFz2Kck3WNkU9FIkiQNm8WfJLWcikaSJC1mDvuUJEnq\nsCR7JvlKkkuSXJzkNW37zknOSHJ5+32nUccqqdss/iRJkrrtLuD1VbUv8DTgqCT7AquAM6tqb+DM\n9rkkzcriT5IkqcOqakNVXdA+vg24FNgdOARY2662Fjh0NBFKGhde8ydJ2qJlq07/hefrVh88okgk\nJVkGPAk4B9i1qja0i66juWmVJM3Knj9JkqQxkGRH4DPAa6vq1t5lVVVAzbLdyiTnJTlv06ZNCxCp\npK6y50+aANN7bqbYgyNJ4yHJDjSF3yeq6rNt8/VJdquqDUl2AzbOtG1VrQHWACxfvnzGAlHSZLDn\nT5IkqcPSTD56PHBpVb2zZ9FpwBHt4yOAUxc6NknjxZ4/SZKkbns68FLgoiQXtm1vAlYDpyQ5ErgK\nOGxE8UkaExZ/kiRJHVZVZwOZZfGBCxmLpPHmsE9JkiRJmgAWf5IkSZI0ASz+JEmSJGkCWPxJkiRJ\n0gTwhi+SpIHMNG+kc0ZKktR99vxJkiRJ0gSw+JMkSZKkCWDxJ0mSJEkTwGv+pAk207Vb4PVbkiRJ\ni5HFn6R7sSiUJElafBz2KUmSJEkTwJ4/Sdtstp5CsLdQkrR1vf9H/L8hDY89f5IkSZI0Aez5kyRt\ns+m9v35yL0lS9yzK4m9LQ9AkaaGYiyRJUpcsyuJP0nDMpZjxzqGSJEndYPEnSZI0QRyVIE0ub/gi\nSZIkSRPA4k+SJEmSJoDFnyRJkiRNAIs/SZIkSZoAFn+SJEmSNAG826ekkXAKCEnSTHr/P/g/QZpf\n9vxJkiRJ0gSw+JPUKc4/JUmasmzV6f5fkOaRxZ8kSZIkTQCv+ZMkzbt+Pqn3Wh5JkhZW53r+khyU\n5LIkVyRZNep4JAnMTZK6ydwkaRCdKv6SbAe8H3gusC9weJJ9RxuVpElnbpLUReYmSYPq2rDP/YEr\nquoHAElOBg4BLhlpVJImnblJUhdNZG6aaVi5w8il/nSt+NsduLrn+TXAU0cUiyRNMTcNQT8ncF47\nKG2RuUnSQLpW/G1VkpXAyvbpHUm+N8p45skuwA2jDmIb+Rq6YTG8BoB9Rh3AXEzPT5z//HHPTwv+\n95S/mfdtFsN7YjG8Blgcr8PcNCLT3uf3+luaS+7ogMXwnvA1dEPfualrxd96YM+e53u0bXerqjXA\nGoAk51XV8oULbzgWw+vwNXTDYngN0LyOUccwzVZzEyy+/ORr6IbF8BpgcbwOc1M3LIbXAIvjdfga\numGQ3NSpG74A3wL2TrJXkvsCK4DTRhyTJJmbJHWRuUnSQDrV81dVdyV5FfBFYDvghKq6eMRhSZpw\n5iZJXWRukjSoThV/AFX1eeDzfa6+ZpixLKDF8Dp8Dd2wGF4DdPB1DJiboIOvYQ58Dd2wGF4DLI7X\n0bnXYG4aa4vhdfgauqHv15CqGmYgkiRJkqQO6No1f5IkSZKkIRjb4i/JQUkuS3JFklWjjmdQSU5I\nsnGcp6pIsmeSryS5JMnFSV4z6pjmIsn9k5yb5Dvt63jrqGOaqyTbJfl2ks+NOpa5SLIuyUVJLuzg\nXfX6Mu65CcxPXWFu6hbz0+iZm7rB3NQtg+amsRz2mWQ74D+BZ9NMaPot4PCqumSkgQ0gyW8DtwMf\nraonjDqeuUiyG7BbVV2Q5MHA+cCh4/R7AEgS4EFVdXuSHYCzgddU1TdHHNrAkrwOWA48pKqeP+p4\nBpVkHbC8qsZyvp3FkJvA/NQV5qZuMT+NnrmpG8xN3TJobhrXnr/9gSuq6gdVdSdwMnDIiGMaSFWd\nBdw06ji2RVVtqKoL2se3AZcCu482qsFV4/b26Q7t19h9KpJkD+Bg4B9GHcsEG/vcBOanrjA3aZ6N\nfX4yN3WDuWm8jWvxtztwdc/zaxizN85ik2QZ8CTgnNFGMjdtt/+FwEbgjKoax9fxbuANwM9HHcg2\nKODLSc5PsnLUwcyBuamDxjk/mZs6xfykeWVuGrmJzE3jWvypQ5LsCHwGeG1V3TrqeOaiqjZX1X7A\nHsD+ScZqOEmS5wMbq+r8UceyjZ7R/h6eCxzVDvGR5mzc85O5qVPMT5o35qbRmuTcNK7F33pgz57n\ne7RtWmDtWO/PAJ+oqs+OOp5tVVW3AF8BDhp1LAN6OvDCdtz3ycDvJPn4aEMaXFWtb79vBP6JZpjS\nODE3dchiyk/mptEzP2m+mJs6YWJz07gWf98C9k6yV5L7AiuA00Yc08RpL/g9Hri0qt456njmKsmS\nJA9rHz+A5mL47482qsFU1dFVtUdVLaN5P/xbVb1kxGENJMmD2ovfSfIg4PeAcbujm7mpIxZDfjI3\ndYf5SfPF3NQNk5ybxrL4q6q7gFcBX6S5UPaUqrp4tFENJslJwDeAfZJck+TIUcc0B08HXkrzacmF\n7dfzRh3UHOwGfCXJd2n+OZ5RVWN7y98xtitwdpLvAOcCp1fVF0Yc00AWQ24C81OHmJu6w/zUAeam\nzjA3dcfAuWksp3qQJEmSJA1mLHv+JEmSJEmDsfiTJEmSpAlg8SdJkiRJE8DiT5IkSZImgMWfJEmS\nJE0Aiz9JkiRJmgAWf5IkSZI0ASz+JEmSJGkC/P90Od2MJyyk4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40823873c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure(1, figsize=(15,5), )\n",
    "plt.suptitle('Velocity Distribution with Berendsen Thermostat') # Figure super title\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "plot_h = df[ df['elem'] == 'H'  ]['vel'].plot(kind='hist', bins=50, xlim=(0.,5.))\n",
    "plot_h.set_title('Hydrogen')\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plot_c = df[ df['elem'] == 'C'  ]['vel'].plot(kind='hist', bins=20, xlim=(0.,5.))\n",
    "plot_c.set_title('Carbon')\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plot_cl= df[ df['elem'] == 'CL' ]['vel'].plot(kind='hist', bins=20, xlim=(0.,5.))\n",
    "plot_cl.set_title('Chlorine')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do the same for a different file (the simulation used a different thermostat algorithm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_bussi = pd.read_table('nvt_bussi.gro', sep='\\s+', skiprows=2, names=gro_header)\n",
    "df_bussi['vel'] = (df_bussi.vx **2 + df_bussi.vy **2 + df_bussi.vz **2) **0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f407e346ac8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0vJ+iepifJI0jc5OksdPvNX+fA+7b8/x+bZskjZr5SdI4MjdJGjv9Fn87V9Wt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f407e420198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure(1, figsize=(15,5), )\n",
    "plt.suptitle('Velocity Distribution with Bussi Thermostat')\n",
    "\n",
    "plt.subplot(131)\n",
    "plot_h = df_bussi[ df_bussi['elem']=='H' ]['vel'].plot(kind='hist', bins=50, xlim=(0.,5.))\n",
    "plot_h.set_title('Hydrogen')\n",
    "\n",
    "plt.subplot(132)\n",
    "plot_c = df_bussi[ df_bussi['elem']=='C' ]['vel'].plot(kind='hist', bins=20, xlim=(0.,5.))\n",
    "plot_c.set_title('Carbon')\n",
    "\n",
    "plt.subplot(133)\n",
    "plot_cl= df_bussi[ df_bussi['elem']=='CL']['vel'].plot(kind='hist', bins=20, xlim=(0.,5.))\n",
    "plot_cl.set_title('Chlorine')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "While one can recognize the expected shape, the distribution seems to have many outliers.  The Analysis should be repeated with a larger system and/or a larger number of simulation frames."
   ]
  }
 ],
 "metadata": {
  "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}