Particle Distribution Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, import the processing tools that contain classes and methods to read, plot and process standard unit particle distribution files. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import processing_tools as pt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The module consists of a class 'ParticleDistribution' that initializes to a dictionary containing the following entries given a filepath:\n",
    "\n",
    "|key | value     |\n",
    "|----|-----------|\n",
    "|'x' | x position|\n",
    "|'y' | y position|\n",
    "|'z' | z position|\n",
    "|'px'| x momentum|\n",
    "|'py'| y momentum|\n",
    "|'pz'| z momentum|\n",
    "|'NE'| number of electrons per macroparticle|\n",
    "\n",
    "The units are in line with the Standard Unit specifications, but can be converted to SI by calling the class method SU2SI\n",
    "\n",
    "Values can then be called by calling the 'dict':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.20390857e-05,   1.19987648e-05,  -5.26944824e-05, ...,\n",
       "        -1.00743962e-05,  -2.33553430e-06,  -8.14314855e-05])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath = './example/example.h5'\n",
    "\n",
    "data = pt.ParticleDistribution(filepath)\n",
    "data.su2si\n",
    "data.dict['x']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively one can ask for a pandas dataframe where each column is one of the above properties of a macroparticle per row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NE</th>\n",
       "      <th>px</th>\n",
       "      <th>py</th>\n",
       "      <th>pz</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31210.986267</td>\n",
       "      <td>0.006629</td>\n",
       "      <td>-0.000466</td>\n",
       "      <td>468.491974</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>49.293173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31210.986267</td>\n",
       "      <td>0.007306</td>\n",
       "      <td>0.001122</td>\n",
       "      <td>466.837388</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>-0.000057</td>\n",
       "      <td>49.293423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31210.986267</td>\n",
       "      <td>0.008277</td>\n",
       "      <td>-0.000318</td>\n",
       "      <td>470.839422</td>\n",
       "      <td>-0.000053</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>49.292852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31210.986267</td>\n",
       "      <td>0.008169</td>\n",
       "      <td>0.005924</td>\n",
       "      <td>465.713097</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>-0.000049</td>\n",
       "      <td>49.293612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31210.986267</td>\n",
       "      <td>0.010275</td>\n",
       "      <td>0.001735</td>\n",
       "      <td>472.664255</td>\n",
       "      <td>-0.000079</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>49.292614</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             NE        px        py          pz         x         y          z\n",
       "0  31210.986267  0.006629 -0.000466  468.491974  0.000012 -0.000009  49.293173\n",
       "1  31210.986267  0.007306  0.001122  466.837388  0.000012 -0.000057  49.293423\n",
       "2  31210.986267  0.008277 -0.000318  470.839422 -0.000053 -0.000002  49.292852\n",
       "3  31210.986267  0.008169  0.005924  465.713097  0.000024 -0.000049  49.293612\n",
       "4  31210.986267  0.010275  0.001735  472.664255 -0.000079  0.000025  49.292614"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panda_data = data.DistFrame()\n",
    "panda_data[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This allows for quick plotting using the inbuilt pandas methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P474CDuctTKqcq4gATD2MYvwPvkIn+sbUsS8Xy6CmxIlwTFSljBP5hS0UjCAI2Lt3L7Zu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\nHOv0yKPnQlgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3be9b19f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "matplotlib.style.use('ggplot') #optional\n",
    "\n",
    "x_axis = 'py'\n",
    "y_axis = 'px'\n",
    "\n",
    "\n",
    "plot = panda_data.plot(kind='scatter',x=x_axis,y=y_axis)\n",
    "#sets axis limits  \n",
    "plot.set_xlim([panda_data[x_axis].min(),panda_data[x_axis].max()])\n",
    "plot.set_ylim([panda_data[y_axis].min(),panda_data[y_axis].max()])\n",
    "plt.show(plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If further statistical analysis is required, the class 'Statistics' is provided. This contains methods to process standard properties of the electron bunch. This is called by giving a filepath to 'Statistics' The following operations can be performed:\n",
    "\n",
    "| Function            | Effect and dict keys                                                                                                     |\n",
    "|---------------------|-------------------------------------------------------------------------------------------------------------------------------|\n",
    "| calc_emittance | Calculates the emittance of all the slices, accessible by 'e_x' and 'e_y'                                                     |\n",
    "| calc_CoM                 | Calculates the weighed averages and standard deviations per slice of every parameter and beta functions, see below for keys.  |\n",
    "| calc_current         | Calculates current per slice, accessible in the dict as 'current'.                                                       |\n",
    "|slice | Slices the data in equal slices of an integer number. |\n",
    "\n",
    "This is a subclass of the ParticleDistribution and all the methods previously described work.\n",
    "\n",
    "\n",
    "| CoM Keys                | Parameter (per slice)                                      |\n",
    "|------------------------|------------------------------------------------------------|\n",
    "| CoM_x, CoM_y, CoM_z    | Centre of mass of x, y, z positions                        |\n",
    "| std_x, std_y, std_z    | Standard deviation of x, y, z positions                    |\n",
    "| CoM_px, CoM_py, CoM_pz | Centre of mass of x, y, z momenta                          |\n",
    "| std_px, std_py, std_pz | Standard deviation of x, y, z momenta                      |\n",
    "| beta_x, beta_y         | Beta functions (assuming Gaussian distribution) in x and y |\n",
    "\n",
    "\n",
    "Furthermore, there is a 'Step_Z' which returns the size of a slice as well as 'z_pos' which gives you central position of a given slice.\n",
    "\n",
    "And from this class both the DistFrame (containing the same data as above) and StatsFrame can be called:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CoM_px</th>\n",
       "      <th>CoM_py</th>\n",
       "      <th>CoM_pz</th>\n",
       "      <th>CoM_x</th>\n",
       "      <th>CoM_y</th>\n",
       "      <th>CoM_z</th>\n",
       "      <th>beta_x</th>\n",
       "      <th>beta_y</th>\n",
       "      <th>current</th>\n",
       "      <th>e_x</th>\n",
       "      <th>e_y</th>\n",
       "      <th>slice_z</th>\n",
       "      <th>std_px</th>\n",
       "      <th>std_py</th>\n",
       "      <th>std_pz</th>\n",
       "      <th>std_x</th>\n",
       "      <th>std_y</th>\n",
       "      <th>std_z</th>\n",
       "      <th>z_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.090988e-24</td>\n",
       "      <td>-9.604622e-26</td>\n",
       "      <td>1.302979e-19</td>\n",
       "      <td>-0.000034</td>\n",
       "      <td>-0.000007</td>\n",
       "      <td>49.292099</td>\n",
       "      <td>0.235018</td>\n",
       "      <td>0.655127</td>\n",
       "      <td>4.635644</td>\n",
       "      <td>1.390731e-07</td>\n",
       "      <td>1.365778e-07</td>\n",
       "      <td>49.292098</td>\n",
       "      <td>6.944301e-25</td>\n",
       "      <td>3.385691e-24</td>\n",
       "      <td>1.315555e-23</td>\n",
       "      <td>0.000109</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>49.292098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.119572e-24</td>\n",
       "      <td>3.881272e-25</td>\n",
       "      <td>1.302565e-19</td>\n",
       "      <td>-0.000041</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>49.292115</td>\n",
       "      <td>0.180635</td>\n",
       "      <td>0.827244</td>\n",
       "      <td>5.751632</td>\n",
       "      <td>1.297608e-07</td>\n",
       "      <td>1.489005e-07</td>\n",
       "      <td>49.292115</td>\n",
       "      <td>8.234089e-25</td>\n",
       "      <td>3.774013e-24</td>\n",
       "      <td>1.259130e-23</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>49.292115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.093419e-24</td>\n",
       "      <td>-1.290899e-25</td>\n",
       "      <td>1.302164e-19</td>\n",
       "      <td>-0.000032</td>\n",
       "      <td>-0.000007</td>\n",
       "      <td>49.292133</td>\n",
       "      <td>0.106912</td>\n",
       "      <td>0.409024</td>\n",
       "      <td>10.387276</td>\n",
       "      <td>2.116124e-07</td>\n",
       "      <td>1.782308e-07</td>\n",
       "      <td>49.292133</td>\n",
       "      <td>9.638639e-25</td>\n",
       "      <td>2.905490e-24</td>\n",
       "      <td>1.109932e-23</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>49.292133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.187010e-24</td>\n",
       "      <td>-1.562527e-25</td>\n",
       "      <td>1.301782e-19</td>\n",
       "      <td>-0.000034</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>49.292151</td>\n",
       "      <td>0.071391</td>\n",
       "      <td>0.316972</td>\n",
       "      <td>12.619253</td>\n",
       "      <td>1.981626e-07</td>\n",
       "      <td>1.770423e-07</td>\n",
       "      <td>49.292150</td>\n",
       "      <td>1.076412e-24</td>\n",
       "      <td>2.479309e-24</td>\n",
       "      <td>1.108139e-23</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>49.292150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.206306e-24</td>\n",
       "      <td>-1.290574e-25</td>\n",
       "      <td>1.301406e-19</td>\n",
       "      <td>-0.000035</td>\n",
       "      <td>-0.000011</td>\n",
       "      <td>49.292168</td>\n",
       "      <td>0.080260</td>\n",
       "      <td>0.435046</td>\n",
       "      <td>13.306015</td>\n",
       "      <td>1.717004e-07</td>\n",
       "      <td>1.798360e-07</td>\n",
       "      <td>49.292168</td>\n",
       "      <td>1.242799e-24</td>\n",
       "      <td>2.735644e-24</td>\n",
       "      <td>1.122382e-23</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>49.292168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CoM_px        CoM_py        CoM_pz     CoM_x     CoM_y      CoM_z  \\\n",
       "0  1.090988e-24 -9.604622e-26  1.302979e-19 -0.000034 -0.000007  49.292099   \n",
       "1  1.119572e-24  3.881272e-25  1.302565e-19 -0.000041  0.000019  49.292115   \n",
       "2  1.093419e-24 -1.290899e-25  1.302164e-19 -0.000032 -0.000007  49.292133   \n",
       "3  1.187010e-24 -1.562527e-25  1.301782e-19 -0.000034 -0.000010  49.292151   \n",
       "4  1.206306e-24 -1.290574e-25  1.301406e-19 -0.000035 -0.000011  49.292168   \n",
       "\n",
       "     beta_x    beta_y    current           e_x           e_y    slice_z  \\\n",
       "0  0.235018  0.655127   4.635644  1.390731e-07  1.365778e-07  49.292098   \n",
       "1  0.180635  0.827244   5.751632  1.297608e-07  1.489005e-07  49.292115   \n",
       "2  0.106912  0.409024  10.387276  2.116124e-07  1.782308e-07  49.292133   \n",
       "3  0.071391  0.316972  12.619253  1.981626e-07  1.770423e-07  49.292150   \n",
       "4  0.080260  0.435046  13.306015  1.717004e-07  1.798360e-07  49.292168   \n",
       "\n",
       "         std_px        std_py        std_pz     std_x     std_y     std_z  \\\n",
       "0  6.944301e-25  3.385691e-24  1.315555e-23  0.000109  0.000180  0.000005   \n",
       "1  8.234089e-25  3.774013e-24  1.259130e-23  0.000092  0.000211  0.000005   \n",
       "2  9.638639e-25  2.905490e-24  1.109932e-23  0.000090  0.000162  0.000005   \n",
       "3  1.076412e-24  2.479309e-24  1.108139e-23  0.000071  0.000142  0.000005   \n",
       "4  1.242799e-24  2.735644e-24  1.122382e-23  0.000071  0.000168  0.000005   \n",
       "\n",
       "       z_pos  \n",
       "0  49.292098  \n",
       "1  49.292115  \n",
       "2  49.292133  \n",
       "3  49.292150  \n",
       "4  49.292168  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = pt.Statistics(filepath)\n",
    "\n",
    "#preparing the statistics\n",
    "stats.slice(100)\n",
    "stats.calc_emittance()\n",
    "stats.calc_CoM()\n",
    "stats.calc_current()\n",
    "\n",
    "#display pandas example\n",
    "panda_stats = stats.StatsFrame()\n",
    "panda_stats[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P1VdbuPXWXJYvb8XZs/qvbf9+EzfdFMB99/nWyYSeR44YiYz0oWtXvbG/JEWB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\nY33hlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3be691fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = panda_stats.plot(x='z_pos',y='CoM_y')\n",
    "panda_stats.plot(ax=ax, x='z_pos',y='std_y',c='b') #first option allows shared axes\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "And finally there is the FEL_Approximations which calculate simple FEL properties per slice. This is a subclass of statistics and as such every method described above is callable.\n",
    "\n",
    "This class conatins the 'undulator' function that calculates planar undulator parameters given a period and either a peak magnetic field or K value.\n",
    "\n",
    "The data must be sliced and most statistics have to be run before the other calculations can take place.\n",
    "These are 'pierce' which calculates the pierce parameter and 1D gain length for a given slice, 'gain length' which calculates the Ming Xie gain and returns three entries in the dict 'MX_gain', '1D_gain', 'pierce', which hold an array for these values per slice. \n",
    "'FELFrame' returns a pandas dataframe with these and 'z_pos' for reference.\n",
    "\n",
    "To make this easier, the class ProcessedData takes a filepath, number of slcies, undulator period, magnetic field or K and performs all the necessary steps automatically. As this is a subclass of FEL_Approximations all the values written above are accessible from here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1D_gain</th>\n",
       "      <th>MX_gain</th>\n",
       "      <th>pierce</th>\n",
       "      <th>z_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.663068</td>\n",
       "      <td>2.806309e+07</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>49.292098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.615747</td>\n",
       "      <td>4.773164e+07</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>49.292115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.460577</td>\n",
       "      <td>2.203503e+08</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>49.292133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.382122</td>\n",
       "      <td>3.744164e+08</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>49.292150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.395079</td>\n",
       "      <td>2.084933e+08</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>49.292168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    1D_gain       MX_gain    pierce      z_pos\n",
       "0  0.663068  2.806309e+07  0.000191  49.292098\n",
       "1  0.615747  4.773164e+07  0.000205  49.292115\n",
       "2  0.460577  2.203503e+08  0.000274  49.292133\n",
       "3  0.382122  3.744164e+08  0.000331  49.292150\n",
       "4  0.395079  2.084933e+08  0.000320  49.292168"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FEL = pt.ProcessedData(filepath,num_slices=100,undulator_period=0.00275,k_fact=2.7)\n",
    "\n",
    "panda_FEL = FEL.FELFrame()\n",
    "panda_stats= FEL.StatsFrame()\n",
    "panda_FEL[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If it is important to plot the statistical data alongside the FEL data, that can be easily achieved by concatinating the two sets as shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QbzkZ2SxLKyZIUtGl14WkLEkSg/3KWFARTFx5do0Nc11ad4zjbX9flqzbzy3X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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3be9ac7f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "cat = pd.concat([panda_FEL,panda_stats], axis=1, join_axes=[panda_FEL.index]) #joins the two if you need to plot\n",
    "#FEL parameters as well as slicel statistics on the same plot\n",
    "cat['1D_gain']=cat['1D_gain']*40000000000 #one can scale to allow for visual comparison if needed\n",
    "az = cat.plot(x='z_pos',y='1D_gain')\n",
    "cat.plot(ax=az, x='z_pos',y='MX_gain',c='b')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}