genkuroki/SoS %put --to Julia.ipynb

## SoS %put --to Julia.ipynb
{
  "cells": [
    {
      "metadata": {
        "kernel": "R",
        "trusted": true
      },
      "cell_type": "code",
      "source": "mtcars",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<table>\n<thead><tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr></thead>\n<tbody>\n\t<tr><th scope=row>Mazda RX4</th><td>21.0 </td><td>6    </td><td>160.0</td><td>110  </td><td>3.90 </td><td>2.620</td><td>16.46</td><td>0    </td><td>1    </td><td>4    </td><td>4    </td></tr>\n\t<tr><th scope=row>Mazda RX4 Wag</th><td>21.0 </td><td>6    </td><td>160.0</td><td>110  </td><td>3.90 </td><td>2.875</td><td>17.02</td><td>0    </td><td>1    </td><td>4    </td><td>4    </td></tr>\n\t<tr><th scope=row>Datsun 710</th><td>22.8 </td><td>4    </td><td>108.0</td><td> 93  </td><td>3.85 </td><td>2.320</td><td>18.61</td><td>1    </td><td>1    </td><td>4    </td><td>1    </td></tr>\n\t<tr><th scope=row>Hornet 4 Drive</th><td>21.4 </td><td>6    </td><td>258.0</td><td>110  </td><td>3.08 </td><td>3.215</td><td>19.44</td><td>1    </td><td>0    </td><td>3    </td><td>1    </td></tr>\n\t<tr><th scope=row>Hornet Sportabout</th><td>18.7 </td><td>8    </td><td>360.0</td><td>175  </td><td>3.15 </td><td>3.440</td><td>17.02</td><td>0    </td><td>0    </td><td>3    </td><td>2    </td></tr>\n\t<tr><th scope=row>Valiant</th><td>18.1 </td><td>6    </td><td>225.0</td><td>105  </td><td>2.76 </td><td>3.460</td><td>20.22</td><td>1    </td><td>0    </td><td>3    </td><td>1    </td></tr>\n\t<tr><th scope=row>Duster 360</th><td>14.3 </td><td>8    </td><td>360.0</td><td>245  </td><td>3.21 </td><td>3.570</td><td>15.84</td><td>0    </td><td>0    </td><td>3    </td><td>4    </td></tr>\n\t<tr><th scope=row>Merc 240D</th><td>24.4 </td><td>4    </td><td>146.7</td><td> 62  </td><td>3.69 </td><td>3.190</td><td>20.00</td><td>1    </td><td>0    </td><td>4    </td><td>2    </td></tr>\n\t<tr><th scope=row>Merc 230</th><td>22.8 </td><td>4    </td><td>140.8</td><td> 95  </td><td>3.92 </td><td>3.150</td><td>22.90</td><td>1    </td><td>0    </td><td>4    </td><td>2    </td></tr>\n\t<tr><th scope=row>Merc 280</th><td>19.2 </td><td>6    </td><td>167.6</td><td>123  </td><td>3.92 </td><td>3.440</td><td>18.30</td><td>1    </td><td>0    </td><td>4    </td><td>4    </td></tr>\n\t<tr><th scope=row>Merc 280C</th><td>17.8 </td><td>6    </td><td>167.6</td><td>123  </td><td>3.92 </td><td>3.440</td><td>18.90</td><td>1    </td><td>0    </td><td>4    </td><td>4    </td></tr>\n\t<tr><th scope=row>Merc 450SE</th><td>16.4 </td><td>8    </td><td>275.8</td><td>180  </td><td>3.07 </td><td>4.070</td><td>17.40</td><td>0    </td><td>0    </td><td>3    </td><td>3    </td></tr>\n\t<tr><th scope=row>Merc 450SL</th><td>17.3 </td><td>8    </td><td>275.8</td><td>180  </td><td>3.07 </td><td>3.730</td><td>17.60</td><td>0    </td><td>0    </td><td>3    </td><td>3    </td></tr>\n\t<tr><th scope=row>Merc 450SLC</th><td>15.2 </td><td>8    </td><td>275.8</td><td>180  </td><td>3.07 </td><td>3.780</td><td>18.00</td><td>0    </td><td>0    </td><td>3    </td><td>3    </td></tr>\n\t<tr><th scope=row>Cadillac Fleetwood</th><td>10.4 </td><td>8    </td><td>472.0</td><td>205  </td><td>2.93 </td><td>5.250</td><td>17.98</td><td>0    </td><td>0    </td><td>3    </td><td>4    </td></tr>\n\t<tr><th scope=row>Lincoln Continental</th><td>10.4 </td><td>8    </td><td>460.0</td><td>215  </td><td>3.00 </td><td>5.424</td><td>17.82</td><td>0    </td><td>0    </td><td>3    </td><td>4    </td></tr>\n\t<tr><th scope=row>Chrysler Imperial</th><td>14.7 </td><td>8    </td><td>440.0</td><td>230  </td><td>3.23 </td><td>5.345</td><td>17.42</td><td>0    </td><td>0    </td><td>3    </td><td>4    </td></tr>\n\t<tr><th scope=row>Fiat 128</th><td>32.4 </td><td>4    </td><td> 78.7</td><td> 66  </td><td>4.08 </td><td>2.200</td><td>19.47</td><td>1    </td><td>1    </td><td>4    </td><td>1    </td></tr>\n\t<tr><th scope=row>Honda Civic</th><td>30.4 </td><td>4    </td><td> 75.7</td><td> 52  </td><td>4.93 </td><td>1.615</td><td>18.52</td><td>1    </td><td>1    </td><td>4    </td><td>2    </td></tr>\n\t<tr><th scope=row>Toyota Corolla</th><td>33.9 </td><td>4    </td><td> 71.1</td><td> 65  </td><td>4.22 </td><td>1.835</td><td>19.90</td><td>1    </td><td>1    </td><td>4    </td><td>1    </td></tr>\n\t<tr><th scope=row>Toyota Corona</th><td>21.5 </td><td>4    </td><td>120.1</td><td> 97  </td><td>3.70 </td><td>2.465</td><td>20.01</td><td>1    </td><td>0    </td><td>3    </td><td>1    </td></tr>\n\t<tr><th scope=row>Dodge Challenger</th><td>15.5 </td><td>8    </td><td>318.0</td><td>150  </td><td>2.76 </td><td>3.520</td><td>16.87</td><td>0    </td><td>0    </td><td>3    </td><td>2    </td></tr>\n\t<tr><th scope=row>AMC Javelin</th><td>15.2 </td><td>8    </td><td>304.0</td><td>150  </td><td>3.15 </td><td>3.435</td><td>17.30</td><td>0    </td><td>0    </td><td>3    </td><td>2    </td></tr>\n\t<tr><th scope=row>Camaro Z28</th><td>13.3 </td><td>8    </td><td>350.0</td><td>245  </td><td>3.73 </td><td>3.840</td><td>15.41</td><td>0    </td><td>0    </td><td>3    </td><td>4    </td></tr>\n\t<tr><th scope=row>Pontiac Firebird</th><td>19.2 </td><td>8    </td><td>400.0</td><td>175  </td><td>3.08 </td><td>3.845</td><td>17.05</td><td>0    </td><td>0    </td><td>3    </td><td>2    </td></tr>\n\t<tr><th scope=row>Fiat X1-9</th><td>27.3 </td><td>4    </td><td> 79.0</td><td> 66  </td><td>4.08 </td><td>1.935</td><td>18.90</td><td>1    </td><td>1    </td><td>4    </td><td>1    </td></tr>\n\t<tr><th scope=row>Porsche 914-2</th><td>26.0 </td><td>4    </td><td>120.3</td><td> 91  </td><td>4.43 </td><td>2.140</td><td>16.70</td><td>0    </td><td>1    </td><td>5    </td><td>2    </td></tr>\n\t<tr><th scope=row>Lotus Europa</th><td>30.4 </td><td>4    </td><td> 95.1</td><td>113  </td><td>3.77 </td><td>1.513</td><td>16.90</td><td>1    </td><td>1    </td><td>5    </td><td>2    </td></tr>\n\t<tr><th scope=row>Ford Pantera L</th><td>15.8 </td><td>8    </td><td>351.0</td><td>264  </td><td>4.22 </td><td>3.170</td><td>14.50</td><td>0    </td><td>1    </td><td>5    </td><td>4    </td></tr>\n\t<tr><th scope=row>Ferrari Dino</th><td>19.7 </td><td>6    </td><td>145.0</td><td>175  </td><td>3.62 </td><td>2.770</td><td>15.50</td><td>0    </td><td>1    </td><td>5    </td><td>6    </td></tr>\n\t<tr><th scope=row>Maserati Bora</th><td>15.0 </td><td>8    </td><td>301.0</td><td>335  </td><td>3.54 </td><td>3.570</td><td>14.60</td><td>0    </td><td>1    </td><td>5    </td><td>8    </td></tr>\n\t<tr><th scope=row>Volvo 142E</th><td>21.4 </td><td>4    </td><td>121.0</td><td>109  </td><td>4.11 </td><td>2.780</td><td>18.60</td><td>1    </td><td>1    </td><td>4    </td><td>2    </td></tr>\n</tbody>\n</table>\n",
            "text/markdown": "\n| <!--/--> | mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | \n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| Mazda RX4 | 21.0  | 6     | 160.0 | 110   | 3.90  | 2.620 | 16.46 | 0     | 1     | 4     | 4     | \n| Mazda RX4 Wag | 21.0  | 6     | 160.0 | 110   | 3.90  | 2.875 | 17.02 | 0     | 1     | 4     | 4     | \n| Datsun 710 | 22.8  | 4     | 108.0 |  93   | 3.85  | 2.320 | 18.61 | 1     | 1     | 4     | 1     | \n| Hornet 4 Drive | 21.4  | 6     | 258.0 | 110   | 3.08  | 3.215 | 19.44 | 1     | 0     | 3     | 1     | \n| Hornet Sportabout | 18.7  | 8     | 360.0 | 175   | 3.15  | 3.440 | 17.02 | 0     | 0     | 3     | 2     | \n| Valiant | 18.1  | 6     | 225.0 | 105   | 2.76  | 3.460 | 20.22 | 1     | 0     | 3     | 1     | \n| Duster 360 | 14.3  | 8     | 360.0 | 245   | 3.21  | 3.570 | 15.84 | 0     | 0     | 3     | 4     | \n| Merc 240D | 24.4  | 4     | 146.7 |  62   | 3.69  | 3.190 | 20.00 | 1     | 0     | 4     | 2     | \n| Merc 230 | 22.8  | 4     | 140.8 |  95   | 3.92  | 3.150 | 22.90 | 1     | 0     | 4     | 2     | \n| Merc 280 | 19.2  | 6     | 167.6 | 123   | 3.92  | 3.440 | 18.30 | 1     | 0     | 4     | 4     | \n| Merc 280C | 17.8  | 6     | 167.6 | 123   | 3.92  | 3.440 | 18.90 | 1     | 0     | 4     | 4     | \n| Merc 450SE | 16.4  | 8     | 275.8 | 180   | 3.07  | 4.070 | 17.40 | 0     | 0     | 3     | 3     | \n| Merc 450SL | 17.3  | 8     | 275.8 | 180   | 3.07  | 3.730 | 17.60 | 0     | 0     | 3     | 3     | \n| Merc 450SLC | 15.2  | 8     | 275.8 | 180   | 3.07  | 3.780 | 18.00 | 0     | 0     | 3     | 3     | \n| Cadillac Fleetwood | 10.4  | 8     | 472.0 | 205   | 2.93  | 5.250 | 17.98 | 0     | 0     | 3     | 4     | \n| Lincoln Continental | 10.4  | 8     | 460.0 | 215   | 3.00  | 5.424 | 17.82 | 0     | 0     | 3     | 4     | \n| Chrysler Imperial | 14.7  | 8     | 440.0 | 230   | 3.23  | 5.345 | 17.42 | 0     | 0     | 3     | 4     | \n| Fiat 128 | 32.4  | 4     |  78.7 |  66   | 4.08  | 2.200 | 19.47 | 1     | 1     | 4     | 1     | \n| Honda Civic | 30.4  | 4     |  75.7 |  52   | 4.93  | 1.615 | 18.52 | 1     | 1     | 4     | 2     | \n| Toyota Corolla | 33.9  | 4     |  71.1 |  65   | 4.22  | 1.835 | 19.90 | 1     | 1     | 4     | 1     | \n| Toyota Corona | 21.5  | 4     | 120.1 |  97   | 3.70  | 2.465 | 20.01 | 1     | 0     | 3     | 1     | \n| Dodge Challenger | 15.5  | 8     | 318.0 | 150   | 2.76  | 3.520 | 16.87 | 0     | 0     | 3     | 2     | \n| AMC Javelin | 15.2  | 8     | 304.0 | 150   | 3.15  | 3.435 | 17.30 | 0     | 0     | 3     | 2     | \n| Camaro Z28 | 13.3  | 8     | 350.0 | 245   | 3.73  | 3.840 | 15.41 | 0     | 0     | 3     | 4     | \n| Pontiac Firebird | 19.2  | 8     | 400.0 | 175   | 3.08  | 3.845 | 17.05 | 0     | 0     | 3     | 2     | \n| Fiat X1-9 | 27.3  | 4     |  79.0 |  66   | 4.08  | 1.935 | 18.90 | 1     | 1     | 4     | 1     | \n| Porsche 914-2 | 26.0  | 4     | 120.3 |  91   | 4.43  | 2.140 | 16.70 | 0     | 1     | 5     | 2     | \n| Lotus Europa | 30.4  | 4     |  95.1 | 113   | 3.77  | 1.513 | 16.90 | 1     | 1     | 5     | 2     | \n| Ford Pantera L | 15.8  | 8     | 351.0 | 264   | 4.22  | 3.170 | 14.50 | 0     | 1     | 5     | 4     | \n| Ferrari Dino | 19.7  | 6     | 145.0 | 175   | 3.62  | 2.770 | 15.50 | 0     | 1     | 5     | 6     | \n| Maserati Bora | 15.0  | 8     | 301.0 | 335   | 3.54  | 3.570 | 14.60 | 0     | 1     | 5     | 8     | \n| Volvo 142E | 21.4  | 4     | 121.0 | 109   | 4.11  | 2.780 | 18.60 | 1     | 1     | 4     | 2     | \n\n\n",
            "text/latex": "\\begin{tabular}{r|lllllllllll}\n  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n\\hline\n\tMazda RX4 & 21.0  & 6     & 160.0 & 110   & 3.90  & 2.620 & 16.46 & 0     & 1     & 4     & 4    \\\\\n\tMazda RX4 Wag & 21.0  & 6     & 160.0 & 110   & 3.90  & 2.875 & 17.02 & 0     & 1     & 4     & 4    \\\\\n\tDatsun 710 & 22.8  & 4     & 108.0 &  93   & 3.85  & 2.320 & 18.61 & 1     & 1     & 4     & 1    \\\\\n\tHornet 4 Drive & 21.4  & 6     & 258.0 & 110   & 3.08  & 3.215 & 19.44 & 1     & 0     & 3     & 1    \\\\\n\tHornet Sportabout & 18.7  & 8     & 360.0 & 175   & 3.15  & 3.440 & 17.02 & 0     & 0     & 3     & 2    \\\\\n\tValiant & 18.1  & 6     & 225.0 & 105   & 2.76  & 3.460 & 20.22 & 1     & 0     & 3     & 1    \\\\\n\tDuster 360 & 14.3  & 8     & 360.0 & 245   & 3.21  & 3.570 & 15.84 & 0     & 0     & 3     & 4    \\\\\n\tMerc 240D & 24.4  & 4     & 146.7 &  62   & 3.69  & 3.190 & 20.00 & 1     & 0     & 4     & 2    \\\\\n\tMerc 230 & 22.8  & 4     & 140.8 &  95   & 3.92  & 3.150 & 22.90 & 1     & 0     & 4     & 2    \\\\\n\tMerc 280 & 19.2  & 6     & 167.6 & 123   & 3.92  & 3.440 & 18.30 & 1     & 0     & 4     & 4    \\\\\n\tMerc 280C & 17.8  & 6     & 167.6 & 123   & 3.92  & 3.440 & 18.90 & 1     & 0     & 4     & 4    \\\\\n\tMerc 450SE & 16.4  & 8     & 275.8 & 180   & 3.07  & 4.070 & 17.40 & 0     & 0     & 3     & 3    \\\\\n\tMerc 450SL & 17.3  & 8     & 275.8 & 180   & 3.07  & 3.730 & 17.60 & 0     & 0     & 3     & 3    \\\\\n\tMerc 450SLC & 15.2  & 8     & 275.8 & 180   & 3.07  & 3.780 & 18.00 & 0     & 0     & 3     & 3    \\\\\n\tCadillac Fleetwood & 10.4  & 8     & 472.0 & 205   & 2.93  & 5.250 & 17.98 & 0     & 0     & 3     & 4    \\\\\n\tLincoln Continental & 10.4  & 8     & 460.0 & 215   & 3.00  & 5.424 & 17.82 & 0     & 0     & 3     & 4    \\\\\n\tChrysler Imperial & 14.7  & 8     & 440.0 & 230   & 3.23  & 5.345 & 17.42 & 0     & 0     & 3     & 4    \\\\\n\tFiat 128 & 32.4  & 4     &  78.7 &  66   & 4.08  & 2.200 & 19.47 & 1     & 1     & 4     & 1    \\\\\n\tHonda Civic & 30.4  & 4     &  75.7 &  52   & 4.93  & 1.615 & 18.52 & 1     & 1     & 4     & 2    \\\\\n\tToyota Corolla & 33.9  & 4     &  71.1 &  65   & 4.22  & 1.835 & 19.90 & 1     & 1     & 4     & 1    \\\\\n\tToyota Corona & 21.5  & 4     & 120.1 &  97   & 3.70  & 2.465 & 20.01 & 1     & 0     & 3     & 1    \\\\\n\tDodge Challenger & 15.5  & 8     & 318.0 & 150   & 2.76  & 3.520 & 16.87 & 0     & 0     & 3     & 2    \\\\\n\tAMC Javelin & 15.2  & 8     & 304.0 & 150   & 3.15  & 3.435 & 17.30 & 0     & 0     & 3     & 2    \\\\\n\tCamaro Z28 & 13.3  & 8     & 350.0 & 245   & 3.73  & 3.840 & 15.41 & 0     & 0     & 3     & 4    \\\\\n\tPontiac Firebird & 19.2  & 8     & 400.0 & 175   & 3.08  & 3.845 & 17.05 & 0     & 0     & 3     & 2    \\\\\n\tFiat X1-9 & 27.3  & 4     &  79.0 &  66   & 4.08  & 1.935 & 18.90 & 1     & 1     & 4     & 1    \\\\\n\tPorsche 914-2 & 26.0  & 4     & 120.3 &  91   & 4.43  & 2.140 & 16.70 & 0     & 1     & 5     & 2    \\\\\n\tLotus Europa & 30.4  & 4     &  95.1 & 113   & 3.77  & 1.513 & 16.90 & 1     & 1     & 5     & 2    \\\\\n\tFord Pantera L & 15.8  & 8     & 351.0 & 264   & 4.22  & 3.170 & 14.50 & 0     & 1     & 5     & 4    \\\\\n\tFerrari Dino & 19.7  & 6     & 145.0 & 175   & 3.62  & 2.770 & 15.50 & 0     & 1     & 5     & 6    \\\\\n\tMaserati Bora & 15.0  & 8     & 301.0 & 335   & 3.54  & 3.570 & 14.60 & 0     & 1     & 5     & 8    \\\\\n\tVolvo 142E & 21.4  & 4     & 121.0 & 109   & 4.11  & 2.780 & 18.60 & 1     & 1     & 4     & 2    \\\\\n\\end{tabular}\n",
            "text/plain": "                    mpg  cyl disp  hp  drat wt    qsec  vs am gear carb\nMazda RX4           21.0 6   160.0 110 3.90 2.620 16.46 0  1  4    4   \nMazda RX4 Wag       21.0 6   160.0 110 3.90 2.875 17.02 0  1  4    4   \nDatsun 710          22.8 4   108.0  93 3.85 2.320 18.61 1  1  4    1   \nHornet 4 Drive      21.4 6   258.0 110 3.08 3.215 19.44 1  0  3    1   \nHornet Sportabout   18.7 8   360.0 175 3.15 3.440 17.02 0  0  3    2   \nValiant             18.1 6   225.0 105 2.76 3.460 20.22 1  0  3    1   \nDuster 360          14.3 8   360.0 245 3.21 3.570 15.84 0  0  3    4   \nMerc 240D           24.4 4   146.7  62 3.69 3.190 20.00 1  0  4    2   \nMerc 230            22.8 4   140.8  95 3.92 3.150 22.90 1  0  4    2   \nMerc 280            19.2 6   167.6 123 3.92 3.440 18.30 1  0  4    4   \nMerc 280C           17.8 6   167.6 123 3.92 3.440 18.90 1  0  4    4   \nMerc 450SE          16.4 8   275.8 180 3.07 4.070 17.40 0  0  3    3   \nMerc 450SL          17.3 8   275.8 180 3.07 3.730 17.60 0  0  3    3   \nMerc 450SLC         15.2 8   275.8 180 3.07 3.780 18.00 0  0  3    3   \nCadillac Fleetwood  10.4 8   472.0 205 2.93 5.250 17.98 0  0  3    4   \nLincoln Continental 10.4 8   460.0 215 3.00 5.424 17.82 0  0  3    4   \nChrysler Imperial   14.7 8   440.0 230 3.23 5.345 17.42 0  0  3    4   \nFiat 128            32.4 4    78.7  66 4.08 2.200 19.47 1  1  4    1   \nHonda Civic         30.4 4    75.7  52 4.93 1.615 18.52 1  1  4    2   \nToyota Corolla      33.9 4    71.1  65 4.22 1.835 19.90 1  1  4    1   \nToyota Corona       21.5 4   120.1  97 3.70 2.465 20.01 1  0  3    1   \nDodge Challenger    15.5 8   318.0 150 2.76 3.520 16.87 0  0  3    2   \nAMC Javelin         15.2 8   304.0 150 3.15 3.435 17.30 0  0  3    2   \nCamaro Z28          13.3 8   350.0 245 3.73 3.840 15.41 0  0  3    4   \nPontiac Firebird    19.2 8   400.0 175 3.08 3.845 17.05 0  0  3    2   \nFiat X1-9           27.3 4    79.0  66 4.08 1.935 18.90 1  1  4    1   \nPorsche 914-2       26.0 4   120.3  91 4.43 2.140 16.70 0  1  5    2   \nLotus Europa        30.4 4    95.1 113 3.77 1.513 16.90 1  1  5    2   \nFord Pantera L      15.8 8   351.0 264 4.22 3.170 14.50 0  1  5    4   \nFerrari Dino        19.7 6   145.0 175 3.62 2.770 15.50 0  1  5    6   \nMaserati Bora       15.0 8   301.0 335 3.54 3.570 14.60 0  1  5    8   \nVolvo 142E          21.4 4   121.0 109 4.11 2.780 18.60 1  1  4    2   "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "kernel": "SoS",
        "trusted": true
      },
      "cell_type": "code",
      "source": "%get mtcars --from R",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Loading required package: feather\nWarning message:\n\"package 'feather' was built under R version 3.4.3\"",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "kernel": "SoS",
        "trusted": true
      },
      "cell_type": "code",
      "source": "%preview mtcars",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "kernel": "SoS",
        "trusted": true
      },
      "cell_type": "code",
      "source": "%put --to Julia mtcars",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Raw index is ignored because Julia DataFrame does not support raw index.\nFailed to put variable mtcars to julia\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "kernel": "Julia",
        "trusted": true
      },
      "cell_type": "code",
      "source": "mtcars",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "error",
          "ename": "LoadError",
          "evalue": "\u001b[91mUndefVarError: mtcars not defined\u001b[39m",
          "traceback": [
            "\u001b[91mUndefVarError: mtcars not defined\u001b[39m",
            "",
            "Stacktrace:",
            " [1] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m.\\loading.jl:522\u001b[22m\u001b[22m"
          ]
        }
      ]
    },
    {
      "metadata": {
        "kernel": "R",
        "trusted": true
      },
      "cell_type": "code",
      "source": "v = c(1,2,3,4,5)",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "kernel": "SoS",
        "trusted": true
      },
      "cell_type": "code",
      "source": "%get v --from R",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "kernel": "SoS",
        "trusted": true
      },
      "cell_type": "code",
      "source": "%preview v",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "kernel": "SoS",
        "trusted": true
      },
      "cell_type": "code",
      "source": "%put v --to Julia",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "kernel": "Julia",
        "trusted": true
      },
      "cell_type": "code",
      "source": "v",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "5-element Array{Int64,1}:\n 1\n 2\n 3\n 4\n 5"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "kernel": "R",
        "trusted": true
      },
      "cell_type": "code",
      "source": "X = matrix(rnorm(10^6), 1000, 1000)",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "kernel": "SoS",
        "trusted": true
      },
      "cell_type": "code",
      "source": "%get X --from R",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {
        "kernel": "SoS",
        "trusted": true
      },
      "cell_type": "code",
      "source": "%put X --to Julia",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "kernel": "SoS",
        "trusted": true
      },
      "cell_type": "code",
      "source": "%preview X",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {
        "kernel": "Julia",
        "trusted": true
      },
      "cell_type": "code",
      "source": "typeof(X), size(X)",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": "(Array{Array{Float64,1},1}, (1000,))"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "kernel": "Julia",
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "X = vcat([transpose(a) for a in X]...)\ntypeof(X), size(X)",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": "(Array{Float64,2}, (1000, 1000))"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "kernel": "Julia",
        "trusted": true
      },
      "cell_type": "code",
      "source": "λ, P = eig(X);",
      "execution_count": 17,
      "outputs": []
    },
    {
      "metadata": {
        "kernel": "Julia",
        "trusted": true
      },
      "cell_type": "code",
      "source": "using PyPlot\nfigure(figsize=(3,3))\nscatter(real(λ), imag(λ), s=5)\ngrid(ls=\":\")\naxes()[:set_aspect](\"equal\")",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000003E8454E0>)",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "_draft": {
      "nbviewer_url": "https://gist.github.com/68e5b51cb2c37c93881b15932d921ce4"
    },
    "gist": {
      "id": "68e5b51cb2c37c93881b15932d921ce4",
      "data": {
        "description": "SoS %put --to Julia",
        "public": true
      }
    },
    "kernelspec": {
      "name": "sos",
      "display_name": "SoS",
      "language": "sos"
    },
    "language_info": {
      "mimetype": "text/x-sos",
      "name": "sos",
      "file_extension": ".sos",
      "pygments_lexer": "sos",
      "codemirror_mode": "sos",
      "nbconvert_exporter": "sos_notebook.converter.SoS_Exporter"
    },
    "sos": {
      "default_kernel": "SoS",
      "kernels": [
        [
          "Julia",
          "julia-0.6",
          "Julia",
          "#ebd8eb"
        ],
        [
          "SoS",
          "sos",
          "",
          ""
        ],
        [
          "R",
          "ir",
          "R",
          "#DCDCDA"
        ],
        [
          "Python3",
          "python3",
          "Python3",
          "#FFE771"
        ],
        [
          "julia-0.5-old",
          "julia-0.5-old",
          "",
          ""
        ],
        [
          "julia-0.6-pauto",
          "julia-0.6-pauto",
          "",
          ""
        ],
        [
          "julia-pro-0.6-mkl",
          "julia-pro-0.6-mkl",
          "",
          ""
        ],
        [
          "julia-pro-0.6-mkl-pauto",
          "julia-pro-0.6-mkl-pauto",
          "",
          ""
        ],
        [
          "ruby",
          "ruby",
          "",
          ""
        ]
      ],
      "panel": {
        "displayed": true,
        "height": 0,
        "style": "side"
      }
    },
    "toc": {
      "nav_menu": {},
      "number_sections": true,
      "sideBar": true,
      "skip_h1_title": false,
      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": false,
      "toc_position": {},
      "toc_section_display": true,
      "toc_window_display": false
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}