matplotlib-commons.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exporting figures\n",
    "\n",
    "In the presented demo we show how you can dump to files plots when you don't want to write them in files jupyter notebooks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "data1 = [1, 2, 3, 4]\n",
    "data2 = [10, -10, 10, -10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPython2CWL has a category of types which called *dumpables*. *Dumpables* are variables that you want to dump them into files at the compiled version (as a CWL command line tool) but you don't want to write them to files when you work in the Jupyter Notebook. For more information about dumpables see the [docs](https://ipython2cwl.readthedocs.io/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A very common case of a dumpable variable is when you have matplotlib plots in the Jupyter Notebook. For such simple usecases there is build in support with the `CWLPNGPlot` & `CWLPNGFigure`. The difference between them is that the `CWLPNGPlot` can be used to write multiple lines in the same file, in contrast to `CWLPNGFigure` which creates a new figure and then write only that to a file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell as a step in the cwl will create an image with name p1.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1: 'CWLPNGFigure' = plt.plot(data1, 'r-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell as a step in the cwl will create an image with name p2.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p2: 'CWLPNGFigure' = plt.plot(data2, 'b-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to create a image with multiple plots. Jupyter Notebook automaticly groups plots which belong to the same cell. In order to say to the ipython2cwl to group them together you have to the `CWLPNGPlot`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lw7Sxdq19gBd9iJd2mz8/fmPmhg03fZgfdFDpH/QNGqR8COgjghRZtsz+qDt3tvaCzpVm+XIbQeblwVtvhY4m9X791S4TnXkmPPZYCl7wzz8TuzwTr768CDRrtvkHeYsWW364t2iRFjXDfUQQ2NZb28Tx9dfD55/bCYFz8dx9t33mFBaGjiSM7baDSy+1shP9+sHOO1fiSVTtmmxpl2SK35Yv3/Lna9bc9IG+005wyCHxz96bNYMamf8x6iOCFPrzT/ub2n13azPoXElF80n/8z/w6quhowlnwQIbQXfrBv/6V7EHNmwo//p70Qf+mjVbPnG9eqVfkil+Jt+4sZUGyDI+IkgD9erZGc7VV8OHH9qcj3PF3XGHLSTJuU2Ia9ZsdrbebN48rtirE8OePoT+cy6n04ov7fH5820ytqTGjTd9kB96aNnX390WfESQYqtXW7mJtm2t3WAuLAt0ifn9dxsNHH88PPdc6GiSZPnysi/LFN0WL97iRxfLtrRjJkdt/TUvHnxH2WfymdylJ4V8RJAm6tSxgnSXXALvvAPHHhs6Ipcubr3VLh+m/eZDVfvgTmSCNV77sVq1Nn2Id+xo18HiTLA2btaMawqrM2TIkUwoPJJ9M7KbeWbwEUEAa9faBFiTJtZ20EcF7rff7Jr4qafCP/8ZKIj16+3ifHkTrL/9Zn/EJdWvX/b696Jbo0YJ/9EvXWqjpIMOsjadrmp8RJBGatWyPQXnnw+vvZbdxcRcYm6+2T5b8/MjePLVq8tf+z5vnk3Cxrv+vu22mz7Ed9659Msz9esnPfRttrFdxv37w5dfwgEHJP0lHD4iCGb9ets1WrcuTJiQlQsUXILmzLHVZH/7Gzz8cII/pLr59feybn/8seXPV68OzZuXvu69+Ad8JXexJsuKFTZa2msvePfdoKFkvJSPCESkNfAE1pxGgZGqeneJY7oCrwKzYne9pKo5sV6iRg07+/vf/7Xdo6efHjoiF8qNN9rn+sCB2Bn5okXlr32fN8/qUJRUu/amD/Fdd4Ujjoj/Ad+kSca0OqtfH/r2td35H38Mh1Wq67krS2QjAhFpCbRU1QmxdpXjgZNUdWqxY7oCvVT1xIo8dzaMCMCWRO+5p30IfPttxvy7dJWxfr0tfSzxYT7ru7V0fGYIFzZ5hQdqX2PHrFu35c9vvXVi698bNszKSadVq2zU1L49fPRRVr7FlEj5iCDWd3he7OvlIjINaAVMLfMHc0j16tam7/TT4emnbXTgMsyqVYltblq40DJ+CYV1nqI6Gxiw60vQ7sjSP+zr1g3w5tLHVlvZPMEVV8B778HRR4eOKLukZI5ARNoCHwOdVHVZsfu7Ai8Cc4BfsdHBlFKeoyfQE6BNmzb7/ZwlXeE3boT99rProNOmZcVu9cxXsjxwWROtS5du+fM1amy6/l7G7fulzdltr5pccQXceWfq32amWbPGKpNut52V5fdRQcWVNiKIPBGISH3gI+BGVX2pxGNbAxtVdYWInADcraodynvObLk0VKRo5dCoUbaSyEVk40bbtZXIGfyqVVv+fGnlgUtOtjZpktDs/9lnW1nymTMtb7jyPfSQVSd97TU4sUIXlB0ESgQiUhN4HXhbVe9I4PifgDxV/b2s47ItEajasrj58+H774Mv0sg869aVfdZe9Nj8+XatvqRttkls/fvWWyftNHTKFNhjDytEeMstSXnKnLBuHeyyi/3Kxo/3UUFFhVg1JMAoYFppSUBEWgDzVVVFpDPWOjNOrdfsJmKVJo891kYFl1wSOqI0UVQeuLw18L/HOW8QgaZNN32I77FH6WfzAcoD5+fbapjevVP+0hmtZk37f3fuufDyy3DKKaEjyg5Rrho6BPgE+BYo2qXSH2gDoKojRORy4BJgPbAKuFZVPy/vubNtRAA2KjjsMLtMMGNGWpQuj4aqrWtPZP17eeWBy6r/3qyZHZuGJk6EffaxTYVDhoSOJvNs2GAVfGvUgEmTfLVdRQSbI4hCNiYCsGVxXbvaxOHVV4eOpoLKKg9c8ow+XnngunUTuzyTBeWBu3WzgoOzZtlqT1dxzz4LPXpYieqzzgodTebwRJAhjjrK9hTMnGllq4Mras9X3tn7ggXx2/M1alT2B3vR2XyA9nwhfPWVzQfdeKMth3SVs3Ej7L23Vc+YOtVX2yXKaw1liMJCK7B1333WzSwyK1YkdnkmTnlgqlXb1J6vRQv7F1nah3ydOhG+icwzaJAtKrryytCRZLZq1axnw8knw5NPwnnnhY4os/mIIA395S9WYGvWLFuokrB45YFLO5tfsWLLn69Vq+y6M0W3pk39FKwSPvnE5oFuuw169QodTeZThf33tz/56dN9tV0i/NJQBhk/3hqXFxTE6s9s2BC/PHC8a/GJlgeO94HfuHFOXJ4JQdU60n33Hfz4Y85vFE6aMWPsxGnECLjootDRpD9PBOmojPLAJ79xAR/+vgeztt2fRr//UHp7vkQmWCMoD+wq5v33bf7nnnusTIJLDlU4+GCYPRt++MGvRJbH5whSpag8cCITrEuWbPnz1apB8+YUNF7BXvOf5vbtbmfoRV9teTbv7fkyhqp1pdt+e7jwwtDRZJeiPThHHQUjR/rcS2X5iCBRqluWBy7tVl554LKuwzdt+t+F0T16WFemWbPsbpeZii5fPPiglUdwyaVq1banTbPVdn7ZrXR+aag0JcsDl3Ym/9tv8csDN2iQ2OWZSpQHnj7dNs5ce61NMLrMo2rzPUuW2PxAmu5xy3iffgqHHuoT8eXxRADWHXz69M0/4EspD0yTJolNsEa82P/cc+H5522CsWXLSF/KRaCoDMJjj9nv0kXn2GNtocWsWXZ+5rbkiQBsrdmvv5Z/9t68edqsRfvxR2sTe+mlNtHoMsfGjdZece1aKzLnK26jNXYsdOkCQ4fCgAGho0lPngjAzvwzcHlkz57w+ONWg6h169DRuEQ984yVP/AyCKnj5TvKVloiyOyiLRWVgUkAbMUJ2JmOywzr18PgwdCpE5x5ZuhockdBgdU0vKPcoveuuNxKBBmqTRsbFTzyiK2KcOnvqadscnjIkIyvkZdR9t4bTjsN7rorfnVyF5//iWaI/v3tGnNhYehIXHnWrbMz0332sVo4LrUGD7YKKr7SLnGeCDJEy5Y2YfzEE3am6dLXY4/ZyK2wMGOvRma03XeHv/7VCjfOnx86mszgiSCDXH+9NazxZibpa80aSwBdusAJJ4SOJnfl59vvwtuAJibyRCAix4nIdyIyQ0T6xnm8tog8G3v8KxFpG3VMmapZM9tC/8wzMHly6GhcPA89ZHVvhg710UBIHTrAOefA8OEwZ07oaNJfpIlARKoD9wPHA7sBZ4nIbiUOuwBYoqrtgTuBYVHGlOl69bLNMvn5oSNxJa1caQ1nDjsMjjwydDRu0CDby3HTTaEjSX9Rjwg6AzNUdaaqrgWeAbqXOKY78Hjs6xeAI2ON710cjRtbyYmXXoIJE0JH44obPtwqkfjcQHpo2xYuuAAefhh++il0NOkt6kTQCphd7Ps5sfviHqOq64GlwLYln0hEeorIOBEZt3DhwojCzQxXX20dIAcNCh2JK7JihV2PPvpoGxG49DBggC3f9dV2ZcuYyWJVHamqeaqa1zTHS3Fusw306QNvvGGdzFx499xj69b9Aye9bL89XHyx7cz/4YfQ0aSvqBPBXKB4UYTtY/fFPUZEagDbAIsijivjXX65laYeODB0JO6PP2zN+okn2mohl1769rXSYb7arnRRJ4KvgQ4i0k5EagE9gNEljhkNFNVlPA34QDOxAFKK1a8P/frBe+/Bxx+Hjia33XmnJYOCgtCRuHhatLCucP/6F0ydGjqa9BRpIohd878ceBuYBjynqlNEpEBEusUOGwVsKyIzgGuBLZaYuvguvhi2285qEXnqDGPRIksEp55qO4ldeurd2yrG+2q7+CKfI1DVMaraUVV3UtUbY/cNUtXRsa9Xq+rpqtpeVTurqlfTSdBWW1npiU8+sZGBS71//MMmiv2yQ3pr0gSuuQZeeAEmTgwdTfrJrTLUWWjNGujY0UpQfPGFL1tMpfnzYccdoXt3u+zg0tsff0C7draq69VXQ0cThpehzlK1a9uE8VdfWW9clzrDhsHq1VbkzKW/hg3huutg9GhrYuM28RFBFli3DnbZxZaVjh/vo4JUmDsX2reHHj3g0UdDR+MStXy5jQry8uCtt0JHk3o+IshiNWvaWel//mM9cl30brrJms/4pr7M0qCBFW98+21reO+MjwiyxIYN1g2renWYNMn+66Lx889W1Oz882HEiNDRuIpaudLmdnbdFT78MHQ0qeUjgixXvbqtXJkyBZ57LnQ02a2olpA3SM9Mdevaart//xs++CB0NOnBRwRZZONGW8u+erUlhBo1QkeUfWbMsPmYyy6Du+8OHY2rrNWrbVTXujV89lnuzKv5iCAHVKtmo4Lvv4cnnwwdTXYqKLByBf36hY7EVUWdOrYR84svcnPSuCQfEWQZVdh/f1i8GKZPtw8tlxzTplkbxF694NZbQ0fjqmrtWth5Z9h2W/j669wYFfiIIEeI2DXsWbN8WWOyDR5sZQr69AkdiUuGWrVs1df48bm7wayIJ4IsdNxxcNBB1i5x9erQ0WSHSZNsEv6qq6xcgcsOf/ubzRUUdTPLVZ4IslDRqGDOHOuh66ouP9827F13XehIXDLVqGEjvW+/heefDx1NOJ4IstQRR0DXrtZDd+XK0NFktnHj7NLBdddZZziXXc480+Z+Bg+2/Ti5yBNBFisstMJoDzwQOpLMNnCg9Yq+6qrQkbgoFO3BmT49d4sHeiLIYoccAscea710ly8PHU1m+uwzW154/fWw9daho3FROflk2HtvGxWsWxc6mtTzRJDlCguteco994SOJDMNHAjNmtkGMpe9ihrcz5xp/Y1zTSSJQERuE5HpIvKNiLwsIg1LOe4nEflWRCaKiG8MiMD++0O3btZA5Y8/QkeTWT780G79+9uyUZfd/vIX6zldUGB9PnJJVCOCd4FOqron8D1Q1j7Mw1V173ibHFxyFBRYErjjjtCRZA5VGw20agUXXRQ6GpcKRavtZs+Ghx8OHU1qRZIIVPWdWL9igC+B7aN4HZeYvfaC00+Hu+6yy0SufG+/bfMDAwZYOQKXG446Cg491FbbrVoVOprUScUcwfnAm6U8psA7IjJeRHqW9SQi0lNExonIuIULFyY9yGw3eLD11r3tttCRpL+i0cAOO8AFF4SOxqVS0ahg3jwYPjx0NKlT6VpDIvIe0CLOQwNU9dXYMQOAPOAUjfNCItJKVeeKSDPsctIVqvpxea/ttYYq53//1xrXzJwJzZuHjiZ9vfoqnHQSjBplPQdc7jn6aNtNPnMm1K8fOprkSXqtIVU9SlU7xbkVJYHzgBOBs+MlgdhzzI39dwHwMtC5svG48uXn2yTYLbeEjiR9bdxo5Qbat4dzzgkdjQulsBAWLoR77w0dSWpEtWroOKAP0E1V4+5rFZF6ItKg6GvgGGByFPE406EDnHuuDXnnzAkdTXp68UX45hu7lOb9HHLXAQfYKqLbboOlS0NHE72o5gjuAxoA78aWho4AEJHtRGRM7JjmwKciMgkYC7yhql4ZPGIDB9pZ7003hY4k/WzYYKOm3XazpvQutxUUwJIltsgi23k/ghx06aW2PO7776Ft29DRpI8nn7RqlM8/D6edFjoalw5OOQXef9/KujduHDqaqvN+BO6/BgzYtJPSmXXr7HLQXnvZP37nwGoQLV9uGzKzmSeCHNSqFVxyiW2l/+GH0NGkhyeegB9/tORYzf9VuJg99rDqpHffDQsWhI4mOv4nn6P69oXate2MJ9etWWPXgzt3hhNPDB2NSzeDB1uDp2HDQkcSHU8EOap5c7jiCiu7O3Vq6GjCGjUKfvnFkkEu9K11FbPzzjZ39MAD8OuvoaOJhieCHNa7t22WGTw4dCThrFpl5QQOOQSOOSZ0NC5dDRoE69dn72o7TwQ5bNtt4eqrbZXMxImhowljxAg7yyss9NGAK92OO9ou84cestFjtvFEkOOuvRYaNrT187nmzz9tl/WRR1pbT+fKcsMN9t+hQ8PGEQVPBACv5roAABFGSURBVDmuYUPo1QtGj4axY0NHk1r33WcrQXwZrUtE69bQsyc88oitMMsmnggcV14JTZrYddBcsWwZ3HorHH88HHhg6GhcpujfH2rWtIUF2cQTgaNBA+vJ+/bb8OmnoaNJjbvugsWLfTTgKqZlS2tb+uST1uw+W3gicICVnWjRwmoRZbvFi+H2263U9H77hY7GZZrrr4ettsqu1XaeCBwAdevasPff/4YPPggdTbRuv93KBmTb8N6lRtOmcNVV8OyzVqk2G3jROfdfq1dbqerWra1NYzYup1y4ENq1sx3EzzwTOhqXqRYvtr+jI46wZk+ZwovOuXLVqWOXhr74At7K0oLgw4bZJrJsGta71GvcGK67Dl55BcaPDx1N1fmIwG1m3TrbUt+4MXz9dXaNCubNs41BZ5xhBfecq4ply2xU0KULjBlT/vHpIOUjAhEZLCJzY41pJorICaUcd5yIfCciM0Skb1TxuMTUrGnLSMePt9692eTmmy3R5dIyWRedrbeGPn3gzTdtFJ3JIhsRiMhgYIWqllrJW0SqA98DRwNzgK+Bs1S1zDJoPiKI1vr1sPvuVp104sTsKMv8yy+bWnWOHBk6Gpct/vzTRpl77AHvvRc6mvKl6xxBZ2CGqs5U1bXAM0D3wDHlvBo17Br6t9/CCy+EjiY5brzR/ltUJsC5ZKhXz0q6v/++rbjLVFEngstF5BsReUREGsV5vBUwu9j3c2L3bUFEeorIOBEZt3DhwihidcWceaaNCvLzrZdvJps508oCXHghtGkTOhqXbS6+GLbbzhZaZOCUK1DFRCAi74nI5Di37sBwYCdgb2AecHtVXktVR6pqnqrmNW3atCpP5RJQrZqts58+3XoWZLKCAhvl9O8fOhKXjbbaytq/fvopvPNO6GgqJyWrhkSkLfC6qnYqcf+BwGBVPTb2fT8AVb25rOfzOYLUULWdt0uXWkKoWTN0RBX33Xew225Wbvv2Kp2KOFe6NWtstV2zZvDVV+m72i7EqqGWxb49GZgc57CvgQ4i0k5EagE9gNFRxeQqRsRq8cycmbnLLQcPtjO2668PHYnLZrVr26Whr7+G118PHU3FRTlHcKuIfCsi3wCHA9cAiMh2IjIGQFXXA5cDbwPTgOdUdUqEMbkKOuEEWyddWGhnPZnk22+tDMCVV9qZmnNROucc2GknSwgbN4aOpmIiSwSq+jdV3UNV91TVbqo6L3b/r6p6QrHjxqhqR1XdSVVvjCoeVzlFo4JffoGHHw4dTcXk51tl1V69QkfickHNmjYCnTQJXnopdDQVE3r5qMsARx0Fhx1mSzBXrQodTWImTLAaMNdea7uknUuFs86CXXfNvNV2nghcuYpGBfPmwfDhoaNJzKBB0KiRTRI7lyrVq9uoYOrUzCpq6InAJeSww+Doo63H74oVoaMp2xdfwBtvQO/esM02oaNxuea002DPPS0hrF8fOprEeCJwCSsstDLO990XOpKyDRpkNeOvuCJ0JC4XFe3BmTEDnngidDSJ8UTgEtalC/zlL9brd+nS0NHE99FHVvOlb1+oXz90NC5XdesGeXmWENauDR1N+TwRuAopKIAlS6znb7pRtaV7LVvCJZeEjsblsqJ5tZ9/tvIm6c4TgauQffeFU06BO+6wLk3p5L334JNPbLv/VluFjsblumOPhYMOgqFDrftfOvNE4CpsyBDr+fuPUguMp56qVRZt3Rr+/vfQ0Thno4KhQ2HuXHjwwdDRlM0TgauwTp2gRw+45x5YsCB0NOaNN2DsWJsorl07dDTOmcMPt9tNN1nvgnTlicBVSn6+bS4bNix0JLadf+BAaxBy7rmho3Fuc4WFdsJ0//2hIymdJwJXKTvvbLVVHngAfv01bCwvv2yd1AYPzswKqS67HXwwHHecrbZbtix0NPF5InCVNmiQbZi5ucyi4dHasMFGJ7vsAn/9a7g4nCtLQQEsWgR33x06kvg8EbhKa9cOLrjAegD/8kuYGJ59FqZMsdFA9ephYnCuPPvvD927W0+MJUtCR7MlTwSuSgYMsP8OHZr6116/3hLAHnvA6aen/vWdq4iCAtuImY4NkjwRuCpp3Rouusg2zfz4Y2pf+5//hB9+sH9g1fwv2aW5PfeEM86wy0O//x46ms35Px9XZf36Qa1a9oGcKmvX2uvtt58NuZ3LBIMHw8qVNnGcTiJJBCLyrIhMjN1+EpGJpRz3U6yL2UQR8SbEGaplS7jsMnjySettnAqPPgo//WRL89K1P6xzJe26qy1quO8++O230NFsEkkiUNUzVXVvVd0beBEoq1/P4bFjt2io7DJHnz5W1mHw4Ohfa/VqSwAHHmjL8pzLJPn5NqINudqupEgvDYmIAGcAT0f5Oi68pk2tCcyzz8I330T7WiNH2rb9oUN9NOAyT/v2cN55MGIEzJ4dOhoT9RzBocB8Vf2hlMcVeEdExotIz7KeSER6isg4ERm3cOHCpAfqqu6666wRTH5+dK+xcqVt1+/aFY44IrrXcS5KAwdafawb06RLe6UTgYi8JyKT49yKT92dRdmjgUNUdV/geOAyETmstANVdaSq5qlqXtOmTSsbtotQo0aWDF55BcaPj+Y17r8f5s+3S0POZaoddoALL4RRo2DWrNDRgKhqNE8sUgOYC+ynqnMSOH4wsEJVy61pmZeXp+PG+dxyOlq2zDaaHXCAFYJLpuXL7bnz8uCtt5L73M6l2ty5sNNO1vD+0UdT85oiMj7efGyUl4aOAqaXlgREpJ6INCj6GjgGmBxhPC4Ftt7aJo7HjLHewcl09922TT+Vy1Sdi0qrVnDppdbO8vvvw8YSZSLoQYnLQiKynYiMiX3bHPhURCYBY4E3VNXP87LA5ZdDs2Z2HTRZ/vjDdmR26wadOyfveZ0LqW9fqFPHenyEFFkiUNXzVHVEift+VdUTYl/PVNW9YrfdVTVNpk1cVdWrZ5vM3n8f/v3v5DznHXdYMvDRgMsmzZrBFVfA00/D5IDXQyKbI4iSzxGkv9WrbZlcu3bw8cdVW+b5++/2PMcfD889l7wYnUsHixbZ3/cxx8ALL0T7WiHmCFwOq1PHCtJ9+im8+27Vnuu226y7Uyo2qzmXattuC9dcAy++CP/5T5gYfETgIrN2LXTsaMPfr76q3Kjgt9+s89gpp1gJC+ey0R9/2N/5wQfDa69F9zo+InApV6uWNa/5+mt4/fXKPcctt1hCiXKTmnOhNWwIvXrZv5Ovvkr96/uIwEVq/XortFWvHkyYULFy0XPm2DzD2WfbxhvnstmKFTZXsM8+8M470byGjwhcEDVq2LX9SZPgpbJKD8Zx442bGtM7l+3q17flpO++awssUslHBC5yGzZYUw6wgnSJtJScNQt23hn+/nd44IFo43MuXaxcabuNO3a0pdfJLqroIwIXTPXqNiqYOhWeeSaxnykstMtIRa0wncsFdeva3/zHH9s+nFTxEYFLiY0bYd997Yxn6lS7ZFSaH36weYUrroA770xdjM6lgzVroEMHK0Hx+efJHRX4iMAFVa2a7Qr+4QfrNVyWIUOgdm27XupcrqldG264Ab780mp2pYKPCFzKqEKXLrBggRXZqlVry2OmTIE99oDevWHYsNTH6Fw6WLcOdtnF+nuMH5+8UYGPCFxwInbt/+ef4ZFH4h8zeLCtnujTJ6WhOZdWata0PTj/+Q+8/HL0r+eJwKXUMcfY7smhQ60eUXETJ1qtlauvtm33zuWys8+2lXP5+TbHFiVPBC6lRCwJzJ0LDz64+WODBtkOy2uvDRObc+mkaA/O5MnRF1v0ROBSrqjf8M03WzE5gLFjrcZKr16WDJxzcMYZ0KmTjQrWr4/udTwRuCAKC6338P332/cDB9rloCuvDBuXc+mkaLXd99/DU09F+DpV+WEROV1EpojIRhHJK/FYPxGZISLficixpfx8OxH5KnbcsyISZx2Jy0YHHWT9BW691ZbIvfOOLRdt0CB0ZM6ll5NOsj04Q4bYaqIoVHVEMBk4BdisMoaI7Ia1qtwdOA54QETiFRYYBtypqu2BJcAFVYzHZZCCAmvKceqp0KKF9W91zm1OxP6tzJoVXZP7KiUCVZ2mqt/Feag78IyqrlHVWcAMYLNOsyIiwBFAUU+ex4GTqhKPyyx5eXa2s3o19O9v2+udc1s64QQ44AC7pFpytV0ylLHRv0paAV8W+35O7L7itgX+UNX1ZRzzXyLSE+gJ0KZNm+RF6oK6/XYrvXvhhaEjcS59Fa22GzXKylXXqZPc5y83EYjIe0CLOA8NUNVXkxtO6VR1JDASbGdxql7XRWvHHa0xvXOubEceabcolJsIVPWoSjzvXKB1se+3j91X3CKgoYjUiI0K4h3jnHMuYlEtHx0N9BCR2iLSDugAjC1+gFqRow+B02J3nQukbIThnHPOVHX56MkiMgc4EHhDRN4GUNUpwHPAVOAt4DJV3RD7mTEisl3sKa4HrhWRGdicgTckdM65FPPqo845lyO8+qhzzrm4PBE451yO80TgnHM5zhOBc87luIycLBaRhcDPlfzxJsDvSQwnpGx5L9nyPsDfS7rKlvdS1fexg6o2LXlnRiaCqhCRcfFmzTNRtryXbHkf4O8lXWXLe4nqffilIeecy3GeCJxzLsflYiIYGTqAJMqW95It7wP8vaSrbHkvkbyPnJsjcM45t7lcHBE455wrxhOBc87luKxNBCJynIh8JyIzRKRvnMdri8izsce/EpG2qY+yfAm8j/NEZKGITIzd/h4izkSIyCMiskBEJpfyuIjIPbH3+o2I7JvqGBORwPvoKiJLi/1OBqU6xkSJSGsR+VBEporIFBG5Ks4xaf97SfB9ZMTvRUTqiMhYEZkUey9D4hyT3M8vVc26G1Ad+BHYEagFTAJ2K3HMpcCI2Nc9gGdDx13J93EecF/oWBN8P4cB+wKTS3n8BOBNQIADgK9Cx1zJ99EVeD10nAm+l5bAvrGvGwDfx/kbS/vfS4LvIyN+L7H/z/VjX9cEvgIOKHFMUj+/snVE0BmYoaozVXUt8AzQvcQx3YHHY1+/ABwpIpLCGBORyPvIGKr6MbC4jEO6A0+o+RLrYNcyNdElLoH3kTFUdZ6qToh9vRyYxpa9w9P+95Lg+8gIsf/PK2Lf1ozdSq7qSernV7YmglbA7GLfz2HLP4r/HqPWKnMp1hwnnSTyPgBOjQ3ZXxCR1nEezxSJvt9McGBsaP+miOweOphExC4v7IOdgRaXUb+XMt4HZMjvRUSqi8hEYAHwrqqW+jtJxudXtiaCXPIa0FZV9wTeZdNZggtnAlbTZS/gXuCVwPGUS0TqAy8CV6vqstDxVFY57yNjfi+qukFV98Z6uXcWkU5Rvl62JoK5QPEz4+1j98U9RkRqANsAi1ISXeLKfR+qukhV18S+fRjYL0WxRSGR31vaU9VlRUN7VR0D1BSRJoHDKpWI1MQ+PJ9S1ZfiHJIRv5fy3kem/V4AVPUPrLf7cSUeSurnV7Ymgq+BDiLSTkRqYZMpo0scMxo4N/b1acAHGpt5SSPlvo8S12q7YddGM9Vo4JzYKpUDgKWqOi90UBUlIi2KrteKSGfs31m6nWQAtiII6xU+TVXvKOWwtP+9JPI+MuX3IiJNRaRh7OutgKOB6SUOS+rnV43K/mA6U9X1InI58Da28uYRVZ0iIgXAOFUdjf3R/FNEZmATfz3CRRxfgu/jShHpBqzH3sd5wQIuh4g8ja3caCIic4B8bCIMVR0BjMFWqMwAVgL/FybSsiXwPk4DLhGR9cAqoEcanmQUORj4G/Bt7Jo0QH+gDWTU7yWR95Epv5eWwOMiUh1LVs+p6utRfn55iQnnnMtx2XppyDnnXII8ETjnXI7zROCccznOE4FzzuU4TwTOOZfjPBE451yO80TgnHM57v8DLKkM/oK601cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data1, 'r-')\n",
    "p3: 'CWLPNGPlot' = plt.plot(data2, 'b-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the Jupyter Notebook developer handles management of the figures so you can use both `CWLPNGPlot` & `CWLPNGFigure` and you will have the same result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "p4: 'CWLPNGPlot' = plt.plot(data1, 'r-')\n",
    "plt.figure()\n",
    "p5: 'CWLPNGPlot' = plt.plot(data2, 'b-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following lines illustrate how to compile the repo. Uncomment them without saving the notebook to execute the demo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-09 20:50:28,198 - repo2cwl - INFO - copy repo to temp directory: /var/folders/k8/800hfw_n2md_2zb44lhhtqqr0000gn/T/repo2cwl_zoslrkuk/repo\n",
      "[Repo2Docker] Picked Local content provider.\n",
      "\n",
      "[Repo2Docker] Using local repo /var/folders/k8/800hfw_n2md_2zb44lhhtqqr0000gn/T/repo2cwl_zoslrkuk/repo.\n",
      "\n",
      "[Repo2Docker] Using PythonBuildPack builder\n",
      "\n",
      "[Repo2Docker] Step 1/51 : FROM buildpack-deps:bionic\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> 45b76b488328\n",
      "\n",
      "[Repo2Docker] Step 2/51 : ENV DEBIAN_FRONTEND=noninteractive\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 13e933248fc7\n",
      "\n",
      "[Repo2Docker] Step 3/51 : RUN apt-get -qq update &&     apt-get -qq install --yes --no-install-recommends locales > /dev/null &&     apt-get -qq purge &&     apt-get -qq clean &&     rm -rf /var/lib/apt/lists/*\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 60a9e5683647\n",
      "\n",
      "[Repo2Docker] Step 4/51 : RUN echo \"en_US.UTF-8 UTF-8\" > /etc/locale.gen &&     locale-gen\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 452fc536d5d9\n",
      "\n",
      "[Repo2Docker] Step 5/51 : ENV LC_ALL en_US.UTF-8\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 8c95af955e52\n",
      "\n",
      "[Repo2Docker] Step 6/51 : ENV LANG en_US.UTF-8\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> c59f415e92c2\n",
      "\n",
      "[Repo2Docker] Step 7/51 : ENV LANGUAGE en_US.UTF-8\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> ca88318e1a01\n",
      "\n",
      "[Repo2Docker] Step 8/51 : ENV SHELL /bin/bash\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 2f84d28929b9\n",
      "\n",
      "[Repo2Docker] Step 9/51 : ARG NB_USER\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 0541bbae21b7\n",
      "\n",
      "[Repo2Docker] Step 10/51 : ARG NB_UID\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 1f9d2fc0eb2b\n",
      "\n",
      "[Repo2Docker] Step 11/51 : ENV USER ${NB_USER}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> c08002d8ff34\n",
      "\n",
      "[Repo2Docker] Step 12/51 : ENV HOME /home/${NB_USER}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 62233407ec9b\n",
      "\n",
      "[Repo2Docker] Step 13/51 : RUN groupadd         --gid ${NB_UID}         ${NB_USER} &&     useradd         --comment \"Default user\"         --create-home         --gid ${NB_UID}         --no-log-init         --shell /bin/bash         --uid ${NB_UID}         ${NB_USER}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> a45cc8768ea4\n",
      "\n",
      "[Repo2Docker] Step 14/51 : RUN wget --quiet -O - https://deb.nodesource.com/gpgkey/nodesource.gpg.key |  apt-key add - &&     DISTRO=\"bionic\" &&     echo \"deb https://deb.nodesource.com/node_10.x $DISTRO main\" >> /etc/apt/sources.list.d/nodesource.list &&     echo \"deb-src https://deb.nodesource.com/node_10.x $DISTRO main\" >> /etc/apt/sources.list.d/nodesource.list\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 2fcfa92cea96\n",
      "\n",
      "[Repo2Docker] Step 15/51 : RUN apt-get -qq update &&     apt-get -qq install --yes --no-install-recommends        less        nodejs        unzip        > /dev/null &&     apt-get -qq purge &&     apt-get -qq clean &&     rm -rf /var/lib/apt/lists/*\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 2148a7230ceb\n",
      "\n",
      "[Repo2Docker] Step 16/51 : EXPOSE 8888\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 2da171311dca\n",
      "\n",
      "[Repo2Docker] Step 17/51 : ENV APP_BASE /srv\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 526353fd07bf\n",
      "\n",
      "[Repo2Docker] Step 18/51 : ENV NPM_DIR ${APP_BASE}/npm\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 43468cd037fa\n",
      "\n",
      "[Repo2Docker] Step 19/51 : ENV NPM_CONFIG_GLOBALCONFIG ${NPM_DIR}/npmrc\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 7f3bc42e2eef\n",
      "\n",
      "[Repo2Docker] Step 20/51 : ENV CONDA_DIR ${APP_BASE}/conda\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 051adc2efb40\n",
      "\n",
      "[Repo2Docker] Step 21/51 : ENV NB_PYTHON_PREFIX ${CONDA_DIR}/envs/notebook\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 013346965fe8\n",
      "\n",
      "[Repo2Docker] Step 22/51 : ENV KERNEL_PYTHON_PREFIX ${NB_PYTHON_PREFIX}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> a7a63412bb13\n",
      "\n",
      "[Repo2Docker] Step 23/51 : ENV PATH ${NB_PYTHON_PREFIX}/bin:${CONDA_DIR}/bin:${NPM_DIR}/bin:${PATH}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 840da235892b\n",
      "\n",
      "[Repo2Docker] Step 24/51 : COPY build_script_files/-2fusers-2fdks-2f-2epyenv-2fversions-2f3-2e6-2e10-2flib-2fpython3-2e6-2fsite-2dpackages-2frepo2docker-2fbuildpacks-2fconda-2factivate-2dconda-2esh-52434a /etc/profile.d/activate-conda.sh\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 090433256342\n",
      "\n",
      "[Repo2Docker] Step 25/51 : COPY build_script_files/-2fusers-2fdks-2f-2epyenv-2fversions-2f3-2e6-2e10-2flib-2fpython3-2e6-2fsite-2dpackages-2frepo2docker-2fbuildpacks-2fconda-2fenvironment-2epy-2d3-2e7-2efrozen-2eyml-5b16b8 /tmp/environment.yml\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 146f86884638\n",
      "\n",
      "[Repo2Docker] Step 26/51 : COPY build_script_files/-2fusers-2fdks-2f-2epyenv-2fversions-2f3-2e6-2e10-2flib-2fpython3-2e6-2fsite-2dpackages-2frepo2docker-2fbuildpacks-2fconda-2finstall-2dminiconda-2ebash-90a909 /tmp/install-miniconda.bash\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 97c4e5e2ac07\n",
      "\n",
      "[Repo2Docker] Step 27/51 : RUN mkdir -p ${NPM_DIR} && chown -R ${NB_USER}:${NB_USER} ${NPM_DIR}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 84e996ae6305\n",
      "\n",
      "[Repo2Docker] Step 28/51 : USER ${NB_USER}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 6b0b7e4fc614\n",
      "\n",
      "[Repo2Docker] Step 29/51 : RUN npm config --global set prefix ${NPM_DIR}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 700d15d87867\n",
      "\n",
      "[Repo2Docker] Step 30/51 : USER root\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 3a966a41a99b\n",
      "\n",
      "[Repo2Docker] Step 31/51 : RUN bash /tmp/install-miniconda.bash && rm /tmp/install-miniconda.bash /tmp/environment.yml\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 6495a59deb7a\n",
      "\n",
      "[Repo2Docker] Step 32/51 : ARG REPO_DIR=${HOME}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 7810a6cdadd4\n",
      "\n",
      "[Repo2Docker] Step 33/51 : ENV REPO_DIR ${REPO_DIR}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> fa68e043afaa\n",
      "\n",
      "[Repo2Docker] Step 34/51 : WORKDIR ${REPO_DIR}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 363c51dcd6b2\n",
      "\n",
      "[Repo2Docker] Step 35/51 : ENV PATH ${HOME}/.local/bin:${REPO_DIR}/.local/bin:${PATH}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 19349b083785\n",
      "\n",
      "[Repo2Docker] Step 36/51 : ENV CONDA_DEFAULT_ENV ${KERNEL_PYTHON_PREFIX}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 711419c84a8f\n",
      "\n",
      "[Repo2Docker] Step 37/51 : COPY src/requirements.txt ${REPO_DIR}/requirements.txt\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 404af101881a\n",
      "\n",
      "[Repo2Docker] Step 38/51 : USER root\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> ffc8611e7152\n",
      "\n",
      "[Repo2Docker] Step 39/51 : RUN chown -R ${NB_USER}:${NB_USER} ${REPO_DIR}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 22fe6f87fe95\n",
      "\n",
      "[Repo2Docker] Step 40/51 : USER ${NB_USER}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> be5b99f711ff\n",
      "\n",
      "[Repo2Docker] Step 41/51 : RUN ${KERNEL_PYTHON_PREFIX}/bin/pip install --no-cache-dir -r \"requirements.txt\"\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> cbd7a1a6b97c\n",
      "\n",
      "[Repo2Docker] Step 42/51 : USER root\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Using cache\n",
      "\n",
      "[Repo2Docker]  ---> 9f93dbd9780d\n",
      "\n",
      "[Repo2Docker] Step 43/51 : COPY src/ ${REPO_DIR}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> 4549052d36a5\n",
      "\n",
      "[Repo2Docker] Step 44/51 : RUN chown -R ${NB_USER}:${NB_USER} ${REPO_DIR}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Running in a339f8b551ed\n",
      "\n",
      "[Repo2Docker] Removing intermediate container a339f8b551ed\n",
      "\n",
      "[Repo2Docker]  ---> 871a6844ab2b\n",
      "\n",
      "[Repo2Docker] Step 45/51 : LABEL repo2docker.ref=\"None\"\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Running in dd190c6e64a2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Repo2Docker] Removing intermediate container dd190c6e64a2\n",
      "\n",
      "[Repo2Docker]  ---> 32bf07d4ed9c\n",
      "\n",
      "[Repo2Docker] Step 46/51 : LABEL repo2docker.repo=\"local\"\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Running in 4c3616ce6e78\n",
      "\n",
      "[Repo2Docker] Removing intermediate container 4c3616ce6e78\n",
      "\n",
      "[Repo2Docker]  ---> 376d4a9058a5\n",
      "\n",
      "[Repo2Docker] Step 47/51 : LABEL repo2docker.version=\"0.11.0\"\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Running in 4b3b644cf0bf\n",
      "\n",
      "[Repo2Docker] Removing intermediate container 4b3b644cf0bf\n",
      "\n",
      "[Repo2Docker]  ---> c6b30a95b975\n",
      "\n",
      "[Repo2Docker] Step 48/51 : USER ${NB_USER}\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Running in d5d7fbabdb1e\n",
      "\n",
      "[Repo2Docker] Removing intermediate container d5d7fbabdb1e\n",
      "\n",
      "[Repo2Docker]  ---> 9db0c76cba4f\n",
      "\n",
      "[Repo2Docker] Step 49/51 : COPY /repo2docker-entrypoint /usr/local/bin/repo2docker-entrypoint\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> f7711fbf0372\n",
      "\n",
      "[Repo2Docker] Step 50/51 : ENTRYPOINT [\"/usr/local/bin/repo2docker-entrypoint\"]\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Running in c92922ab2a5d\n",
      "\n",
      "[Repo2Docker] Removing intermediate container c92922ab2a5d\n",
      "\n",
      "[Repo2Docker]  ---> 1a23a841cd6d\n",
      "\n",
      "[Repo2Docker] Step 51/51 : CMD [\"jupyter\", \"notebook\", \"--ip\", \"0.0.0.0\"]\n",
      "[Repo2Docker] \n",
      "\n",
      "[Repo2Docker]  ---> Running in 9a7c2bffd547\n",
      "\n",
      "[Repo2Docker] Removing intermediate container 9a7c2bffd547\n",
      "\n",
      "[Repo2Docker]  ---> c8c65561fbe8\n",
      "\n",
      "[Repo2Docker] {\"aux\": {\"ID\": \"sha256:c8c65561fbe8b475162469c415d597b071b92b66758fd8bde316156540d05106\"}}\n",
      "[Repo2Docker] Successfully built c8c65561fbe8\n",
      "\n",
      "[Repo2Docker] Successfully tagged r2d-2fvar-2ffolders-2fk8-2f800hfw-5fn2md-5f2zb44lhhtqqr0000gn-2ft-2frepo2cwl-5fzoslrkuk-2frepo1594324230:latest\n",
      "\n",
      "2020-07-09 20:50:38,681 - repo2cwl - INFO - Generated image id: r2d-2fvar-2ffolders-2fk8-2f800hfw-5fn2md-5f2zb44lhhtqqr0000gn-2ft-2frepo2cwl-5fzoslrkuk-2frepo1594324230\n",
      "2020-07-09 20:50:38,681 - repo2cwl - INFO - Creating CWL command line tool: demo1.cwl\n",
      "2020-07-09 20:50:38,688 - repo2cwl - INFO - Creating CWL command line tool: matplotlib-commons.cwl\n",
      "2020-07-09 20:50:38,694 - repo2cwl - INFO - Cleaning local temporary directory /var/folders/k8/800hfw_n2md_2zb44lhhtqqr0000gn/T/repo2cwl_zoslrkuk/repo...\n"
     ]
    }
   ],
   "source": [
    "#!jupyter-repo2cwl . -o ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arguments:\r\n",
      "- --\r\n",
      "baseCommand: /app/cwl/bin/matplotlib-commons\r\n",
      "class: CommandLineTool\r\n",
      "cwlVersion: v1.1\r\n",
      "hints:\r\n",
      "  DockerRequirement:\r\n",
      "    dockerImageId: r2d-2fvar-2ffolders-2fk8-2f800hfw-5fn2md-5f2zb44lhhtqqr0000gn-2ft-2frepo2cwl-5fzoslrkuk-2frepo1594324230\r\n",
      "inputs: {}\r\n",
      "outputs:\r\n",
      "  p1:\r\n",
      "    outputBinding:\r\n",
      "      glob: p1.png\r\n",
      "    type: File\r\n",
      "  p2:\r\n",
      "    outputBinding:\r\n",
      "      glob: p2.png\r\n",
      "    type: File\r\n",
      "  p3:\r\n",
      "    outputBinding:\r\n",
      "      glob: p3.png\r\n",
      "    type: File\r\n",
      "  p4:\r\n",
      "    outputBinding:\r\n",
      "      glob: p4.png\r\n",
      "    type: File\r\n",
      "  p5:\r\n",
      "    outputBinding:\r\n",
      "      glob: p5.png\r\n",
      "    type: File\r\n"
     ]
    }
   ],
   "source": [
    "# !cat matplotlib-commons.cwl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#!/usr/bin/env ipython\r\n",
      "\"\"\"\r\n",
      "DO NOT EDIT THIS FILE\r\n",
      "THIS FILE IS AUTO-GENERATED BY THE ipython2cwl.\r\n",
      "FOR MORE INFORMATION CHECK https://github.com/giannisdoukas/ipython2cwl\r\n",
      "\"\"\"\r\n",
      "\r\n",
      "\r\n",
      "def main():\r\n",
      "    get_ipython().run_line_magic('matplotlib', 'inline')\r\n",
      "    import matplotlib.pyplot as plt\r\n",
      "    data1 = [1, 2, 3, 4]\r\n",
      "    data2 = [10, -10, 10, -10]\r\n",
      "    import matplotlib.pyplot as plt\r\n",
      "    plt.figure()\r\n",
      "    p1 = plt.plot(data1, 'r-')\r\n",
      "    p1[-1].figure.savefig('p1.png')\r\n",
      "    import matplotlib.pyplot as plt\r\n",
      "    plt.figure()\r\n",
      "    p2 = plt.plot(data2, 'b-')\r\n",
      "    p2[-1].figure.savefig('p2.png')\r\n",
      "    plt.plot(data1, 'r-')\r\n",
      "    p3 = plt.plot(data2, 'b-')\r\n",
      "    p3[-1].figure.savefig('p3.png')\r\n",
      "    plt.figure()\r\n",
      "    p4 = plt.plot(data1, 'r-')\r\n",
      "    p4[-1].figure.savefig('p4.png')\r\n",
      "    plt.figure()\r\n",
      "    p5 = plt.plot(data2, 'b-')\r\n",
      "    p5[-1].figure.savefig('p5.png')\r\n",
      "    get_ipython().system('jupyter-repo2cwl . -o .')\r\n",
      "\r\n",
      "\r\n",
      "if __name__ == '__main__':\r\n",
      "    import argparse\r\n",
      "    import pathlib\r\n",
      "    parser = argparse.ArgumentParser()\r\n",
      "    args = parser.parse_args()\r\n",
      "    main()\r\n"
     ]
    }
   ],
   "source": [
    "# !docker run $(grep 'dockerImageId' matplotlib-commons.cwl | awk '{print $2}') cat /app/cwl/bin/matplotlib-commons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;30mINFO\u001b[0m /Users/dks/.pyenv/versions/3.6.10/bin/cwltool 3.0.20200706173533\n",
      "\u001b[1;30mINFO\u001b[0m Resolved 'matplotlib-commons.cwl' to 'file:///Users/dks/Workspaces/ipython2cwl-demo/matplotlib-commons.cwl'\n",
      "\u001b[1;30mINFO\u001b[0m [job matplotlib-commons.cwl] /private/tmp/docker_tmpu7r7dhn1$ docker \\\n",
      "    run \\\n",
      "    -i \\\n",
      "    --mount=type=bind,source=/private/tmp/docker_tmpu7r7dhn1,target=/fRONcG \\\n",
      "    --mount=type=bind,source=/private/tmp/docker_tmp8i9133vi,target=/tmp \\\n",
      "    --workdir=/fRONcG \\\n",
      "    --read-only=true \\\n",
      "    --net=none \\\n",
      "    --user=501:20 \\\n",
      "    --rm \\\n",
      "    --env=TMPDIR=/tmp \\\n",
      "    --env=HOME=/fRONcG \\\n",
      "    --cidfile=/private/tmp/docker_tmp_3drpwzg/20200709205111-379808.cid \\\n",
      "    r2d-2fvar-2ffolders-2fk8-2f800hfw-5fn2md-5f2zb44lhhtqqr0000gn-2ft-2frepo2cwl-5fzoslrkuk-2frepo1594324230 \\\n",
      "    /app/cwl/bin/matplotlib-commons \\\n",
      "    --\n",
      "2020-07-09 19:51:17,810 - repo2cwl - INFO - copy repo to temp directory: /tmp/repo2cwl_ouj4sz5c/repo\n",
      "[Repo2Docker] ERROR | \n",
      "Docker client initialization error: Error while fetching server API version: ('Connection aborted.', FileNotFoundError(2, 'No such file or directory')).\n",
      "Check if docker is running on the host.\n",
      "\n",
      "\u001b[1;30mINFO\u001b[0m [job matplotlib-commons.cwl] Max memory used: 483MiB\n",
      "\u001b[1;30mINFO\u001b[0m [job matplotlib-commons.cwl] completed success\n",
      "{\n",
      "    \"p1\": {\n",
      "        \"location\": \"file:///Users/dks/Workspaces/ipython2cwl-demo/p1.png\",\n",
      "        \"basename\": \"p1.png\",\n",
      "        \"class\": \"File\",\n",
      "        \"checksum\": \"sha1$88115cdcd7bae2c593c230a3f5585ff75711850a\",\n",
      "        \"size\": 9281,\n",
      "        \"path\": \"/Users/dks/Workspaces/ipython2cwl-demo/p1.png\"\n",
      "    },\n",
      "    \"p2\": {\n",
      "        \"location\": \"file:///Users/dks/Workspaces/ipython2cwl-demo/p2.png\",\n",
      "        \"basename\": \"p2.png\",\n",
      "        \"class\": \"File\",\n",
      "        \"checksum\": \"sha1$aac28be944c0ef01d78068ef03b5635bc6f1fae6\",\n",
      "        \"size\": 13357,\n",
      "        \"path\": \"/Users/dks/Workspaces/ipython2cwl-demo/p2.png\"\n",
      "    },\n",
      "    \"p3\": {\n",
      "        \"location\": \"file:///Users/dks/Workspaces/ipython2cwl-demo/p3.png\",\n",
      "        \"basename\": \"p3.png\",\n",
      "        \"class\": \"File\",\n",
      "        \"checksum\": \"sha1$f305bfc114a784b8aa2b229274d20347669cf184\",\n",
      "        \"size\": 14232,\n",
      "        \"path\": \"/Users/dks/Workspaces/ipython2cwl-demo/p3.png\"\n",
      "    },\n",
      "    \"p4\": {\n",
      "        \"location\": \"file:///Users/dks/Workspaces/ipython2cwl-demo/p4.png\",\n",
      "        \"basename\": \"p4.png\",\n",
      "        \"class\": \"File\",\n",
      "        \"checksum\": \"sha1$88115cdcd7bae2c593c230a3f5585ff75711850a\",\n",
      "        \"size\": 9281,\n",
      "        \"path\": \"/Users/dks/Workspaces/ipython2cwl-demo/p4.png\"\n",
      "    },\n",
      "    \"p5\": {\n",
      "        \"location\": \"file:///Users/dks/Workspaces/ipython2cwl-demo/p5.png\",\n",
      "        \"basename\": \"p5.png\",\n",
      "        \"class\": \"File\",\n",
      "        \"checksum\": \"sha1$aac28be944c0ef01d78068ef03b5635bc6f1fae6\",\n",
      "        \"size\": 13357,\n",
      "        \"path\": \"/Users/dks/Workspaces/ipython2cwl-demo/p5.png\"\n",
      "    }\n",
      "}\n",
      "\u001b[1;30mINFO\u001b[0m Final process status is success\n"
     ]
    }
   ],
   "source": [
    "# !cwltool matplotlib-commons.cwl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After executing the generated CWL CommandLineTool we take the following images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# from IPython.display import Image\n",
    "# from IPython.display import display\n",
    "# images = [Image(f\"p{p}.png\") for p in range(1,6)]\n",
    "# display(*images)"
   ]
  }
 ],
 "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}