jupyter-intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Jupyter Notebook Intro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "livereveal": {
     "height": 1600,
     "width": 2560
    }
   },
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<img src=\"http://imgs.xkcd.com/comics/python.png\" style=\"min-height:400px;height:0.8vh;margin:auto;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "```python\n",
    "import antigravity\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## General Info\n",
    "\n",
    "`Jupyter notebooks` = the notebook front-end (and more), used to be IPython notebooks, but can do much more. Can also run other Kernels, like `Julia`, `R`, ..., but we'll focus on the python+notebook features.\n",
    "\n",
    "Think of a notebook as a script that you can execute step-by-step (or in different order). Just another way (like a console) to talk to the `Kernel`/Namespace. You can even do both at once."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Useful for:\n",
    "\n",
    "- slides (see also the [RISE-Extensions](https://github.com/damianavila/RISE))\n",
    "- (live) lectures\n",
    "- ... even books"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Can also run/convert a notebook with \n",
    "\n",
    "    jupyter nbconvert --execute <notebook>\n",
    "    jupyter nbconvert --to python <notebook>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## More information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Follow along: <http://goo.gl/xLbtwq>\n",
    "\n",
    "[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/birnstiel/jupyter-intro/master?filepath=jupyter-intro.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Many Examples: <http://nb.bianp.net/sort/views/>\n",
    "\n",
    "Relevant for this week: [Star Wars API](http://nbviewer.jupyter.org/github/rasbt/pattern_classification/blob/master/data_viz/swapi_viz.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Connecting to a remote notebook server"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remote machine (note port, like 8889 or set it with `--port=8889`):\n",
    "\n",
    "    jupyter notebook --no-browser\n",
    "\n",
    "Local machine (terminal): open a tunnel\n",
    "\n",
    "    ssh -N -f -L localhost:8888:localhost:8889 remote_user@remote_host\n",
    "    \n",
    "Local machine (browser):\n",
    "\n",
    "    localhost:8888"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Markdown\n",
    "\n",
    "Markdown is great to mark up text without cluttering it.\n",
    "\n",
    "- You \n",
    "- can\n",
    "- do\n",
    "- lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "1. You\n",
    "2. can\n",
    "3. do\n",
    "3. numbered lists\n",
    "\n",
    "You can typeset $\\LaTeX$ or also `code`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For example python\n",
    "\n",
    "```python\n",
    "def hello_world():\n",
    "    print('Hello, world!')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Or Fortran\n",
    "\n",
    "```fortran\n",
    "program main\n",
    "implicit none\n",
    "write(*,*) 'Hello, world!'\n",
    "end program main\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Tables\n",
    "\n",
    "| Number |  Text  |\n",
    "| :----: | :----- |\n",
    "|    1   |  one   |\n",
    "|    2   |  two   |\n",
    "|    3   |  three |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Editing\n",
    "\n",
    "- select cell with arrow keys\n",
    "- press `Return` to edit it, escape to stop editing it\n",
    "- press `Ctrl+Return` to run cell in place\n",
    "- press `Shift+Return` to run and proceed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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pzEjOmX+5aVo+nnomY7rmYKnxce0ba/lpb6XUYQlCixRr7kz54hM5bnmqJqYt2Y5SO9e+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\nTj6MMcZc7v8ADSEb733NRrQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1154db0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import xkcd\n",
    "with xkcd():\n",
    "    f,ax=plt.subplots()\n",
    "    ax.plot(np.sin(np.linspace(0,2*np.pi,100)))\n",
    "    ax.set_xlabel('x',fontsize=16)\n",
    "    ax.set_ylabel('sin(x)',fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Editing (cont.)\n",
    "\n",
    "- in edit mode:\n",
    "    - **`h` for help**\n",
    "    - `a` to add cell above\n",
    "    - `b` to add cell below\n",
    "- selected cell:\n",
    "    - `m` to Markdown\n",
    "    - `y` to code\n",
    "    - `l` turns on line numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Misc Stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"text-align:center; width:50%; margin:auto;\">\n",
       "<h2>Whoa, Jupyter can render HTML!</h2>\n",
       "<!-- <iframe src=\"https://giphy.com/embed/kBxxT2NqezJXW\" width=\"480\" height=\"384\" frameBorder=\"0\" class=\"giphy-embed\" allowFullScreen></iframe><p><a href=\"https://giphy.com/gifs/secretsandliesedit-kBxxT2NqezJXW\">via GIPHY</a></p> -->\n",
       "<img src=\"https://media.giphy.com/media/kBxxT2NqezJXW/giphy.gif\" width=\"100%\"</img>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<div style=\"text-align:center; width:50%; margin:auto;\">\n",
    "<h2>Whoa, Jupyter can render HTML!</h2>\n",
    "<!-- <iframe src=\"https://giphy.com/embed/kBxxT2NqezJXW\" width=\"480\" height=\"384\" frameBorder=\"0\" class=\"giphy-embed\" allowFullScreen></iframe><p><a href=\"https://giphy.com/gifs/secretsandliesedit-kBxxT2NqezJXW\">via GIPHY</a></p> -->\n",
    "<img src=\"https://media.giphy.com/media/kBxxT2NqezJXW/giphy.gif\" width=\"100%\"</img>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Show videos\n",
    "for whatever reason"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/vWkb7VahaXQ\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x114882320>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "# a short video about using NumPy arrays, from Enthought\n",
    "YouTubeVideo('vWkb7VahaXQ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Profiling\n",
    "\n",
    "See also [here](http://pynash.org/2013/03/06/timing-and-profiling/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    }
   ],
   "source": [
    "%%prun\n",
    "a = np.random.rand(int(1e7))\n",
    "a.sort()\n",
    "b = a.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "If you installed `line_profiler` (e.g. `conda install line_profiler`), you can do line-profiling. But need to load the extension first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext line_profiler\n",
    "import numpy as np\n",
    "\n",
    "def testfunc(n):\n",
    "    y = np.arange(n)\n",
    "    for i in range(n):\n",
    "        y[i] += i\n",
    "    y += y\n",
    "    print('done')\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "%lprun -f testfunc testfunc(10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Bash"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "run one bash command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "requirements.txt\r\n"
     ]
    }
   ],
   "source": [
    "!ls *.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "more bash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "requirements.txt\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "for i in *.txt; do\n",
    "    echo $i;\n",
    "done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "can also use  local variables with brackets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mask = '*.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--@ 1 birnstiel  staff  56 10 Dez 20:56 requirements.txt\r\n"
     ]
    }
   ],
   "source": [
    "!ls -l {mask}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Open a console in current workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%qtconsole"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Writing Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "writing to file \"by hand\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "<!--associate:\n",
    "x.dat\n",
    "y.dat\n",
    "z.dat\n",
    "#Z = np.cos(X*Y)+np.sin(X*Y)\n",
    "-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting x.dat\n"
     ]
    }
   ],
   "source": [
    "%%file x.dat\n",
    "-5. -3.88888889 -2.77777778 -1.66666667 -0.55555556 0.55555556 1.66666667 2.77777778 3.8888888 5.        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting y.dat\n"
     ]
    }
   ],
   "source": [
    "%%file y.dat\n",
    "-1. -0.89473684 -0.78947368 -0.68421053 -0.57894737  -0.47368421 -0.36842105 -0.26315789 -0.15789474 -0.05263158  0.05263158 0.15789474 0.26315789 0.36842105 0.47368421  0.57894737 0.68421053 0.78947368 0.89473684 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting z.dat\n"
     ]
    }
   ],
   "source": [
    "%%file z.dat\n",
    "-6.75262e-01 -1.41319e+00 -5.78704e-01 8.99684e-01 1.37702e+00 3.22192e-01 -1.09113e+00 -1.29039e+00 -5.38712e-02 1.24259e+00\n",
    "-1.20809e+00 -1.27498e+00 -1.82188e-01 1.07632e+00 1.35584e+00 4.02123e-01 -9.17352e-01 -1.40243e+00 -6.11896e-01 7.35201e-01\n",
    "-1.41392e+00 -9.26094e-01 2.29793e-01 1.21991e+00 1.33002e+00 4.80679e-01 -7.15410e-01 -1.39542e+00 -1.06881e+00 2.88163e-02\n",
    "-1.23704e+00 -4.24172e-01 6.22268e-01 1.32606e+00 1.29965e+00 5.57592e-01 -4.91504e-01 -1.26995e+00 -1.34910e+00 -6.85368e-01\n",
    "-7.25329e-01 1.47843e-01 9.61919e-01 1.39149e+00 1.26484e+00 6.32599e-01 -2.52510e-01 -1.03668e+00 -1.40646e+00 -1.21404e+00\n",
    "-1.72905e-02 6.95428e-01 1.21991e+00 1.41420e+00 1.22570e+00 7.05442e-01 -5.76364e-03 -7.15410e-01 -1.23141e+00 -1.41411e+00\n",
    "6.95428e-01 1.12810e+00 1.37435e+00 1.39350e+00 1.18238e+00 7.75874e-01 2.41160e-01 -3.33406e-01 -8.52877e-01 -1.23141e+00\n",
    "1.21991e+00 1.37435e+00 1.41212e+00 1.33002e+00 1.13501e+00 8.43653e-01 4.80679e-01 7.69009e-02 -3.33406e-01 -7.15410e-01\n",
    "1.41420e+00 1.39350e+00 1.33002e+00 1.22570e+00 1.08376e+00 9.08548e-01 7.05442e-01 4.80679e-01 2.41160e-01 -5.76364e-03\n",
    "1.22570e+00 1.18238e+00 1.13501e+00 1.08376e+00 1.02881e+00 9.70337e-01 9.08548e-01 8.43653e-01 7.75874e-01 7.05442e-01\n",
    "7.05442e-01 7.75874e-01 8.43653e-01 9.08548e-01 9.70337e-01 1.02881e+00 1.08376e+00 1.13501e+00 1.18238e+00 1.22570e+00\n",
    "-5.76364e-03 2.41160e-01 4.80679e-01 7.05442e-01 9.08548e-01 1.08376e+00 1.22570e+00 1.33002e+00 1.39350e+00 1.41420e+00\n",
    "-7.15410e-01 -3.33406e-01 7.69009e-02 4.80679e-01 8.43653e-01 1.13501e+00 1.33002e+00 1.41212e+00 1.37435e+00 1.21991e+00\n",
    "-1.23141e+00 -8.52877e-01 -3.33406e-01 2.41160e-01 7.75874e-01 1.18238e+00 1.39350e+00 1.37435e+00 1.12810e+00 6.95428e-01\n",
    "-1.41411e+00 -1.23141e+00 -7.15410e-01 -5.76364e-03 7.05442e-01 1.22570e+00 1.41420e+00 1.21991e+00 6.95428e-01 -1.72905e-02\n",
    "-1.21404e+00 -1.40646e+00 -1.03668e+00 -2.52510e-01 6.32599e-01 1.26484e+00 1.39149e+00 9.61919e-01 1.47843e-01 -7.25329e-01\n",
    "-6.85368e-01 -1.34910e+00 -1.26995e+00 -4.91504e-01 5.57592e-01 1.29965e+00 1.32606e+00 6.22268e-01 -4.24172e-01 -1.23704e+00\n",
    "2.88163e-02 -1.06881e+00 -1.39542e+00 -7.15410e-01 4.80679e-01 1.33002e+00 1.21991e+00 2.29793e-01 -9.26094e-01 -1.41392e+00\n",
    "7.35201e-01 -6.11896e-01 -1.40243e+00 -9.17352e-01 4.02123e-01 1.35584e+00 1.07632e+00 -1.82188e-01 -1.27498e+00 -1.20809e+00\n",
    "1.24259e+00 -5.38712e-02 -1.29039e+00 -1.09113e+00 3.22192e-01 1.37702e+00 8.99684e-01 -5.78704e-01 -1.41319e+00 -6.75262e-01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Load files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "x = np.loadtxt('x.dat')\n",
    "y = np.loadtxt('y.dat')\n",
    "z = np.loadtxt('z.dat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Could also save to `txt`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savetxt('x.dat',x,fmt='%.6e')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5.000000e+00\r\n",
      "-3.888889e+00\r\n",
      "-2.777778e+00\r\n",
      "-1.666667e+00\r\n",
      "-5.555556e-01\r\n",
      "5.555556e-01\r\n",
      "1.666667e+00\r\n",
      "2.777778e+00\r\n",
      "3.888889e+00\r\n",
      "5.000000e+00\r\n"
     ]
    }
   ],
   "source": [
    "%cat x.dat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plot Contour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MiVB81JxFFVtSxcRurERXVxeAxM62ubBx40Y0NTXB6/Xi5ptvhslkwtSpU/Hp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dd0c9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots()\n",
    "c=ax.contourf(x,y,z,cmap='viridis')\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "cb=plt.colorbar(c)\n",
    "cb.set_label('z');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Import Fortran Routines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a toy fortran code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting fortrantest.f90\n"
     ]
    }
   ],
   "source": [
    "%%file fortrantest.f90\n",
    "subroutine test_fortran(N,a,b)  \n",
    "    implicit none  \n",
    "    integer, intent(in) :: N  \n",
    "    integer, intent(in) :: a  \n",
    "    doubleprecision, intent(inout) :: b(N,N)\n",
    "\n",
    "    b = a*b\n",
    "\n",
    "end subroutine test_fortran\n",
    "    \n",
    "doubleprecision function square(a)\n",
    "    implicit none\n",
    "    doubleprecision, intent(in) :: a\n",
    "    square = a**2\n",
    "end function square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[39mrunning build\u001b[0m\n",
      "\u001b[39mrunning config_cc\u001b[0m\n",
      "\u001b[39munifing config_cc, config, build_clib, build_ext, build commands --compiler options\u001b[0m\n",
      "\u001b[39mrunning config_fc\u001b[0m\n",
      "\u001b[39munifing config_fc, config, build_clib, build_ext, build commands --fcompiler options\u001b[0m\n",
      "\u001b[39mrunning build_src\u001b[0m\n",
      "\u001b[39mbuild_src\u001b[0m\n",
      "\u001b[39mbuilding extension \"my_fortran_routine\" sources\u001b[0m\n",
      "\u001b[39mf2py options: []\u001b[0m\n",
      "\u001b[39mf2py:> /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/my_fortran_routinemodule.c\u001b[0m\n",
      "\u001b[39mcreating /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6\u001b[0m\n",
      "Reading fortran codes...\n",
      "\tReading file 'fortrantest.f90' (format:free)\n",
      "Post-processing...\n",
      "\tBlock: my_fortran_routine\n",
      "\t\t\tBlock: test_fortran\n",
      "\t\t\tBlock: square\n",
      "Post-processing (stage 2)...\n",
      "Building modules...\n",
      "\tBuilding module \"my_fortran_routine\"...\n",
      "\t\tConstructing wrapper function \"test_fortran\"...\n",
      "\t\t  test_fortran(a,b,[n])\n",
      "\t\tCreating wrapper for Fortran function \"square\"(\"square\")...\n",
      "\t\tConstructing wrapper function \"square\"...\n",
      "\t\t  square = square(a)\n",
      "\tWrote C/API module \"my_fortran_routine\" to file \"/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/my_fortran_routinemodule.c\"\n",
      "\tFortran 77 wrappers are saved to \"/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/my_fortran_routine-f2pywrappers.f\"\n",
      "\u001b[39m  adding '/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/fortranobject.c' to sources.\u001b[0m\n",
      "\u001b[39m  adding '/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6' to include_dirs.\u001b[0m\n",
      "\u001b[39mcopying /Users/birnstiel/anaconda3/lib/python3.6/site-packages/numpy/f2py/src/fortranobject.c -> /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6\u001b[0m\n",
      "\u001b[39mcopying /Users/birnstiel/anaconda3/lib/python3.6/site-packages/numpy/f2py/src/fortranobject.h -> /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6\u001b[0m\n",
      "\u001b[39m  adding '/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/my_fortran_routine-f2pywrappers.f' to sources.\u001b[0m\n",
      "\u001b[39mbuild_src: building npy-pkg config files\u001b[0m\n",
      "\u001b[39mrunning build_ext\u001b[0m\n",
      "\u001b[39mcustomize UnixCCompiler\u001b[0m\n",
      "\u001b[39mcustomize UnixCCompiler using build_ext\u001b[0m\n",
      "\u001b[39mcustomize Gnu95FCompiler\u001b[0m\n",
      "\u001b[39mFound executable /usr/local/bin/gfortran\u001b[0m\n",
      "\u001b[39mcustomize Gnu95FCompiler\u001b[0m\n",
      "\u001b[39mcustomize Gnu95FCompiler using build_ext\u001b[0m\n",
      "\u001b[39mbuilding 'my_fortran_routine' extension\u001b[0m\n",
      "\u001b[39mcompiling C sources\u001b[0m\n",
      "\u001b[39mC compiler: gcc -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/Users/birnstiel/anaconda3/include -arch x86_64 -I/Users/birnstiel/anaconda3/include -arch x86_64\n",
      "\u001b[0m\n",
      "\u001b[39mcreating /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/var\u001b[0m\n",
      "\u001b[39mcreating /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/var/folders\u001b[0m\n",
      "\u001b[39mcreating /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/var/folders/r_\u001b[0m\n",
      "\u001b[39mcreating /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn\u001b[0m\n",
      "\u001b[39mcreating /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T\u001b[0m\n",
      "\u001b[39mcreating /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf\u001b[0m\n",
      "\u001b[39mcreating /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6\u001b[0m\n",
      "\u001b[39mcompile options: '-I/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6 -I/Users/birnstiel/anaconda3/lib/python3.6/site-packages/numpy/core/include -I/Users/birnstiel/anaconda3/include/python3.6m -c'\u001b[0m\n",
      "\u001b[39mgcc: /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/my_fortran_routinemodule.c\u001b[0m\n",
      "In file included from /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/my_fortran_routinemodule.c:19:\n",
      "In file included from /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/fortranobject.h:13:\n",
      "In file included from /Users/birnstiel/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/arrayobject.h:4:\n",
      "In file included from /Users/birnstiel/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/ndarrayobject.h:18:\n",
      "In file included from /Users/birnstiel/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/ndarraytypes.h:1809:\n",
      "/Users/birnstiel/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: \"Using deprecated NumPy API, disable it by \"          \"#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-W#warnings]\n",
      "#warning \"Using deprecated NumPy API, disable it by \" \\\n",
      " ^\n",
      "/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/my_fortran_routinemodule.c:147:12: warning: unused function 'f2py_size' [-Wunused-function]\n",
      "static int f2py_size(PyArrayObject* var, ...)\n",
      "           ^\n",
      "2 warnings generated.\n",
      "\u001b[39mgcc: /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/fortranobject.c\u001b[0m\n",
      "In file included from /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/fortranobject.c:2:\n",
      "In file included from /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/fortranobject.h:13:\n",
      "In file included from /Users/birnstiel/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/arrayobject.h:4:\n",
      "In file included from /Users/birnstiel/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/ndarrayobject.h:18:\n",
      "In file included from /Users/birnstiel/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/ndarraytypes.h:1809:\n",
      "/Users/birnstiel/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: \"Using deprecated NumPy API, disable it by \"          \"#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-W#warnings]\n",
      "#warning \"Using deprecated NumPy API, disable it by \" \\\n",
      " ^\n",
      "/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/fortranobject.c:138:18: warning: comparison of integers of different signs: 'Py_ssize_t' (aka 'long') and 'unsigned long' [-Wsign-compare]\n",
      "        if (size < sizeof(notalloc)) {\n",
      "            ~~~~ ^ ~~~~~~~~~~~~~~~~\n",
      "2 warnings generated.\n",
      "\u001b[39mcompiling Fortran sources\u001b[0m\n",
      "\u001b[39mFortran f77 compiler: /usr/local/bin/gfortran -Wall -g -ffixed-form -fno-second-underscore -m64 -fPIC -O3 -funroll-loops\n",
      "Fortran f90 compiler: /usr/local/bin/gfortran -Wall -g -fno-second-underscore -m64 -fPIC -O3 -funroll-loops\n",
      "Fortran fix compiler: /usr/local/bin/gfortran -Wall -g -ffixed-form -fno-second-underscore -Wall -g -fno-second-underscore -m64 -fPIC -O3 -funroll-loops\u001b[0m\n",
      "\u001b[39mcompile options: '-I/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6 -I/Users/birnstiel/anaconda3/lib/python3.6/site-packages/numpy/core/include -I/Users/birnstiel/anaconda3/include/python3.6m -c'\u001b[0m\n",
      "\u001b[39mgfortran:f90: fortrantest.f90\u001b[0m\n",
      "\u001b[39mgfortran:f77: /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/my_fortran_routine-f2pywrappers.f\u001b[0m\n",
      "\u001b[39m/usr/local/bin/gfortran -Wall -g -m64 -Wall -g -undefined dynamic_lookup -bundle /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/my_fortran_routinemodule.o /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/fortranobject.o /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/fortrantest.o /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf/src.macosx-10.7-x86_64-3.6/my_fortran_routine-f2pywrappers.o -L/usr/local/Cellar/gcc/7.2.0/lib/gcc/7/gcc/x86_64-apple-darwin17.0.0/7.2.0 -L/Users/birnstiel/anaconda3/lib -lgfortran -o ./my_fortran_routine.cpython-36m-darwin.so\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing build directory /var/folders/r_/b16rpth9643dx5csqdw29wn40000gn/T/tmpuf7vicjf\r\n"
     ]
    }
   ],
   "source": [
    "!f2py -c fortrantest.f90 -m my_fortran_routine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Importing & Calling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.  3.  3.]\n",
      " [ 3.  3.  3.]\n",
      " [ 3.  3.  3.]]\n"
     ]
    }
   ],
   "source": [
    "import my_fortran_routine\n",
    "a=3\n",
    "b=np.asfortranarray(np.ones([3,3]))\n",
    "my_fortran_routine.test_fortran(a,b)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_fortran_routine.square(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Open Notebook as `reveal.js` Slides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook jupyter-intro.ipynb to slides\n",
      "[NbConvertApp] Writing 287603 bytes to jupyter-intro.slides.html\n",
      "[NbConvertApp] Redirecting reveal.js requests to https://cdnjs.cloudflare.com/ajax/libs/reveal.js/3.5.0\n",
      "Serving your slides at http://127.0.0.1:8000/jupyter-intro.slides.html\n",
      "Use Control-C to stop this server\n",
      "WARNING:tornado.access:404 GET /custom.css (127.0.0.1) 1.80ms\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbconvert jupyter-intro.ipynb --to slides --post serve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Thanks for your attention!"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}