{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Jupyter and multiple Languages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: This has been written as [a notebook](https://github.com/carreau/posts) so you can download it to run it yourself using [jupyter](https://jupyter.org) or [nteract](https://nteract.io/), view it [on GitHub](https://github.com/Carreau/posts/blob/master/23-Cross-Language-Integration.ipynb), or [on nbviewer](https://nbviewer.jupyter.org/github/Carreau/posts/blob/master/23-Cross-Language-Integration.ipynb) if you prefer the classical notebook rendering. It originally should have appeared on [my blog](http://carreau.github.io/posts/23-Cross-Language-Integration.ipynb.html).\n", "\n", "I would be happy to know if you can get it to work on [binder](http://mybinder.org/).\n", "\n", "--- \n", "\n", "[UPDATE] \n", "\n", "Thanks to [Michael Pacer](https://github.com/michaelpacer) for copy-editting this. \n", "Also note that this notebook is basedaround multiple example that have been written by various people across many years.\n", "\n", "---\n", "\n", "An often requested feature for the [Jupyter Notebook](https://jupyter.org) is the ability to have multiple kernels, often in many languages, for a single notebook. \n", "\n", "While the request in spirit is a perfectly valid one, it is often a misunderstanding of what having a single kernel means. In particular having multiple language is often easier if you have a single process which handle the dispatching of various instructions to potentially multiple underlying languages. It is possible to do that in a _Single Kernel_ which does orchestrate dispatching instruction and moving data around.\n", "\n", "Whether the multiple languages that get orchestrated together are remote processes, or simply library calls or more complex mechanisms becomes an implementation detail. \n", "\n", "[Python](https://python.org) is known to be a good \"glue\" language, and over the year the [IPython kernel](https://ipython.org) have seen a growing number of extensions showing that dynamic cross language integration can be seamless form the point of view of the user.\n", "\n", "In the following we only scratch the surface of what is possible across a variety of languages. The approach shown here is one among many. The [Calysto](https://github.com/Calysto) organisation for example has several projects taking different approaches on the problem.\n", "\n", "In the following I will show a quick overview on how you can in single notebook interact with many languages, via subprocess call (Bash, Ruby), Common Foreign function interface (C, Rust, Fortran, ...), or even crazier approaches (Julia).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# IPython and cross language integration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The rest of this is mostly a demo on how cross-language integration works in a Jupyter notebook by using the features of the _Reference IPython Kernel implementation_. These features are completely handled in the kernel so need to be reimplemented on a per-kernel basis. Though they **also** work on pure terminal IPython, nbconvert or any other programmatic use of IPython.\n", "\n", "Most of what you will see here are _just_ thin wrappers around already existing libraries. These libraries (and their respective authors) do all the heavy lifting. I just show how seamless a cross language environment can be from the user point of view. The installation of these library might not be easy either and getting all these language to play together can be complex task. It is though becoming easier and easier.\n", "\n", "The term _just_ does not imply that the wrappers are simple, or easy to write. It indicate that the wrappers are far from being complete. What is shown here is completely doable using standard Python syntax and bit of manual work. SO what you'll see here is mostly _convenience_. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The good old example of Fibonacci" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Understanding the multiple languages themselves is not necessary; most of the code here should self explanatory and straightforward. We'll define many function that compute the nth `Fibonacci` number more or less efficiently. We'll define them either using the classic recursive implementation, or sometime using an unrolled optimized version. As a reminder the Fibonacci sequence is defines a the following:\n", "\n", "$$ F_n = \\begin{cases} 1 &\\mbox{if } n \\leq 2 \\\\ \n", "F_{n-1}+F_{n-2} & \\mbox{otherwise }\\end{cases}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fact that we calculate the Fibonacci sequence as little importance, except that the value of $F_n$ can grow _really_ fast in $O(e^n)$ if I remember correctly. And the recursive implementation will have a hard time getting beyond (n=100) as the number of call will be greater than $O(e^n)$ as well. Be careful especially if you calculate $F_{F_n}$ or more composition. Remembering that n=5 is stable via $F$ might be useful. \n", "\n", "Here are the first terms of the Fibonacci sequence:\n", "\n", " 1. 1\n", " 1. 1\n", " 1. 1+1 = 2\n", " 1. 2+1 = 3\n", " 1. 3+2 = 5\n", " 1. 5+3 = 8\n", " 1. 8+5 = 13\n", " ...\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic Python cross-language integration" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "sys.version_info(major=3, minor=5, micro=2, releaselevel='final', serial=0)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import sys\n", "sys.version_info" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python offer many facilities to call into other languages, whether we \"shell-out\" or use the C-API. The easiest for the end user is often to use [`subprocess`](https://docs.python.org/3/library/subprocess.html) , with the recently added [`run`](https://docs.python.org/3/library/subprocess.html#subprocess.run) command. Though it might be annoying to define all foreign code in strings and calling the subprocess run function manually." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is why in the following you will see 2 constructs:\n", "\n", "```\n", "optional_python_lhs = %language (language RHS expression)\n", "```\n", "\n", "As well as \n", "\n", "```\n", "%%language --cli like arguments\n", "A block:\n", " containing expressions and statement\n", "from another:\n", " language\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IPython define these which are called Magics. A single percent : `%something` for line magics, act only on the rest of the line, and cells magics (start with `%%`)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One example of line magic is `%timeit`, which runs the following statement multiple time to get statistics about runtime:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000 loops, best of 3: 34.9 µs per loop\n" ] } ], "source": [ "%timeit [x for x in range(1000)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Streamlining calling subprocess" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The IPython team special cased a couple of these for foreign languages, here _ruby_. We define the `fibonacci` function, compute the 15th value, print it to standard out from Ruby, and capture the value variable in the python `fib15`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%ruby --out fib15\n", "def fibonacci( n )\n", " return n if ( 0..1 ).include? n\n", " ( fibonacci( n - 1 ) + fibonacci( n - 2 ) )\n", "end\n", "puts fibonacci( 15 )" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'610\\n'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib15" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now from with Python we can do a crude parsing of the previous string output, and get the value of Fibonacci of 15." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "610" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "int(fib15.strip())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, that's somewhat useful, but not really that much. It's convenient for self contain code. You cannot pass variable in... or can't you ? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Send variables in " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calling subprocess can be quite cumbersome when working interactively, we saw above that `%%` cells-magics can be of help, but you might want to _shell out_ in the middle of a python function. Let's create a bunch of random file-names and fake some subprocess operations with it. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "import string\n", "\n", "def rand_names(k=10,l=10):\n", " for i in range(k):\n", " yield '_' + ''.join(random.choice(string.ascii_letters) for i in range(l))+'.o'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `!something` expression is – for the purpose of these demo – equivalent to `%sh something $variable` where `$variable` is looked up in `locals()` and replaced by it's `__repr__`" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "creating file _XiKZxtsLwX.o\n", "creating file _DKCjzvlTuF.o\n", "creating file _ShTGoJMxCp.o\n", "creating file _nbockcrTbT.o\n", "creating file _cZnVpuYsxJ.o\n", "creating file _UYxnHlwJwy.o\n", "creating file _fxoVMPQbJV.o\n", "creating file _wJJUbrPzpq.o\n", "creating file _ngMMBxaDkG.o\n", "creating file _uKbssAzHBP.o\n" ] } ], "source": [ "for f in rand_names():\n", " print('creating file',f)\n", " !touch $f" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_DKCjzvlTuF.o\r\n", "_ShTGoJMxCp.o\r\n", "_UYxnHlwJwy.o\r\n", "_XiKZxtsLwX.o\r\n", "_cZnVpuYsxJ.o\r\n", "_fxoVMPQbJV.o\r\n", "_nbockcrTbT.o\r\n", "_ngMMBxaDkG.o\r\n", "_uKbssAzHBP.o\r\n", "_wJJUbrPzpq.o\r\n" ] } ], "source": [ "ls -1 *.o" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can as well get values back using the `!` syntax" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['_DKCjzvlTuF.o',\n", " '_ShTGoJMxCp.o',\n", " '_UYxnHlwJwy.o',\n", " '_XiKZxtsLwX.o',\n", " '_cZnVpuYsxJ.o',\n", " '_fxoVMPQbJV.o',\n", " '_nbockcrTbT.o',\n", " '_ngMMBxaDkG.o',\n", " '_uKbssAzHBP.o',\n", " '_wJJUbrPzpq.o']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "files = !ls *.o\n", "files" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!rm -rf *.{o,c,so} Cargo.* src target" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(_Who said I was going to use [rust-lang.org](https://www.rust-lang.org/en-US/) later ?_)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ls: *.o: No such file or directory\r\n" ] } ], "source": [ "ls *.o" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, our directory is clean !" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Add some state" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, that was kind of cute, fire-up a subprocess, serialize, pipe data in as a string, pipe-data out as a string, kill subprocess... What about something less state-less, or more stateful?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's define the `fibonacci` function in python:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fib(n):\n", " \"\"\"\n", " A simple definition of fibonacci manually unrolled\n", " \"\"\"\n", " if n<2:\n", " return 1\n", " x,y = 1,1\n", " for i in range(n-2):\n", " x,y = y,x+y\n", " return y" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 1, 2, 3, 5, 8, 13, 21, 34]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[fib(i) for i in range(1,10)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Store the value from 1 to 30 in `Y`, and graph it." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdIAAAE1CAYAAABTIWEMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAMTQAADE0B0s6tTgAAIABJREFUeJzt3XuYJHV97/H3h91eRjbMRhDk4l6AFQM5KzMeL0E0C0Iy\nBp8knAHZqEGQoxwvJ5qD8gQmJ15yESWJ55A8RsBoFonJInG8JcooYDYCgtGdcZFjuC17URZBLjPL\n6jKd3e/5o36zWzQzszNTM91VPZ/X8/Sz3fWrrvp2TW1/un7162pFBGZmZjYzB7S6ADMzsypzkJqZ\nmRXgIDUzMyvAQWpmZlaAg9TMzKwAB6mZmVkBDlIzM7MCHKQ25yR9QNK35mC575P0kKQRSWdK+oSk\na3LtD0q6cLbX2yyNr8fak6Q3SdqS9uO3t7oem76FrS7Aqk3SDmDsqh4HAguAnwFK038jtc3qlT8k\nHQV8BHh5RGxIk786m+totYh4R6trsLklaQFwNfDmiOifheWdD/xpRCydwrx7gFMj4t+Krne+8xGp\nFRIRB0dEZ0R0AlcA/5Yej02/bY5WfSxZWH9/jpZv84CkWotLOBJ4DjA0S8sb+wBrTeQgtWaRpPen\nrtifSrpK0gG5xqMkfVbSjyQ9nO4/b4IFXQh8PT18UtJImr5W0mcaZl8m6WZJOyRtlPTrDcu6QNJd\nkp5M7W/OtS2XtEfSmyUNpa632yX9Um6ecyR9V9Ljkh6R9CVJKxrW8TpJ307zPCrpc7m2oyX9vaRt\nqYYNkrpS29+N83ryyz1X0g/S8x6V9PVc24GSPizpfkmPSfrXseWm9gWSrpC0XdJP0rzfkvT+htd+\nbO45q9O0/N9tbNs8mbbjmnHmP1vSPZKGJX1d0pG5eTok/WlqH0n1npdrP1PSHWnb3SPp9ybaHmn+\nlZJuSeu6W9JbUw3LUvv5aVu/U9KDwGNp+hJJ16S2RyT9i6Tjc211SSvT42VpmX+RW+9XJfWl+6dJ\n+ndJT6R9/VuSloxT6+nAf6SHG9Prf0Habrel5z6W9t+Tcs9bmup7LLffniLpVcAngKOU7e8jkt4w\n2fayWRIRvvk2KzfgT4Bbxpn+AWAU+F9kpxNeSPYGdn5qXwT8EPgo0AEcBFwLfH2Sda0GdgPKTfs7\n4DO5xw8CPwF+hexD44XALmB5aj8bGAZOJfskfzqwA/it1L4c2EMW2oenOm8Avplbx68Dq9L9Q4Av\nAbfl2s8g6+ruTa99EXBaausA7gH+FnhumvZLwNLxXk/D638O8DSwOrcNT821r011H5le+zvTtliS\n2v8Q2AS8CKgBf5z+Ru/PvfbdwLHjbPMD0uMLgM1Ad3r8yrQ9X5mbfw9wHfALwMHArcDf5Zb598Ad\nwPHp8RFAV7p/KvDk2OsCTgS2AG+YYJssIAumT5CdZjgSuD3VvCzNcz5QB64i28860vSvADcDh6W/\ny/8BtgIHpfbbgXel+29L67krPa4BTwEvTY9/xL59eyHwCuA5E9Q8tp2PyU07mWyfXQAsTrVuBhbm\nttnVQC09fiH79unzga1T/P+6B/jVVr9vtMOt5QX41j43Jg/S+xumfQ74eLrfC2xraD86/Uc/aoJ1\nPeNNPU0bL0ivaHjeHcAfpvs3An/Z0P5/ga+m+2NBekqu/UzgqUm2QXeqa3F6/BXgryaY9xzg0bE3\nyHHa9xekO4B3AIc2tB2S6l7ZMP1e4I25++/MtR1AFrTTCdLvA29tWMc1wDUN8x+da38ncHe6/7xU\nZ/cEr/FLZOf78tP6gG9MMP8pZCF5UG7a63h2kI4CB+bmOSLV8V9y0xamv8256fGHgC+l+zcAb0nt\nRwOvAX6ae+4DZB9Mxt13G2p+1nYeZ57npvp+OT3+dNo2J44zr4O0BTd37VqzPNTweCfZEQrASuCI\n1H33uKTHgR8APweWFVzvg+M8HhuIsZTsTS/v/oZ1BrC9oe7njHVvpm64m5R1WT8J/Gua7/D07zFk\nR53jWQFsjoj/nNpLyRUV8XPgtcCvAfdI+r6kd6fmlenf7+S26RPAUWRv/AAvILdtImIP2RHYdLwQ\n+FjDOn6H7Egwr3H7jf3dV5Bt33snWf7vNyz/D4DnTzD/0cDjEfGz3LTN48z3SEQ8nXu8NNWxaWxC\n+ptsYd++8HXgVEkHkgXn14BbgB6yXoebc8v7LeA44HuS7pX0wXx3+P5IWiXpy8pOczyZ6gr27VPv\nJdtP+5WdBvmUpMMnWt4kfC51lnjUrpXBw2SB8sI5WPYx4zz+Srq/jewNL28lUwwUZQNVvgJ8kKw7\n+GfpPOT3yLqKIXsjP36CRWwGVkhaOMMwvQ24LdWyGrhR0t3AfWRvki+OiB9N8PQfkds26Y0+P9Jz\nR3oNi3PTjuaZtgMfiIi/n27tyeb07/HA4DjtDwP/EBF/OsXl/Rg4RNLiiNiZpq0YZ749DY+3pX+P\nA+6CvaNpl7FvX7iDbJv+PvCjiHhY2TnpnvS8q8YWFhF3A29Ky3kxWQhvAz41xddxA9kI9N+NiBFJ\nvwg8TtqnIuIJsjB9bzrf/FngL4HzxnltE4qIBVOd1ybnI1Irg36gJumPJXUCSDpc0rmzsOw3S3qF\nssE1FwAnkb3xQHZu8sJ0VHmApNeQddldnXu+mNgisvNpT6YQPQpofNO/Evjvkv6bpJqyQUCvSW3/\nDDwB/I2kQ5U5QdJUvrrwfGUDncYGsQyTvYnWI2Ir8EXg47lBNgdLeq2ksaO5a8neiF8kaRFZ9/tz\nx5YfEY+THQm9NW27Y8nevBtf2x9Jemmq/cB0/yX7qz+t46fAP6Y6xwb2HCGpO7f835P0mlTDAkm/\nLOnVEyzyDrIehiskPSf9PS6bQh0PkwXXX6b97jlkI9CfBv4lzbObrLfhUvYNdBsg6xXoGpuW/sYX\naN9AuR3Af6bbRBr3sU5gBNgh6RCy87V7jx4lrZF0rCSRHeE/nVv+w8Dz0vOsSRyk1nIR8RTZAItl\nwF2pO+tWYKI3zCkvmuxI4cNkg1beC5wVEZvTev8pTfsbskC7Enh3RHypYRkT1b0TeCtZmIyQvel+\nrmGebwBvIHtDf4TsyOSi1LaLrJvwF8iOhJ4gG0gylTdBAW8HHkjr/hzQF/u+E/hGsiPjb0gaJhvM\n9Vb2vWl/lOwDzPpU00LgzoZ1vBk4LdX1GeCTDa/tr8iOxq8iGzy2jSyADppC/WPelmr4mrLvJN9K\nNqiI9Hc4j+x84yNk53A/CRw63oJS2P1mev5PgG+w70PTrv3UcR7ZEfIGsqPQE4Azcke2kIVlZ/qX\ndLT/Y+C+9OFlzDnAD9Lr+SbpXPck627cxy4EziUL4dvJPnDlnUTWrTxC1vvwBPC+1HYL2fnTe1N3\n+O9MtNI0svdXJ6nLpkjppLOZzXPKrj71jYj441bXMlsknUXWPTydcDebllIdkUpaJOmv0wn67yt9\nh07Zd8NuU/Y9sjslnZh7TjPaTmjWNjCzmZP0K5JemO6/iGy07Wcnf5ZZMaUKUrLupj0RcXxEnMS+\n7oqrgasi4kVkXUdrc89pRtu1s/HizEquHbqnjgS+Lukpsq7d24GLW1uStbvSdO1KOohsFODR6ZzZ\n2PTDyM4DHJKG6CNpO9l3xnY0sy0i9g6PNzMzg3J9/eU4siHefyhp7GowHyIbJLJ9LNSSrWQDU0aa\n3LY3SNOIuaPIgtfMzKrtYOChmMHRZZmCtEZ2lY8fRMRlyr6P9w2yK5M0Dg+f7CsJzWo7iuy7eGZm\n1h5eQDYSe1rKFKRbyC6V9Q8AETGk7KLSy8muenNA7ihxKdlR4g7gyCa25e0A2LZtG52dnbO0CZqn\nr6+PD3/4w60uY9qqWjdUt/aq1g3Vrb2qdUM1ax8ZGWHp0qUwwx7G0gRpRDwm6WayLzh/TdIxZFcl\nuZXsu13nAddKOofsuqybACQ1ta1RZ2dnJYN00aJFrrvJqlp7VeuG6tZe1bqh2rXPVGmCNHkH8ClJ\nHyU7Or0oIrYr+9X4tcp+pmiY7OozY5rdZmZmtlepgjQiHiS70kvj9HvJfqJpvOc0ta1d9PT0tLqE\nGalq3VDd2qtaN1S39qrWDdWufaZK8/WXqlF2Tdjh4eHhedeNYWbWTkZGRliyZAlkv9c7Mt3nl+2C\nDGZmZpXiIDUzMyvAQWpmZlaAg9TMzKwAB6mZmVkBDlIzM7MCHKRmZmYFOEjNzMwKcJCamZkV4CA1\nMzMrwEFqZmZWgIPUzMysAAepmZlZAQ5SMzOzAhykZmZmBThIzczMCnCQmpmZFeAgNTMzK8BBamZm\nVoCD1MzMrAAHqZmZWQELW12AmZmVW71ep7+/n6GhIbq6uujt7aVWq7W6rNJQRLS6hkqS1AkMDw8P\n09nZ2epyzMzmRL1eZ/XqHgYHt1Cvn06tdjPd3ctZv36gbcJ0ZGSEJUuWACyJiJHpPt9du2ZmNqH+\n/n4GB7ewa9dGdu++hl27NjI4uJn+/v5Wl1YaDlIzM5vQ0NAQ9frpwOI0ZTH1+hkMDQ21sqxSKVWQ\nStos6YeSBiVtkPT6NH2lpNsk3SPpTkkn5p7TjLYTmrUNzMzKpKuri1rtZmBnmrKTWu0murq6WllW\nqZQqSIE9wLkR0R0RL4mIG9L0q4GrIuJFwBXA2txzmtF27ay8OjOziunt7aW7ezkdHatYsOAiOjpW\n0d29gt7e3laXVhqlGmwk6UHgtyNiY27aYcB9wCERsSdN2w6cAuxoZltEbMrV5cFGZjYvtPuo3aKD\njcr49ZfPSBLwHeAyYCmwfSzUkq3AMmCkyW2bMDObZ2q1GmvWrGHNmjWtLqWUyhakr46IH0laAPwZ\nWZfqHwFqmK/xccva+vr6WLRoEQA9PT309PRMshgzMyuDgYEBBgYGABgdHS20rFJ17eZJOgK4BzgO\neAB4rrt2zcxstrXN90glHSRpSW7SG4ENEfFT4HvAeWm+c4BtEbEpIh4FNjSrbY43gZmZVVBpjkgl\nHQN8nizcRXY+8j0RsVXS8WSjag8FhoG3RMTd6XlNbcvV6yNSM7M2UPSItDRBWjUOUjOz9tA2Xbtm\nZmZV5CA1MzMrwEFqZmZWgIPUzMysAAepmZlZAQ5SMzOzAhykZmZmBThIzczMCnCQmpmZFeAgNTMz\nK8BBamZmVoCD1MzMrAAHqZmZWQEOUjMzswIcpGZmZgU4SM3MzApwkJqZmRXgIDUzMyvAQWpmZlaA\ng9TMzKwAB6mZmVkBDlIzM7MCHKRmZmYFOEjNzMwKcJCamZkV4CA1MzMroHRBKuktkvZI+q30+DBJ\nX5N0r6SNkl6dm7epbWZmZo1KFaSSlgFvBb6dm/wR4NsRcTxwIfAPkha0qM3MzOwZShOkkgR8Cvif\nwGiu6VzgKoCI+C7wELC6iW0/zrWZmZk9Q2mCFLgY+FZEDI5NkHQIsDAiHsnNtxlY1sS2LcCyoi/O\nzMza08JWFwAg6UTgHOBV4zRH4+wtbDMzM3uGUgQp8KtkR333pS7eI4BrgA8CuyUdnjtKXA5siYjH\nJTWrbetEhff19bFo0SIAenp66OnpmY3tYWZmc2hgYICBgQEARkdH9zP35BTReADWepK+CXwsIr4i\n6dNkIfchSS8D+oEVEbG72W0NNXYCw8PDw3R2djZpy5iZ2WwbGRlhyZIlAEsiYmS6zy/LEWmjYF+X\n6qXAdZLuBZ4G3pQLtWa3mZmZPUMpj0irwEekZmbtoegRaZlG7ZqZmVWOg9TMzKwAB6mZmVkBDlIz\nM7MCHKRmZmYFOEjNzMwKcJCamZkV4CA1MzMrwEFqZmZWgIPUzMysAAepmZlZAQ5SMzOzAhykZmZm\nBThIzczMCnCQmpmZFeAgNTMzK8BBamZmVoCD1MzMrAAHqZmZWQEOUjMzswIcpGZmZgU4SM3MzApY\n2OoCzMysNer1Ov39/QwNDdHV1UVvby+1Wq3VZVWOIqLVNVSSpE5geHh4mM7OzlaXY2Y2LfV6ndWr\nexgc3EK9fjq12s10dy9n/fqBeRemIyMjLFmyBGBJRIxM9/nu2jUzm4f6+/sZHNzCrl0b2b37Gnbt\n2sjg4Gb6+/tbXVrlOEjNzOahoaEh6vXTgcVpymLq9TMYGhpqZVmV5CA1M5uHurq6qNVuBnamKTup\n1W6iq6urlWVVUqmCVNKApCFJg5LWS+pK01dKuk3SPZLulHRi7jnNaDuhWdvAzKwZent76e5eTkfH\nKhYsuIiOjlV0d6+gt7e31aVVTqkGG0nqHDvRK+ks4IMR0SXpZmBtRFwn6WzgDyLi5Wm+prbla8WD\njcyswjxqN1N0sFGpgjRP0gXAu4Ee4H7guRGxJ7VtB04BdgD3AYc0oy0iNuXqc5CambWBokFauu+R\nSroWOA0I4ExgKfDQWKglW4FlwAiwvYltmzAzM8spXZBGxPkAks4DrgD+CFDDbI2PW9bW19fHokWL\nAOjp6aGnp2eSxZiZWRkMDAwwMDAAwOjoaKFllbZrF0DSz4DluGvXzMzmSNtckEHSEklH5h6fBfw0\nIh4Fvgecl6afA2yLiE2pbUOz2uZ+K5iZWdWU5ohU0jLgBqCD7PzoI8D7ImKjpOOBtcChwDDwloi4\nOz2vqW25en1EambWBtp21G7ZOUjNzNpD23TtmpmZVdG0g1TSorkoxMzMrIqmFKSSTpW0TtKjwC5J\no5I2SOqTdMgc12hmZlZa+w1SSV8lu8JQP/AS4ECyQThvJ/se6i2SXjuXRZqZmZXVfgcbSVoVEXdN\n0l4DjomIe2e7uDLzYCMzs/Yw55cInCxEU3sdmFchamZmNmbKlwiU9IvA/wCOyz8vIi6cg7rMzMwq\nYTrX2v0n4FHg28DuuSnHzMysWqYTpEdGxBlzVomZmVkFTed7pA+k7l0zMzNLpnNE+jNgg6QbgV1j\nEyPi4lmvyszMrCKmE6Q/TDczMzNLphykEfGhuSzEzMysiqZyZaNz99N+lKRXzl5JZmZm1TGVI9KX\nSXo/8HngTuAnZL8Z+iLgtcAKsu+XmpmZzTtT+j1SSUcBFwKnAi8gG3i0key7pf8S8/BHTX2JQDOz\n9uAf9m4RB6mZWXuY82vt5kk6GTgdCOCmiLhzuis0MzNrJ1O+IIOk9wHryH5C7XnA9ZL8HVIzM5vX\npnNEehHwkoh4DEDSnwB3AB+bi8LMzMyqYDqXCBwZC1GAiHgcmHZfspmZWTvZ7xGppBenu7dIWgt8\nKj2+ALhpbsoyMzOrhql07X6p4fHq3P0ALp29cszMzKplv0EaEcc0oxAzM7MqmkrX7uKI2Jm+N/ks\nM/nOjZmZWbuYStfuk0At/RuAcm0BLJiDuszMzCphKqN2f5D+/XZELIiIA3K3WQtRSQdK+oKk/5A0\nKGlA0nGp7TBJX5N0r6SNkl6de15T28zMzPKmckR6oKQ1wBGSfpNnHpESEV+exXqujogbASS9C/hb\n4DTgo2RB/huSXgp8QdKKiNgNfKTJbWZmZntNJUgvBd4OHA40XskogFkJ0oh4GrgxN+kO4L3p/uuB\n49J835X0ENno4VuAc5vQ9uNcm5mZ2V5TGbX7ZeDLkq6MiPc0oaYx7wa+KOkQYGFEPJJr2wwsa2Lb\nFmDZ7LwsM7O5V6/X6e/vZ2hoiK6uLnp7e6nVaq0uqy1N+RKBzQxRSX3ASrLfOT2I7Mj3GbPkS2ty\nm5lZqdXrdVav7mFwcAv1+unUan1ceeXVrF8/4DCdA9P69ZdmSBfHPws4PSJ2Absk7ZZ0eO4ocTmw\nJSIeb2Lb1vHq7evrY9GiRQD09PTQ09Mzm5vDzGza+vv7GRzcwq5dG4HF7N69k8HBVfT397NmzZpW\nl1cKAwMDDAwMADA6OlpoWaX6PdL0azJvJAvR4dz0T5OF3IckvQzoB1ZExO5mt+Vq8u+RmlkpXXbZ\nZfz5nz/G7t3X7J22YMFFXHLJoVx++eUtrKycmvp7pHNJ0tHAXwAPAN+UJGBXRJxMNuDpOkn3Ak8D\nb8qFWrPbzMxKrauri1qtj927dwKLgZ3UajfR1eUQnQulOiKtEh+RmllZ7TtHupl6/QxqtZvo7l7h\nc6QTKHpE6iCdIQepmZWZR+1OnYO0RRykZmbtoWiQTueHvc3MzKyBg9TMzKwAB6mZmVkBDlIzM7MC\nHKRmZmYFOEjNzMwKcJCamZkV4CA1MzMrwEFqZmZWgIPUzMysAAepmZlZAQ5SMzOzAhykZmZmBThI\nzczMCnCQmpmZFeAgNTMzK8BBamZmVoCD1MzMrAAHqZmZWQELW12AmZlNT71ep7+/n6GhIbq6uujt\n7aVWq7W6rHlLEdHqGipJUicwPDw8TGdnZ6vLMbN5ol6vs3p1D4ODW6jXT6dWu5nu7uWsXz/gMJ2h\nkZERlixZArAkIkam+3x37ZqZVUh/fz+Dg1vYtWsju3dfw65dGxkc3Ex/f3+rS5u3HKRmZhUyNDRE\nvX46sDhNWUy9fgZDQ0OtLGtec5CamVVIV1cXtdrNwM40ZSe12k10dXW1sqx5zUFqZlYhvb29dHcv\np6NjFQsWXERHxyq6u1fQ29vb6tLmrdIEqaQrJT0oaY+kF+emr5R0m6R7JN0p6cQmt53QjNdvZjYV\ntVqN9esHWLv2ci655FDWrr3cA41arDSjdiW9CtgE3AqcFREb0/SbgbURcZ2ks4E/iIiXt6KtoV6P\n2jUzawNFR+2WJkjHSHoQ+O2I2CjpMOA+4JCI2JPatwOnADua2RYRmxrqdJCambWBokFa9gsyLAW2\nj4VashVYBow0ue0ZQWpmZgblD9LxqERt9PX1sWjRIgB6enro6emZbHYzMyuBgYEBBgYGABgdHS20\nLHftTrHNXbtmZu2pra9sFBGPAhuA8wAknQNsi4hNzW5r2os2M7NKKc0RqaSrgNcBzwceA3ZExPGS\njgfWAocCw8BbIuLu9JymtjXU6yNSM7M20HajdqvCQWpm1h7aumvXzMys7Ko4atfMrC35d0aryV27\nM+SuXTObTf6d0dZx166ZWRvw74xWl4PUzKwE/Duj1eUgNTMrAf/OaHU5SM3MSsC/M1pdHmw0Qx5s\nZGazzaN2W8MXZGgRB6mZWXvwqF0zM7MWcpCamZkV4CsbmZk1gc9/ti+fI50hnyM1s6nyVYvKzedI\nzcxKzlctam8OUjOzOearFrU3B6mZ2RzzVYvam4PUzGyO+apF7c2DjWbIg43MbMxURuR61G55+cpG\nLeIgNTPwiNx24FG7ZmYt5BG55iA1MyvAI3LNQWpmNoF6vc7111/PZZddxvXXX0+9Xn/WPB6Raz5H\nOkM+R2rW3qZ67nPffJup18+gVruJ7u4VPkdaIUXPkfpau2Zm48if+4TF7N69k8HBVfT397NmzZq9\n89VqNdavH8iNyL3cI3LnGQepmc1L+/s6ymTnPvNBClmYrlmz5lnTbX5wkJpZ29lfSD6727aPK6+8\n+hndsdm5zz52795JFqZj5z4vb82LstLyOdIJSFoJXAs8D3gSuCAifphr9zlSsxaYfkg++9zm9ddf\nzwUX9O3ttoWddHSsYu3ay/ceVfrc5/zhc6Rz52rgqoi4TtLZZKH68hbXZLZfs3WVnaleiaeZ65vK\nkeRUzm1OpdvW5z5tyiLCt4YbcBjZUegBuWnbgWNzjzuBGB4ejnYzOjoa69ati0svvTTWrVsXo6Oj\nM5pnNpfl9U19npNPPi06Oo6NBQveFh0dx8bJJ5/2jHlna55WrG/dunXR0XFswFMBEfBUdHQcE+vW\nrds7z6WXXhoLFrwttWe3BQveFpdeeum0lmPzx/DwcAABdMZMMmMmT2r3G/AS4IcN0+4ETs09bssg\nrfKbbDuvbzaDZrbmacX6Zisk923PY9L2PGbc7Wnzg4O0eUH6nfkQpFV+k23n9c1m0MzWPK1Y32yG\n5FR7Haz9FQ1SX9lofNuAIyXlt89SYGvjjH19fVx88cVcfPHFDAwMNK3AuTKVy51N9ZJos7Usr2/q\nNU3lKjuzNU8r1jeVnyMbO7e5du3lXHLJoaxde/m4A4TGvrJy+eXZACOf+5xfBgYG9r539/X1FVvY\nTNJ3PtyAW4Dz0/1zgO80tPuItERHbO2+vqnWNJWjsdmapxXrG5vXR5I2m9y1O3dBejxwO3APWbfu\nLze0t2WQVvlNtp3XN9tBU9UBV2ZzoWiQ+nukM9TO3yOt6lcj2n19/mFos7nhH/ZukXYOUjOz+cQ/\n7G1mZtZCDlIzM7MCHKRmZmYFOEjNzMwKcJCamZkV4CA1MzMrwEFqZmZWgIPUzMysAAepmZlZAQ5S\nMzOzAhykZmZmBThIzczMCnCQmpmZFeAgNTMzK8BBamZmVoCD1MzMrAAHqZmZWQEOUjMzswIcpGZm\nZgU4SM3MzApwkJqZmRXgIDUzMyvAQWpmZlaAg9TMzKwAB6mZmVkBDlIzM7MCShGkks6U9O+Sdkn6\nWEObJP21pPsl3SvpXa1qaycDAwOtLmFGqlo3VLf2qtYN1a29qnVDtWufqVIEKXAvcCFwxTht5wG/\nFBErgVcAl0g6oUVtbaOqO3tV64bq1l7VuqG6tVe1bqh27TNViiCNiPsj4i5g9zjN5wKfTPM9AVwP\nvKFFbWZmZs+wsNUFTMEyYEvu8WayI8VWtD3LyMjIZLWX1ujoaCVrr2rdUN3aq1o3VLf2qtYN1ay9\naL1NCVJJtwMrGycDAXRHxI+ns7iStB0MsHTp0kmeWm4f//jHW13CjFS1bqhu7VWtG6pbe1XrhkrX\nfjAw7VRtSpBGxCsLPH0rsBy4Mz1enqa1oi3vIeAFwI4ZvCYzMyuXg8ne16etjF27jUeANwBvk/RP\nwBJgDXBmk9te11hkRAQwnSNpMzMrrxn375YiSCW9BriW7BOBJJ0NvDMi/hm4DngpcB+wB/iLiPh/\n6anNart7Tl64mZlVnrIDKzMzM5uJUnz9pcokfUDSI5I2SBqUdF2ra5qMpJWSbpN0j6Q7q/QdWUmb\nJf0wbecNkl7f6prGI+lKSQ9K2iPpxbnppd/2k9Re6m0v6UBJX5D0H6nGAUnHpbbDJH0tXWBlo6RX\nt7reMRPUfWxq+1dJm9L23iDpPa2ut1GqdyjVvl5SV5pe6n19nLpPStNntp9HhG8FbsAHgI+1uo5p\n1HszcF7ii6agAAAEDElEQVS6fzbwnVbXNI3aNwGrWl3HFOp8FXBUqvfFVdr2k9Re6m0PHAi8Nvf4\nXcA30/1PA+9P918KbAMWtLrmSeq+Jd3/JvCbra5xP/V35u6fBQyl+6Xe1yep+8GZ7Oc+Ip0dk319\npjQkHQb8V+CzABHxeWDp2CfgChAV2NYRcWtEPESu1qps+/FqT0q97SPi6Yi4MTfpDrIR9wCvB65K\n832XbJDg6uZWOL4J6l6Re1zabQ4QEfkBOr8I7En7+ksp8b4+Tt35iwFNe5s7SGfHOakr4CZJp7a6\nmEksBbZHxJ7ctK1kF6Gois9I+r6kT0p6XquLmQZv++Z6N/BFSYcACyPikVzbFsq73d8NfDH3+CNp\nm/+jpGNaVdRkJF0raSvwIbJLrC4FHir7vt5Q95tzTdPezx2k+yHp9nQONH97NP17NPAJYEVEdAPv\nB66XVKWrNJT6E2+DV0dEF/AS4DGykd5V5m0/ByT1kV0Api9NahxRWcrtPk7dvxsRJ0bEScCtwD+3\nrLhJRMT5EbEM+N/su176eD0apTJB3TPazz1qd5ZJuhG4OiK+0OpaGqUul/uAQ8Y+LUraDpwSEZta\nWtw0SToCuCcilrS6lolIehD47YjYWLVtn699nLbSbntJ7yO7XvbpEbEjTdsBHDd2VCrpTuCyiLil\ndZU+03h1jzPPz4GjIrsGeClJ+hlZl/r9wHOrsK/D3rqPzm/b6eznPiItKB2Vjt1/IXAScFfrKppY\nRDwKbCDrfkHSOcC2su7ceZIOkpTfod8IDLaqnunytp97ki4Gfgf4tYYwugF4R5rnZWSDqdY3v8Lx\njVe3pAWSDs/NczbwcJlCVNISSUfmHp8F/DTt69+jpPv6RHUDu2a6n/uItCBJa8m6AXYD/wl8uIxH\no2MkHQ+sBQ4FhoG3RAUuOJHOD32e7MOfyEaRvicixrt8Y0tJuorsaljPJ+se2hERx1dh249XO/Dr\nQD8l3vbpA+024AGymgXsioiTUyBdBxwDPA28KyL+rWXF5kxUN3A6WdgvIuuafhS4OLJfySoFScvI\nPqR0kNX4CPC+1ANT2n19grrfCzzFDN9jHKRmZmYFuGvXzMysAAepmZlZAQ5SMzOzAhykZmZmBThI\nzczMCnCQmpmZFeAgNTMzK8BBamZmVoCD1MzMrAAHqZmNS9IeSZdJulPSA5IuaHVNZmXkIDWzyfw8\nIl4BnAn8lSS/Z5g18LV2zWxckvYAR+R+fuwxYFVEPNTayszKxZ8uzWwiQfZLJGP2AAtbVItZaTlI\nzWwianUBZlXgIDWziTSe9/F5ILNx+BypmZlZAT4iNTMzK8BBamZmVoCD1MzMrAAHqZmZWQEOUjMz\nswIcpGZmZgU4SM3MzApwkJqZmRXw/wEb9PF203s8jgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "X = np.arange(1,30)\n", "Y = np.array([fib(x) for x in X])\n", "import matplotlib.pyplot as plt\n", "fig, ax = plt.subplots()\n", "ax.scatter(X, Y)\n", "ax.set_xlabel('n')\n", "ax.set_ylabel('fib(n)')\n", "ax.set_title('The Fibonacci sequence grows fast !')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It may not surprise you, but this looks like an exponential, so if we were to look at $log(fib(n))$ × $n$ it would look approximately like a line. We can try to do a linear regression using this model. R is a language many people use to do statistics. So, let's use R. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's enable integration between Python and R using the [__`RPy2`__](https://rpy2.readthedocs.io/en/version_2.8.x/) python package developed by Laurent Gautier and the rest of the rpy2 team." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(Side note, you might need to change the environment variable passed to your kernel for this to work.\n", "Here is what I had to do only once.)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#!a2km add-env 'python 3' DYLD_FALLBACK_LIBRARY_PATH=$HOME/anaconda/pkgs/icu-54.1-0/lib:/Users/bussonniermatthias/anaconda/pkgs/zlib-1.2.8-3/lib " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import rpy2.rinterface\n", "\n", "%load_ext rpy2.ipython" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Following will \"Send\" the X and Y array to R." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%Rpush Y X" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now let's try to fit a linear model ($ln(Y) = A.X + B$) using R. I'm not a R user myself, so don't take this as idiomatic R." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAYAAAB91L6VAAAD8GlDQ1BJQ0MgUHJvZmlsZQAAOI2N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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "my_summary = summary(lm(log(Y)~X))\n", "val <- my_summary$coefficients\n", "\n", "plot(X, log(Y))\n", "abline(my_summary)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "lm(formula = log(Y) ~ X)\n", "\n", "Residuals:\n", " Min 1Q Median 3Q Max \n", "-0.183663 -0.013497 -0.004137 0.006046 0.296094 \n", "\n", "Coefficients:\n", " Estimate Std. Error t value Pr(>|t|) \n", "(Intercept) -0.775851 0.026173 -29.64 <2e-16 ***\n", "X 0.479757 0.001524 314.84 <2e-16 ***\n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", "\n", "Residual standard error: 0.06866 on 27 degrees of freedom\n", "Multiple R-squared: 0.9997,\tAdjusted R-squared: 0.9997 \n", "F-statistic: 9.912e+04 on 1 and 27 DF, p-value: < 2.2e-16\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "my_summary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Good, we have now the some statistics on the fit, which also looks good. __And__ we were able to not only send variable to R, but to plot directly from R !" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are happy as $F_n = \\left[\\frac{\\phi^n}{\\sqrt 5}\\right]$, where `[]` is closest integer and $\\phi = \\frac{1+\\sqrt 5}{2}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also look at the variables more carefully" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " Estimate Std. Error t value Pr(>|t|)\n", "(Intercept) -0.7758510 0.026172673 -29.64355 3.910319e-22\n", "X 0.4797571 0.001523832 314.83597 1.137181e-49\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "val" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or even the following that _looks_ more like python" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ -7.75850975e-01, 2.61726725e-02, -2.96435519e+01,\n", " 3.91031947e-22],\n", " [ 4.79757090e-01, 1.52383191e-03, 3.14835966e+02,\n", " 1.13718145e-49]])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%R val" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can even get the variable back from R as Python objects:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-0.77585097534858738, 0.4797570904348315)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coefs = %Rget val\n", "y0,k = coefs.T[0]\n", "y0,k" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's all from the R part. I hope this shows you some of the power of IPython, both in notebook and command line. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# CFFI" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! We were able to send data back and forth! If does not works for all objects, but at least for the basic ones. It requires quite some work from the authors of the underlying library to allow you to do that. Though we are still limited to data. We can't (yet) send functions over which limits the utility." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Mix and Match : C" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the critical point of any code may at some point be performance. Python is known to not be the most performant language, though it is convenient and quick to write and has a large ecosystem. Most of the function you requires are probably available in a package, battle tested and optimized. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You might still need here and there the raw power of an ubiquitous language which is known for its speed when you know how to wield it well: C. \n", "\n", "Though one of the disadvantage of C is the (relatively) slow iteration process due to the necessity of compilation/run part of the cycle. Let see if we can improve that by leveraging the excellent [CFFI project](https://cffi.readthedocs.io/), using my own small [cffi_magic](https://pypi.python.org/pypi/cffi_magic) wrapper. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import cffi_magic" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rm -rf *.o *.c *.so Cargo.* src target" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ls: *.c: No such file or directory\r\n", "ls: *.h: No such file or directory\r\n", "ls: *.o: No such file or directory\r\n" ] } ], "source": [ "ls *.c *.h *.o" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the `%%cffi` magic we can define in the middle of our python code some C function:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cffi int cfib(int);\n", "\n", "int cfib(int n)\n", "{\n", " int res=0;\n", " if (n <= 1){\n", " res = 1;\n", " } else {\n", " res = cfib(n-1)+cfib(n-2);\n", " }\n", " return res;\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first line take the \"header\" of the function we declare, and the rest of the cell takes the body of this function. The `cfib` function will automatically be made available to you in the main python namespace." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cfib(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Oops there is a mistake as we should have `fib(5) == 5`. Luckily we can redefine the function on the fly. I could edit the above cell, but here as this will be rendered statically for the sake of demo purpose, I'm going to make a second cell:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cffi int cfib(int);\n", "\n", "int cfib(int n)\n", "{\n", " int res=0;\n", " if (n <= 2){ /*mistake was here*/\n", " res = 1;\n", " } else {\n", " res = cfib(n-1)+cfib(n-2);\n", " }\n", " return res;\n", "}" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cfib(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great ! Let's compare the timing." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The slowest run took 73.42 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1000000 loops, best of 3: 379 ns per loop\n" ] } ], "source": [ "%timeit cfib(10)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The slowest run took 4.65 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1000000 loops, best of 3: 853 ns per loop\n" ] } ], "source": [ "%timeit fib(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not so bad considering the C implementation is recursive, and the Python version is manually hand-rolled. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation detail" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So how do we do that magically under the hood? The knowledgeable reader is aware that CPython extensions cannot be reloaded. Though here we redefine the function... how come? \n", "\n", "Using the user provided code we compile a shared object with a random name, import this as a module and alias using a user friendly name in the `__main__` namespace. If the user re-execute we just get a new name, and change the alias mapping. \n", "\n", "If one wan to optimize you can use a hash of the codecell string to not recompile if the user hasn't changed the code. " ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_cffi_cWtAstIlGT.c _cffi_cWtAstIlGT.o _cffi_yxGAKIqXRR.c _cffi_yxGAKIqXRR.o\r\n" ] } ], "source": [ "ls *.o *.c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this in mind you can guess the same can be done for any language which can be compiled to a shared object, or a dynamically loadable library. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Mix and Match : rust" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `cffi` module also allows you to do the same with [Rust](https://www.rust-lang.org), a new language designed by Mozilla, which provide the same C-like level of control, while incorporating more recent understanding of programming and provide better memory safety. Let's see how we would do the same with Rust:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "injecting rfib in user ns\n" ] } ], "source": [ "%%rust int rfib(int);\n", "\n", "#[no_mangle]\n", "pub extern fn rfib(n: i32) -> i32 {\n", " match n {\n", " 0 => 1,\n", " 1 => 1,\n", " 2 => 1,\n", " _ => rfib(n-1)+rfib(n-2)\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 1, 2, 3, 5, 8, 13, 21, 34]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[rfib(x) for x in range(1,10)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I'm not a Rustacean, but the above seem pretty straightforward to me. Again this might not be idiomatic Rust but you should be able to decipher what's above. The same than for C applies. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Still in development" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both the C and Rust example shown above use the `cffi_magic` on which I spent roughly 4 hours total, so the functionalities can be really crude and the documentation minimal at best. Feel free to send PRs if you are interested. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fortran" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [fortran magic](https://pypi.python.org/pypi/fortran-magic) does the same as above, but has been developed by [mgaitan](https://github.com/mgaitan/fortran_magic) and is slightly older. Again no surprise except you are supposed to mark fortran variable that are used to return the values. " ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/bussonniermatthias/anaconda/lib/python3.5/site-packages/fortranmagic.py:147: UserWarning: get_ipython_cache_dir has moved to the IPython.paths module since IPython 4.0.\n", " self._lib_dir = os.path.join(get_ipython_cache_dir(), 'fortran')\n" ] }, { "data": { "application/javascript": [ "$.getScript(\"https://raw.github.com/marijnh/CodeMirror/master/mode/fortran/fortran.js\", function () {\n", "IPython.config.cell_magic_highlight['magic_fortran'] = {'reg':[/^%%fortran/]};});\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%load_ext fortranmagic" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%fortran\n", "RECURSIVE SUBROUTINE ffib(n, fibo) \n", " IMPLICIT NONE\n", " INTEGER, INTENT(IN) :: n\n", " INTEGER, INTENT(OUT) :: fibo\n", " INTEGER :: tmp\n", " IF (n <= 2) THEN \n", " fibo = 1\n", " ELSE\n", " CALL ffib(n-1,fibo)\n", " CALL ffib(n-2,tmp)\n", " fibo = fibo + tmp\n", " END IF\n", "END SUBROUTINE ffib" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 1, 2, 3, 5, 8, 13, 21, 34]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[ffib(x) for x in range(1,10)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No surprise here, you are well aware of what we are doing." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cython" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IPython used to ship with the Cython magic that is now part of [Cython](http://cython.org/) itself.\n", "Cython is a superset of Python that compiles to C and importable from Python. You should be a ble to take your python code as is, type annotate it, and get c-like speed.\n", "The same principle applies:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import cython" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext cython" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython\n", "\n", "def cyfib(int n): # note the `int` here\n", " \"\"\"\n", " A simple definition of fibonacci manually unrolled\n", " \"\"\"\n", " cdef int x,y # and the `cdef int x,y` here\n", " if n < 2:\n", " return 1\n", " x,y = 1,1\n", " for i in range(n-2):\n", " x,y = y,x+y\n", " return y" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 1, 2, 3, 5, 8, 13, 21, 34]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[cyfib(x) for x in range(1,10)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### benchmark" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 648 ns per loop\n" ] } ], "source": [ "%timeit -n100 -r3 fib(5)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The slowest run took 11.60 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "100 loops, best of 3: 578 ns per loop\n" ] } ], "source": [ "%timeit -n100 -r3 cfib(5)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 147 ns per loop\n" ] } ], "source": [ "%timeit -n100 -r3 ffib(5)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 45.6 ns per loop\n" ] } ], "source": [ "%timeit -n100 -r3 cyfib(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The benchmark result can be astonishing, but keep in mind that the Python and Cython version use manually unrolled loop. Main point being that we reached our goal and used Fortran, Cython, C (and Rust) in the middle of our Python program." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[let's skip the Rust fib version, it tends to segfault, and it would be sad to segfault now :-) ]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# %timeit rfib(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Cake is not a lie!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So can we do a layer cake? Can we call rust from Python from Fortran from Cython? Or Cython from C from Fortran? Or Fortron from Cytran from Cust?" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pray the demo-gods it wont segfault even without rust...\n" ] } ], "source": [ "import itertools\n", "lookup = {'c':cfib,\n", " # 'rust': rfib, # as before Rust may segfault, but I dont' know why ...\n", " 'python': fib,\n", " 'fortran': ffib,\n", " 'cython': cyfib\n", " }\n", "\n", "print(\"Pray the demo-gods it wont segfault even without rust...\")" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for function in lookup.values():\n", " assert function(5) == 5, \"Make sure all is correct or will use 100% CPU for a looong time.\"" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fortran -> cython -> python -> c : 5\n", "fortran -> cython -> c -> python : 5\n", "fortran -> python -> cython -> c : 5\n", "fortran -> python -> c -> cython : 5\n", "fortran -> c -> cython -> python : 5\n", "fortran -> c -> python -> cython : 5\n", "cython -> fortran -> python -> c : 5\n", "cython -> fortran -> c -> python : 5\n", "cython -> python -> fortran -> c : 5\n", "cython -> python -> c -> fortran : 5\n", "cython -> c -> fortran -> python : 5\n", "cython -> c -> python -> fortran : 5\n", "python -> fortran -> cython -> c : 5\n", "python -> fortran -> c -> cython : 5\n", "python -> cython -> fortran -> c : 5\n", "python -> cython -> c -> fortran : 5\n", "python -> c -> fortran -> cython : 5\n", "python -> c -> cython -> fortran : 5\n", "c -> fortran -> cython -> python : 5\n", "c -> fortran -> python -> cython : 5\n", "c -> cython -> fortran -> python : 5\n", "c -> cython -> python -> fortran : 5\n", "c -> python -> fortran -> cython : 5\n", "c -> python -> cython -> fortran : 5\n" ] } ], "source": [ "for order in itertools.permutations(lookup):\n", " t = 5\n", " for f in order:\n", " t = lookup[f](t)\n", " \n", " print(' -> '.join(order), ':', t)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "It worked ! I can run all the permutations !\n" ] } ], "source": [ "print('It worked ! I can run all the permutations !')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The Cherry on the Layer Cake, with Julia" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you have a small idea about how the above layer-cake is working you'll understand that there is (still) a non-negligible overhead as between each language switch we need to go back to Python-land. And the scope in which we can access function is still quite limited. The following is some really _Dark Magic_ concocted by Fernando Perez and Steven Johnson using the [Julia](http://julialang.org/) programming language. I can't even pretend to understand how this possible, but it's really impressive to see. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try to handwave what's happening. I would be happy to get corrections." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The crux is that the Python and Julia interpreters can be started in a way where they each have access to the other process memory. Thus the Julia and Python interpreter can share live objects. You then \"just\" need to teach the Julia language about the structure of Python objects and it can manipulate these as desired, either directly (if the memory layout allow it) or using proxy objects that \"delegate\" the functionality to the python process.\n", "\n", "The result being that Julia can import and use Python modules (using the Julia `PyCall` package), and Julia functions are available from within Python using the `pyjulia` module. \n", "\n", "Let's see how this look like." ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initializing Julia interpreter. This may take some time...\n" ] } ], "source": [ "%load_ext julia.magic" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "julia_version = %julia VERSION\n", "julia_version # you can see this is a wrapper" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "He we tell the _julia_ process to import the _python_ matplotlib module, as well as numpy." ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%julia @pyimport matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%julia @pyimport numpy as np" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAAEbCAYAAAB+50uWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%julia\n", " # Note how we mix numpy and julia:\n", "t = linspace(0, 2*pi,1000); # use the julia `linspace` and `pi`\n", "s = sin(3*t + 4*np.cos(2*t)); # use the numpy cosine and julia sine\n", "fig = plt.gcf() # **** WATCH THIS VARIABLE ****\n", "plt.plot(t, s, color=\"red\", linewidth=2.0, linestyle=\"--\", label=\"sin(3t+4.cos(2t))\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the above block of code is Julia, where, `linspace`,`pi`,`sin` are builtins of Julia. `np.*` and `plt.*` are referencing Python function and methods.\n", "\n", "We see that `t` is a Julia \"Array\" (technically a `1000-element LinSpace{Float64}`), which can get sent to `numpy.cos`, multiply by a Julia int, (..etc) and end up being plotted via matplotlib (Python), and displayed inline.\n", "\n", "Let's finish our graph in Python" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAAEmCAYAAADGA1/lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niqIoiu34f8/KIEdEHcvcAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "fig = %julia fig\n", "fig.axes[0].plot(X[:6], np.log(Y[:6]), '--', label='fib')\n", "fig.axes[0].set_title('A weird Julia function and Fib')\n", "fig.axes[0].legend()\n", "\n", "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above we get the reference to our previously defined figure (in Julia), plot the log of our `fib` function.\n", "The key value here is that we get the _same_ object from within Python and Julia. But let's push even further." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above we had _explicit_ transition between the Julia code and the Python code. Can we be more sneaky?\n", "\n", "One toy example is to define the Fibonacci function using the recursive form and _explicitly_ pass the function with which we recurse.\n", "\n", "We'll define such a function both on the Julia and Python side, ask the Julia function to recurse by calling the Python one, and the Python one to recurse using the Julia one.\n", "\n", "Let's print `(P` when we enter Python Kingdom, `(J` when we enter Julia Realm, and close the parenthesis accordingly:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function\n", "\n", "\n", "# julia fib function\n", "jlfib = %julia _fib(n, pyfib) = n <= 2 ? 1 : pyfib(n-1, _fib) + pyfib(n-2, _fib)\n", "\n", "\n", "def pyfib(n, _fib):\n", " \"\"\"\n", " Python fib function\n", " \"\"\"\n", " print('(P', end='')\n", " if n <= 2:\n", " r = 1\n", " else:\n", " print('(J', end='')\n", " # here we tell julia (_fib) to recurse using Python\n", " r = _fib(n-1, pyfib) + _fib(n-2, pyfib)\n", " print(')',end='')\n", " print(')',end='')\n", " return r" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(P(J(P(J(P(J(P(J(P)(P)))(P(J))(P(J))(P)))(P(J(P(J))(P)(P)(P)))(P(J(P(J))(P)(P)(P)))(P(J(P)(P)))))(P(J(P(J(P(J))(P)(P)(P)))(P(J(P)(P)))(P(J(P)(P)))(P(J))))(P(J(P(J(P(J))(P)(P)(P)))(P(J(P)(P)))(P(J(P)(P)))(P(J))))(P(J(P(J(P)(P)))(P(J))(P(J))(P)))))" ] }, { "data": { "text/plain": [ "55" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fibonacci = lambda x: pyfib(x, jlfib)\n", "\n", "fibonacci(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cross language is Easy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I hope you enjoyed that, I find it quite interesting and useful when you need to leverage the tools available across multiple domains. I'm sure there are plenty of other tools that allow this kind of things and a host of other languages that can interact with each other in this way.\n", "\n", "From the top of my head I know of a few magics (SQL, Redis...) that provide such integration. Every language has its strong and weak points, and knowing what to use is often hard. I hope I convinced you that mixing languages is not such a daunting task.\n", "\n", "The other case when this is useful is when you are learning a new language, you can leverage your current expertise temporarily and get something that work before learning the idiomatic way and available libraries.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comments\n", "\n", "If you have comments suggestions please [open an issue on GitHub](https://github.com/carreau/posts/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cat Tax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a Fibonacci cat to thank you from reading until the end, sorry, no banana for scale." ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pfiew\n" ] }, { "data": { "image/jpeg": "/9j/4QAYRXhpZgAASUkqAAgAAAAAAAAAAAAAAP/sABFEdWNreQABAAQAAABQAAD/7gAOQWRvYmUA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\nAMh83/4P9+p+fJ539/8Acf/Z\n", "text/plain": [ "" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "print('Pfiew')\n", "Image('http://static.boredpanda.com/blog/wp-content/uploads/2016/02/fibonacci-composition-cats-furbonacci-91__700.jpg')" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python 3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }