{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function arities of different Scala projects\n",
    "\n",
    "I was curious what is a distribution of function arities in different Scala projects.\n",
    "Scala is a functional programming language, so I assumed the average number of arguments would be more than 2.\n",
    "\n",
    "So I did a small research. I created a Scala compiler plugin that gathered defined functions arities.\n",
    "In this notebook I visualize collected data for the following projects:\n",
    "- Scala 2.12.4 compiler and standard library\n",
    "- Dotty 0.5.0-RC1 compiler\n",
    "- Akka 2.4\n",
    "- Spark 2.2\n",
    "- Cats 1.0\n",
    "- Scalaz 7.2.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt  \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I don't count constructors, and do _not_ count `this` as implicit argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def readStats(filename):\n",
    "    x = []\n",
    "    with open(filename, \"r\") as file:    \n",
    "        for l in file:\n",
    "            name, num, implicits = l.strip().split()\n",
    "            n = int(num)\n",
    "            if name == \"<init>\":\n",
    "                continue    \n",
    "            x.append(n)\n",
    "    return np.asarray(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scalaz = readStats(\"data/scalaz8.txt\")\n",
    "akka   = readStats(\"data/akka2.4.txt\")\n",
    "scala  = readStats(\"data/scala2.12.txt\")\n",
    "dotty  = readStats(\"data/dotty.txt\")\n",
    "spark  = readStats(\"data/spark2.2.txt\")\n",
    "cats   = readStats(\"data/cats1.0.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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atULvvrtCLi4uGjDgWUmSt/cdxQrypTz1S30fvby8JElnzpxWXNwERUT8XZJk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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110374750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "fig = plt.figure(figsize=(12, 5))\n",
    "fig.suptitle(\"Function arities in Scala projects\")\n",
    "plt.subplot(121)\n",
    "plt.title(\"Scala 2.12\")\n",
    "plt.text(4, 8000, \"mean: %.2f  std: %.2f\" % (np.mean(scala), np.std(scala)))\n",
    "n, bins, patches = plt.hist(scala, bins=range(8),  alpha=0.6, histtype='bar')\n",
    "plt.subplot(122)\n",
    "plt.title(\"Dotty 0.5.0-RC1\")\n",
    "plt.text(4, 2500, \"mean: %.2f  std: %.2f\" % (np.mean(dotty), np.std(dotty)))\n",
    "n, bins, patches = plt.hist(dotty, bins=range(8),  alpha=0.6, histtype='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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duuull2Zr2LBB2rYtS7/88stJn2fUqGEqKyurtGz48JFasmShHn74AZWVlenG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110b17b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 5))\n",
    "fig.suptitle(\"Function arities in Scala projects\")\n",
    "plt.subplot(121)\n",
    "plt.title(\"Akka 2.4\")\n",
    "plt.text(4, 10000, \"mean: %.2f  std: %.2f\" % (np.mean(akka), np.std(akka)))\n",
    "n, bins, patches = plt.hist(akka, bins=range(8),  alpha=0.6, histtype='bar')\n",
    "plt.subplot(122)\n",
    "plt.title(\"Spark 2.2\")\n",
    "plt.text(4, 8000, \"mean: %.2f  std: %.2f\" % (np.mean(spark), np.std(spark)))\n",
    "n, bins, patches = plt.hist(spark, bins=range(8),  alpha=0.6, histtype='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nLqpfP1w7dmzTW2/NVrNmzZWVlaVWrSI1YMBgjR4dpw0bUtWqVaQmTZpe6LiG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\nAAAQIhkAAEIkAwBAiGQAAAiRDAAAcQDRJ8CWEKKzbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111054e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 5))\n",
    "fig.suptitle(\"Function arities in Scala projects\")\n",
    "plt.subplot(121)\n",
    "plt.title(\"Cats 1.0\")\n",
    "plt.text(4, 800, \"mean: %.2f  std: %.2f\" % (np.mean(cats), np.std(cats)))\n",
    "n, bins, patches = plt.hist(cats, bins=range(8),  alpha=0.6, histtype='bar')\n",
    "plt.subplot(122)\n",
    "plt.title(\"ScalaZ 8\")\n",
    "plt.text(4, 250, \"mean: %.2f  std: %.2f\" % (np.mean(scalaz), np.std(scalaz)))\n",
    "n, bins, patches = plt.hist(scalaz, bins=range(8),  alpha=0.6, histtype='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions\n",
    "\n",
    "There are no conclusions. Just a general 'Aha, that's how it is'. I was surprised by the number of zero argument functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Future work\n",
    "\n",
    "I'd like to gather similar statistics for Haskell projects.\n",
    "\n",
    "Please, contribute!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}