{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benford's Law"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AF154ITvvvHNBMQ0dOrTl+Zw5c1izZg1DhgxpiS0iGDZsGADz589/3/zDhw/fsB1SAJcA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\nNer/A5BS/Rah2PFLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x79fb5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicted 1st Digit Ratios:\n",
      "\t [30.1, 17.6, 12.5, 9.7, 7.9, 6.7, 5.8, 5.1, 4.5]\n",
      "Observed 1st Digit Ratios:\n",
      "\t [29.0, 17.4, 13.5, 10.7, 7.7, 6.7, 5.8, 5.0, 4.1] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\"\"\"\n",
    "Exploring Benford's Law by applying it to the files sizes\n",
    "on a local hard drive\n",
    "\n",
    "Note: this is a 'hacky' version that works but is NOT very Pythonic\n",
    "\"\"\" \n",
    "\n",
    "\n",
    "import os\n",
    "\n",
    "# The significand ranges from 1 thru 9 inclusive (0 is excluded)\n",
    "\n",
    "FIRST_DIGIT_PERCENTAGES = [\n",
    "                            30.1,    ## '1'\n",
    "                            17.6,\n",
    "                            12.5,\n",
    "                             9.7,\n",
    "                             7.9,\n",
    "                             6.7,\n",
    "                             5.8,\n",
    "                             5.1,\n",
    "                             4.5     ## '9'\n",
    "                          ]\n",
    "\n",
    "# 2nd and subsequent digits range from 0 thru 9 inclusive \n",
    "# (0 is included)\n",
    "\n",
    "SECOND_DIGIT_PERCENTAGES = [\n",
    "                             12.0,  ## '0'\n",
    "                             11.4,\n",
    "                             10.9,\n",
    "                             10.4,\n",
    "                             10.0,\n",
    "                              9.7,\n",
    "                              9.3,\n",
    "                              9.0,\n",
    "                              8.8,\n",
    "                              8.5   ## '9'\n",
    "                            ]\n",
    "\n",
    "\n",
    "class BenfordDigit(object):\n",
    "    '''\n",
    "    Class to test whether given number digits adhere to Benford's Law\n",
    "    Class Attributes:\n",
    "      __experiment_name: <string> unique label for each Benford test\n",
    "      __digit_position:  int>     significand = 1, 2nd digit, \n",
    "                                  counting from LHS = 2 ...\n",
    "      __digit_count:     <dict>   tally of numbers encountered in given\n",
    "                                  digit position\n",
    "      __current_number:  <int>    most recent number reported in digit\n",
    "                                  position, used to\n",
    "                                  increment the corrsponding entry in\n",
    "                                  digit_count tally\n",
    "      __number_count:    <int>    sum of all numbers found in a given\n",
    "                                  digit_position\n",
    "      __digit_percent:   <list>   list of percentages corresponding to\n",
    "                                  relative frequencies \n",
    "                                  for each number for a given \n",
    "                                  digit_position (1-9 for significand,\n",
    "                                  or 0-9 otherwise)\n",
    "    '''\n",
    "    \n",
    "    def __init__(self, experiment_name, digit_position):\n",
    "        self.__experiment_name = experiment_name\n",
    "        self.__digit_position = digit_position\n",
    "        \n",
    "        self.__digit_count = {\n",
    "                    '0': 0,\n",
    "                    '1': 0,\n",
    "                    '2': 0,\n",
    "                    '3': 0,\n",
    "                    '4': 0,\n",
    "                    '5': 0,\n",
    "                    '6': 0,\n",
    "                    '7': 0,\n",
    "                    '8': 0,\n",
    "                    '9': 0\n",
    "                   }\n",
    "        self.__current_number = '1'\n",
    "        self.__number_count = 0\n",
    "        \n",
    "        self.__digit_percent = []\n",
    "        \n",
    "    def __str__(self):\n",
    "        return ('Experiment: %s   Digit position: %i' % \\\n",
    "                (self.__experiment_name,self.__digit_position))\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return str(self)\n",
    "    \n",
    "    @property\n",
    "    def experiment_name(self):\n",
    "        return (self.__experiment_name)\n",
    "    \n",
    "    @property\n",
    "    def digit_position(self):\n",
    "        return (self.__digit_position)\n",
    "    \n",
    "    @property\n",
    "    def digit_count(self):\n",
    "        # return [(k,v) for k,v in \n",
    "        # sorted([(k,v) for k,v in self.__digit_count.items()])]\n",
    "        return sorted([(k,v) for k,v in self.__digit_count.items()])       \n",
    "    \n",
    "    @property\n",
    "    def current_number(self):\n",
    "        return self.__current_number\n",
    "    \n",
    "    @current_number.setter\n",
    "    def current_number(self, number):\n",
    "        self.__current_number = number\n",
    "        self.__digit_count[self.__current_number] += 1       \n",
    "        \n",
    "    @property\n",
    "    def number_count (self):\n",
    "        if self.__digit_position == 1:\n",
    "            # return all numbers excl. 0's\n",
    "            return sum(value for key, \n",
    "                value in self.__digit_count.items() if key != '0')\n",
    "        else: \n",
    "            return sum(value for key,\n",
    "                value in self.__digit_count.items()) ## Simplify!\n",
    "    @property\n",
    "    def digit_percent (self):\n",
    "        self.__digit_percent = []  \n",
    "        if self.__digit_position == 1:\n",
    "            # return ratio of all numbers excl. 0's\n",
    "            for i in range(1,10):\n",
    "                d1 = self.__digit_count[str(i)] / self.number_count            \n",
    "                self.__digit_percent.append(round(100.0*d1, 1))\n",
    "            return self.__digit_percent\n",
    "        else:\n",
    "            # return ratio of all numbers incl. 0's\n",
    "            for i in range(0,10):\n",
    "                d2 = self.__digit_count[str(i)] / self.number_count\n",
    "                self.__digit_percent.append(round(100.0*d2, 1))\n",
    "            return self.__digit_percent\n",
    "        \n",
    "### Significand\n",
    "benford1 = BenfordDigit('Local P: Drive Files - Significand', 1)\n",
    "                \n",
    "### 2nd Digit\n",
    "benford2 = BenfordDigit('Local P: Drive Files - 2nd Digit', 2)\n",
    "\n",
    "for root, dirs, files in os.walk('P:/'):\n",
    "    for name in files:\n",
    "        filename = os.path.join(root, name)\n",
    "        filesize = os.path.getsize(filename)\n",
    "        \n",
    "        digit1Strng = (str(filesize))[0]\n",
    "        benford1.current_number = digit1Strng\n",
    "        \n",
    "        if filesize > 9:\n",
    "            benford2.current_number = str(filesize)[1]\n",
    "\n",
    "# Plot predicted 1st digits frequencies versus actual\n",
    "\n",
    "# Show plots in notebook (rather than in separate window)\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X = np.arange(len(benford1.digit_percent))\n",
    "plt.bar(X + 0.0, benford1.digit_percent, facecolor='blue',\n",
    "        edgecolor='white', width=0.5, label=\"Measured\")\n",
    "plt.bar(X + 0.25, FIRST_DIGIT_PERCENTAGES, facecolor='red',\n",
    "        edgecolor='white', width=0.5, label=\"Predicted\")\n",
    "\n",
    "plt.title('1st digit frequencies for P-Drive file sizes')\n",
    "plt.xlabel('First Digit: 1-9')\n",
    "plt.ylabel('Percentage')\n",
    "plt.legend(loc='upper right', frameon=False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print ('\\nPredicted 1st Digit Ratios:\\n\\t',\n",
    "       FIRST_DIGIT_PERCENTAGES)        \n",
    "print ('Observed 1st Digit Ratios:\\n\\t', \n",
    "       benford1.digit_percent, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7b15b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicted 2nd Digit Ratios:\n",
      "\t [12.0, 11.4, 10.9, 10.4, 10.0, 9.7, 9.3, 9.0, 8.8, 8.5]\n",
      "Observed 2nd Digit Ratios:\n",
      "\t [12.1, 10.3, 10.7, 10.3, 10.6, 9.8, 10.0, 8.8, 8.8, 8.5]\n"
     ]
    }
   ],
   "source": [
    "# 2nd digit frequencies\n",
    "\n",
    "X = np.arange(len(benford2.digit_percent))\n",
    "plt.bar(X + 0.0, benford2.digit_percent, facecolor='blue',\n",
    "        edgecolor='white', width=0.5, label=\"Measured\")\n",
    "plt.bar(X + 0.25, SECOND_DIGIT_PERCENTAGES, facecolor='red',\n",
    "        edgecolor='white', width=0.5, label=\"Predicted\")\n",
    "\n",
    "plt.title('2nd digit frequencies for P-Drive file sizes')\n",
    "plt.xlabel('Second Digit: 0-9')\n",
    "plt.ylabel('Percentage')\n",
    "plt.legend(loc='upper right', frameon=False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print ('\\nPredicted 2nd Digit Ratios:\\n\\t', \n",
    "       SECOND_DIGIT_PERCENTAGES) \n",
    "print ('Observed 2nd Digit Ratios:\\n\\t',\n",
    "       benford2.digit_percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "python3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}