{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "structural-optimization",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4147524\n"
     ]
    }
   ],
   "source": [
    "def count(f):\n",
    "    sums = {}\n",
    "    lines = 0\n",
    "    for l in f:\n",
    "        for p, ch in enumerate(l.rstrip()):\n",
    "            sums[p] = sums.get(p, 0) + int(ch)\n",
    "        lines += 1\n",
    "\n",
    "    return sums, lines\n",
    "\n",
    "def rates(f):\n",
    "    sums, lines = count(f)\n",
    "    digits = len(sums)\n",
    "    gamma = 0\n",
    "    epsilon = 0\n",
    "    for p, s in sums.items():\n",
    "        if s > lines/2:\n",
    "            gamma |= 1<<(digits-p-1)\n",
    "        else:\n",
    "            epsilon |= 1<<(digits-p-1)\n",
    "\n",
    "    return gamma, epsilon\n",
    "\n",
    "import import_ipynb\n",
    "import helper\n",
    "f = helper.open_file('2021_3.txt')\n",
    "\n",
    "gamma, epsilon = rates(f)\n",
    "\n",
    "print(gamma*epsilon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "intellectual-railway",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3570354"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Node:\n",
    "    def __init__(self):\n",
    "        self.children = [None, None]\n",
    "        \n",
    "def insert(node, bitstring):\n",
    "    for bit in bitstring:\n",
    "        bit = int(bit)\n",
    "        if node.children[bit] is None:\n",
    "            node.children[bit] = Node()\n",
    "        node = node.children[bit]\n",
    "        \n",
    "def count_leafs(node):\n",
    "    if node is None:\n",
    "        return 0\n",
    "    \n",
    "    c = 0\n",
    "    \n",
    "    leaf = True\n",
    "    for n in node.children:\n",
    "        if n:\n",
    "            c += count_leafs(n)\n",
    "            leaf = False\n",
    "            \n",
    "    if leaf:\n",
    "        c += 1\n",
    "    \n",
    "    return c\n",
    "\n",
    "import import_ipynb\n",
    "import helper\n",
    "f = helper.open_file('2021_3.txt')\n",
    "\n",
    "root = Node()\n",
    "\n",
    "for l in f:\n",
    "    insert(root, l.rstrip())\n",
    "    \n",
    "def rating(node, bit_criteria):\n",
    "    r = 0\n",
    "    while node:        \n",
    "        c0 = count_leafs(node.children[0])\n",
    "        c1 = count_leafs(node.children[1])\n",
    "        \n",
    "        if c0 == 0 and c1 == 0:\n",
    "            break\n",
    "    \n",
    "        r <<= 1\n",
    "        if not (c0 == 0 and c1 == 1) and \\\n",
    "            (bit_criteria(c0, c1) or (c0 == 1 and c1 == 0)):\n",
    "            \n",
    "            node = node.children[0]\n",
    "        else:\n",
    "            node = node.children[1]\n",
    "            r |= 1          \n",
    "            \n",
    "    return r\n",
    "\n",
    "import operator\n",
    "\n",
    "oxygen_generator_rate = rating(root, operator.gt)\n",
    "c02_scrubber_rate =  rating(root, operator.le)\n",
    "\n",
    "oxygen_generator_rate * c02_scrubber_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "wrapped-deputy",
   "metadata": {},
   "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": "ipython3",
   "version": "3.9.2"
  },
  "title": "Binary Diagnostic"
 },
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 "nbformat_minor": 5
}