Advent of Code 2020 - 24.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import timeit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(r'C:\\Users\\stefa\\OneDrive\\Documents\\Python Scripts\\Problem 24 Input.txt') as f:\n",
    "    lines = f.read().split('\\n')\n",
    "lines = lines[:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "move_dict = {'nw': [-.5,.5],\n",
    "             'ne': [.5,.5],\n",
    "             'sw': [-.5,-.5],\n",
    "             'se': [.5,-.5],\n",
    "             'e': [1,0],\n",
    "             'w': [-1,0]\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_commands(line):\n",
    "    comm_list = []\n",
    "    skip_next = 0 \n",
    "\n",
    "    for x in range(len(line)):\n",
    "        char = line[x]\n",
    "        if char == 's' or char =='n':\n",
    "            command = line[x:x+2]\n",
    "            skip_next = 1\n",
    "            comm_list.append(command)\n",
    "        elif skip_next == 1:\n",
    "            skip_next = 0 \n",
    "            pass\n",
    "        else: \n",
    "            command = char\n",
    "            comm_list.append(command)\n",
    "\n",
    "    return comm_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "388"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xy_list = []\n",
    "for line in lines:\n",
    "    commands = make_commands(line)\n",
    "    x = 0 \n",
    "    y = 0 \n",
    "    for comm in commands:\n",
    "        x += move_dict.get(comm)[0]\n",
    "        y += move_dict.get(comm)[1]\n",
    "    xy_item = [x,y]\n",
    "    if xy_item in xy_list:\n",
    "        xy_list.remove(xy_item)\n",
    "\n",
    "    else:\n",
    "        xy_list.append(xy_item)\n",
    "len(xy_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dot in xy_list:\n",
    "    plt.scatter(x=dot[0],y=dot[1], c='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_borders([-16.5, 0.5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "move_dict.get('w')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Define extreme edges, count spots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_borders(xy):\n",
    "    neighbors = 0\n",
    "    for key in move_dict.keys():\n",
    "        x = xy[0]\n",
    "        y = xy[1]\n",
    "        x = x + move_dict.get(key)[0]\n",
    "        y = y + move_dict.get(key)[1]\n",
    "        if [x,y] in xy_list:\n",
    "            neighbors += 1\n",
    "    return neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_limits(xy_list):\n",
    "    array_xy = np.array(xy_list)\n",
    "    max_x = np.max(array_xy[:,0])+1\n",
    "    max_y = np.max(array_xy[:,1])+1\n",
    "    min_x = np.min(array_xy[:,0])-1\n",
    "    min_y = np.min(array_xy[:,1])-1\n",
    "    return [min_x,max_x,min_y,max_y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_gen(xy_list):\n",
    "    flip_to_white = []\n",
    "    flip_to_black = []\n",
    "    limits = find_limits(xy_list)\n",
    "    for x in np.arange(limits[0],limits[1],.5):\n",
    "        for y in np.arange(limits[2],limits[3],.5):\n",
    "            neighbors = count_borders([x,y])\n",
    "            if [x,y] in xy_list and (neighbors == 0 or neighbors > 2):\n",
    "                flip_to_white.append([x,y])\n",
    "            if [x,y] not in xy_list and (neighbors == 2):\n",
    "                flip_to_black.append([x,y])\n",
    "    xy_list = [x for x in xy_list if x not in flip_to_white] + [x for x in flip_to_black]\n",
    "    \n",
    "    return xy_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 294\n",
      "0.416417899999999\n",
      "1 329\n",
      "0.2517887999999999\n",
      "2 357\n",
      "0.24182720000000302\n",
      "3 387\n",
      "0.24329840000000047\n",
      "4 356\n",
      "0.27550899999999956\n",
      "5 395\n",
      "0.2549974000000077\n",
      "6 387\n",
      "0.28332619999999054\n",
      "7 461\n",
      "0.3034899000000024\n",
      "8 468\n",
      "0.34877699999999834\n",
      "9 462\n",
      "0.3647398000000095\n",
      "10 495\n",
      "0.3791685000000058\n",
      "11 511\n",
      "0.4209102999999885\n",
      "12 535\n",
      "0.476188900000011\n",
      "13 577\n",
      "0.5132733000000087\n",
      "14 567\n",
      "0.630539899999988\n",
      "15 619\n",
      "0.638005800000002\n",
      "16 609\n",
      "0.7044262000000003\n",
      "17 671\n",
      "0.6870731999999862\n",
      "18 677\n",
      "0.8001237999999944\n",
      "19 682\n",
      "0.7689347000000026\n",
      "20 720\n",
      "0.8276074999999992\n",
      "21 762\n",
      "0.9374102000000022\n",
      "22 821\n",
      "0.9752489999999909\n",
      "23 772\n",
      "1.0447814999999991\n",
      "24 813\n",
      "1.0380448000000086\n",
      "25 858\n",
      "1.107656299999988\n",
      "26 856\n",
      "1.2574190999999928\n",
      "27 896\n",
      "1.2788903999999945\n",
      "28 907\n",
      "1.381590899999992\n",
      "29 902\n",
      "1.3896280000000019\n",
      "30 1033\n",
      "1.4570931999999885\n",
      "31 1051\n",
      "1.682578499999991\n",
      "32 1061\n",
      "1.7876182999999912\n",
      "33 1011\n",
      "1.826243500000004\n",
      "34 1085\n",
      "1.8392821999999995\n",
      "35 1125\n",
      "2.0162943999999925\n",
      "36 1225\n",
      "2.0944366999999886\n",
      "37 1222\n",
      "2.312247599999992\n",
      "38 1259\n",
      "2.413101700000013\n",
      "39 1268\n",
      "2.608984200000009\n",
      "40 1321\n",
      "2.7043701000000056\n",
      "41 1341\n",
      "2.857092599999987\n",
      "42 1341\n",
      "2.9329358999999897\n",
      "43 1429\n",
      "3.036224200000021\n",
      "44 1417\n",
      "3.303377299999994\n",
      "45 1509\n",
      "3.3333430000000135\n",
      "46 1519\n",
      "3.558354400000013\n",
      "47 1651\n",
      "3.6819000999999787\n",
      "48 1588\n",
      "4.091324500000013\n",
      "49 1643\n",
      "4.004078100000015\n",
      "50 1585\n",
      "4.334784600000006\n",
      "51 1719\n",
      "4.299912699999993\n",
      "52 1804\n",
      "4.769926699999985\n",
      "53 1746\n",
      "5.078613399999995\n",
      "54 1842\n",
      "5.015753499999988\n",
      "55 1804\n",
      "5.481806899999981\n",
      "56 1949\n",
      "5.275841200000002\n",
      "57 1975\n",
      "5.778767799999997\n",
      "58 2000\n",
      "6.139333299999976\n",
      "59 2050\n",
      "6.2607147000000225\n",
      "60 2070\n",
      "6.603420999999997\n",
      "61 2076\n",
      "6.862960900000019\n",
      "62 2179\n",
      "6.83933540000001\n",
      "63 2099\n",
      "7.28117979999999\n",
      "64 2302\n",
      "7.407297099999994\n",
      "65 2261\n",
      "8.087605100000019\n",
      "66 2320\n",
      "8.158465799999988\n",
      "67 2411\n",
      "8.437531799999988\n",
      "68 2540\n",
      "8.849150699999996\n",
      "69 2482\n",
      "9.627412099999958\n",
      "70 2520\n",
      "9.983527999999978\n",
      "71 2575\n",
      "10.157382900000016\n",
      "72 2662\n",
      "10.51681149999996\n",
      "73 2557\n",
      "11.325431399999957\n",
      "74 2736\n",
      "10.765063700000042\n",
      "75 2701\n",
      "11.909081599999979\n",
      "76 2863\n",
      "11.883562900000015\n",
      "77 2817\n",
      "13.201197100000002\n",
      "78 3025\n",
      "13.694479000000001\n",
      "79 2847\n",
      "14.021811499999956\n",
      "80 2901\n",
      "16.058468600000026\n",
      "81 3034\n",
      "16.238383199999987\n",
      "82 3089\n",
      "15.929549099999974\n",
      "83 3065\n",
      "16.13210190000001\n",
      "84 3173\n",
      "16.39941349999998\n",
      "85 3239\n",
      "18.782319099999995\n",
      "86 3302\n",
      "18.62075320000008\n",
      "87 3323\n",
      "22.591032399999904\n",
      "88 3373\n",
      "29.243697300000008\n",
      "89 3307\n",
      "31.651087000000075\n",
      "90 3532\n",
      "25.78653470000006\n",
      "91 3634\n",
      "44.454575699999964\n",
      "92 3567\n",
      "51.70604490000005\n",
      "93 3683\n",
      "39.469132700000046\n",
      "94 3722\n",
      "34.73447269999997\n",
      "95 3715\n",
      "39.70162259999995\n",
      "96 3809\n",
      "35.51797529999999\n",
      "97 3828\n",
      "33.278270099999986\n",
      "98 4041\n",
      "32.70419270000002\n",
      "99 4002\n",
      "37.121770800000036\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4002"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for x in range(100):\n",
    "    start = timeit.default_timer()\n",
    "    \n",
    "    xy_list = run_gen(xy_list)\n",
    "    print(x, len(xy_list))\n",
    "    \n",
    "    stop = timeit.default_timer()\n",
    "    print(stop-start)\n",
    "    \n",
    "    \n",
    "len(xy_list)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dot in xy_list:\n",
    "    plt.scatter(x=dot[0],y=dot[1], c='black', s=.5)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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