8.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inp_file = open('8.txt', 'r')\n",
    "inp = inp_file.read()\n",
    "lines = inp.split('\\n')[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def rect(num_row, num_col, display):\n",
    "    display[0:num_col, 0:num_row] = 1\n",
    "    return display\n",
    "\n",
    "def rot_row(row, num, display):\n",
    "    display[row] = np.roll(display[row], num)\n",
    "    return display\n",
    "\n",
    "def rot_col(col, num, display):\n",
    "    display[:, col] = np.roll(display[:, col], num)\n",
    "    return display\n",
    "\n",
    "def apply_command(display, command):\n",
    "    pat_rect = r'rect ([0-9]+)x([0-9]+)'\n",
    "    pat_rot_row = r'rotate row y=([0-9]+) by ([0-9]+)'\n",
    "    pat_rot_col = r'rotate column x=([0-9]+) by ([0-9]+)'\n",
    "    patterns = [pat_rect, pat_rot_row, pat_rot_col]\n",
    "    m_rect, m_row, m_col = map(lambda p: re.match(p, command), patterns)\n",
    "    \n",
    "    if sum(map(lambda x: x is not None, [m_rect, m_row, m_col])) != 1:\n",
    "        print('Pattern match error')\n",
    "        \n",
    "    if m_rect is not None:\n",
    "        return rect(int(m_rect.group(1)), int(m_rect.group(2)), display)\n",
    "    elif m_row is not None:\n",
    "        return rot_row(int(m_row.group(1)), int(m_row.group(2)), display)\n",
    "    elif m_col is not None:\n",
    "        return rot_col(int(m_col.group(1)), int(m_col.group(2)), display)\n",
    "    else:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end_display = reduce(apply_command, lines, np.zeros((6, 50)))\n",
    "np.sum(end_display)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10aa4a950>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Jr+NlSbBa23SKI0m7MljHNgpBzLOLmx9n4g1np4g/816SCzrvn1qeXR2HGnKOuZPRwXmI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\nCCGEGAVybIUQQgghxCj4/9tkuv4mAM8dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a6562d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,100))\n",
    "plt.imshow(end_display)"
   ]
  }
 ],
 "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",
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