2014_08_NumberCross.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Oq1xibzMT1Wl"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import itertools\n",
    "from IPython.display import Markdown, display,HTML,Image\n",
    "\n",
    "from z3 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "ICTfvjZUT1Wr"
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Puzzle as published \n",
       "https://www.janestreet.com/puzzles/number-cross-index/"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"inner-wrapper\">\n",
       "<p>Place the digits 1 thru 9 (no zeroes) in the crossword grid below so that all of\n",
       "the clues are satisfied. No digit is repeated in any one grid entry (e.g.\n",
       "1-across is a grid entry), and no grid entry is used more than once within the\n",
       "puzzle.</p>\n",
       "<p>For your answer, submit the sum of the 8 5-digit grid entries in the completed\n",
       "grid (e.g. 4-down, 18-across, etc.)</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url='https://www.janestreet.com/puzzles/number-cross-index/'\n",
    "res = requests.get(url)\n",
    "soup = BeautifulSoup(res.content, 'html.parser')\n",
    "display(Markdown('### Puzzle as published \\n'+url))\n",
    "display(Image('https://www.janestreet.com/puzzles/number_crossword_with_clues.png',width=500))\n",
    "HTML(str(soup.find_all('div', {'class' :'inner-wrapper'})[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "844RhDFpT1Wu"
   },
   "outputs": [],
   "source": [
    "grid = np.array([[0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n",
    "                 [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n",
    "                 [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
    "                 [1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n",
    "                 [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
    "                 [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n",
    "                 [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
    "                 [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1],\n",
    "                 [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
    "                 [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n",
    "                 [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0]\n",
    "                ],dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "844RhDFpT1Wu"
   },
   "outputs": [],
   "source": [
    "# some utility functions\n",
    "def mult(x):\n",
    "    n=len(x)\n",
    "    return Sum([x[i] *10**((n-1)-i)  for i in range(n)]) \n",
    "\n",
    "def sum_dig(x):\n",
    "    return Sum([i for i in x])\n",
    "\n",
    "def make_across(x,y,n):\n",
    "    return [X[x][y+i] for i in range(n)]\n",
    "\n",
    "def make_down(x,y,n):\n",
    "    return [X[x+i][y] for i in range(n)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "844RhDFpT1Wu"
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Solution 407,358 found after 3.0690 seconds**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "# Setup # 11x11 matrix of integer variables and solver \n",
    "X = np.array(IntVector(\"x\",11*11),dtype=object).reshape((11,11))\n",
    "s = Tactic(\"pqffd\").solver()\n",
    "\n",
    "# each non-black cell contains a value in {1, ..., 9} black cells = 0\n",
    "s += [And(e>0,e<=9) if grid[i,j] !=1 else e==0 for (i,j),e in np.ndenumerate(X)]\n",
    "\n",
    "# Set up the clues \n",
    "across = {}\n",
    "across[1]  = make_across(0,0,3)\n",
    "across[4]  = make_across(0,4,3)\n",
    "across[7]  = make_across(0,8,3)\n",
    "across[10] = make_across(1,0,3)\n",
    "across[11] = make_across(1,4,3)\n",
    "across[12] = make_across(1,8,3)\n",
    "across[13] = make_across(2,0,3)\n",
    "across[14] = make_across(2,4,7)\n",
    "across[16] = make_across(3,2,6)\n",
    "across[18] = make_across(4,0,5)\n",
    "across[20] = make_across(4,6,5)\n",
    "across[24] = make_across(5,0,4)\n",
    "across[25] = make_across(5,7,4)\n",
    "across[26] = make_across(6,0,5)\n",
    "across[28] = make_across(6,6,5)\n",
    "across[29] = make_across(7,3,6)\n",
    "across[31] = make_across(8,0,7)\n",
    "across[34] = make_across(8,8,3)\n",
    "across[37] = make_across(9,0,3)\n",
    "across[38] = make_across(9,4,3)\n",
    "across[39] = make_across(9,8,3)\n",
    "across[40] = make_across(10,0,3)\n",
    "across[41] = make_across(10,4,3)\n",
    "across[42] = make_across(10,8,3)\n",
    "\n",
    "down = {}\n",
    "down[1] = make_down(0,0,3)\n",
    "down[2] = make_down(0,1,3)\n",
    "down[3] = make_down(0,2,7)\n",
    "down[4] = make_down(0,4,5)\n",
    "down[5] = make_down(0,5,4)\n",
    "down[6] = make_down(0,6,5)\n",
    "down[7] = make_down(0,8,3)\n",
    "down[8] = make_down(0,9,3)\n",
    "down[9] = make_down(0,10,3)\n",
    "down[15] = make_down(2,7,6)\n",
    "down[17] = make_down(3,3,6)\n",
    "down[18] = make_down(4,0,3)\n",
    "down[19] = make_down(4,1,3)\n",
    "down[21] = make_down(4,8,7)\n",
    "down[22] = make_down(4,9,3)\n",
    "down[23] = make_down(4,10,3)\n",
    "down[27] = make_down(6,4,5)\n",
    "down[28] = make_down(6,6,5)\n",
    "down[30] = make_down(7,5,4)\n",
    "down[31] = make_down(8,0,3)\n",
    "down[32] = make_down(8,1,3)\n",
    "down[33] = make_down(8,2,3)\n",
    "down[35] = make_down(8,9,3)\n",
    "down[36] = make_down(8,10,3)\n",
    "\n",
    "clues =[v for k,v in across.items()]+[v for k,v in down.items()]\n",
    "\n",
    "# distinct clues and letters in clues\n",
    "s += Distinct([mult(i) for i in clues])\n",
    "s += [Distinct([x for x in i]) for i in clues]\n",
    "\n",
    "# The clues \n",
    "\n",
    "#squares\n",
    "square_numbers = [i**2 for i in range(10,32)]\n",
    "sq_across = [1,4,7,11,12]\n",
    "s += [Or([mult(across[i]) == x for x in square_numbers]) for i in sq_across]\n",
    "sq_down = [1,7,8,18,19,36]\n",
    "s += [Or([mult(down[i]) == x for x in square_numbers]) for i in sq_down]\n",
    "\n",
    "#primes\n",
    "prime_numbers = [*sympy.primerange(100,1000)]\n",
    "pr_across = [39]\n",
    "s += [Or([mult(across[i]) == x*2 for x in prime_numbers]) for i in pr_across]\n",
    "pr_down = [9,22]\n",
    "s += [Or([mult(down[i]) == x for x in prime_numbers]) for i in pr_down]\n",
    "\n",
    "# simple across\n",
    "s += mult(across[10]) < mult(down[23])\n",
    "s += And([mult(across[13]) <= mult(i) for i in clues])\n",
    "s += sum_dig(across[14]) ==42\n",
    "s += mult(across[16]) % 6 ==0\n",
    "s += mult(across[18]) == (mult(across[37])+mult(down[6]))\n",
    "s += And([And(mult(across[20]) >= mult(i),mult(across[20][::-1]) > mult(i)) for i in clues if len(i) == 5])\n",
    "s += Or([And([across[24][i]==j[i] for i in range(4)]) for j in itertools.permutations(down[5],4)])\n",
    "s += And([across[28][i-1] < across[28][i] for i in range(1,len(across[28]))])\n",
    "s += mult(across[25]) == (mult(across[42])+mult(down[7]))\n",
    "s += sum_dig(across[26]) == 26\n",
    "s += And([across[28][i-1] < across[28][i] for i in range(1,len(across[28]))])\n",
    "s += mult(across[29]) == (mult(across[4])*mult(down[35]))\n",
    "s += mult(across[31]) % 3 ==0\n",
    "s += mult(across[34]) % 3 ==0\n",
    "s += And([across[37][i-1] < across[37][i] for i in range(1,len(across[37]))])\n",
    "s += mult(across[38]) == (mult(across[39])-mult(across[13]))\n",
    "s += mult(across[40]) > mult(across[1])\n",
    "s += mult(across[41]) % 25 ==0\n",
    "s += mult(across[42]) %  2 ==1\n",
    "\n",
    "# simple down\n",
    "s += mult(down[2]) % 11 ==0\n",
    "s += And([mult(down[3]) >= mult(i) for i in clues])\n",
    "s += sum_dig(down[4]) == 16\n",
    "s += Or([mult(down[5]) * i  == mult(down[9]) for i in range(1,10)]+[mult(down[5]) == mult(down[9])*i for i in range(1,10)])\n",
    "s += sum_dig(down[6]) == 35\n",
    "s += mult(down[15]) % 11 ==0\n",
    "s += mult(down[17]) % 9 ==0\n",
    "s += sum_dig(down[21]) == 41\n",
    "s += mult(down[23]) > mult(down[32])\n",
    "s += mult(down[27])*2 == mult(down[6])\n",
    "s += mult(down[28]) % 125 ==0\n",
    "s += And([down[30][i-1] > down[30][i] for i in range(1,len(down[30]))])\n",
    "s += sum_dig(down[31]) == 6\n",
    "s += Or([mult(down[32]) *i  == mult(down[31]) for i in range(1,10)]+[mult(down[32]) == mult(down[31])*i for i in range(1,10)])\n",
    "s += Or([mult(down[33]) *i  == mult(down[31]) for i in range(1,10)]+[mult(down[33]) == mult(down[31])*i for i in range(1,10)])\n",
    "s += mult(down[35]) % 2 ==0\n",
    "\n",
    "# Solve and print \n",
    "if s.check() == sat:\n",
    "    m = s.model()\n",
    "    evalu = np.vectorize(lambda x:m.evaluate(x).as_long())\n",
    "    r = evalu(X)\n",
    "    display(Markdown(\"**Solution {:,.0f} found after {:.4f} seconds**\".format(sum([mult(evalu(x)) for x in clues if len(x) ==5]),time.time()-start)))\n",
    "    annot = r.astype('str')\n",
    "    annot[annot==\"0\"] =\"\"\n",
    "    fig,ax = plt.subplots(1,1,figsize=(5,5))\n",
    "    ax =sns.heatmap(grid,annot=annot,cbar=False,cmap=\"Greys\",fmt=\"\",linewidths=2,annot_kws={\"size\":14},linecolor='k')\n",
    "    ax.axis(\"off\")\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"Failed\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Puzzle as published \n",
       "https://www.janestreet.com/puzzles/number-cross-solution/"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"inner-wrapper\">\n",
       "<p>The answer to August’s puzzle is 407358. Congratulations to Chen-Yu Yang, this\n",
       "month’s randomly chosen winner and proud recipient of a Jane Street t-shirt!</p>\n",
       "<p>The full solution is presented here.</p>\n",
       "<h4 class=\"omit-as-of\" id=\"correct-submissions-from\"></h4>\n",
       "<p class=\"correct-submissions\" data-directory=\"2014-08-01-number-cross\"></p>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url='https://www.janestreet.com/puzzles/number-cross-solution/'\n",
    "res = requests.get(url)\n",
    "soup = BeautifulSoup(res.content, 'html.parser')\n",
    "display(Markdown('### Puzzle as published \\n'+url))\n",
    "display(Image('https://www.janestreet.com/puzzles/Aug14_Solution.png',width=500))\n",
    "HTML(str(soup.find_all('div', {'class' :'inner-wrapper'})[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = np.array([[3,2,4,0,2,5,6,0,5,2,9],\n",
    "                     [6,5,9,0,1,6,9,0,7,8,4],\n",
    "                     [1,3,2,0,4,8,5,3,6,9,7],\n",
    "                     [0,0,1,3,6,2,7,8,0,0,0],\n",
    "                     [6,9,7,1,3,0,8,7,2,1,9],\n",
    "                     [2,6,5,8,0,0,0,1,4,3,7],\n",
    "                     [5,1,8,9,3,0,3,4,5,7,8],\n",
    "                     [0,0,0,2,4,9,8,5,6,0,0],\n",
    "                     [2,6,8,4,7,5,1,0,7,9,8],                 \n",
    "                     [1,3,5,0,8,4,2,0,9,7,4],\n",
    "                     [3,9,2,0,9,2,5,0,8,6,1]\n",
    "                    ],dtype=int)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "name": "JaneSt-Feb18.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}