{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from __future__ import division\n",
    "from itertools import permutations, combinations\n",
    "import copy as cp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How big is your chess board?8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{(1, 1), (2, 5), (3, 8), (4, 6), (5, 3), (6, 7), (7, 2), (8, 4)},\n",
       " {(1, 1), (2, 6), (3, 8), (4, 3), (5, 7), (6, 4), (7, 2), (8, 5)},\n",
       " {(1, 1), (2, 7), (3, 4), (4, 6), (5, 8), (6, 2), (7, 5), (8, 3)},\n",
       " {(1, 1), (2, 7), (3, 5), (4, 8), (5, 2), (6, 4), (7, 6), (8, 3)},\n",
       " {(1, 2), (2, 4), (3, 6), (4, 8), (5, 3), (6, 1), (7, 7), (8, 5)},\n",
       " {(1, 2), (2, 5), (3, 7), (4, 1), (5, 3), (6, 8), (7, 6), (8, 4)},\n",
       " {(1, 2), (2, 5), (3, 7), (4, 4), (5, 1), (6, 8), (7, 6), (8, 3)},\n",
       " {(1, 2), (2, 6), (3, 1), (4, 7), (5, 4), (6, 8), (7, 3), (8, 5)},\n",
       " {(1, 2), (2, 6), (3, 8), (4, 3), (5, 1), (6, 4), (7, 7), (8, 5)},\n",
       " {(1, 2), (2, 7), (3, 3), (4, 6), (5, 8), (6, 5), (7, 1), (8, 4)},\n",
       " {(1, 2), (2, 7), (3, 5), (4, 8), (5, 1), (6, 4), (7, 6), (8, 3)},\n",
       " {(1, 2), (2, 8), (3, 6), (4, 1), (5, 3), (6, 5), (7, 7), (8, 4)},\n",
       " {(1, 3), (2, 1), (3, 7), (4, 5), (5, 8), (6, 2), (7, 4), (8, 6)},\n",
       " {(1, 3), (2, 5), (3, 2), (4, 8), (5, 1), (6, 7), (7, 4), (8, 6)},\n",
       " {(1, 3), (2, 5), (3, 2), (4, 8), (5, 6), (6, 4), (7, 7), (8, 1)},\n",
       " {(1, 3), (2, 5), (3, 7), (4, 1), (5, 4), (6, 2), (7, 8), (8, 6)},\n",
       " {(1, 3), (2, 5), (3, 8), (4, 4), (5, 1), (6, 7), (7, 2), (8, 6)},\n",
       " {(1, 3), (2, 6), (3, 2), (4, 5), (5, 8), (6, 1), (7, 7), (8, 4)},\n",
       " {(1, 3), (2, 6), (3, 2), (4, 7), (5, 1), (6, 4), (7, 8), (8, 5)},\n",
       " {(1, 3), (2, 6), (3, 2), (4, 7), (5, 5), (6, 1), (7, 8), (8, 4)},\n",
       " {(1, 3), (2, 6), (3, 4), (4, 1), (5, 8), (6, 5), (7, 7), (8, 2)},\n",
       " {(1, 3), (2, 6), (3, 4), (4, 2), (5, 8), (6, 5), (7, 7), (8, 1)},\n",
       " {(1, 3), (2, 6), (3, 8), (4, 1), (5, 4), (6, 7), (7, 5), (8, 2)},\n",
       " {(1, 3), (2, 6), (3, 8), (4, 1), (5, 5), (6, 7), (7, 2), (8, 4)},\n",
       " {(1, 3), (2, 6), (3, 8), (4, 2), (5, 4), (6, 1), (7, 7), (8, 5)},\n",
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       " {(1, 3), (2, 7), (3, 2), (4, 8), (5, 6), (6, 4), (7, 1), (8, 5)},\n",
       " {(1, 3), (2, 8), (3, 4), (4, 7), (5, 1), (6, 6), (7, 2), (8, 5)},\n",
       " {(1, 4), (2, 1), (3, 5), (4, 8), (5, 2), (6, 7), (7, 3), (8, 6)},\n",
       " {(1, 4), (2, 1), (3, 5), (4, 8), (5, 6), (6, 3), (7, 7), (8, 2)},\n",
       " {(1, 4), (2, 2), (3, 5), (4, 8), (5, 6), (6, 1), (7, 3), (8, 7)},\n",
       " {(1, 4), (2, 2), (3, 7), (4, 3), (5, 6), (6, 8), (7, 1), (8, 5)},\n",
       " {(1, 4), (2, 2), (3, 7), (4, 3), (5, 6), (6, 8), (7, 5), (8, 1)},\n",
       " {(1, 4), (2, 2), (3, 7), (4, 5), (5, 1), (6, 8), (7, 6), (8, 3)},\n",
       " {(1, 4), (2, 2), (3, 8), (4, 5), (5, 7), (6, 1), (7, 3), (8, 6)},\n",
       " {(1, 4), (2, 2), (3, 8), (4, 6), (5, 1), (6, 3), (7, 5), (8, 7)},\n",
       " {(1, 4), (2, 6), (3, 1), (4, 5), (5, 2), (6, 8), (7, 3), (8, 7)},\n",
       " {(1, 4), (2, 6), (3, 8), (4, 2), (5, 7), (6, 1), (7, 3), (8, 5)},\n",
       " {(1, 4), (2, 6), (3, 8), (4, 3), (5, 1), (6, 7), (7, 5), (8, 2)},\n",
       " {(1, 4), (2, 7), (3, 1), (4, 8), (5, 5), (6, 2), (7, 6), (8, 3)},\n",
       " {(1, 4), (2, 7), (3, 3), (4, 8), (5, 2), (6, 5), (7, 1), (8, 6)},\n",
       " {(1, 4), (2, 7), (3, 5), (4, 2), (5, 6), (6, 1), (7, 3), (8, 8)},\n",
       " {(1, 4), (2, 7), (3, 5), (4, 3), (5, 1), (6, 6), (7, 8), (8, 2)},\n",
       " {(1, 4), (2, 8), (3, 1), (4, 3), (5, 6), (6, 2), (7, 7), (8, 5)},\n",
       " {(1, 4), (2, 8), (3, 1), (4, 5), (5, 7), (6, 2), (7, 6), (8, 3)},\n",
       " {(1, 4), (2, 8), (3, 5), (4, 3), (5, 1), (6, 7), (7, 2), (8, 6)},\n",
       " {(1, 5), (2, 1), (3, 4), (4, 6), (5, 8), (6, 2), (7, 7), (8, 3)},\n",
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       " {(1, 5), (2, 1), (3, 8), (4, 6), (5, 3), (6, 7), (7, 2), (8, 4)},\n",
       " {(1, 5), (2, 2), (3, 4), (4, 6), (5, 8), (6, 3), (7, 1), (8, 7)},\n",
       " {(1, 5), (2, 2), (3, 4), (4, 7), (5, 3), (6, 8), (7, 6), (8, 1)},\n",
       " {(1, 5), (2, 2), (3, 6), (4, 1), (5, 7), (6, 4), (7, 8), (8, 3)},\n",
       " {(1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 7), (7, 3), (8, 6)},\n",
       " {(1, 5), (2, 3), (3, 1), (4, 6), (5, 8), (6, 2), (7, 4), (8, 7)},\n",
       " {(1, 5), (2, 3), (3, 1), (4, 7), (5, 2), (6, 8), (7, 6), (8, 4)},\n",
       " {(1, 5), (2, 3), (3, 8), (4, 4), (5, 7), (6, 1), (7, 6), (8, 2)},\n",
       " {(1, 5), (2, 7), (3, 1), (4, 3), (5, 8), (6, 6), (7, 4), (8, 2)},\n",
       " {(1, 5), (2, 7), (3, 1), (4, 4), (5, 2), (6, 8), (7, 6), (8, 3)},\n",
       " {(1, 5), (2, 7), (3, 2), (4, 4), (5, 8), (6, 1), (7, 3), (8, 6)},\n",
       " {(1, 5), (2, 7), (3, 2), (4, 6), (5, 3), (6, 1), (7, 4), (8, 8)},\n",
       " {(1, 5), (2, 7), (3, 2), (4, 6), (5, 3), (6, 1), (7, 8), (8, 4)},\n",
       " {(1, 5), (2, 7), (3, 4), (4, 1), (5, 3), (6, 8), (7, 6), (8, 2)},\n",
       " {(1, 5), (2, 8), (3, 4), (4, 1), (5, 3), (6, 6), (7, 2), (8, 7)},\n",
       " {(1, 5), (2, 8), (3, 4), (4, 1), (5, 7), (6, 2), (7, 6), (8, 3)},\n",
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       " {(1, 6), (2, 2), (3, 7), (4, 1), (5, 3), (6, 5), (7, 8), (8, 4)},\n",
       " {(1, 6), (2, 2), (3, 7), (4, 1), (5, 4), (6, 8), (7, 5), (8, 3)},\n",
       " {(1, 6), (2, 3), (3, 1), (4, 7), (5, 5), (6, 8), (7, 2), (8, 4)},\n",
       " {(1, 6), (2, 3), (3, 1), (4, 8), (5, 4), (6, 2), (7, 7), (8, 5)},\n",
       " {(1, 6), (2, 3), (3, 1), (4, 8), (5, 5), (6, 2), (7, 4), (8, 7)},\n",
       " {(1, 6), (2, 3), (3, 5), (4, 7), (5, 1), (6, 4), (7, 2), (8, 8)},\n",
       " {(1, 6), (2, 3), (3, 5), (4, 8), (5, 1), (6, 4), (7, 2), (8, 7)},\n",
       " {(1, 6), (2, 3), (3, 7), (4, 2), (5, 4), (6, 8), (7, 1), (8, 5)},\n",
       " {(1, 6), (2, 3), (3, 7), (4, 2), (5, 8), (6, 5), (7, 1), (8, 4)},\n",
       " {(1, 6), (2, 3), (3, 7), (4, 4), (5, 1), (6, 8), (7, 2), (8, 5)},\n",
       " {(1, 6), (2, 4), (3, 1), (4, 5), (5, 8), (6, 2), (7, 7), (8, 3)},\n",
       " {(1, 6), (2, 4), (3, 2), (4, 8), (5, 5), (6, 7), (7, 1), (8, 3)},\n",
       " {(1, 6), (2, 4), (3, 7), (4, 1), (5, 3), (6, 5), (7, 2), (8, 8)},\n",
       " {(1, 6), (2, 4), (3, 7), (4, 1), (5, 8), (6, 2), (7, 5), (8, 3)},\n",
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       " {(1, 7), (2, 3), (3, 1), (4, 6), (5, 8), (6, 5), (7, 2), (8, 4)},\n",
       " {(1, 7), (2, 3), (3, 8), (4, 2), (5, 5), (6, 1), (7, 6), (8, 4)},\n",
       " {(1, 7), (2, 4), (3, 2), (4, 5), (5, 8), (6, 1), (7, 3), (8, 6)},\n",
       " {(1, 7), (2, 4), (3, 2), (4, 8), (5, 6), (6, 1), (7, 3), (8, 5)},\n",
       " {(1, 7), (2, 5), (3, 3), (4, 1), (5, 6), (6, 8), (7, 2), (8, 4)},\n",
       " {(1, 8), (2, 2), (3, 4), (4, 1), (5, 7), (6, 5), (7, 3), (8, 6)},\n",
       " {(1, 8), (2, 2), (3, 5), (4, 3), (5, 1), (6, 7), (7, 4), (8, 6)},\n",
       " {(1, 8), (2, 3), (3, 1), (4, 6), (5, 2), (6, 5), (7, 7), (8, 4)},\n",
       " {(1, 8), (2, 4), (3, 1), (4, 3), (5, 6), (6, 2), (7, 7), (8, 5)}]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Determine the size of the board\n",
    "\n",
    "input_text = raw_input('How big is your chess board?')\n",
    "N = int(input_text)\n",
    "x = range(1, N + 1)\n",
    "\n",
    "\n",
    "master_list = []\n",
    "for item in permutations(range(1, N + 1)):\n",
    "    y = item\n",
    "    new_list = []\n",
    "    for x_value, y_value in zip(x, y):\n",
    "        new_list.append((x_value, y_value))\n",
    "    master_list.append(new_list)\n",
    "\n",
    "\n",
    "def IsDiagonal(point1, point2):\n",
    "    x1 = point1[0]\n",
    "    y1 = point1[1]\n",
    "    x2 = point2[0]\n",
    "    y2 = point2[1]\n",
    "    gradient = (y2 - y1) / (x2 - x1)\n",
    "    if gradient == -1 or gradient == 1:\n",
    "        return(True)\n",
    "    else:\n",
    "        return(False)\n",
    "\n",
    "\n",
    "solutions = []\n",
    "for possible_solution in master_list:\n",
    "    diagonal_clash_list = []\n",
    "    for piece1, piece2 in combinations(possible_solution, 2):\n",
    "        diagonal_clash_list.append(IsDiagonal(piece1, piece2))\n",
    "\n",
    "    if True not in diagonal_clash_list:\n",
    "        solutions.append(possible_solution)\n",
    "\n",
    "solutions = [set(solution) for solution in solutions]\n",
    "\n",
    "solutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{(1, 6), (2, 3), (3, 7), (4, 4), (5, 1), (6, 8), (7, 2), (8, 5)},\n",
       " {(1, 6), (2, 4), (3, 1), (4, 5), (5, 8), (6, 2), (7, 7), (8, 3)},\n",
       " {(1, 6), (2, 4), (3, 7), (4, 1), (5, 8), (6, 2), (7, 5), (8, 3)},\n",
       " {(1, 7), (2, 2), (3, 4), (4, 1), (5, 8), (6, 5), (7, 3), (8, 6)},\n",
       " {(1, 7), (2, 2), (3, 6), (4, 3), (5, 1), (6, 4), (7, 8), (8, 5)},\n",
       " {(1, 7), (2, 3), (3, 1), (4, 6), (5, 8), (6, 5), (7, 2), (8, 4)},\n",
       " {(1, 7), (2, 3), (3, 8), (4, 2), (5, 5), (6, 1), (7, 6), (8, 4)},\n",
       " {(1, 7), (2, 4), (3, 2), (4, 5), (5, 8), (6, 1), (7, 3), (8, 6)},\n",
       " {(1, 7), (2, 4), (3, 2), (4, 8), (5, 6), (6, 1), (7, 3), (8, 5)},\n",
       " {(1, 7), (2, 5), (3, 3), (4, 1), (5, 6), (6, 8), (7, 2), (8, 4)},\n",
       " {(1, 8), (2, 3), (3, 1), (4, 6), (5, 2), (6, 5), (7, 7), (8, 4)},\n",
       " {(1, 8), (2, 4), (3, 1), (4, 3), (5, 6), (6, 2), (7, 7), (8, 5)}]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove symmetry elements\n",
    "\n",
    "# Define C4 rotation\n",
    "def c4(points):\n",
    "    transformed_points = []\n",
    "    for point in points:\n",
    "        x, y = point\n",
    "        transformed_points.append(((N + 1) - y, x))\n",
    "    return(set(transformed_points))\n",
    "\n",
    "\n",
    "# Define y=x mirror plane\n",
    "def mirror(points):\n",
    "    transformed_points = []\n",
    "    for point in points:\n",
    "        x, y = point\n",
    "        transformed_points.append((y, x))\n",
    "    return(set(transformed_points))\n",
    "\n",
    "\n",
    "# Define solutions that are equivalent\n",
    "def symmetry_equivalent_solutions(solution):\n",
    "    equivalent_solutions = []\n",
    "    equivalent_solutions.append(solution)\n",
    "    equivalent_solutions.append(mirror(solution))\n",
    "    equivalent_solutions.append(c4(solution))\n",
    "    equivalent_solutions.append(mirror(c4(solution)))\n",
    "    equivalent_solutions.append(c4(c4(solution)))\n",
    "    equivalent_solutions.append(mirror(c4(c4(solution))))\n",
    "    equivalent_solutions.append(c4(c4(c4(solution))))\n",
    "    equivalent_solutions.append(mirror(c4(c4(c4(solution)))))\n",
    "    return(equivalent_solutions)\n",
    "\n",
    "\n",
    "#remove symmetry equivalent duplicates\n",
    "unique_solutions = cp.deepcopy(solutions)\n",
    "for n, solution in enumerate(solutions):\n",
    "    found_in_solutions = False\n",
    "    for related_solution in symmetry_equivalent_solutions(solution):\n",
    "        if related_solution in solutions[n + 1:]:\n",
    "            found_in_solutions = True\n",
    "            break\n",
    "    if found_in_solutions:\n",
    "        unique_solutions.remove(solution)\n",
    "\n",
    "unique_solutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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8ByP+3iyqxyxKx5qHElwLQmY1J+AKHanvLl2CjY04Qz0Y1GtjMIjv39gYGYzS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Wk4LtMpseZZYBalYc1plVjzKEk2RZYkSZIkSVJznNCRJEmSJEnqGSd0JEmSJEmSeqbRPXSkE/b2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\nkiRJUsc4oSNJkiRJktQx/x/vIdOyHFWUXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x36e7978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display solutions\n",
    "\n",
    "def plot_solution(points, savefile_name=None):\n",
    "    Z1 = np.zeros(N * N).reshape(N, N)\n",
    "    for r, x in enumerate(Z1):\n",
    "        for c, y in enumerate(x):\n",
    "            if (r + c) % 2 == 0:\n",
    "                Z1[r, c] = 1\n",
    "    plt.imshow(Z1, cmap=plt.cm.gray, interpolation='nearest',\n",
    "               extent=(0.5, N + 0.5, 0.5, N + 0.5))\n",
    "\n",
    "    row_labels = range(1, N + 1)\n",
    "    col_labels = [letter for letter in \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"][:N]\n",
    "    plt.xticks(range(1, N + 1), col_labels)\n",
    "    plt.yticks(range(1, N + 1), row_labels)\n",
    "    plt.xlim(0.5, N + 0.5)\n",
    "    plt.ylim(0.5, N + 0.5)\n",
    "    ax = plt.gca()\n",
    "    for line in ax.xaxis.get_ticklines():\n",
    "        line.set_visible(False)\n",
    "    for line in ax.yaxis.get_ticklines():\n",
    "        line.set_visible(False)\n",
    "    #plt.show()\n",
    "    for point in points:\n",
    "        plt.scatter(*point, color='r', s=500)\n",
    "    if savefile_name:\n",
    "        plt.savefig(savefile_name)\n",
    "    #return(plt.gcf())\n",
    "    \n",
    "def plot_solutions(list_of_solutions):\n",
    "    plt.gcf().set_size_inches(20,20)\n",
    "    for n, solution in enumerate(list_of_solutions):\n",
    "        plt.subplot(4,4,n+1)\n",
    "        plot_solution(solution)\n",
    "        \n",
    "\n",
    "plot_solutions(unique_solutions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}