TSP_GM_Solver.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import googlemaps as gm\n",
    "from googlemaps import distance_matrix\n",
    "from operator import itemgetter\n",
    "%matplotlib inline\n",
    "# Importing standard Qiskit libraries and configuring account\n",
    "from qiskit import QuantumCircuit, execute, Aer, IBMQ\n",
    "from qiskit.compiler import transpile, assemble\n",
    "from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "# Important stuffs\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.axes as axes\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "from itertools import combinations\n",
    "from docplex.mp.model import Model\n",
    "import math\n",
    "import googlemaps as gm\n",
    "from googlemaps import distance_matrix\n",
    "from operator import itemgetter\n",
    "\n",
    "# Qiskit stuff\n",
    "import qiskit\n",
    "from qiskit import Aer\n",
    "from qiskit import BasicAer\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "from qiskit.aqua import run_algorithm\n",
    "from qiskit.aqua.input import EnergyInput\n",
    "from qiskit.optimization.ising import docplex, max_cut, tsp\n",
    "from qiskit.aqua.algorithms import VQE, ExactEigensolver\n",
    "from qiskit.aqua.components.optimizers import SPSA\n",
    "from qiskit.aqua.components.variational_forms import RY\n",
    "from qiskit.aqua import QuantumInstance, aqua_globals\n",
    "from qiskit.optimization.ising.common import sample_most_likely\n",
    "\n",
    "# setup aqua logging\n",
    "import logging\n",
    "from qiskit.aqua import set_qiskit_aqua_logging\n",
    "# set_qiskit_aqua_logging(logging.DEBUG)  # choose INFO, DEBUG to see the logq\n",
    "\n",
    "IBMQ.save_account(\"5ad49ab7112adacf305682866bfa4538c541f1d3cfe5b98813e26661374a3e6a940a420f86296d302519f14989469f5eaae8905e9161d43d0c1cb2a103b9d8e7\", overwrite = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "maps = gm.Client('AIzaSyA9BHpsNjLIuM5uuUNcA7Qa0A0rMu_npHc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[42.3592444, -71.0931389], [42.2611926, -71.79496069999999], [40.7534672, -73.9699388], [42.3634422, -71.0257812], [41.8309534, -71.4149965]]\n",
      "[{'elements': [{'distance': {'text': '1 m', 'value': 0}, 'duration': {'text': '1 min', 'value': 0}, 'status': 'OK'}, {'distance': {'text': '73.3 km', 'value': 73310}, 'duration': {'text': '54 mins', 'value': 3247}, 'status': 'OK'}, {'distance': {'text': '335 km', 'value': 335080}, 'duration': {'text': '3 hours 32 mins', 'value': 12735}, 'status': 'OK'}, {'distance': {'text': '8.4 km', 'value': 8439}, 'duration': {'text': '18 mins', 'value': 1107}, 'status': 'OK'}, {'distance': {'text': '90.3 km', 'value': 90343}, 'duration': {'text': '1 hour 3 mins', 'value': 3802}, 'status': 'OK'}]}, {'elements': [{'distance': {'text': '70.9 km', 'value': 70901}, 'duration': {'text': '53 mins', 'value': 3198}, 'status': 'OK'}, {'distance': {'text': '1 m', 'value': 0}, 'duration': {'text': '1 min', 'value': 0}, 'status': 'OK'}, {'distance': {'text': '276 km', 'value': 276464}, 'duration': {'text': '2 hours 57 mins', 'value': 10591}, 'status': 'OK'}, {'distance': {'text': '78.1 km', 'value': 78083}, 'duration': {'text': '53 mins', 'value': 3206}, 'status': 'OK'}, {'distance': {'text': '62.3 km', 'value': 62315}, 'duration': {'text': '46 mins', 'value': 2764}, 'status': 'OK'}]}, {'elements': [{'distance': {'text': '334 km', 'value': 334350}, 'duration': {'text': '3 hours 31 mins', 'value': 12678}, 'status': 'OK'}, {'distance': {'text': '277 km', 'value': 276775}, 'duration': {'text': '2 hours 55 mins', 'value': 10520}, 'status': 'OK'}, {'distance': {'text': '1 m', 'value': 0}, 'duration': {'text': '1 min', 'value': 0}, 'status': 'OK'}, {'distance': {'text': '342 km', 'value': 341532}, 'duration': {'text': '3 hours 31 mins', 'value': 12685}, 'status': 'OK'}, {'distance': {'text': '286 km', 'value': 285629}, 'duration': {'text': '3 hours 0 mins', 'value': 10794}, 'status': 'OK'}]}, {'elements': [{'distance': {'text': '10.3 km', 'value': 10255}, 'duration': {'text': '17 mins', 'value': 1028}, 'status': 'OK'}, {'distance': {'text': '79.1 km', 'value': 79098}, 'duration': {'text': '56 mins', 'value': 3330}, 'status': 'OK'}, {'distance': {'text': '341 km', 'value': 340868}, 'duration': {'text': '3 hours 34 mins', 'value': 12819}, 'status': 'OK'}, {'distance': {'text': '1 m', 'value': 0}, 'duration': {'text': '1 min', 'value': 0}, 'status': 'OK'}, {'distance': {'text': '83.2 km', 'value': 83238}, 'duration': {'text': '58 mins', 'value': 3480}, 'status': 'OK'}]}, {'elements': [{'distance': {'text': '84.4 km', 'value': 84423}, 'duration': {'text': '1 hour 2 mins', 'value': 3707}, 'status': 'OK'}, {'distance': {'text': '62.7 km', 'value': 62709}, 'duration': {'text': '47 mins', 'value': 2825}, 'status': 'OK'}, {'distance': {'text': '285 km', 'value': 284692}, 'duration': {'text': '3 hours 2 mins', 'value': 10926}, 'status': 'OK'}, {'distance': {'text': '81.9 km', 'value': 81941}, 'duration': {'text': '56 mins', 'value': 3353}, 'status': 'OK'}, {'distance': {'text': '1 m', 'value': 0}, 'duration': {'text': '1 min', 'value': 0}, 'status': 'OK'}]}]\n",
      "[[  0.   73.3 335.    8.4  90.3]\n",
      " [ 70.9   0.  276.   78.1  62.3]\n",
      " [334.  277.    0.  342.  286. ]\n",
      " [ 10.3  79.1 341.    0.   83.2]\n",
      " [ 84.4  62.7 285.   81.9   0. ]]\n"
     ]
    }
   ],
   "source": [
    "locations = ('77 Massachusetts Ave, Cambridge, MA 02139','2 Washington Square, Worcester, MA 01604','885 2nd Ave, New York, NY 10017','1 Harborside Dr, Boston, MA 02128', '82 Smith St, Providence, RI 02903')\n",
    "coordinates = []\n",
    "for i in locations:\n",
    "    coordinates.append(list(maps.geocode(i)[0]['geometry']['location'].values()))\n",
    "\n",
    "print(coordinates)\n",
    "raw_matrix = maps.distance_matrix(coordinates, coordinates, mode='driving')\n",
    "rows_dist = raw_matrix['rows']\n",
    "print(rows_dist)\n",
    "\n",
    "distance_matrix = []\n",
    "for i in rows_dist:\n",
    "    h = list(i.values())[0]\n",
    "    list_thing = []\n",
    "    for j in range(len(h)):\n",
    "#         print(j, h[j]['distance'])\n",
    "        if h[j]['distance']['text'].replace(' km', '') == '1 m':\n",
    "            list_thing.append(0)\n",
    "        else:\n",
    "            list_thing.append(float(h[j]['distance']['text'].replace(' km', '')))\n",
    "#         print(list_thing)\n",
    "        if(j == 4):\n",
    "            distance_matrix.append(list_thing)\n",
    "    \n",
    "# print(distance_matrix)\n",
    "weight_matrix = np.array(distance_matrix)\n",
    "print(weight_matrix)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 1), (0, 2), (0, 3), (0, 4), (1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]\n",
      "[(0, 1), (0, 2), (0, 3), (0, 4), (1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]\n",
      "distance\n",
      " [[ 0.  7. 60. 13. 95.]\n",
      " [ 7.  0. 54.  7. 92.]\n",
      " [60. 54.  0. 47. 50.]\n",
      " [13.  7. 47.  0. 85.]\n",
      " [95. 92. 50. 85.  0.]]\n",
      "[[ 0.  7. 60. 13. 95.]\n",
      " [ 7.  0. 54.  7. 92.]\n",
      " [60. 54.  0. 47. 50.]\n",
      " [13.  7. 47.  0. 85.]\n",
      " [95. 92. 50. 85.  0.]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:579: MatplotlibDeprecationWarning: \n",
      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
      "  if not cb.iterable(width):\n"
     ]
    },
    {
     "data": {
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LliwBm81GQEAA7VbmhPQgodsIXC4XPj4+MDc3h62tLdXlCJWpqSkyMzNFclQik8lEcHAwduzYgWfPngn9+sLy888/Iz4+HkpKSrC1tcW5c+dotzonJB8J3UbYsmULqqur4enpSXUpQqekpARjY2OR7d7S19fHkiVL4OPjg8rKSpHcQxjU1NTg5eWF9evXIyoqCm5ubnjz5g3VZRFShIRuAx0/fhxXr17F2rVrJXpSoT6iGB373Pjx42FgYIAtW7aI7B7CYmZmhpiYGJiZmcHJyQmHDh0i42WEUJDQbYC0tDTs2LEDmzdvlvhJhfqIOnQZDAZ8fHzw999/46+//hLZfYRFUVERM2fOxN69e/HXX39h+vTpePLkCdVlERKOhO53ZGdnw9vbG4GBgejUqRPV5YiUKPu6fC1atEBgYCCCgoIkZldYp06dsGvXLkyePBkLFy7E1q1bKTtJjZB8JHTrUVJSAjc3N7i4uKBfv35UlyNySkpK6NGjB+7duyfS+5iZmcHe3h6+vr4S8yM7g8HAhAkTEBcXh9zcXNjZ2eH69etUl0VIIBK6deBwOPDx8YGlpSUmT55MdTliI+oWA9/06dMBAPv27RP5vYRJW1sbwcHB8PLyQmBgIPz8/FBYWEh1WYQEIaFbhy1btoDD4cDDw4PqUsRKXKErJyeHgIAAxMbGinxlLQo//PAD4uLioKWlBVtbWyQlJZHxMqJBSOjWIjExEf/88w9CQkIgLy9PdTliZWpqioyMDLE82rxt27Zgs9lgs9n4+PGjyO8nbKqqqnB3d8fmzZsRHR2NRYsW4fXr11SXRdAcCd2vpKamYteuXVJxpkJTKCsri6WvyzdkyBAMHjwYQUFBErtSNDExwYEDB9C/f39MmzYNBw4coMWRlgQ9kdD9zKtXr+Dj44OgoCCpn1Soj7haDHyurq548eIFTp48KbZ7CpuCggKmTZuG/fv349q1a3B2dsbDhw+pLougIRK6//n48SPc3Nwwb948WFpaUl0OpczNzcUaukpKSggODsa2bdsk/im+enp6CA8Ph5OTE5YuXYrQ0FCxtGoIyUFCF58mFVasWIEBAwbA2tqa6nIoZ2ZmhoyMDJSXl4vtnoaGhliwYAF8fHwk/ohFBoOBsWPHIi4uDoWFhbCzs0NycjLVZRE0QUIXQGhoKBgMBtzc3KguhRaUlZXRvXt3sU8VTJo0CXp6eti2bZtY7ysqLVu2xJo1a8Bms7Fu3Tr4+Pjg/fv3VJdFUEzmQzchIQHXr19HcHCwzE0q1EfcfV3g0wqRzWbj0qVLuHr1qljvLUr9+/dHXFwcdHR0YGdnh5MnT0rsi4ZE88l06N68eRMREREICwtDixYtqC6HVlgsFlJTU8V+Xw0NDQQGBiIgIABv374V+/1FRVlZGUuWLMH27dsRHx+PefPm4eXLl1SXRVBAZkP35cuXWLlyJUJCQtCxY0eqy6EdKvq6fH369MHkyZOxatUqidkm3FDdu3fHvn378NNPP2HGjBn4/fffUV1dTXVZhBjJZOgWFxfDzc0N8+fPB4vForocWlJWVoaRkRH+/fdfSu4/a9YsVFdX4+DBg5TcX5Tk5eXh6OiI6Oho3LlzB05OTpR9nQnxk7nQrampgbe3N3744QdMmjSJ6nJojaoWA/ApmAICAhATE4P79+9TUoOo6erqYsuWLZg1axY8PT2xfv16lJaWUl0WIWIyF7qhoaFQUFCAq6sr1aXQnqgeVtlQOjo68Pb2xsqVK1FSUkJZHaLEYDAwcuRIxMfHo6KiAlOmTJGIs4aJppOp0I2Pj8fNmzfJpEIDmZqa4smTJ5T0dfmGDRuGAQMGICQkRKpf8dfQ0ICfnx/WrFmDLVu2wMvLS6peSCT+n8yE7o0bN7B7926EhYVBXV2d6nIkgoqKCqV9XT53d3dkZGTgzJkzlNYhDhYWFoiNjYW+vj4cHByQmJgodS8myjqZCN2srCyw2WysXbsWenp6VJcjUajs6/Lxtwlv3rxZJsasmEwmFixYgIiICJw6dQouLi54/vw51WURQiL1ocufVFi4cCHMzc2pLkfisFgspKWlUV0Gunbtirlz50rFNuGG6tKlC/bs2YORI0di9uzZiIyMlJnPXZpJdejyJxV+/PFH/Prrr1SXI5HMzMzw+PFjWjwTbPLkyWjXrh127NhBdSliIycnB1tbWxw+fBiPHj2Co6Mjbt++TXVZRDNIbejyeDxs2LABioqKZFKhGVRUVNCtWzdaPN2BwWDAz88P586dk7kDZNq2bYtNmzYJDgUKDg6WyIPfCSkO3fj4eNy+fRvBwcGQk5PaT1MsqB4d+5ympiYCAwOxZs0aFBQUUF2OWDEYDAwbNgxHjhwBg8GAra0t/vzzT6me6pBGUplG165dw549exAWFgY1NTWqy5F4VBx+Ux9zc3NMnDhRKrcJN0SLFi2wYsUKhISEYNeuXfDw8EBeXh7VZRENJHWh++LFC/j5+WHt2rXo0KED1eVIBTr1dfnmzJmDsrIyxMTEUF0KZfr06YOYmBgYGxvD0dERcXFxMvlNSNJIVejyJxUWL16Mvn37Ul2O1KBTX5dPXl4eQUFBOHDggEw/FofJZGLOnDmIiorC+fPnMXPmTGRmZlJdFlEPqQndmpoaeHl54eeff8b48eOpLkfq0GV07HO6urpYvnw5Vq5cKfOPxDEwMEBkZCQmTJiAefPmITw8HJWVlVSXRdRCKkKXx+Nh/fr1UFFRweLFi6kuRyrRra/LN2LECLBYLKxbt47qUignJycHa2trxMbG4uXLl7C3t8fNmzepLov4ilSEblxcHO7evYugoCAyqSAiZmZmePToEa36unweHh548OABkpKSqC6FFlq3bo1169bBzc0N/v7+WLNmDYqKiqgui/iPxCdUcnIy9u7di7CwMKiqqlJdjtRSVVVFt27dKD+HoTYqKioIDg5GaGgosrOzqS6HNoYMGYL4+HioqKjA1tYW//vf/8h4GQ1IdOg+f/4cq1atwrp169C+fXuqy5F6dG0xAICRkRHmzJkDHx8f8iSGz6iqqmLZsmXYtGkT9u7dC1dXV+Tk5FBdlkyT2NAtKiqCm5sbXF1d0adPH6rLkQnm5ua0DV0AsLW1RatWrbBz506qS6GdXr16ITo6Gn379oWzszNiYmLA4XCoLksmSWToVldXY9myZRg2bBjGjRtHdTkyo3fv3nj06BFtXxVnMBhYtWoVzp49i+vXr1NdDu0oKChgxowZ2Lt3L/7++29Mnz4djx8/prosmSNxocufVFBXV8eiRYuoLkemqKqqomvXrrTs6/JpaWnB398ffr6+8Jk+HeO6dMGQdu3g2Ls3khMSqC6PFjp16oQdO3bA1tYWixcvxpYtWyg9qF7WSFzoHj58GPfv30dgYCCZVKAAnfu6fP0sLODL4aDVkSOIqKnBZU1NzH/7Ft5OTsiJjKS6PFpgMBgYP348YmNj8fbtW9jZ2eHatWtUlyUTJCq1kpOTceDAAYSGhpJJBYpIQuji+HH88Pw5bFRUoCwvDzkNDfzYvj3aKynh0Zo1wNOnVFdIG9ra2ggMDIS3tzeCg4Ph5+eHDx8+UF2WVJOY0H327BlWrVqF9evXQ1dXl+pyZFbv3r2Rnp5O274uAGDXLjCUlNBeTw8F796hvKIC72tq8LKmBoZycoAMn9dQl0GDBiEuLg7a2tqws7PD6dOnyXiZiEhE6BYWFsLNzQ1ubm4wMzOjuhyZpqqqii5dutCyr1taWorHjx+j7P59vCsvR8G7dwCDgcznz+GemYlfVFTQSVUVoNEZEnSioqKCpUuXYsuWLTh8+DAWLlyIV69eUV2W1FGguoDv4U8qjBw5EmPHjqW6HAL/fw6DhYWFWO/L5XJRUFCA7Ozsb369fv0aFRUV0NPTwwYOB8qVlVBSVYWcvDz88/OhyGTCmcdDTlYWnisq4nVsLAYNGoSOHTuCwWCI9fOgO2NjYxw4cACHDh3C9OnT4ezsDCcnJygo0D4uJAKDV8/PEBYWFpQ+lJDH4yEgIADFxcVYv349eeGMJpKTk7Fv3z5EiuBFqaqqKuTm5tYZrGpqatDT00OHDh2gp6f3xS9tbe1PAbppExAeDl7LlvB+9gw51dWIMjKCEoMBzocPuLNkCf4oKUFycjIUFBQwcOBADBo0CJaWluS1gq/k5OQgODgYBQUFYLPZ6NmzJ9UlSYT6spPWoRsTE4MzZ84gKiqK/GOgkbKyMowaNQp//vknmExmoz/+48ePeP36da3BWlBQgLZt2wqC9OtwbdDfg6IiYNIkBN++jbsVFQjV0UEHVVWgvBwYPRrYvh2QkwOPx8Pz58+RnJyM5ORk3L9/H8bGxhg0aBAGDhyIbt26kVUwPi1+zp49i7CwMIwaNQrz588n/x6/QyJD9+rVqwgODsbevXuho6NDSQ1E3aZPn47FixeDxWJ98zYul4t3797VGqrZ2dmorq7+ZpXKD1cdHR2h/Bj75tEjjB8xArzcXKjIyYGhrAzo6sJn0yaMqWNDTXl5OdLS0pCcnIyUlBSUlpYKVsH9+/eHpqZms+uSZEVFRQgLC0Nqaiq8vb0xePBgqkuiLYkL3adPn2LevHkIDQ2Fqamp2O9PfF9YWBjKy8sxZMgQwY/+n7cB1NXVvwlW/q+WLVuKZQXJ4XAwZMgQnP/f/6Cqrt7oj8/OzkZKSgpSUlJw69YtGBgYCFbBPXv2lNl2140bNxAUFAQTExN4enqiVatWVJdEOxIVuh8+fMBvv/2G+fPnY8yYMWK9N/Gl4uLib3qq/N9nZWWhpKQE48aNq3XVqqKiQnX5eP78Odzc3HD8+PFmX6uqqgp3794VhHBeXh769++PgQMHYuDAgWjTpo0QKpYcFRUViIqKwokTJ7Bw4UJMmDBBZr8J1UZiQreqqgoLFiyAubk5FixYILb7yioul4u3b9/WGaw1NTV1rlbV1dVhZWXV5L6uOJw7dw7nzp3Dxo0bhX7t/Px8XLt2DSkpKbh+/TratWsnaEX07t0bioqKQr8nHT158gSBgYFQVlbGypUr0blzZ6pLooX6spM2MyA8Hg8hISHQ1tbGvHnzqC5HalRVVdX6otXr16+Rk5MDDQ2NL1aoP/74o+C/tbS06m0DGBoa4v79+zA3NxfjZ9RwmZmZ6Nq1q0iu3bZtW0yYMAETJkwAh8PBgwcPkJKSgm3btuHFixdgsViCEJbmB6QaGRlh3759iIuLw8yZM+Hg4IDffvtNZr7pNAVtVroHDx7E2bNnERUVRYsfTSUFj8fDx48f63zRqrCwEDo6OrW+aNWhQwcoKys3+d5bt26FsrIyXFxchPgZCY+7uzusrKwwfPhwsd63sLAQN27cEExFqKurCwKYxWI162tOZ7m5uVi7di1ycnLAZrNleiMT7Ve6V65cwaFDh7Bv3z4SuLXgcrnIz8+vM1h5PN4XoWpqaooxY8agQ4cO0NHREVmvzcLCAvv37xfJtYVBlCvd+mhpaWHkyJEYOXIkuFwuMjMzkZycjP3792PFihUwNTUVhLCBgYHUjKXp6OggLCwMFy5cEDwkdtGiRVBvwouY0ozylW5GRgYWLFiAzZs3y/TgdWVlZZ2zq7m5udDU1Ky1t9qhQwdoampS8g+3tLQUo0ePpmVft6ysDL/88guuXr1Kqxd4SktLkZqaKlgFczgcDBo0SLA5o0WLFlSXKBTFxcXYunUrkpOTsWzZMgwdOpTqksSKtivd9+/fw93dHZ6enlIfuDweD0VFRbW+YJWdnY2ioiLo6up+sRlgwIABgmBVUlKi+lP4hpqaGgwMDGjZ183MzISBgQGtAhf49DX76aef8NNPP4HH4+Hly5dITk7G8ePH4e/vDyMjI8FERI8ePWhXf0NpaGiAzWYjLS0NgYGBSEpKwrJly9C2bVuqS6McZaFbVVUFT09PWFlZYdSoUVSVIVRcLveLLaxfB6ucnNwXK9TevXvDysoKenp6aNu2rUT+A7OwsMCtW7doGbrdunWjuox6MRgMdO7cGZ07d4aDgwMqKyuRlpaGlJQUrFq1CoWFhRgwYAAGDhyIAQMGQFtbm+qSG83c3ByxsbH4/fff4eDggHnz5sHGxkYi/64LCyXtBR6PB39/f1RUVMjmdp8AAB8uSURBVCAkJESi/gAqKiq+2QjweRtAS0urzjErDQ0NqssXun/++QcHDx7Erl27qC7lC+vXr0eHDh0wdepUqktpsjdv3gjmgm/evImOHTsKesGmpqaQl5enusRGefbsGQIDAwEAbDYbhoaGFFckOrSb092/fz/Onz+PqKgo2r2Sy+PxUFhYWOuIVXZ2NoqLi9G+ffs6pwHo1tsUNbr2dV1cXDBr1iz079+f6lKEoqamBvfu3UNKSgqSk5ORk5MDS0tLQStCUrbKc7lcJCYmYteuXZg8eTJmzpxJq783wkKr0P3rr7+wbt067Nu3j7L+DofDQV5eXp3TAAoKCnW+aNWmTRuJWpmLw7Rp07B06VLatBh4PB6GDx+O+Ph4qd2i+v79e1y7dg3Jycm4du0atLW1Bavgvn370j7I8vPzsWHDBjx79gwrV66kzd8dYaHNC2n83StbtmwReeCWl5fXuSkgLy8P2traX7xo9csvvwiCVRrbAKJEt77u27dvIS8vL5E90IbS1tbG2LFjMXbsWHC5XDx69AjJycmIiIhAZmYm+vTpI5iKoOOZwW3btsWGDRtw6dIlsNlsDBo0CEuWLJGJf3uiCd3qahzx9cWpuDhkFhZilJkZlm7aBI/Vq+Hl5QUTE5Nm34LH4+HDhw91rlZLS0u/+LHfwMBAsNtKV1eX9isBSWJubo7o6GjMmTOH6lIA/P98Lt2CRlTk5ORgYmICExMTzJ49G8XFxbh586bgmYIKCgqCALawsKDVsYxDhw6FpaUlwsPDYWtrCw8PD4wYMUKq/+yE314oLwemT8fF/+YjU6qqUFFdjflcLm47OWFMeHiDL8XhcPDmzZs6V6yKiop1nrvaunVr0gYQk5KSEowZM4Y2fd0DBw7g7du38PDwoLoUyvF4PDx79kxwXCWdzwy+d+8eAgMD0b59e3h7e0tMn7o24m0vhIcDN25gmK4uwGDgQX4+3paXg6esjNEXLgB5eUC7doJ3Lysrq3W8Kjs7G/n5+WjduvUXYdqzZ88vDl0hqKeurg4DAwM8ePAAffv2pbocZGZm1nrOryxiMBjo0qULunTpAmdnZ5SVleHWrVtISUnB8uXLUV5eLngxbsCAAZT+eG9mZoaYmBgcOHAAU6dOxezZs2FnZyd1iyfhrnQ5HID/j+6/Fc/658+RXVaGEH19oLAQaUOH4nzXrrW2Ab7+paurSw7OkBBbtmyBmpoaZs+eTXUpcHR0BJvNFkobS9q9evXqizODu3TpIghhKs8MzsrKQlBQECoqKsBms2FkZERJHU0lvpVucTFQVgZoaQEAeADef/iAyv9+xNFkMJD/99/IBNCuXTsYGxtDR0cH6urqgl9qampQV1eHsrIyqquroaCgQJsff4i6sVgsxMTEUB66NTU1ePHihVTPgApTx44d0bFjR9ja2qKqqgp37txBSkoKAgMD8fbtW8HmjIEDB6J169Ziq6tz587YtWsXTp48KTiv18XFhZY7MxtLuCvdqirA1BRQUQH+e+TKxpcvkVVcjKWqqminqIg3kyYhfexYlJSUoLS0FCUlJfX+vrKyEmpqaoIw/jqcv/59be+jpqZGnmQqYiUlJRg7diwuXLhAaV/32bNn8PDwwLFjxyirQVrwzwxOTk7GjRs3oKOjIwhgcZ4ZXFBQgI0bNyI9PR0+Pj7o16+fWO7bHOJb6TKZgI0NEBsL/Deuo6qsDG0FBbRSUkJRXh6SlJTg2IiT9jkcToPC+e3bt4Lff/22kpISKCoq1hnaDQlzNTU1KCsrk1V3HdTV1dG5c2fK+7pUnSwmjWo7Mzg5ORlbt25FVlYWWCyWYCqiffv2IqujVatWCAkJwdWrV7FmzRpYWFhg6dKl0PrvJ2pJI/zln4cH8Pff4Lx6BY6qKrhcLniVlVBVUQFz1SrIq6nBzs4O9vb2cHZ2/u5RjvLy8tDQ0GhWg5/H46GiouK7q+vc3Nwvgvrr96upqak1rOsL6treT9peGOBjsVi4desWCV0pJC8vDzMzM5iZmWHevHkoLCzE9evXkZycjMjISKirqwsmIkR1ZvCPP/4IFouFnTt3wtbWFkuXLsWYMWMkbiEkmh1pHz4gcu5cRJ45A3C5n9oNbdrAxcMDLi4uyMnJwfbt23Hnzh0sWLAAY8eOlYggqqmpqTO06wrqr39fVlYGZWXlBgV1XatxdXV1KCoq0u4v29WrV3H48GHs2LGDshrc3d0xbtw4DBs2jLIaZA2Xy0VGRoZgLO3Ro0cwNTUVhLAozgx+8OABAgMD0apVK6xYsYJ2T+eg1Tbgz927dw9hYWGorq6Gm5ubTIz58Hg8lJeX1xvSDel3c7ncRgf11++nqqoq1G92Hz9+hJWVFaV93QkTJmD79u3o1KkTJfcnPvX3b968KTgngsfjCQK4X79+Qhv1rKmpEYyYTZ8+HY6OjrQ5BIi2oQt8CqHz589j27Zt6N69O5YsWUL+wTRAVVVVvavuhqzIKyoqoKKi0qD+dn3h/vkLKs7OzvDw8ECfPn3E/jUpLS3FqFGjcOXKFYn4yUkW8Hg8ZGVlCVbBd+/ehZGRkSCEu3fv3uw/q1evXiEkJARFRUVgs9kwNjYWUvVNR+vQ5auqqsLhw4dx4MABWFlZYfbs2TKxD5tKXC4XZWVljQrq2t5HTk5OEMAvX76Euro6LC0tGzVxoqKi0uwfQe/du4cNGzbg4MGDQvoKEcJWUVEhODM4OTkZxcXFX2zOaNmyZZOuy+PxkJSUhC1btmDMmDGYN28epY/+kojQ5Xv//j0iIiJw8eJFzJw5E5MnTyYbJGiMx+OhqqpKEMKXL1/GyZMnsWDBgga3SoQ1Gnju3Dmkp6fDz8+P6i8L0UA5OTmCsbSbN2+iU6dOzToz+MOHDwgLC8OdO3fg7e2NQYMGiajy+klU6PI9e/YMmzdvRnZ2NlxdXTFkyBDavXBEfIvf1/3zzz8b9c2yoaOB9a3GMzIyoKKiAgMDAzIaKIGqq6vx77//Cp4f9+bNG1haWgpaEe0+Oz7ge1JSUhASEgJTU1N4eHiI/cQ5iQxdvpSUFISFhUFLSwvu7u7o0aMHpfUQ3+fk5IRly5ahd+/eYr3vnDlz4OzsDGNj4wYHtbBHAz//PekrN09BQcEXZwa3atVKEMANOTO4vLwcu3fvxunTp7Fo0SKMHz9ebN9MJTp0gU+roBMnTiAiIgIDBw7EggULyAPuaCwsLAyampqYOXOm2O7J4/EwbNgwJCQkNHtVQ0YD6YfL5SI9PV3wglxmZib69u0rCOH6zgx+/PgxAgICoK6uDh8fH7G8UC/xoctXWlqKffv2ISEhocGbKwjxu3LlCmJjY8U6r5ufnw8nJyecO3dObPesjzSPBtJBcXExrl+/Ljish8lkCgK4tjODORyO4AGZU6dOxbRp00R6NIDUhC7fmzdvsH37dty+fRvz58+HlZWV1P2lkmRN7es2R3JyMqKjoyndmCEKdB0NpBMej4enT58KVsEPHjyAiYmJIIQ/P9A+JycHa9euRV5eHnx9fdGrVy/+RVB86RLWBAXhWlYWtPT0sMjbG6NHj25STVIXunz//vsvwsLCUFlZCTc3N1hYWFBdEvEfcfd1Dxw4gHfv3sHd3V0s95Mkn48G1hfajRkNbMh2d1GNBn5PWVkZUlNTkZKSgn/++QdVVVWCAO7fvz9atGiBc+fOITQ0FCNGjMBCBweoLlwIn6tXweNw4KupiScAXGtqsPfPP2HYhLlfqQ1d4NN3uQsXLmDbtm3o1q0bXF1dyeYKGggNDYWWlpbY+rq+vr6wtLTEhAkTxHI/WfP1aGBj+tvfGw2sL7Rr+/+NOTWQx+Ph1atXghfk0tLSBGcGm5mZ4X9nz2JoRAR6MRgYW1SEI126oBOTCfB48Hv2DG0sLbG4CS0r2jyYUhQYDAZ++eUX/PTTT4iNjcWMGTMwduxYzJkzh2yuoJCFhQXi4uLEFrqZmZlwcHAQy71kEYPBgJKSEpSUlJr1hOWmnBpYW8g39tTAzp07w8TEBEwmEy9evMDdu3cRGhoK9awsdC8txbXqalTX1ECX36ZkMNBNQwNpN28CHz4ATdy0URuJD10+JpOJadOmYdy4cYiMjISNjQ1mzJiBKVOm0LYXJc369OkDNpuN6upqkX/9a2pqkJWVRQ4ulwB0OzVwdGEhqiorUcrjQbGmBvfu3UOfPn0gLycHdQUFlPJ4wOPHwIABQvsaSE3o8mlra8Pb2xu2trbYvHkz4uPj4erqip9++omM3oiRhoYGOnbsiPT0dJiZmYn0Xi9fvoSOjo5IjhMk6IfBYEBFRQUqKioNPpe7NjU1NSiLiQGWL4dWeTmqOBy01NKC3H85UcrlQo3BAIT8tAqpfcnf0NAQW7duhZeXF3bu3Im5c+fi0aNHVJclU1gsllheE8jIyCBn6BKN8urVK+zYsQMz9u5FeUUFzLS1wVRWhkKHDoLF2ZPSUhi2aPHpaThCJLWhyzdw4EAcOnQIY8aMgaurK/z9/ZGfn091WTLBwsICt27dEvl9yMHlRENUV1fj/PnzmD9/PmbNmgUACDtwAO18fdFOTg7DVFWx6+1blHM4uPv+Pf76+BFWK1cKHj0mLFIfusCnPtKkSZOQmJiItm3bwt7eHpGRkSgvL6e6NKnWp08f/Pvvv6ipqRHpfUjoEvV59eoVtm7dCisrKyQkJGDSpEk4ffr0/x8j6+4OsNnwbt8elZWV+OXRI/gUFmKFjw8M584Vej1S19Otj5qaGhYsWIBJkyYhPDwcNjY2mDdvHsaNG0c2V4iAhoYG9PT08PDhQ5H2dUnoEl+rrq7GX3/9hcTERGRkZGDcuHGIioqqfZyUwQBmzICGkxM2ZWUB8vJA586AiDJBpkKXT1dXF4GBgbh//z5CQ0MRGxsLd3d3srlCBPgtBlGFbklJCT58+AA9PT2RXJ+QLNnZ2Th27BhOnToFAwMD2NjY4Oeff27Yk0wUFQExfPOWydDl69WrF/bs2YM///wTa9asQdeuXbF06VKyuUKIWCwW4uPjMWPGDJFc/+nTpzA0NCQ/qciw6upqXLlyBQkJCcjIyICVlRV2796Nzp07U11arWQ6dIFP4ycjRozAkCFDBJsrxowZgzlz5kBTU5Pq8iRe37594evri5qaGpEcMEJaC7Lr9evXOHbsGE6ePAl9fX3Y2Nhg6NChlD2fr6HI8uA//M0VR48eBYfDgY2NDQ4dOoTq6mqqS5Non/d1RYGErmypqanBxYsXsXDhQvz222+orq5GZGQkIiMjMWrUKNoHLkBC9xstW7bE8uXLERkZievXr8PW1haXLl1CPUdUEN/BYrFENjpGZnRlQ05ODsLDw2FlZYXY2FiMHz8eSUlJcHNzg76+PtXlNQoJ3ToYGhpiy5YtWL58OXbt2oW5c+ciPT2d6rIkkqhCl8fjITMzE926dRP6tQnq8Ve1ixYtwrRp01BZWYmIiAhERkZi9OjRErGqrY3M93S/Z8CAATh8+DBOnDiBpUuXYsCAAVi4cCF5ckUjmJubw8/PT+h93fz8fDCZzCY/QZagp5ycHBw/fhwnT55Ex44dYW1tjdDQUIkN2a+RlW4DyMnJYdKkSTh27BjatWsHe3t7REREoKysjOrSJAK/ryvsnxRIP1d61NTU4NKlS1i8eDGcnZ1RUVGBnTt3Yvfu3RgzZozUBC5AQrdRVFVVsWDBAhw6dAjZ2dmwsbHByZMnweVyqS6N9kTRYiChK/lycnKwY8cOjBs3DjExMRgzZgySkpLg7u4OAwMDqssTCRK6TaCjo4OAgABs3LgRJ06cgJOTE27evEl1WbQmitDNyMgg/VwJxOFwcPnyZSxZsgTOzs4oLy/Hjh07EBUVhbFjx0JJyKd60Q3p6TZDz549ERUVhYsXLyIgIABdu3aFq6srbYeyqdS3b1+h93UzMzMxdepUoVyLEL03b94IerXt27eHjY0NNmzYIPUh+zUSus3EYDAwfPhw/Pjjj4iLi8OsWbMwatQouLi4kM0Vn9HU1ESHDh2Qnp4OUyEclVdTU4OXL1+Sg8tpjsPh4O+//0ZCQgLu37+PsWPHIjw8XKb/3Eh7QUiYTCacnZ0RHx8PHo8HGxsbxMTEkM0Vn2GxWEhLSxPKtbKysqCjoyNzqyRJkZubi127dmHcuHHYv38/Ro0ahaSkJHh6esp04AIkdIWuZcuW8PLywu7du3Hz5k1MmTIFFy9eJJsrINy+Lunn0g+Hw8GVK1fg6uoKR0dHfPz4Edu3b8fvv/8OKysr8mSP/5D2gogYGBhg8+bNuH79OsLCwnD48GG4ubnBxMSE6tIoY25ujlWrVgmlr0smF+gjLy8Px48fx4kTJ6CjowNra2usW7eOhGwdSOiKWP/+/XHo0CGcPHkS7u7u6NevHxYtWiSTmyv4fd1Hjx6hV69ezbpWZmYmfv31VyFVRjQWh8PBP//8g8TERNy7dw+jR4/G1q1byTfCBiDtBTGQk5PDxIkTkZiYCB0dHdjb22PXrl0yubnC3NxcKC0GstKlRl5eHiIjIzF+/Hjs3bsXw4cPR1JSEry8vMifRwOR0BWjzzdXvH79WiY3Vwijr/vx40cUFRWhQ4cOQqqKqA+Xy8XVq1fh5uYGBwcHFBYWYsuWLdi7dy/Gjx9P2giNRNoLFOBvrnjw4IHgyRVLly5Fv379qC5N5MzNzbF69WpwOBzIy8s36Rrk4HLxyM/Px/Hjx3H8+HG0bdsW1tbWCA4OhoqKCtWlSTQSuhTib664dOkSgoKC0KVLF6nfXKGlpQVdXV08evQIPXv2bNI1SGtBdLhcLpKTk5GYmIg7d+5g1KhR2Lx5M4yMjKguTWqQ0KUYg8HAsGHDMHjwYBw5ckQmNlfwWwwkdOkjPz8fJ06cwPHjx9G6dWvY2NggKCiIrGpFgPx8RhNMJhNOTk44evToF5srqqqqqC5N6FgsFlJTU5v88WRGVzi4XC7++ecfuLu7w97eHgUFBQgLC8P+/fsxYcIEErgiQkKXZrS0tASbK1JTU2Frayt1myvMzc1x9+5dcDicRn8sj8fD06dPyUq3Gd6+fYuoqChMmDABERER+Omnn3D69Gl4e3uTNoIYkPYCTRkYGCAsLAw3btxAWFgYDh06BHd3d6nYXKGlpQUdHZ0m9XXz8vKgpKQELS0tEVUnnbhcLq5du4aEhATcvn0bv/zyCzZt2oTu3btTXZrMIaFLc/369UNMTAxOnTol2FyxcOFCtGvXjurSmqWpfV3Sz22ct2/f4uTJkzh+/DhatmwJa2trBAQEQFVVlerSZBZpL0gAOTk5/Prrr0hMTISuri4cHBywc+dOid5cYWFh0aR5XdLP/T7+BIKnpyfs7OyQl5eHDRs24MCBA5g4cSIJXIqR0JUgqqqqmD9/Pg4fPow3b97A2toaJ06ckMjNFX379sWdO3ca3dclK926vXv3Dr///jt+/fVX7Ny5Ez/88ANOnz4NHx8f9OjRg+ryiP+Q0JVA7dq1w5o1axAaGopTp05h6tSpuHHjBtVlNUrLli2ho6ODx48fN+rjSOh+icvlIiUlBcuWLcOUKVOQm5uL9evX4+DBg5g0aRJZ1dIQ6elKMBMTE+zevVuwucLQ0BCurq7Q19enurQG4Y+ONfTFwerqarx69Upqn53VGAUFBYK5Wg0NDVhbW2P16tUkZCUAWelKOP7mivj4eLBYLMyePRvr169HYWEh1aV9V2PPYcjKykL79u1l9uBy/gSCl5cXJk+ejDdv3mDdunWIjo6GtbU1CVwJQUJXSny+uYLBYGDy5MmIjo6m9eaKvn37NmpeNyMjQyZbCwUFBdi7dy8mTpyIbdu2YcCAATh9+jRWrlwJY2NjqssjGomErpTR0tLCsmXLEBUVhbS0NFo/uUJbWxtt27ZtcF9Xlvq5XC4X169fF6xqX79+jbVr1wpWtWpqalSXSDQR6elKKX19fYSGhtJ+cwV/dKwhdWVmZmLSpEliqIo679+/x8mTJ3Hs2DGoqanBxsYGq1atIiErRUjoSjn+5orTp0/Dw8MDlpaWtNpcwWKxcOrUKTg7O3/3fTMzM6VyRpfL5SI1NRUJCQm4ceMGhg0bhpCQEBgbG4PBYFBdHiFkpL0gA+Tk5DBhwgQkJCSgffv2cHBwwI4dO2ixuaKh87rFxcX4+PEjdHV1xVSZ6L1//x779++HtbU1Nm/eDEtLS5w6dQq+vr4wMTEhgSulSOjKEFVVVcybNw+HDx9Gbm4urK2tcfz4cUo3VzS0rystB5dzuVzcuHED3t7esLGxQVZWFoKCghATE4PJkydDXV2d6hIJESPtBRnE31zx8OFDhIWFIS4uDm5ubpQ9uYI/OlZfX1fSX0R7//49Tp8+jWPHjkFZWRnW1tZgs9kkZGUQCV0ZZmJigsjISFy6dAnBwcHQ19eHq6ur2DcfsFgsnDlzpt6+riT2c7lcLm7duoXExERcu3YNQ4cORUBAAHr27ElaBzKMhK6M+/zJFfHx8ZgzZw5GjhwJFxcXsR2faG5ujsDAQHC53DrbBxkZGRg5cqRY6mmuDx8+4NSpUzh27BiUlJRgbW0NHx8ftGjRgurSCBqQ7AYZITRMJhNTp079YnPFwYMHxbK54nt9XUk4uJzH4yE1NRU+Pj6wtrbG8+fPsWbNGhw+fBi2trYkcAkBstIlvsDfXDFlyhRs3boVR48exZIlSzBs2DCR/khsbm6OW7du1brDKjc3FyoqKrR8ZlxhYaFgVauoqAgbGxusWLGChCxRJxK6RK34mytu3rz5xeaKpj5M8ntYLBaSkpLg5OT0zdvo9iIaj8dDWloaEhISkJycjJ9//hn+/v4wNTUlvVriu0joEvWytLREdHQ0Tp8+DU9PT1hYWGDhwoXQ0dER6n1YLBaCgoJq7evS5eDywsJCnD59GomJiVBQUICNjQ28vb2hoaFBdWmEBCE9XeK7Pt9coaenB0dHR6FvrtDW1kabNm3w5MmTb95G5UqXv6pduXIlJk6ciIyMDPj7+yMuLg52dnYkcIlGI6FLNJiqqirmzp2L2NhY5OXlCX1zRV1HPVIRukVFRYINC2vXroWpqSlOnjyJ1atXw8zMjLQRiCYjoUs0Wtu2bbF69WqEhoYiKSkJjo6OuH79erOvW1voVlVVITs7Wyyzw/xVLZvNxq+//orHjx/Dz88PcXFxsLe3J6taQihIT5doMhMTE0RERODy5csICQlB586dsXTp0iYHpLm5OYKDg7/o67548QIdOnQAk8kUZulfKCoqwpkzZ5CYmAgGgwFra2t4eXmRkCVEgoQu0SwMBgNDhw79ZnPFnDlz0LJly0Zdq1WrVmjVqhWePHkieJCiqFoLPB4Pd+7cQWJiIq5evYohQ4aAzWajd+/epHVAiBQJXUIoFBUV4ejoCCsrK+zevRtTpkzBtGnTYG9v36hVKr/FIKrQLS4uFpyBwOPxYG1tjWXLlpFVLSE2pKdLCJWmpiY8PT2xZ88e3LlzB5MnT8aFCxca/OSKr/u6wghd/qrWz88PEyZMQHp6Onx8fBAfHw9HR0cSuIRYkZUuIRKdO3f+ZnOFh4fHdzdXsFgshISECPq6zTnopri4GGfOnMGxY8fA4XBgY2MDDw8PWu5sI2QHCV1CpL7eXMFisbBo0aI6N1fw+7oZGRnQ1dVFSUlJozZi8Hg83Lt3DwkJCbhy5QoGDx4Mb29v9O3bl/RqCVogoUuIHH9zxYgRIxAdHQ1HR0dMnjwZ06dPr/Wx4fxzGHr06IEuXbo06ODy4uJiJCUlITExERwOB9bW1nB3dxfbSWkE0VCkp0uIjaqqKlxcXBAbG4v8/HxMmjQJx44d+2ZzhWXv3nh34gSeRkXhr//9D76+vrVej7+qXbVqFX799Vf8+++/8Pb2xtGjRzF16lQSuAQtMXj1vMJhYWGB1NRUcdZDyJD09HSEhYWhuLgYbm5u6N+/P/DHH6jx8kLO06fw53LBYTDQycAAAZcvA23bAgA+fvwoWNVWV1fD2toa48aNIyFL0EZ92UnaCwRljI2NERERgb/++gtr167FEEVFLLp+HYotWuBPOTkocrnoq6WF/Nxc8OztcX/DBiScOoXLly9j0KBB8PLygrm5OenVEhKFhC5BKQaDgZ9//hk//PAD3gwahNy3b1FVXY0jNTVYzuPhobw8ynk85N68ieOLF6PL/PlwdXVt9MYLgqALEroELSjW1KBTQQFqjIwQ8OIFfuRyocnl4t27d6hWUEDLli3B7tcPjHqeo0YQkoCELkEfPB6eVVcjXU4Oe4yMwKuqQpvyclTX1EBZSYnq6ghCKEjoEvSgogL07YvU5GTkVFXB5uVLAEA5jwcOj4dnJSWIGTWK4iIJovlI6BL04ekJa2dnjNLSApSVAQAHCwqQU1KCFb16AcOHU1wgQTQfmdMl6GPQIChv24ZWyspoVV6OVuXlUK2shJKuLlomJAAiPN6RIMSFrHQJehk7FhgxArhxAyguhku3bgANno9GEMJCQpegHyYTGDyY6ioIQiRIe4EgCEKMSOgSBEGIEQldgiAIMSKhSxAEIUYkdAmCIMSIhC5BEIQY1XuebuvWraGvry/GcgiCICTfixcv8O7du1rfVm/oEgRBEMJF2gsEQRBiREKXIAhCjEjoEgRBiBEJXYIgCDEioUsQBCFG/weDF0UPBGBVpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distance func\n",
    "def dist (point_1: list, point_2: list) -> float:\n",
    "    return math.sqrt((point_1[0]-point_2[0])**2 + (point_1[1]-point_2[1])**2)\n",
    "\n",
    "# Draw sol func\n",
    "def draw_tsp_solution(graph, order, colors, pos):\n",
    "    G2 = graph.copy()\n",
    "    nodes = len(order)\n",
    "    for i in range(nodes):\n",
    "        j = (i + 1) % nodes\n",
    "        G2.add_edge(order[i], order[j])\n",
    "    default_axes = plt.axes(frameon=True)\n",
    "    nx.draw_networkx(G2, node_color=colors, node_size=600, alpha=.8, ax=default_axes, pos=pos)\n",
    "\n",
    "\n",
    "# Points\n",
    "points = coordinates\n",
    "\n",
    "graph = nx.Graph(node_color = \"red\", alpha = 0.3) # Create graph\n",
    "\n",
    "nodes = len(points) # Number of nodes\n",
    "for node in range(nodes): # Add nodes onto graph\n",
    "    graph.add_node(node)\n",
    "\n",
    "ins = tsp.random_tsp(nodes)\n",
    "largest_weight = 0\n",
    "edges = list(combinations(graph.nodes, 2)) # Use graph to get list of edges\n",
    "print(edges)\n",
    "for i in edges:\n",
    "#     print(i)\n",
    "    point_1 = points[i[0]]\n",
    "    point_2 = points[i[1]]\n",
    "    \n",
    "#     weight = dist(point_1, point_2)\n",
    "    weight = weight_matrix[i[0], i[1]]\n",
    "    \n",
    "    \n",
    "    if weight > largest_weight:\n",
    "        largest_weight = weight\n",
    "        \n",
    "#     print(dist(point_1, point_2))\n",
    "    graph.add_edge(i[0], i[1], weight = weight)\n",
    "\n",
    "nx.draw(graph)\n",
    "print(edges)\n",
    "print('distance\\n', ins.w)\n",
    "\n",
    "weight_matrix = (ins.w)\n",
    "print(weight_matrix)\n",
    "\n",
    "pos = nx.spring_layout(graph)\n",
    "colors = ['red' for node in graph.nodes()]\n",
    "default_axes = plt.axes(frameon=True)\n",
    "nx.draw_networkx(graph, node_color=colors, node_size=60, alpha=.8, ax=default_axes, pos=pos)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/qiskit/optimization/ising/max_cut.py:136: DeprecationWarning: get_max_cut_qubitops function has been changed to get_operatorthe method here will be removed after Aqua 0.7+\n",
      "  DeprecationWarning)\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/qiskit/optimization/ising/docplex.py:286: DeprecationWarning: get_qubitops function has been changed to get_operator. The method here will be removed after Aqua 0.7+\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "energy: -178.0\n",
      "tsp objective: -433.0\n",
      "feasible: False\n",
      "solution: [1]\n",
      "solution objective: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:579: MatplotlibDeprecationWarning: \n",
      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
      "  if not cb.iterable(width):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl = Model(name='tsp')\n",
    "x = {(i,p): mdl.binary_var(name='x_{0}_{1}'.format(i,p)) for i in range(nodes) for p in range(nodes)}\n",
    "\n",
    "# Object function\n",
    "tsp_func = mdl.sum(ins.w[i,j] * x[(i,p)] * x[(j,(p+1)%nodes)] for i in range(nodes) for j in range(nodes) for p in range(nodes))\n",
    "mdl.minimize(tsp_func)\n",
    "\n",
    "# Constrains\n",
    "for i in range(nodes):\n",
    "    mdl.add_constraint(mdl.sum(x[(i,p)] for p in range(nodes)) == 1)\n",
    "for p in range(nodes):\n",
    "    mdl.add_constraint(mdl.sum(x[(i,p)] for i in range(nodes)) == 1)\n",
    "    \n",
    "    \n",
    "qubitOp, offset = max_cut.get_max_cut_qubitops(weight_matrix)\n",
    "algo_input = EnergyInput(qubitOp)\n",
    "qubitOp_docplex, offset_docplex = docplex.get_qubitops(mdl)\n",
    "\n",
    "def get_tsp_solution(x):\n",
    "    nodes = int(np.sqrt(len(x)))\n",
    "    z = []\n",
    "    for p in range(nodes):\n",
    "        for i in range(nodes):\n",
    "            if x[i * nodes + p] >= 0.999:\n",
    "#                 assert len(z) == p\n",
    "                z.append(i)\n",
    "    return z\n",
    "\n",
    "\n",
    "ee = ExactEigensolver(qubitOp, k=1)\n",
    "result = ee.run()\n",
    "\n",
    "print('energy:', result['energy'])\n",
    "print('tsp objective:', result['energy'] + offset)\n",
    "x = sample_most_likely(result['eigvecs'][0])\n",
    "print('feasible:', tsp.tsp_feasible(x))\n",
    "\n",
    "\n",
    "z = get_tsp_solution(x)\n",
    "print('solution:', z)\n",
    "print('solution objective:', tsp.tsp_value(z, ins.w))\n",
    "draw_tsp_solution(graph, z, colors = \"red\", pos = pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "energy: -177.37234564339303\n",
      "time: 21.295278072357178\n",
      "feasible: False\n",
      "solution: [1]\n",
      "solution objective: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/qiskit/optimization/ising/tsp.py:279: DeprecationWarning: sample_most_likely function has been moved to qiskit.optimization.ising.common, the method here will be removed after Aqua 0.7+\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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NhG41xcfHU1JSQlxcnF00sTGGOacYDKZNm8bFixctdsyPsUShTTAVEbrVkJCQwC+//MJbb72Fo6OjtYdjdpYI3RuP+THnU7WpiUKbYCwRulX47bffWLVqFfHx8Xh7e1t7OBYRGRnJ0aNHzb60KyIigtjYWGbPnk1xcbFZ72VKotAmGEOEbiUuXLjAq6++ypIlS2jUqJG1h2MxAQEBBAYGcvr0abPfa/DgwbRp04a3337b7PcyNVFoE2pDhG4FcnJyiI2NJSYmhs6dO1t7OBZninPTqsPax/wYSxTahJoSoVsOtVrNSy+9xMCBAxkyZIi1h2MVlpjXNbCFY36MJQptQnWJ0L2FJEksWLCAwMBAJk6caO3hWE1kZCTHjh2zWEtGWzjmxxQMhbaoqChRaBPKJUL3Fp9++ikpKSm8/vrrdaKJTW35+fnRsGFDTp06ZbF72soxP8ZydHTkmWee4fPPPxeFNuE29TdVyrF7924SExNZvnw5rq6u1h6O1UVGRlq0HaMtHfNjCmFhYTcV2hYvXkxBQYG1hyVYmQjdv504cYKlS5eyfPlyAgMDrT0cmxAVFWWRYtqNvL29WbhwIW+++SaZmZkWvbc53FhoUygUjBw5UhTa6jkRukBGRgYvvfQS8+bNo2XLltYejs0wzOtaek7SFo/5MdathbaYmBjS09OtPSzBCup96BYVFREbG8vTTz9N7969rT0cm+Lj40N4eDgnT560+L1t8ZgfUzAU2iIjI3nqqadYt26dKLTVM/U6dHU6HbNnz6Zjx448/vjj1h6OTbLk0rEbGY75SUxMtLljfox1Y6Ht8OHDotBWz9Tr0F2+fDk6nY6XX365zjexqS1LF9NuZMvH/JiCodD23HPPMXPmTFFoqyfqbehu2bKFAwcOsGTJknrRxKa2OnXqxIkTJ9BoNFa5f/fu3W36mB9jKRQKBg4cyFdffQUgCm31QL0M3f/973988sknxMfH4+XlZe3h2DRvb28aN25s1ZMeJk6cSGFhIRs2bLDaGMzNy8uL2bNni0JbPVDvQvfcuXPMnz+fpUuXEhYWZu3h2AVrzesaODo6smDBAj7//HObPubHFEShre6rV6F7/fp1pk2bxvTp07nrrrusPRy7Ye3QBfs65sdYotBWt9Wb0FWr1cyYMYNBgwbx4IMPWns4dqVjx478+eefVu+JYE/H/JiCKLTVTfUidCVJ4vXXXyckJITx48dbezh2x9PTk6ZNm3LixAlrD8Xujvkxlii01T31InT/9a9/kZ6ezvz588XSsFqyhSkGsN9jfox1Y6Htk08+EYU2O1bnQ/e7775j+/btvPPOO7i4uFh7OHbLVkIX7PeYH1Po0KEDGzZsEIU2O6ao7G1KVFSUZCv/o9XG77//zowZM1i1ahXNmze39nDsmkql4v777+e///2vTfzjJUkS8+fPx8nJiblz5/7zgeJi2LcPDh+GAwcgKwskCXx8oGNH6NIF+vYFf3+rjd1U0tLSeOutt7h69SqvvPIKHTp0sPaQhL8pFIrDkiRFlfuxuhq66enpjBkzhvnz59OzZ09rD6dOGDNmDBMnTqRr167WHgog/0Pw5JNPMn78eO7v0gU++gg2bgSNBrRacHEBJyf5xTodlJSAQgFKJTzwAMTGQrNm1v0ijCRJErt372b58uX06dOHKVOmiLXnNqCy0K2T0wuFhYXExsby3HPPicA1IWu0eqyM4Zif7+PiUPfqBZ9+Co6O4O0tP8l6eICzs/zDzQ38/MDXV/7z7dvhwQdhzRo5kO2UKLTZnzoXujqdjri4OKKiohg1apS1h1On2NK8LgCSRKsdO5ibkcHVtDT0vr7/PNlWxsFBDmVXV3jrLXjhBSgtNf94zUgU2uxHnQpdSZJYtmwZSqWSGTNmWHs4dc5dd93FmTNnbKd4tWIFrFiBZ3AwuLnVvOm5k5P85LtvH0yebNdPvAai0Gb76lTobt68mSNHjrBo0SIcHBysPZw6x9XVlVatWnH8+HFrD0Uukr33Hnh7o3B0JDQkhIKCAgpqultNoZCDd+9eWLvWLEO1tBt3tB06dIgnn3xS7GizIXUmdH/++WfWrVtHfHw8np6e1h5OnWXNVo9lVCqIiZHnb//uEOfg4EBYWBgZ6eloavpkp1DI87xLl8L582YYsHWEhYXx/vvvM3bsWLGjzYbUidBNTk7mtddeY+nSpYSGhlp7OHWaTRTTtm6Vl4Ld8o+ru5sb/v7+pKWlUeMykrOzvOrhww9NNkxbIApttsful4xlZ2fz7LPPMnXqVAYOHGjt4dR5paWl9O/fn127duHu7m75AUgS3HcfZGZCOfeXgEuXLuHo6sonWi0HVCrydTrCnZyYEhREz8reBel0UFQEv/0mr3Sog37//XcWLlxIw4YNiYuLEw8pZlJnl4yVlpYyffp0hg4dKgLXQlxcXGjTpg3Hjh2zzgDOnoX0dHkJWDkUQFhoKNdyc/GTJNY0bsy+li2Z2KABcWlppFfWtMfBQQ71H34wz9htgCi0WZ/dhq5er2f+/Pk0adKEcePGWXs49YpVl4799Zf8cyU9NBwdHWkWFsYDKhVBSiVKhYLeXl6EOjlxqqSk8uvrdHDkiAkHbHtEoc267DZ0V61aRWZmJnPnzhVNbCzMqqH7xx/y3GsVPD088PHxIT0jAwm4rtVySa2mWVVbmF1d5S3E9YAotFmHXYbujh07+O6773jnnXdwdna29nDqnfbt25OSkmKdZuLXrsnTABXQSxKlajWFhYU4ODpSWFDAxcuXmXXxIoN9fIioKnQdHCAvz8SDtl2i0GZ5dnci49GjR4mPj2f16tX41dFih61zdnamXbt2HD16lN69e1v03pJSiV6vR11cjFqjQa1WozH8rFaj1elwcnTE2dkZJ2dnnF1dWZqfjwYYqtFwSavFw8MDTw8PnF1cKPc9Uj1c423Y0TZ48GAWLlzI9u3bRaHNTOwqdC9fvsysWbNYsGABzey8UYm9M0wxmCN0NRoNV65cITU1lbS0NNLS0sp+3f233xiZnk6xhwfOzs44Oznh5uaGj7c3Ts7OODk5lQWpJEnMOH2aYhcXVjdrhqMkUaRSUVhYyPVr10ChkAPY0xMPd3d5Q41GA0FBJv+a7IWh0LZhwwaeeuopnn76aUaPHi1OzDYhu/mbzM/PJzY2lvHjx9OtWzdrD6fei4yMZNmyZbX+/Pz8/LIgvTVcs7OzadCgAeHh4YSFhREWFka7du0ICwuj8ZkzeMyaddsa3fIsvnKFFLWaTyMicFHKM2neXl54e3khIR/hVFhYSG5uLunp6bi4uBAAFDZpQkOdrt7uajQU2vr378+SJUv49ttvmTNnjmgdaSJ2sU5Xq9Xy4osvcscdd4ieCjZCo9HQr18/tm/fjre3920f1+l0XL16ldTU1HLDVafTER4eXvbDEK7h4eEEBwdX/GR15Qr07g1eXnKLxgpkaDQ8lJyMrrQUzxvW884JDuZBH5/bXq+XJIpVKtTXrvFh+/bsAbp06UKPHj3o3r07wcHBNf47qgtE68jaset+upIksWjRIrKysli+fDnKSv5HEyzrhRdeoHfv3oSFhd0WrpmZmQQEBNwUpjeGq4+PT+1XnTzxBBw8KDcmr4RKpeJqZiZNIyKqd121Wg7yAwfIzs/n119/Zf/+/fz222/4+vrSo0cPevToQefOnXF1da3d2O1UQUEBK1as4IcffmD69OkMGDBArBqqhF2H7vr169mxYweffPKJdXZA1WN6vZ7MzMzb3v4bfn/58mXc3d3p37//beEaHBxsvpUle/fK7Ri9vStdr3s9J4fSkhJCQkKqd93r12HKFJg+/aY/1uv1nD59mv379/Prr79y6tQp2rdvT/fu3enRowd33HFHvQkgsaOteuw2dH/44QeWLFnCZ599Vm/f3pmbSqW67a2/4fdXrlzB19f3pkC98depqaksXryYTZs2WXbQej2MHi0/7fr6VviyjIwMXFxd8a/OKpeiInme+Pvv5TCv9KVFHDp0qOxJuKSkpCyAu3btWudX1Wi1WjZs2MDnn38uCm0VsMvQPX36NJMnT+a9996jXbt2VhlDXaDX68nOzq6waKVSqcoN1LCwMEJDQys9D02r1dK3b1+++eYbfCsJP7NISwPD1u8KtgSnpKTQICgIj6reIWm1UFgIn30mzxfXUGpqKvv372f//v0cPnyYxo0bl80Fd+jQoc4GUlpaGkuWLCErK0sU2m5hvdDVamH/fjh6VO5/euWK/JTi7Q2RkdCpk/xNfsvcXGZmJs8++ywzZsygX79+tb9/PVFSUkJ6enq5Rav09HS8vLzKnVcNDw8nICDAqLfGU6dOZdiwYfTt29eEX1E1/fwzjB0rNyO/JXgl5H+4W9xxR+WrEDQaOXBnzoTx440eklar5ffffy97Cr506RKRkZFl88Hh4eFG38OW3Fhou/fee5k8ebIotGGN0FWp5IbQH38sf0NrNHLrvFsPCXRykufkhg2DSZOgSROKi4t5/vnn6du3L2PHjq35vesgSZK4du1aufOqqampFBQUEBISUhaoN4ZraGgobhU8CZrC559/zpUrV5g5c6bZ7lGpX37557gdH5+yOV61RsPFlBRatGhR8efm58sPAXPmyOFtBjk5ORw4cKDsSdjNza3sKbhLly51pk4hCm03s2zoHjggN5i+elVuDF3VtkutVv7md3JCP3Mms0+dws3Dg/nz59er/2hqtbrsafXWcE1PT8fNze22t/+GcA0MDLTaqo6TJ0/y2muvsWXLFqvcH5C7jr38stySUaEALy8KiorIyc2lcaNGN79WkuT5W60WwsPh/ffBQm+LJUni7NmzZQW5EydO0Lp167L54FatWtn96hxRaJNZLnTXrYMFC+RtlDU9vUGtJi89neOBgXQ/eBDnOvYWRZIkcnJybtthZfh1Tk4OwcHB5c6thoWF4eHhYe0voVw6nY5+/fqRkJCAv7+/9Qai18vTDR9/DP/7H7mFhSg0GnwMc816vfxuS6+Xj12fOFE+DbiqhwIzKikp4ciRI2VPwbm5uXTr1q3sSTgwMNBqYzOGKLRZKnQ3bIB58+Swrc6JrLfIzc0lOyuLpn5+OPTvD6tX290eeI1GQ0ZGRrnbV9PS0nB0dLxtXtUQrg0bNrTbp5zY2FgGDx7MgAEDrD0UWVYWq6ZO5R5/f9r6+cnTWQEB0L49tG0LjRpVutTMWq5cuVI2F3zw4EEaNmxY9hTcsWNHu2vuVJ8LbeYP3ZMn5XlZV1f5aaKGilQq0lJTaRIRgYuTE+TmwqxZ8lydDZEkifz8/NvmVQ2/v3btGkFBQeUWrcLCwupsgWH9+vWkpqYSFxdn7aGUGT58OG+//bbd9ujQ6XT8+eefZSF87tw5OnbsWPYUHBERYRfTb/W10Gbe0NVoYNAguHixyvWN5SlVq7mYknLzW2iNRi607dwJzZvX+JrG0Gq1ZdtXywtXSZJuCtQbf92wYcN69zYK4NSpU7z66qt8/fXX1h4KIL9t79evHz/88EOd+e+Rn5/PwYMHy6YiFApF2VNwly5dyt2KbUsMhbYff/yR6dOn079/f7v4R6O2zBu6O3bIhbMbKsfVpdPpuJCSQkBAAH63rvPMyZHD/IMPanTN6igoKCh3XjU1NZWsrCwCAwMrLFp5eXnV6W+W2tDr9fTv358tW7bYxDzkyZMnWbBgARs3brT2UMxCkiRSUlLKnoKPHTtG8+bNy56C27VrZ7PNeupLoa2y0DX+MWDVKnnutYog2nL9Otvy8jhbWsr93t7MCwnhcmoqXp6etwcuyE/N330H2dlQw/+R9Xo9V65cqbBopdVqbwrUFi1acN999xEWFkZISAhOtZiTrs+USiWdOnXi8OHD3H///dYeDsnJyZUvFbNzCoWCpk2b0rRpUx5//HHUajXHjh1j//79LF68mKtXr9pss55bW0c+88wzPPHEE3XmHUl1GPekm54O99xTrafcPfn5KBUK9hcWUiJJjFco0Gm1hDdqVH4jaZDndhctglGjbvuQSqUqd141LS2NK1eu4Ofnd9NT6o1Prr6+vuJp1cQ2btzIhQsXeOWVV6w9FN5++22Cg4N58sknrT0Uq8jOzraLZj11udBmvnfdxxIAACAASURBVCfdkyer9ZQL0PfvOaeTxcXk5OdT4uBAkyZNKgxcCdBptVzbvp3fXF1vC9fi4uKb3vY3bdq0rONVaGio3VV67V1UVJTNzOkmJydb/EQLWxIYGMiQIUMYMmQIer2eU6dO8euvv7J27Vri4uJsplmP4Yy23bt3M3PmzHpTaDM+dEtL5d6m1VRcWkpxcTGN2rRBAZSUlqJRq1FrNGU/G45gcQNU//0vhyIiCA8P5+677y4LWn9/f/G0akPuuOMO8vLyyMzMJMiKJy9IklTnpxdqQqlU0rZtW9q2bcvYsWPLmvXs37+fl156idLSUqs26zGc0dajRw9WrFjBqFGj6nyhzbjQzcqq0VpanV7PtawsdA4OnDl9GkmScHRywsnJCWcnJ5ydnfHy8sLFxQUXFxccdDpwcuKNN94wapiC+SmVSiIjIzl06BCDBg2y2jiysrJwdHS07kYNG+bh4UGfPn3o06cP8E+znl27drFo0SKrNeu59Yy2bdu21dlCm3F/o0qlvK2y2i9X4ujkhKTT4ebujouLC5Ikodfp0Op0qIuK0Ofno9PrkfR6nACdoyNzR42Sz7Hy8PjnTKtbfjb8uPXPXVxc6uy/mLbGcG6aNUP37Nmz3HHHHVa7v70JDw9n5MiRjBw5Eo1Gwx9//MGvv/7Ku+++a5VmPfWh0GbcV1Ld5tB/UwDBDRuiLS5GodejLi0lqGHDclvvSYC+sBBtaCiLFy+msLCQoqIiCgsLy34UFRWRlpZW9ue3/lxYWIhOp7spmGsT3B4eHmJFQzVERkayfv16q44hOTlZhG4tOTk50blzZzp37sykSZPIycnht99+Y//+/Xz88ce4ubndtDbYXM16bj2jbefOnXWq0GZc6LZrV+0daDpJQidJ6AFHJydCg4MpKiggPT0dVxcXgho2xOWGaykAB7Uah169aG7EBgmNRkNRUVG5gXzj7zMzM8t+f+trCgsLcXR0rDCQqxvcHh4edrvVtzqaNWtGcXExGRkZ1T+twcTOnj1L165drXLvusbPz48HHniABx544KZmPV9++SVz5841e7OeulpoM27J2PXr0K1blYcEAqzJymJNdvZNf/ZCYCDjAgPJuX6d7GvX8Pb2pkFg4D9vJfLy4J134OGHq/0FmYMkSZSWllYZ3FWFe3FxMa6urkYHt5ubm81OmcTFxdGrVy+GDBlilfs/9thjvPbaa7Ru3doq968viouLOXLkSNnSNHM36zH5jjadDi5ckDdhSZK87LVp01q1MSiPeXekPfOM3NPUyJMDtDod2dnZ5OXlEeDvj7+3N0qNBg4dkltE1gF6vZ7i4mKjgruwsBCNRmN0cHt6epplWd3XX3/NiRMneO2110x+7apoNBr69OnDvn37xJJBC8vIyLipWU9wcLBZmvUYtaNNpYJvv4UvvpBXXikU/zws6vVyELdsCY8/DkOH1qqtgYF5Q/enn+QG0FUcElhdarWazMxMHPLzKRg6lDs2bKjTb8lrQ6vVljsNUpPgLiwsRKlU1ji4y3vtjVtOL1y4wIsvvsi2bdss/jSenJzMnDlz+Oqrryx6X+Fm5m7Wc2PryGoV2vR62LgRFi+WT3x2cJBPGrl15ZVeD8XFcq9lBwf5kNLx42vVNdG8oStJ8OSTcgNpU52TVVJCcWkps6KiuKZUEhMTI+bpTEySJNRqdbmhXFlh8tY/V6lUuLi4lIWwu7s7+/btY/DgwYSEhFQruD09PU0yZbJz505+/vlnFi1aZKK/JcEUzNWsp1o72jIzYfJkOHJEfsdc3SdujUZudn/HHXKrg4iIGo3N/K0d09PB0EvV2KNhdDr5JIn33kMaPJjvv/+eDz74gIiICGJiYuy2VV9dpdfrKSkpuSmU33nnHZo3b06HDh2qFdyFhYWo1Wrc3d2rfOKu7Kl87dq1+Pj48Nxzz1n7r0WogKFZj+H0DGOb9VTaOvLKFXjkEflnX9+avxOXJLmu5OUFX30lB3A1WaaJ+S+/wJgx5R4SWG2GwB07Vj63ynDelVrN119/zWeffcZ9993H+PHjCQgIqN09BLNLSEjg2LFjNdrUotPpKnzCrm5wnzhxAn9/f0JDQ2sd3OVNmQjmc2Oznv3795OZmVmrZj23FdruvhvF4MGQmnrbwbc1lp8vh/Z330E1N91Y7rien36S50DU6pq3eiwqkj9v/Hj5vKtyPjc/P59PP/2Ub775hieeeILRo0eb9dBFoXYuXbrEhAkT2LFjh0XndR944AHWrFmDp6dnrYPbML3i7OxcbijXpIDp5uYm6hE1lJWVVbY2uDbNegyFtqcvXuT+jAwcGzQwzcBycuD+++HDD6uVa5Y9mDI1FWbMgMOH5cqgl1fFg5QkuVl5aan8L8jy5dCrV5W3SEtL48MPP+TYsWNMmDCBIUOGiG9uGyJJEoMGDWL16tU0btzYIvfMyckhOjqavXv3Gh30kiRRXFxsdHCXlJTg5uZmVHAbVpnY6hJBc7qxWc+vv/7KqVOnqtWsR3v8OKqBA7mqUuEXGEiAKfq06PXyE+8nn8DfW6grY/kj2PV62LNHPufs2DE5fNVqMFQYdTq5OqhUyr1yX3gBhg+vUeMcgBMnThAfH09hYSGxsbF079695mMVzGLu3Ll06tSJ4cOHW+R+Bw8eZM2aNfzrX/+yyP2qQ6/XGx3chl2Vxga3h4eH3W+lvbFZz/79+8ua9XTv3p1u3br906xn2jT45hvUnp5cychAo9USEhKCu7HvivPzoWNHqMbJ15YP3RulpcGJE3D8uPxrnQ78/OTBt2kDrVtXubGiMpIksW/fPt5//33Cw8OJiYkR20BtwNatWzlw4AALFy60yP02btxIamoqM2fOtMj9LMmwyqQmwV3ea5ycnIwObnd3d5t5V3n58uWyZWmHDx+mcePG9LnrLp784ANc/PxQODoiIU9LXr16FS8vL4KCgnD4e/z5Oh1vZGTwa2Ehvo6OTGnQgAcqm/81FNZ275Y3UlTCvCdHVCUsTP5hphMFFAoF9913H7169SIhIYFJkybRu3dvJkyYQANTzecINRYVFcXKlSuRJMkib43Pnj1L+/btzX4fa3B2dsbZ2dmotouSJFFSUlJlcGdnZ5OSklJhcJeUlODq6mpUcHt4eODq6mr090WjRo1o1KjRTc16Lq1bx/WcHPKvX8fD3R0PT088PTxo3rw5mVevcu7cOYIbNsTL25slV67gpFDwn5YtOVNSQszly7R0daWZi0v5N1Qo5B8HD1YZupWx7/cbN3BycuLRRx9l0KBBrF27lkcffZRHH32Up556ymyNOYSKhYaG4uTkxMWLF4mo4RrH2jh79izDhg0z+33slUKhwM3NDTc3N6O26Or1elQqVZVP3Onp6ZW+RqvV1iq4b/2YoRFVWbOen3+GgACCfH3L7pmdnY1SocDD0xN/Pz+ysrK4kpvL90VFfNW8Oe5KJR3d3enj5cWOvDxerKwftF4v16vKOc2muupM6Bp4eXnx4osvMmLECFauXMnw4cMZP348Dz/8sFgGZEEKhaKsv665Q1ev13P+/HkxrWQBhl2Mnp6eRl2nOo2oioqKyMzMrPA15TWiGnfkCG1zc1GXluKgVOLk5IS/vz96nY7Sv6dp1KWlpJSUUFpaimN2NvqQEJQKBS1cXDiiUlU+cBcX+Osvo772Ohe6BiEhIbz55pucPHmS+Ph4Nm3aRExMDD179qyXlWBriIqK4pdffmHEiBFmvc/ly5cJCAgQ72jsiJOTE76+vvgasYu1vEZUDWbNwj0/HydXV3Q6HXq9Hp1Wi16vR6FQ4OzsjINSSUlxMW5AekYG3j4+eHp44KlUUqTXV35ThUJeFGCEOhu6Bm3btmX16tX89NNPLF++nA0bNhAbG0vLli2tPbQ6Lyoqivfff9/s87rieJ76SaFQ4Orqiqur6z+bpRo3hlOnyt0QISGvgMjNycGtpAStoyNtW7QoW+tfpNfjUVWRUJLAyIM9baMMaWYKhYJ77rmHL7/8kr59+zJlyhRee+01MjMzrT20Oi0kJAQ3NzcuXLhg1vuI0BXKtG0rr5C6gUarJSs7m7Nnz5KZmYm7hwe9W7TAwdmZbAeHssNxz5SWVlxEMygtlVddGaFehK6Bo6MjI0aMIDExkaCgIB577DFWrlxJUVGRtYdWZxmO8DEnEbpCmfbtwdkZCSgoLOTy5cucP3cOrUZDeHg4zZo2xd/PD09HR/p6ebEqK4tivZ7jKhU/FBQwuKotw0olREYaNcR6FboGHh4eTJo0iU2bNpGZmUl0dDRff/01ulv+hRSMZ4nQFeeiCQZXQkLIzc3l/JkzZGdn4+XlxR0tWsjvum6ZFogLDqZUr2fAmTPMSUtjdnBw5U+6er08vdCtm1FjNP/mCDtw+vRp4uPjycrKYurUqfTu3VsU20wkMzOTxx9/nN27d5tlUX1hYSEPPvggP/zwg80s2hcsS6vV8uOPP5KYmMjJkydZpNPR6fx5nCtb+lUbeXly4FbjHMDKNkeI71KgVatWrFy5kmnTprFixQrGjx/PyZMnrT2sOiEoKAhvb2/OnTtnluufO3eOZs2aicCthy5dusT777/PoEGD2Lx5Mw8++CA7d+6k25o1OLu4yM3ITcXwlDt5stGXqvOrF6pLoVBw99130717d7Zu3cr06dPp0qULkyZNstohi3WFYYrBHPOuYj63flGr1ezdu5eEhAQuXLjA4MGD+de//kWTJk3+eVHLlnK3wo8+ql0f3fLk5cGwYdCjh9GXEo8Ht3BwcGD48OEkJCQQHh7O6NGj+eCDDygsLLT20OyWOed1z549K0K3Hjh//jzLly9n0KBBfPPNN4wcOZIdO3YQExNzc+AaTJ0qh29envE3z8uDhg1h/nzjr4UI3Qq5u7szfvx4Nm/eTE5ODsOHD+fLL79Ea8q3LPVEZGQkR48eRV/VwvNaEE+6dVdJSQnbtm1j7NixTJo0CVdXV9atW8eHH35I//79y7YAl8vZWZ57bdz4nxN/a0qS5M/184PNm406qPJGInSrEBQUxLx58/jwww/5+eefGTVqFHv37qWyAqRws8DAQPz8/Dhz5oxJrytJkli5UAedPn2aJUuWMGjQIL7//nueeeYZduzYwaRJkwgLC6v+hQIC4N//hnvugdxcuXd3danV8ud07AhJSXJ4m4iY062mFi1a8MEHH7B//37ee++9sp1tdbWzlalFRUVx+PBhWrdubbJrZmRk4OHhUeuDDQXbUVRUxK5du0hMTCQnJ4ehQ4eyefNmgoxdgeDnB59+Clu3wuuvyz1xAdzd/+nvbaDVyqcB6/Vyj4V58+Cpp4xqPVseEbo11KNHD7p168b27dt5+eWX6dSpE1OmTCE0NNTaQ7NpUVFR7Ny5k9GjR5vsmuIp175JksSff/5JQkICe/fupUuXLkycOJHu3bubdjWKQiEXwQYPhr175WmHo0flI8JuPFjB0VF+sn3iCbkVrZHbfSsiQrcWlEolDz/8MAMGDGDDhg08+eSTPPzww4wdO1Y8dVUgMjKShQsXotPpTNbtTczn2qf8/Hy+/fZbEhMTKSkpYdiwYXz99dfmP2zWyQkGDpR/SJJ8SnBOjvwxb28IDTX5U215ROgawc3NjXHjxhEdHc3q1at55JFHGDNmDCNGjMDZ2dnaw7Mp/v7+BAUFcfr0adq2bWuSayYnJ3Pvvfea5FqCeUmSxNGjR0lKSuLHH3+kZ8+ezJgxg8jISOussVYoICRE/mFhopBmAgEBAcyZM4fVq1fz22+/MXLkSP773/+KYtstTL10TDzp2r6cnBy++OILRowYweLFi2ndujVJSUksWrSILl261MtNLWIbsBkcOHCA+Ph4XF1diY2NpUOHDtYekk3Ys2cPSUlJvP/++0Zfq6SkhL59+/Ljjz/a/YGLdY1er+fgwYMkJiby66+/cu+99xIdHU2HDh3qzfZ6656RVg917dqV9evX8+233xIXF8edd97JlClTaNSokbWHZlWdO3fm9ddfR6vVGh2U58+fJyIiQgSuDcnKymLbtm0kJSXh6elJdHQ0r7zyCl41POW7rhPfsWaiVCoZPHgw/fr1Y+PGjTz77LMMGjSIcePG4VNV+7g6ytfXl5CQEP766y/uvPNOo64lVi7YBr1ezy+//EJiYiJHjx5lwIABLFmyhDZt2tSbp9qaqn8TKhbm6urK2LFj+eqrr9BoNDzyyCN88cUXqI088sNemWpeV2z/ta6MjAxWr17NkCFD+Pjjj+nTpw87duxgzpw5tG3bVgRuJUToWoi/vz9xcXF8/PHHHD16lBEjRrBr1y6zbI21ZaYKXVFEszytVsuePXuYOnUqo0ePJi8vj/fee49169YxdOhQcUZdNYlCmpUcPnyY+Ph4FAoFsbGxdO7c2dpDsoj8/HyGDBnC999/X/ne+UpIkkT//v3ZsmWL+dd2Cly+fJmkpCS2b99O48aNiY6Opl+/frhUdbRNPSYKaTYoMjKSdevW8Z///Id58+bRunVrpk6dSmMT7vG2Rd7e3jRq1IiTJ09y11131eoa165dQ6lU4u/vb+LRCQaGFoqJiYmcO3eOwYMHs3r1aiIiIqw9NLsnQteKlEolDzzwAH379mXz5s2MGTOG+++/n+effx4/Pz9rD89sIiMjOXToUK1DNzk5mTvuuEPMG5rB+fPnSUpKYufOnbRs2ZJHHnmEPn36iM0+JiTmdG2As7MzTz/9NP/+979RKBSMGDGCtWvXUlpaau2hmYWx87piPte0SkpK2L59O8899xyTJk3CxcWFtWvXsnLlSgYMGCAC18RE6NoQX19fXn75ZT777DP+/PNPHnnkEXbu3Fnnim2dO3fmxIkTtV7BIZaLmcaZM2d46623GDRoELt37+app55i+/btTJ48mfDwcGsPr84S0ws2qHHjxixbtoxjx47x7rvvsnHjRmJjY4mKKnde3u54enrStGlTTpw4UasCYnJyMo8//rgZRlb3qVQqvvvuO5KSkrh27RpDhw5l06ZNNGzY0NpDqzdE6Nqwjh07snbtWnbv3s0bb7xB8+bNmTp1Kk2bNrX20IxmmGKoaehqNBouXrxIs2bNzDSyusfQQjExMZE9e/YQFRXFhAkTTN9CUagWEbo2TqFQMHDgQO69916++uornn/+efr168f48ePtunpvWL3xwgsv1OjzLl68SGhoqFiuVA35+fl89913JCQkUFxcTHR0tGVaKAqVEv/M2QlnZ2dGjx5NQkICLi4ujBw5kk8++YSSmhxBYkM6derEX3/9VeNioSiiVc7QQnHevHk8/PDDHDt2jOnTp5OYmMizzz4rAtcGiCddO+Pt7c306dMZNWoUK1asYPjw4UycOJHBgwfb1VtFd3d3mjdvzu+//06XLl2q/Xli+2/5cnJy2LFjB0lJSQBER0czffp0fH19rTwy4VYidO1UeHg4S5Ys4ffffyc+Pp5NmzYRGxtL165drT20ajOcm1aT0E1OTmbEiBFmHJX90Ov1HDp0iMTERPbv30+fPn149dVXueuuu8QaZhsmQtfOdejQgU8++YS9e/eyaNEiGjduTExMDM2bN7f20KoUFRXFxx9/XKPPEU+6kJ2dzTfffMPWrVvx8PBg2LBhzJkzR7RQtBMidOsAhUJB37596d27N19//TUTJkygT58+TJgwgcDAQGsPr0IdOnTg9OnTlJSU4FqNQwDz8vIoKioiODjYAqOzLXq9nv3795OQkMCRI0fo378/ixcvFi0U7ZD9TAIKVXJycuLxxx/n3//+N15eXowaNYo1a9ZQXFxs7aGVy93dnRYtWnD8+PFqvd7wlFufQubKlSusWbOGhx56iNWrV9OrVy927NjBK6+8Iloo2ikRunWQt7c3MTExrF+/nosXLzJ8+HCSkpJscmdbTbYE15eVC1qtlr179zJ16lSeeOIJcnNzeffdd/n888+Jjo4WLRTtnJheqMNCQ0NZuHAhf/75Z1mxLSYmhh49etjME1JUVBSrVq2q1muTk5NNdpKwLUpNTSUpKYlt27bRqFEjoqOjWbp0abWmXgT7IUK3HmjXrh1r1qzhxx9/5O233yYkJISYmBhatmxp7aHRoUMHkpOTUalUVT7BJScnM3ToUAuNzDLUajX79u0jMTGR5ORkBg8ezKpVq+rErkOhfCJ06wmFQkGfPn24++67SUhIYMqUKfTq1YsJEyYQFBRktXG5urrSunVrjh07Rs+ePSt8nV6v5/z583axKqM6Lly4QGJiIjt37qRFixZER0dz7733io5e9YCY061nHB0dGTVqFAkJCfj7+/PYY4/x0UcfoVKprDam6szrpqam4u/vj4eHh4VGZXolJSXs2LGDcePGMWHCBJydnVm7di0fffQRAwcOFIFbT4jQrac8PT2ZMmUKGzduJCMjg+joaBISEtDpdBYfS3VC156LaGfOnGHp0qUMGjSIXbt2MXr0aHbs2MGUKVNEC8V6SEwv1HPBwcG88cYb/PXXXzcV2+6++26LFdvuvPNOLly4QFFRUYVPsva2KUKlUrFr1y4SExPJzs5m2LBhbNy4sV6uMRZuJkJXAKBNmzasWrWKn3/+mfj4eNavX09sbCytW7c2+72dnZ1p164dR48epVevXuW+Jjk5mQcffNDsYzGGJEmcPHmSxMREvv/+eyIjIxk/fjw9evSwq74YgnmJ0BXKKBQKevfuTc+ePUlKSiImJoZu3boxadIksz+hGc5Nqyx0p06datYx1FZBQQHffvstiYmJqFQqhg0bxldffWXTuwEF6xGhK9zGwcGBRx55hAcffJB169bxxBNPMHz4cJ599lk8PT3Ncs+oqCjeeeedcj+mUqm4fv26Tc1/SpLE8ePHSUxM5IcffqBHjx5MmzaNqKgo8VQrVEqErlAhd3d3Jk6cyCOPPMJHH33E8OHDGTduHMOHD8fR0bTfOu3atePSpUvk5+fj7e1908fOnj1L06ZNbSLMcnNzy1oo6vV6oqOjiY2NrdOnNwumJUJXqFJQUBDz58/nzJkzxMfHs3nzZmJiYrjnnntMVmxzdnamffv2HDt2jHvuueemj1m7iGZooZiUlMT//vc/7rnnHubMmUPHjh1tZmefYD9E6ArV1rJlSz788EP2799PfHw8GzZsIDY21mRbcw1Lx24NXWstF8vOzmbbtm1s3boVNzc3oqOjiYuLu+1JXBBqQoSuUCMKhYKePXvSrVs3tm3bxvTp04mMjGTy5MmEhoYade0u7dqxZd48CAiAv/6CkhJwcyPgf/+jw3PPQXExuLmZ6Cspn6GFYmJiIocPH6Zv374sXLhQdPQSTEYhSVKFH4yKipKq2wFKqJ9UKhXr169n8+bNDBs2jDFjxtS8mfaFC7BuHdKXX3Lp4kXCg4JwcHAApRJJryc9PZ3g8HAcnJ3hscfg6aehSROTfh1Xr15l69atbN26lYCAAIYNG8YDDzwgOnoJtaJQKA5LkhRV7sdE6AqmkJWVxerVq/nxxx8ZM2YMI0aMwMnJqfJPUqvho49g5UrQasHTk0sZGfj6+eH9d3CrNRpSUlJo2aIFaDRQWAgODhATA+PHgxEFPa1Wy88//0xiYiJ//PEH999/P9HR0TbRCEiwbyJ0BYs5e/Ys77//PpcuXWLq1Kncd9995b8tv35dfmL96y/w8ioLz+xr19BqNGXrggsKC8nJyaFxo0b/fK4hfO+8E9auhRoevpiWllbWQjEsLIzo6Gj69+8vWigKJiNCV7C43377jfj4eNzc3Jg2bRp33nnnPx/MzYWRI+VpBR8fuCGUi4uLSc/IoHmzZoBczNLp9TS8tROaJMnXadECtmyBKopbhhaKSUlJnDlzhkGDBjFs2DCa/X0fQTClykJXFNIEs+jWrRsbNmxgx44dzJo1iw4dOsgNXsLCYNo0OH8eylnb6urmhkajQavT4ejgQElpKV7lbchQKOQn3DNn4OWXYdWqm8LbICUlpayFYvPmzRk+fLhooShYlQhdwWyUSiUPPfQQAwYMYMOGDTz99NNMbdKEh/btw8Hfv9zPUSBvylAVFeHt7U1paWnF22kNwfvf/8J338HfvRlKS0v5/vvvSUhI4NKlSzz00EN8+umnNLpxikIQrESErmB2rq6uPPfcc0QPHoymUycuFhTgo1Dg5+eHspynUw93d4pUKjy9vNCo1ZU/lSqV4OoKr75KckQEidu28d1339G+fXueeOIJ7rnnHpPvnhMEY4jvRsFi/I8cAS8vSgMDyczM5Pr16zQMCsLL25sbo9fdw4Pc9HTOFhTwREkJg9PTeTMsrNxr6vR68ktLKb18mc+eeYYmzz3Hhg0bCAkJscwXJQg1JEJXsJx160ChwMXFhUaNGlFUVMTVzEyu/R2+hjWxrq6uaDUall65QqtynnIl5FMYcnNyyM/Px93DgwAfHxa2aoVi/HgLf1GCUDMidAXL0Ovh+HG4YbOBh4cHTZs2JT8vj7S0NNzc3GgQFISLszOHHBxwVqtp5+5Ozt+v1+n15OXlkZubi06nw8/Xl2bNm+Pk6Civ8z16VF7VIHaOCTZMhK5gGZcvy8Hr4HDTHysAHx8fvLy9uX79OikpKTh5erJRrSYOOOroiEarJS09ncKCAjw8PQkKCsLDw+OmKQkcHaGoCDIywMjtyIJgTiJ0Bcu4du22wL2RUqEgMCAAXx8f3rxwgV46HR5aLXn5+RTo9biGhNAwKKjyopiDg7zpQoSuYMNE6AqWUckmnBud12r5y8GBT1u0oCg/H1+NhlIgICCg6k9WKOSnaUGwYSJ0Bcvw8qpW8B5SqUhXqxl+6RIAxZKETpIYfeECG5o2rfyT9foqd6YJgrWJ0BUsIyJCLnbp9fLa2goM9/Xl/huC84tr10jXaJhd1RlthqPjGzc2wWAFwXxE6AqW4ews90lISYEKjlkHcFUqcb0hlN2VSlwUCvyq2uBQXAxtPocKZwAAAZtJREFU2lQa6IJgC8R3qGA5jz8udwirgRcaNKhwY8RNdDq5164g2DgRuoLlDB0qrzDQak17XY1GXjL20EOmva4gmIEIXcFyfHxgwgQoKKj2aoYqSZLcW3fKlEqnLQTBVojQFSxr8mRo2hTy801zvbw8aNlSPkVCEOyACF3BspydYfVqeTtwQYFx1yookJeiffSRUcf2CIIlidAVLK95c9i0ST7ZNyen5lMNhlMjPD3hyy/l5WiCYCdE6ArW0bYt7NoFvXvLAVpYWHX4SpL8dJuXB336wLffylMLgmBHxHsywXoaNoTPPpNPfXj/fTh7Vl765eAALi7ytl5JgtLSf/68VSuYOhUGDBDdxAS7JEJXsC6FQj5m54EH5JOBf/0VDhyA5GQ5bF1c5KDt1k3+0bq1tUcsCEYRoSvYBoVCnnJo2xbGjrX2aATBbMScriAIggWJ0BUEQbAghVRJxVihUGQBFy03HEEQhDqhiSRJDcr7QKWhKwiCIJiWmF4QBEGwIBG6giAIFiRCVxAEwYJE6AqCIFiQCF1BEAQL+j8Fj6gq+33+9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seed = 10598\n",
    "\n",
    "spsa = SPSA(max_trials=300)\n",
    "ry = RY(qubitOp.num_qubits, depth=5, entanglement='linear')\n",
    "vqe = VQE(qubitOp, ry, spsa)\n",
    "\n",
    "backend = BasicAer.get_backend('statevector_simulator')\n",
    "quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed)\n",
    "\n",
    "result = vqe.run(quantum_instance)\n",
    "\n",
    "print('energy:', result['energy'])\n",
    "print('time:', result['eval_time'])\n",
    "#print('tsp objective:', result['energy'] + offset)\n",
    "x = tsp.sample_most_likely(result['eigvecs'][0])\n",
    "print('feasible:', tsp.tsp_feasible(x))\n",
    "z = get_tsp_solution(x)\n",
    "print('solution:', z)\n",
    "print('solution objective:', tsp.tsp_value(z, ins.w))\n",
    "draw_tsp_solution(graph, z, colors, pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}