Vaccine_Distribution.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "anaconda-cloud": {},
    "colab": {
      "name": "Vehicle Transportation.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "QBUsHZZNXwlr",
        "VDmfowvMEuWp",
        "i8OllGCNYJ-B"
      ],
      "include_colab_link": true
    },
    "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": "3.7.6"
    },
    "papermill": {
      "duration": 14348.105008,
      "end_time": "2020-03-12T07:55:21.779384",
      "environment_variables": {},
      "exception": null,
      "input_path": "max_cut_only_15q.ipynb",
      "output_path": "max_cut_only_15q-background-results.ipynb",
      "parameters": {},
      "start_time": "2020-03-12T03:56:13.674376",
      "version": "2.0.0"
    },
    "varInspector": {
      "cols": {
        "lenName": 16,
        "lenType": 16,
        "lenVar": 40
      },
      "kernels_config": {
        "python": {
          "delete_cmd_postfix": "",
          "delete_cmd_prefix": "del ",
          "library": "var_list.py",
          "varRefreshCmd": "print(var_dic_list())"
        },
        "r": {
          "delete_cmd_postfix": ") ",
          "delete_cmd_prefix": "rm(",
          "library": "var_list.r",
          "varRefreshCmd": "cat(var_dic_list()) "
        }
      },
      "types_to_exclude": [
        "module",
        "function",
        "builtin_function_or_method",
        "instance",
        "_Feature"
      ],
      "window_display": false
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VGGatGitHub/Quantum-Chain/blob/master/Vaccine_Distribution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzQQSYwBfDCg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Note you need to do the pip install and git clone only once!\n",
        "#That is, at the begining of your runtime on the Google Colab."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QBUsHZZNXwlr",
        "colab_type": "text"
      },
      "source": [
        "## Preamble - set up the repo and dependences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLSXUw7j9YqJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!curl  -H \"X-Auth-Token: YourAPIkey\" -X DELETE https://cloud.dwavesys.com/sapi/problems/7211d196-cfc6-4630-84d0-f42ae5d55f0f"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X0c97eqQFWC7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "ba1838f0-f479-4869-8bc4-6ebb9101b99f"
      },
      "source": [
        "#for private repo use:\n",
        "#!git clone https://Userneme:Pasword@github.com/YourGitHub/Repo.git\n",
        "!git clone https://github.com/VGGatGitHub/Quantum-Chain.git\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Quantum-Chain'...\n",
            "remote: Enumerating objects: 87, done.\u001b[K\n",
            "remote: Counting objects: 100% (87/87), done.\u001b[K\n",
            "remote: Compressing objects: 100% (86/86), done.\u001b[K\n",
            "remote: Total 87 (delta 41), reused 2 (delta 1), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (87/87), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NkuSg66dEsck",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#https://www.youtube.com/watch?v=bErs0dxC1aY&list=PLPvKnT7dgEsuhec_yW9oxcZ6gg9SfRFFV\n",
        "#https://docs.ocean.dwavesys.com/en/latest/overview/install.html#installoceansoftware\n",
        "#!python -m venv ocean\n",
        "#!\\Scripts\\activate\n",
        "\n",
        "!pip install dwave-ocean-sdk"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xA0zTbl1FBB-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!dwave setup #API endpoint URL: https://cloud.dwavesys.com/sapi/\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42ZF4-e9ciZ9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "beeb4cce-db07-475a-bd4b-b24f05e3e7b0"
      },
      "source": [
        "!git clone https://github.com/perrygeo/simanneal.git"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'simanneal'...\n",
            "remote: Enumerating objects: 37, done.\u001b[K\n",
            "remote: Counting objects: 100% (37/37), done.\u001b[K\n",
            "remote: Compressing objects: 100% (29/29), done.\u001b[K\n",
            "remote: Total 312 (delta 15), reused 24 (delta 8), pack-reused 275\u001b[K\n",
            "Receiving objects: 100% (312/312), 819.56 KiB | 1.13 MiB/s, done.\n",
            "Resolving deltas: 100% (135/135), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BryE2khLdDvT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "958754a5-6ee7-4764-ccf8-80b152c7b004"
      },
      "source": [
        "!cd ./simanneal/ && pip install -e ."
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Obtaining file:///content/simanneal\n",
            "Installing collected packages: simanneal\n",
            "  Found existing installation: simanneal 0.5.0\n",
            "    Can't uninstall 'simanneal'. No files were found to uninstall.\n",
            "  Running setup.py develop for simanneal\n",
            "Successfully installed simanneal\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A5HbfYQHK_WP",
        "colab_type": "text"
      },
      "source": [
        "##Restart here!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsqCUKLMa1QA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "30c823c9-b8a4-447b-b487-2e4db58fffe0"
      },
      "source": [
        "#pip install -e git+https://github.com/perrygeo/simanneal.git  # latest from github\n",
        "#!pip install simanneal  # from pypi\n",
        "\n",
        "!pip show simanneal  # from pypi\n",
        "\n",
        "#you may have to restart you runtime after instalatins are compleat!"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Name: simanneal\n",
            "Version: 0.5.0\n",
            "Summary: Simulated Annealing in Python\n",
            "Home-page: https://github.com/perrygeo/simanneal\n",
            "Author: Matthew Perry\n",
            "Author-email: perrygeo@gmail.com\n",
            "License: BSD\n",
            "Location: /content/simanneal\n",
            "Requires: \n",
            "Required-by: \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDKD8CogcyD0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sys\n",
        "f_loc=\"./simanneal/tests/\"\n",
        "if f_loc not in sys.path: sys.path.append(f_loc)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2-JtBP2cEsY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#you may have to restart you runtime after instalatins are compleat!\n",
        "import random\n",
        "import sys\n",
        "import time\n",
        "\n",
        "from helper import distance, cities, distance_matrix\n",
        "from simanneal import Annealer\n",
        "\n",
        "if sys.version_info.major >= 3:  # pragma: no cover\n",
        "    from io import StringIO\n",
        "else:\n",
        "    from StringIO import StringIO\n",
        "\n",
        "#you may have to restart you runtime after instalatins are compleat!"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ohmx4csYpBOV"
      },
      "source": [
        "##Define a map ploting function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "w48k4xfKpBOX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "2e05e064-50d0-42e3-ad8b-57d95255c389"
      },
      "source": [
        "#!pip install geopandas\n",
        "!pip show geopandas"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Name: geopandas\n",
            "Version: 0.8.1\n",
            "Summary: Geographic pandas extensions\n",
            "Home-page: http://geopandas.org\n",
            "Author: GeoPandas contributors\n",
            "Author-email: kjordahl@alum.mit.edu\n",
            "License: BSD\n",
            "Location: /usr/local/lib/python3.6/dist-packages\n",
            "Requires: shapely, pandas, fiona, pyproj\n",
            "Required-by: \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WlcTHm36pBOc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b54992c5-24e2-4d31-cdcc-e1cc4b4ea216"
      },
      "source": [
        "!pip install shapely"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: shapely in /usr/local/lib/python3.6/dist-packages (1.7.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1tSGNq73pBOf",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import geopandas\n",
        "import matplotlib.pyplot as plt\n",
        "#import argparse\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMok639PRzt9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#sources:\n",
        "#https://geopandas.org/io.html?highlight=states\n",
        "#https://medium.com/@erikgreenj/mapping-us-states-with-geopandas-made-simple-d7b6e66fa20d\n",
        "\n",
        "zipfile = \"zip:///content/Quantum-Chain/states_21basic.zip\"\n",
        "states = geopandas.read_file(zipfile)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bPch3ZUTySq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "3f77b3bd-652c-4ae1-98a9-38a2d21a352b"
      },
      "source": [
        "states.head()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>STATE_NAME</th>\n",
              "      <th>DRAWSEQ</th>\n",
              "      <th>STATE_FIPS</th>\n",
              "      <th>SUB_REGION</th>\n",
              "      <th>STATE_ABBR</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hawaii</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>Pacific</td>\n",
              "      <td>HI</td>\n",
              "      <td>MULTIPOLYGON (((-160.07380 22.00418, -160.0497...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Washington</td>\n",
              "      <td>2</td>\n",
              "      <td>53</td>\n",
              "      <td>Pacific</td>\n",
              "      <td>WA</td>\n",
              "      <td>MULTIPOLYGON (((-122.40202 48.22522, -122.4628...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Montana</td>\n",
              "      <td>3</td>\n",
              "      <td>30</td>\n",
              "      <td>Mountain</td>\n",
              "      <td>MT</td>\n",
              "      <td>POLYGON ((-111.47543 44.70216, -111.48080 44.6...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Maine</td>\n",
              "      <td>4</td>\n",
              "      <td>23</td>\n",
              "      <td>New England</td>\n",
              "      <td>ME</td>\n",
              "      <td>MULTIPOLYGON (((-69.77728 44.07415, -69.85993 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>North Dakota</td>\n",
              "      <td>5</td>\n",
              "      <td>38</td>\n",
              "      <td>West North Central</td>\n",
              "      <td>ND</td>\n",
              "      <td>POLYGON ((-98.73044 45.93827, -99.00683 45.939...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     STATE_NAME  ...                                           geometry\n",
              "0        Hawaii  ...  MULTIPOLYGON (((-160.07380 22.00418, -160.0497...\n",
              "1    Washington  ...  MULTIPOLYGON (((-122.40202 48.22522, -122.4628...\n",
              "2       Montana  ...  POLYGON ((-111.47543 44.70216, -111.48080 44.6...\n",
              "3         Maine  ...  MULTIPOLYGON (((-69.77728 44.07415, -69.85993 ...\n",
              "4  North Dakota  ...  POLYGON ((-98.73044 45.93827, -99.00683 45.939...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qy1iypnYShhn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "1a7e2bf4-0568-435a-d8c2-306840fc19a1"
      },
      "source": [
        "states[states.STATE_ABBR == 'CA'].plot()\n",
        "states[states.SUB_REGION == 'Pacific'].plot()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f732ab1f940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ejfooxaypBOh",
        "colab": {}
      },
      "source": [
        "#original function plot_map from QAlpha team in the CDL-Quantum/Hackathon2020\n",
        "\n",
        "def plot_map(cities,*arg,**kwarg):\n",
        "\n",
        "  #VGG note that our cities list has positive values for the Longitude\n",
        "    data_list=[[key, cities[key][0], cities[key][1]] for key in cities.keys()]\n",
        "    df = pd.DataFrame(data_list)\n",
        "\n",
        "    #City,Latitude,Longitude\n",
        "    df.columns=['City','Latitude','Longitude']\n",
        "    gdf_all = geopandas.GeoDataFrame(\n",
        "        df, geometry=geopandas.points_from_xy(df.Longitude, df.Latitude))\n",
        "\n",
        "    if 'state' in kwarg:\n",
        "      state=kwarg['state']\n",
        "\n",
        "      if state=='CA':\n",
        "        ax = states[states.STATE_ABBR == 'CA'].plot(\n",
        "        color='white', edgecolor='black')\n",
        "        ax.set_xlim(xmin=-125, xmax=-114)\n",
        "        ax.set_ylim(ymin=32, ymax=43)\n",
        "    \n",
        "    else:\n",
        "      world = geopandas.read_file(\n",
        "        geopandas.datasets.get_path('naturalearth_lowres'))\n",
        "\n",
        "      # Restrict to the USA only. 'United States of America'\n",
        "      ax = world[world.name == 'United States of America'].plot(\n",
        "          color='white', edgecolor='black')\n",
        "      \n",
        "      gdf_all.plot(ax=ax, color='gray')\n",
        "      \n",
        "      ax.set_xlim(xmin=-130, xmax=-65)\n",
        "      ax.set_ylim(ymin=20, ymax=55)\n",
        "\n",
        "\n",
        "    if 'itinerary' in kwarg:\n",
        "      itinerary=kwarg['itinerary']\n",
        "\n",
        "      if len(itinerary)>1:\n",
        "        data_list=[[city, cities[city][0], cities[city][1]] for city in itinerary]\n",
        "        df_visit = pd.DataFrame(data_list)\n",
        "        df_visit.columns = ['City','Latitude','Longitude']\n",
        "        df_start = df_visit[df_visit['City'].isin([itinerary[0]])]  \n",
        "        df_end = df_visit[df_visit['City'].isin([itinerary[-1]])] \n",
        "        gdf_visit = geopandas.GeoDataFrame(\n",
        "            df_visit, geometry=geopandas.points_from_xy(df_visit.Longitude, df_visit.Latitude))\n",
        "        gdf_start = geopandas.GeoDataFrame(\n",
        "            df_start, geometry=geopandas.points_from_xy(df_start.Longitude, df_start.Latitude))\n",
        "        gdf_end = geopandas.GeoDataFrame(\n",
        "            df_end, geometry=geopandas.points_from_xy(df_end.Longitude, df_end.Latitude))\n",
        "\n",
        "        # plot the ``GeoDataFrame``\n",
        "        x_values=gdf_visit.values.T[2]\n",
        "        y_values=gdf_visit.values.T[1]\n",
        "        plt.plot(x_values,y_values)\n",
        "\n",
        "        gdf_visit.plot(ax=ax, color='blue')\n",
        "        gdf_start.plot(ax=ax, color='green')\n",
        "        gdf_end.plot(ax=ax, color='red')    \n",
        "        \n",
        "        ax.legend(['Path','All cites', 'To Visit','Start','End'])\n",
        "    \n",
        "    ax.set_yticks([])\n",
        "    ax.set_xticks([])\n",
        "    ax.set_aspect(1.2)\n",
        "\n",
        "    plt.show()"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vAqt2uBX7Szv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_map2(cities, open_cities, closed_cities):\n",
        "    \n",
        "    data_list=[[key, cities[key][0], cities[key][1]] for key in cities.keys()]\n",
        "    df = pd.DataFrame(data_list)\n",
        "    #City,Latitude,Longitude\n",
        "    df.columns=['City','Latitude','Longitude']\n",
        "\n",
        "    data_list=[[city, cities[city][0], cities[city][1]] for city in open_cities]\n",
        "    df_open = pd.DataFrame(data_list)\n",
        "    df_open.columns = ['City','Latitude','Longitude']\n",
        "\n",
        "    data_list=[[city, cities[city][0], cities[city][1]] for city in closed_cities]\n",
        "    df_closed = pd.DataFrame(data_list)\n",
        "    df_closed.columns = ['City','Latitude','Longitude']\n",
        "\n",
        "    gdf_open = geopandas.GeoDataFrame(\n",
        "        df_open, geometry=geopandas.points_from_xy(df_open.Longitude, df_open.Latitude))\n",
        "    gdf_closed = geopandas.GeoDataFrame(\n",
        "        df_closed, geometry=geopandas.points_from_xy(df_closed.Longitude, df_closed.Latitude))\n",
        "\n",
        "    world = geopandas.read_file(\n",
        "        geopandas.datasets.get_path('naturalearth_lowres'))\n",
        "\n",
        "    # We restrict to South America.\n",
        "    ax = world[world.name == 'United States of America'].plot(\n",
        "        color='white', edgecolor='black')\n",
        "\n",
        "    # We can now plot our ``GeoDataFrame``\n",
        "    gdf_open.plot(ax=ax, color='blue')\n",
        "    gdf_closed.plot(ax=ax, color='gray')\n",
        "\n",
        "    ax.set_xlim(xmin=-130, xmax=-65)\n",
        "    ax.set_ylim(ymin=20, ymax=55)\n",
        "    ax.set_yticks([])\n",
        "    ax.set_xticks([])\n",
        "    ax.set_aspect(1.2)\n",
        "\n",
        "    ax.legend(['Delivery target', 'Other cities'])\n",
        "\n",
        "    plt.show()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VDmfowvMEuWp",
        "colab_type": "text"
      },
      "source": [
        "#Step1: Optimize the distribution impact"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "II9qfQURGCvw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "0835abc3-b67b-4ea6-a61e-dbe5103b8d31"
      },
      "source": [
        "import sys\n",
        "!pwd\n",
        "!ls"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "Quantum-Chain  sample_data  simanneal\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RESLPWXBGKc_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "f_loc=\"/content/Quantum-Chain/\"\n",
        "if f_loc not in sys.path: sys.path.append(f_loc)\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eeeAVUOKIRss",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "4099dd34-3aa1-4669-eb7a-86d09d2e5b9a"
      },
      "source": [
        "import os\n",
        "print(os.getcwd())\n",
        "\n",
        "for dirname, _, filenames in os.walk(f_loc): #f_loc\n",
        "    for filename in filenames:\n",
        "      #print(os.path.join(dirname, filename))\n",
        "      try: \n",
        "        if filename.index('knapsack') >= 0:\n",
        "          print(os.path.join(dirname, filename))\n",
        "          pass\n",
        "      except:\n",
        "        pass"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "/content/Quantum-Chain/knapsack.py\n",
            "/content/Quantum-Chain/__pycache__/knapsack.cpython-36.pyc\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0xtQkNNkI9WP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from knapsack import *"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGFtYRyhJT73",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "251b5794-d9ea-4a7f-c4a5-4c970b0d6cbc"
      },
      "source": [
        "csv_data_path=\"/content/Quantum-Chain/\"\n",
        "csv_data_file=csv_data_path+\"H_Beds.csv\"\n",
        "\n",
        "number_of_symulations=3\n",
        "\n",
        "hospital_beds_max=200000 \n",
        "#total vaccines for initial distribution\n",
        "max_people=hospital_beds_max  \n",
        "\n",
        "#the next few coments are related to the GDP-sick people assesment\n",
        "#int(hospital_beds_max/(39/100))  \n",
        "#assuming 3x13% are hospitalized and maxout the full hospital capacity!\n",
        "#we use a fudge factor of 3 insted of the 13% statistical value\n",
        "\n",
        "print(f'Performing of {number_of_symulations} simulations.',\n",
        "      f'Considering full capacity for {max_people} people at one time.')\n",
        "\n",
        "%time solution=solve_nodes_using_csv(csv_data_file,max_people,num_reads=number_of_symulations,verbose=True)\n",
        "len(solution)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Performing of 3 simulations. Considering full capacity for 200000 people at one time.\n",
            "\n",
            "BEST SOLUTION\n",
            "\n",
            "nodes in the knapsack:\n",
            "['Alameda', 'Albany', 'Atlanta', 'Augusta', 'Birmingham', 'Boise', 'Boulder', 'Bradenton', 'Bridgeport', 'Cedar Rapids', 'Chicago', 'Chico', 'Colorado Springs', 'Columbus', 'Denver', 'Des Moines', 'Dothan', 'Dubuque', 'Elgin', 'Evanston', 'Fort Lauderdale', 'Fort Smith', 'Fresno', 'Greeley', 'Honolulu', 'Hudson', 'Huntsville', 'Iowa City', 'Jacksonville', 'Joliet', 'Jonesboro', 'Lakeland', 'Los Angeles', 'Macon', 'Martinez', 'Miami', 'Mobile', 'Montgomery', 'Muncie', 'Napa', 'New Haven', 'Ocala', 'Orlando', 'Ormond Beach', 'Pensacola', 'Peoria', 'Phoenix', 'Pueblo', 'Redding', 'Sacramento', 'San Bernardino', 'San Diego', 'San Francisco', 'San Jose', 'San Luis Obispo', 'San Mateo', 'Santa Barbara', 'Santa Rosa', 'Sarasota', 'Sioux City', 'Springdale', 'Springfield', 'Stockton', 'Tallahassee', 'Tampa', 'Terre Haute', 'Texarkana', 'Tucson', 'Tuscaloosa', 'Urbana', 'Washington', 'Waterloo', 'Wilmington']\n",
            "\n",
            "\n",
            "nodes outside the knapsack:\n",
            "['Anchorage', 'Aurora', 'Bakersfield', 'Bloomington', 'Blue Island', 'Clearwater', 'Davenport', 'Fort Collins', 'Fort Myers', 'Gainesville', 'Grand Junction', 'Hartford', 'Hinsdale', 'Idaho Falls', 'Lafayette', 'Little Rock', 'Mason City', 'Melrose Park', 'Mesa', 'Modesto', 'Rockford', 'Rome', 'Salinas', 'Santa Cruz', 'Savannah', 'St. Petersburg', 'Sun City']\n",
            "\n",
            "\n",
            "Total Impact Value: 64439 of 77020 (83.7%)\n",
            "Used up capacity: 198157 of 200000 (99.1%)\n",
            "\n",
            "NEXT BEST SOLUTION\n",
            "\n",
            "nodes in the knapsack:\n",
            "['Alameda', 'Atlanta', 'Aurora', 'Bakersfield', 'Birmingham', 'Bloomington', 'Blue Island', 'Boise', 'Bradenton', 'Cedar Rapids', 'Chicago', 'Chico', 'Clearwater', 'Columbus', 'Denver', 'Des Moines', 'Dothan', 'Dubuque', 'Elgin', 'Fort Collins', 'Fort Lauderdale', 'Fort Myers', 'Fresno', 'Gainesville', 'Greeley', 'Hartford', 'Honolulu', 'Hudson', 'Iowa City', 'Jacksonville', 'Joliet', 'Lafayette', 'Lakeland', 'Little Rock', 'Los Angeles', 'Martinez', 'Melrose Park', 'Miami', 'Modesto', 'New Haven', 'Orlando', 'Ormond Beach', 'Pensacola', 'Phoenix', 'Pueblo', 'Redding', 'Sacramento', 'San Bernardino', 'San Diego', 'San Francisco', 'San Jose', 'San Luis Obispo', 'San Mateo', 'Santa Cruz', 'Santa Rosa', 'Sioux City', 'Springdale', 'Springfield', 'St. Petersburg', 'Stockton', 'Sun City', 'Tallahassee', 'Tampa', 'Texarkana', 'Tucson', 'Washington']\n",
            "\n",
            "\n",
            "nodes outside the knapsack:\n",
            "['Albany', 'Anchorage', 'Augusta', 'Boulder', 'Bridgeport', 'Colorado Springs', 'Davenport', 'Evanston', 'Fort Smith', 'Grand Junction', 'Hinsdale', 'Huntsville', 'Idaho Falls', 'Jonesboro', 'Macon', 'Mason City', 'Mesa', 'Mobile', 'Montgomery', 'Muncie', 'Napa', 'Ocala', 'Peoria', 'Rockford', 'Rome', 'Salinas', 'Santa Barbara', 'Sarasota', 'Savannah', 'Terre Haute', 'Tuscaloosa', 'Urbana', 'Waterloo', 'Wilmington']\n",
            "\n",
            "\n",
            "Total Impact Value: 64166 of 77020 (83.3%)\n",
            "Used up capacity: 197722 of 200000 (98.9%)\n",
            "CPU times: user 394 ms, sys: 1.99 ms, total: 396 ms\n",
            "Wall time: 395 ms\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIOFYcxasIQV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c6c2e1f9-db4c-4045-b2a9-ecc542e69dd7"
      },
      "source": [
        "%time solution=solve_nodes_using_csv(csv_data_file, max_people, num_reads=number_of_symulations, value_r=0.01, weight_r=0.02, verbose=True)\n",
        "len(solution)\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "BEST SOLUTION\n",
            "\n",
            "nodes in the knapsack:\n",
            "['Alameda', 'Anchorage', 'Atlanta', 'Augusta', 'Aurora', 'Birmingham', 'Bloomington', 'Blue Island', 'Boulder', 'Bridgeport', 'Chicago', 'Chico', 'Clearwater', 'Colorado Springs', 'Denver', 'Des Moines', 'Fort Collins', 'Fort Lauderdale', 'Fort Myers', 'Gainesville', 'Hartford', 'Honolulu', 'Hudson', 'Huntsville', 'Jacksonville', 'Joliet', 'Lakeland', 'Little Rock', 'Los Angeles', 'Macon', 'Martinez', 'Melrose Park', 'Mesa', 'Miami', 'Mobile', 'Muncie', 'Napa', 'New Haven', 'Orlando', 'Pensacola', 'Peoria', 'Phoenix', 'Rockford', 'Rome', 'Sacramento', 'Salinas', 'San Bernardino', 'San Diego', 'San Francisco', 'San Luis Obispo', 'Santa Barbara', 'Savannah', 'Springdale', 'Springfield', 'St. Petersburg', 'Stockton', 'Sun City', 'Tallahassee', 'Tampa', 'Terre Haute', 'Texarkana', 'Urbana', 'Washington', 'Waterloo', 'Wilmington']\n",
            "\n",
            "\n",
            "nodes outside the knapsack:\n",
            "['Albany', 'Bakersfield', 'Boise', 'Bradenton', 'Cedar Rapids', 'Columbus', 'Davenport', 'Dothan', 'Dubuque', 'Elgin', 'Evanston', 'Fort Smith', 'Fresno', 'Grand Junction', 'Greeley', 'Hinsdale', 'Idaho Falls', 'Iowa City', 'Jonesboro', 'Lafayette', 'Mason City', 'Modesto', 'Montgomery', 'Ocala', 'Ormond Beach', 'Pueblo', 'Redding', 'San Jose', 'San Mateo', 'Santa Cruz', 'Santa Rosa', 'Sarasota', 'Sioux City', 'Tucson', 'Tuscaloosa']\n",
            "\n",
            "\n",
            "Total Impact Value: 64647.97 of 77020 (83.9%)\n",
            "Used up capacity: 197643 of 200000 (98.8%)\n",
            "\n",
            "NEXT BEST SOLUTION\n",
            "\n",
            "nodes in the knapsack:\n",
            "['Alameda', 'Albany', 'Anchorage', 'Atlanta', 'Bakersfield', 'Birmingham', 'Bloomington', 'Blue Island', 'Bridgeport', 'Cedar Rapids', 'Chicago', 'Clearwater', 'Colorado Springs', 'Davenport', 'Denver', 'Des Moines', 'Dothan', 'Dubuque', 'Elgin', 'Evanston', 'Fort Collins', 'Fort Lauderdale', 'Fort Myers', 'Fresno', 'Gainesville', 'Grand Junction', 'Hinsdale', 'Huntsville', 'Idaho Falls', 'Jacksonville', 'Jonesboro', 'Lafayette', 'Little Rock', 'Los Angeles', 'Martinez', 'Melrose Park', 'Mesa', 'Miami', 'Mobile', 'Modesto', 'Montgomery', 'New Haven', 'Ocala', 'Orlando', 'Ormond Beach', 'Phoenix', 'Pueblo', 'Redding', 'Rockford', 'Rome', 'Sacramento', 'San Bernardino', 'San Diego', 'San Francisco', 'San Luis Obispo', 'San Mateo', 'Santa Barbara', 'Santa Cruz', 'Sarasota', 'Savannah', 'Sioux City', 'Springdale', 'St. Petersburg', 'Tallahassee', 'Tampa', 'Texarkana', 'Tucson', 'Washington', 'Waterloo']\n",
            "\n",
            "\n",
            "nodes outside the knapsack:\n",
            "['Augusta', 'Aurora', 'Boise', 'Boulder', 'Bradenton', 'Chico', 'Columbus', 'Fort Smith', 'Greeley', 'Hartford', 'Honolulu', 'Hudson', 'Iowa City', 'Joliet', 'Lakeland', 'Macon', 'Mason City', 'Muncie', 'Napa', 'Pensacola', 'Peoria', 'Salinas', 'San Jose', 'Santa Rosa', 'Springfield', 'Stockton', 'Sun City', 'Terre Haute', 'Tuscaloosa', 'Urbana', 'Wilmington']\n",
            "\n",
            "\n",
            "Total Impact Value: 64793.5 of 77020 (84.1%)\n",
            "Used up capacity: 198467 of 200000 (99.2%)\n",
            "CPU times: user 343 ms, sys: 2.97 ms, total: 346 ms\n",
            "Wall time: 348 ms\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sj2pNdDkjkrD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "16a9685e-608e-4f4a-cd89-dc9421573138"
      },
      "source": [
        "cities=solution[0]['closed_cities']+solution[0]['open_cities']\n",
        "len(cities)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rHg3-_18NYu6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#original uscities file from https://simplemaps.com/data/us-cities\n",
        "df0=pd.read_csv(csv_data_file)\n",
        "uscities_file=csv_data_path+\"uscities.csv\"\n",
        "uscities=pd.read_csv(uscities_file)\n",
        "my_cities={}\n",
        "for (i,city) in enumerate(df0[\"City\"]):\n",
        "  for (j,city_ascii) in enumerate(uscities[\"city_ascii\"]):\n",
        "    if city in cities and city==city_ascii and df0[\"State\"][i][1:]==uscities[\"state_id\"][j]:\n",
        "      my_cities[city]=(uscities[\"lat\"][j],uscities[\"lng\"][j])"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9LdOu6HGqXCM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c6a87c2e-42fd-4167-917b-77a6bcd32124"
      },
      "source": [
        "plot_map2(my_cities,solution[0]['open_cities'],solution[0]['closed_cities'])"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dXoBuI8Raql2",
        "colab_type": "text"
      },
      "source": [
        "#Step 2: Vehicle Transportation Schedule\n",
        "based on the annealing for the Travelling Salesman Problem\n",
        "https://github.com/perrygeo/simanneal"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i8OllGCNYJ-B",
        "colab_type": "text"
      },
      "source": [
        "##Define the Annealer functoins and test them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4VuqzfnbOqD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "bee2eb83-7efa-4be5-87dc-6a9b9361598f"
      },
      "source": [
        "%%time\n",
        "#see ./simanneal/examples/salesman.py\n",
        "# -*- coding: utf-8 -*-\n",
        "from __future__ import print_function\n",
        "import math\n",
        "import random\n",
        "from simanneal import Annealer\n",
        "\n",
        "\n",
        "def distance(a, b):\n",
        "    \"\"\"Calculates distance between two latitude-longitude coordinates.\"\"\"\n",
        "    R = 3963  # radius of Earth (miles)\n",
        "    lat1, lon1 = math.radians(a[0]), math.radians(a[1])\n",
        "    lat2, lon2 = math.radians(b[0]), math.radians(b[1])\n",
        "    return math.acos(math.sin(lat1) * math.sin(lat2) +\n",
        "                     math.cos(lat1) * math.cos(lat2) * math.cos(lon1 - lon2)) * R\n",
        "\n",
        "\n",
        "class TravellingSalesmanProblem(Annealer):\n",
        "\n",
        "    \"\"\"Test annealer with a travelling salesman problem.\n",
        "    \"\"\"\n",
        "\n",
        "    # pass extra data (the distance matrix) into the constructor\n",
        "    def __init__(self, state, distance_matrix):\n",
        "        self.distance_matrix = distance_matrix\n",
        "        super(TravellingSalesmanProblem, self).__init__(state)  # important!\n",
        "\n",
        "    def move(self):\n",
        "        \"\"\"Swaps two cities in the route.\"\"\"\n",
        "        # no efficiency gain, just proof of concept\n",
        "        # demonstrates returning the delta energy (optional)\n",
        "        initial_energy = self.energy()\n",
        "\n",
        "        a = random.randint(0, len(self.state) - 1)\n",
        "        b = random.randint(0, len(self.state) - 1)\n",
        "        self.state[a], self.state[b] = self.state[b], self.state[a]\n",
        "\n",
        "        return self.energy() - initial_energy\n",
        "\n",
        "    def energy(self):\n",
        "        \"\"\"Calculates the length of the route.\"\"\"\n",
        "        e = 0\n",
        "        for i in range(len(self.state)):\n",
        "            e += self.distance_matrix[self.state[i-1]][self.state[i]]\n",
        "        return e\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "\n",
        "    # latitude and longitude for the twenty largest U.S. cities\n",
        "    \n",
        "    cities = {\n",
        "        'New York': (40.72, -74.00),\n",
        "        'Los Angeles': (34.05, -118.25),\n",
        "        'Chicago': (41.88, -87.63),\n",
        "        'Houston': (29.77, -95.38),\n",
        "        'Phoenix': (33.45, -112.07),\n",
        "        'Philadelphia': (39.95, -75.17),\n",
        "        'San Antonio': (29.53, -98.47),\n",
        "        'Dallas': (32.78, -96.80),\n",
        "        'San Diego': (32.78, -117.15),\n",
        "        'San Jose': (37.30, -121.87),\n",
        "        'Detroit': (42.33, -83.05),\n",
        "        'San Francisco': (37.78, -122.42),\n",
        "        'Jacksonville': (30.32, -81.70),\n",
        "        'Indianapolis': (39.78, -86.15),\n",
        "        'Austin': (30.27, -97.77),\n",
        "        'Columbus': (39.98, -82.98),\n",
        "        'Fort Worth': (32.75, -97.33),\n",
        "        'Charlotte': (35.23, -80.85),\n",
        "        'Memphis': (35.12, -89.97),\n",
        "        'Baltimore': (39.28, -76.62)\n",
        "    }\n",
        "\n",
        "    # initial state, a randomly-ordered itinerary\n",
        "    init_state = list(cities.keys())\n",
        "    random.shuffle(init_state)\n",
        "\n",
        "    # create a distance matrix\n",
        "    distance_matrix = {}\n",
        "    for ka, va in cities.items():\n",
        "        distance_matrix[ka] = {}\n",
        "        for kb, vb in cities.items():\n",
        "            if kb == ka:\n",
        "                distance_matrix[ka][kb] = 0.0\n",
        "            else:\n",
        "                distance_matrix[ka][kb] = distance(va, vb)\n",
        "\n",
        "    tsp = TravellingSalesmanProblem(init_state, distance_matrix)\n",
        "    tsp.set_schedule(tsp.auto(minutes=0.2))\n",
        "    # since our state is just a list, slice is the fastest way to copy\n",
        "    tsp.copy_strategy = \"slice\"\n",
        "    state, e = tsp.anneal()\n",
        "\n",
        "    while state[0] != 'New York':\n",
        "        state = state[1:] + state[:1]  # rotate NYC to start\n",
        "\n",
        "    print()\n",
        "    print(\"%i mile route:\" % e)\n",
        "    print(\" ➞  \".join(state))\n"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Temperature        Energy    Accept   Improve     Elapsed   Remaining\n",
            "     4.90000       7060.97     4.95%     0.00%     0:00:02    -1:59:59\n",
            " Temperature        Energy    Accept   Improve     Elapsed   Remaining\n",
            "     4.90000       7031.22     4.64%     0.00%     0:00:06     0:00:00"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "6845 mile route:\n",
            "New York ➞  Philadelphia ➞  Baltimore ➞  Charlotte ➞  Jacksonville ➞  Houston ➞  Austin ➞  San Antonio ➞  Phoenix ➞  San Diego ➞  Los Angeles ➞  San Jose ➞  San Francisco ➞  Fort Worth ➞  Dallas ➞  Memphis ➞  Indianapolis ➞  Chicago ➞  Detroit ➞  Columbus\n",
            "CPU times: user 7.99 s, sys: 71.3 ms, total: 8.07 s\n",
            "Wall time: 8.03 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "shXu4MOvpxoR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "339e262d-e410-4f63-a362-cc3379bd93d2"
      },
      "source": [
        "cities.update(my_cities)\n",
        "plot_map(cities,itinerary=state)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFXRoFW4bivu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def make_itinerary(current_location,initial_state):\n",
        "\n",
        "  if current_location not in initial_state:\n",
        "    initial_state.append(current_location) \n",
        "    print(\"\\nAdding your current location to the initial list!\")\n",
        "\n",
        "  # populate your cities list with coordinate information\n",
        "  my_cities={}\n",
        "  for city in initial_state:\n",
        "    if city in cities.keys():\n",
        "      my_cities[city]=cities[city]\n",
        "    else:\n",
        "      print(\"No location coordinates for \",city)\n",
        "      if city==current_location:\n",
        "        print(\"Add your location coordinates!\")\n",
        "        return\n",
        "  \n",
        "  # initial state, a randomly-ordered itinerary\n",
        "  init_state = initial_state\n",
        "  random.shuffle(init_state)\n",
        "\n",
        "  # create a distance matrix\n",
        "  distance_matrix = {}\n",
        "  for ka, va in my_cities.items():\n",
        "      distance_matrix[ka] = {}\n",
        "      for kb, vb in my_cities.items():\n",
        "          if kb == ka:\n",
        "              distance_matrix[ka][kb] = 0.0\n",
        "          else:\n",
        "              distance_matrix[ka][kb] = distance(va, vb)\n",
        "\n",
        "  tsp = TravellingSalesmanProblem(init_state, distance_matrix)\n",
        "  '''\n",
        "  tsp.set_schedule(tsp.auto(minutes=0.2))\n",
        "  # since our state is just a list, slice is the fastest way to copy\n",
        "  tsp.copy_strategy = \"slice\"\n",
        "  '''\n",
        "  itinerary, miles = tsp.anneal()\n",
        "\n",
        "  while itinerary[0] != current_location:\n",
        "      itinerary = itinerary[1:] + itinerary[:1]  # rotate to start correctly\n",
        "\n",
        "  print()\n",
        "  print(\"%i mile route:\" % miles)\n",
        "  print(\" ➞  \".join(itinerary))\n",
        "  return itinerary, miles\n"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SepRptdwXVsD",
        "colab_type": "text"
      },
      "source": [
        "##Create the travel itinerary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWDz4YgLozkQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "71d40465-e4aa-4850-9937-afbca5564cf6"
      },
      "source": [
        "%%time\n",
        "#initial_state = ['New York City', 'Los Angeles','Dallas', 'Philadelphia', 'Houston','San Diego']\n",
        "initial_state = ['Santa Cruz','San Luis Obispo','San Jose','Stockton','Santa Rosa']\n",
        "current_location= 'San Francisco'\n",
        "\n",
        "itinerary, miles = make_itinerary(current_location,initial_state)\n",
        "\n",
        "print(itinerary[0:2])\n",
        "print(itinerary[2:4])\n",
        "print(itinerary[4:6])\n",
        "plot_map(cities,state='CA',itinerary=itinerary)\n"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Temperature        Energy    Accept   Improve     Elapsed   Remaining\n",
            "  3295.64185        586.83    99.80%    43.20%     0:00:00     0:00:01"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Adding your current location to the initial list!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "     2.50000        533.81    20.40%     0.40%     0:00:01     0:00:00"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "533 mile route:\n",
            "San Francisco ➞  San Jose ➞  Santa Cruz ➞  San Luis Obispo ➞  Stockton ➞  Santa Rosa\n",
            "['San Francisco', 'San Jose']\n",
            "['Santa Cruz', 'San Luis Obispo']\n",
            "['Stockton', 'Santa Rosa']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 1.09 s, sys: 138 ms, total: 1.23 s\n",
            "Wall time: 1.15 s\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}