simulate.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "def timemycode(func):\n",
        "    def wrapper(*args, **kwargs):\n",
        "        start_time = time.time()\n",
        "        result = func(*args, **kwargs)\n",
        "        end_time = time.time()\n",
        "        print(f\"Function {func.__name__} took {end_time - start_time:.5f} seconds to execute.\")\n",
        "        return result\n",
        "    return wrapper\n"
      ],
      "metadata": {
        "id": "VWaZLTEZJb3I"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "map={\n",
        "    'Engineering Buiding':{\n",
        "        'latitude':32.214256505009985,\n",
        "        'longitude': -98.21779723918512\n",
        "    },\n",
        "     'Library':{\n",
        "         'latitude':32.215730969726785,\n",
        "         'longitude': -98.21730569278304\n",
        "     },\n",
        "     'Nursing Building':{\n",
        "         'latitude':32.21664588196621,\n",
        "         'longitude': -98.22043371534166\n",
        "     },\n",
        "     'Centennial':{\n",
        "        'latitude':32.217553223855305,\n",
        "        'longitude': -98.2217742964382\n",
        "    },\n",
        "     'Rec':{\n",
        "         'latitude':32.21630562642345,\n",
        "         'longitude':  -98.2224535241938\n",
        "     },\n",
        "      'Science Building':{\n",
        "         'latitude':     32.216883861659866, \n",
        "         'longitude': -98.21972368519181\n",
        "     },\n",
        "\n",
        "     'Student Center':{\n",
        "         'latitude':32.21768176322404, \n",
        "         'longitude': -98.21858371342843,\n",
        "     }\n",
        "     ,'Dining Hall':{\n",
        "        'latitude':32.2178254246564,\n",
        "        'longitude': -98.21791342288013\n",
        "    },\n",
        "     'Honors Hall':{\n",
        "         'latitude':32.216131717543234, \n",
        "         'longitude':  -98.21936125046442,\n",
        "     },\n",
        "     'Heritage Hall':{\n",
        "         'latitude':32.21875543791148, \n",
        "         'longitude':  -98.21975448758607\n",
        "     },\n",
        "     'Hunnewell Hall':{\n",
        "        'latitude':32.21636498651769,\n",
        "        'longitude':  -98.2146443060869, \n",
        "    },\n",
        "     'Mathematic Building':{\n",
        "         'latitude':32.216696108219054, \n",
        "         'longitude': -98.21673716080358\n",
        "     },\n",
        "     'Legends':{\n",
        "         'latitude':32.21772417564671,\n",
        "         'longitude':  -98.2212426209783\n",
        "     }\n",
        "     ,'Legacy':{\n",
        "        'latitude':32.217963653269614,\n",
        "        'longitude':  -98.220472065015\n",
        "    },\n",
        "     'Integrity Hall':{\n",
        "         'latitude':32.21921876315668, \n",
        "         'longitude':  -98.22109181516743\n",
        "     },\n",
        "     'Texan Village':{\n",
        "         'latitude':32.21862007618148, \n",
        "         'longitude':  -98.22219260940068\n",
        "     }\n",
        "\n",
        "}"
      ],
      "metadata": {
        "id": "6C1SAKTbAHmk"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lat_miles_per_degree = 69\n",
        "\n",
        "# 32.214256505009985 \n",
        "lon_miles_per_degree = 69.172\n",
        "\n",
        "\n",
        "for location in map:\n",
        "    map[location]['latitude'] *= lat_miles_per_degree\n",
        "    map[location]['longitude'] *= lon_miles_per_degree\n",
        "\n",
        "G = nx.Graph()\n",
        "for location in map:\n",
        "    latitude = map[location]['latitude']\n",
        "    longitude = map[location]['longitude']\n",
        "    G.add_node(location, pos=(longitude, latitude))\n",
        "\n",
        " \n",
        "pos = nx.get_node_attributes(G, 'pos')\n",
        "nx.draw(G, pos, with_labels=True ,node_color='lightblue', node_size=500, font_size=8)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "2TAiA-b3_WvH",
        "outputId": "157fa25e-b8d8-45f2-b4c6-c4e951d30c8d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "planned = list(map.keys())[0:7]\n",
        "planned"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8EQoSR4UD7bq",
        "outputId": "73a7f1eb-1b18-4760-bf77-58339485b67d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Engineering Buiding',\n",
              " 'Library',\n",
              " 'Nursing Building',\n",
              " 'Centennial',\n",
              " 'Rec',\n",
              " 'Science Building',\n",
              " 'Student Center']"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "@timemycode\n",
        "def find_optimal_path(planned, start, end, map):\n",
        "    distances = []\n",
        "    for i in range(len(planned) - 1):\n",
        "        start_lat = map[planned[i]]['latitude']\n",
        "        start_lon = map[planned[i]]['longitude']\n",
        "        end_lat = map[planned[i+1]]['latitude']\n",
        "        end_lon = map[planned[i+1]]['longitude']\n",
        "        distance = ((end_lat - start_lat)**2 + (end_lon - start_lon)**2)**0.5\n",
        "        distances.append(distance)\n",
        "    best_path = planned\n",
        "    best_distance = sum(distances)\n",
        "    from itertools import permutations\n",
        "    for path in permutations(planned):\n",
        "        new_distances = []\n",
        "        for i in range(len(path) - 1):\n",
        "            start_lat = map[path[i]]['latitude']\n",
        "            start_lon = map[path[i]]['longitude']\n",
        "            end_lat = map[path[i+1]]['latitude']\n",
        "            end_lon = map[path[i+1]]['longitude']\n",
        "            distance = ((end_lat - start_lat)**2 + (end_lon - start_lon)**2)**0.5\n",
        "            new_distances.append(distance)\n",
        "        new_distance = sum(new_distances)\n",
        "        if new_distance < best_distance:\n",
        "            best_path = path\n",
        "            best_distance = new_distance\n",
        "    start_index = best_path.index(start)\n",
        "    end_index = best_path.index(end)\n",
        "    if start_index < end_index:\n",
        "        return best_path[start_index:end_index+1]\n",
        "    else:\n",
        "        return best_path[end_index:start_index+1][::-1]\n"
      ],
      "metadata": {
        "id": "hyQeX8AzHL5t"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def find_optimal_path_new(planned, start, end, map):\n",
        "    distances = []\n",
        "    times = []\n",
        "    for i in range(len(planned) - 1):\n",
        "        start_lat = map[planned[i]]['latitude']\n",
        "        start_lon = map[planned[i]]['longitude']\n",
        "        end_lat = map[planned[i+1]]['latitude']\n",
        "        end_lon = map[planned[i+1]]['longitude']\n",
        "        distance = ((end_lat - start_lat)**2 + (end_lon - start_lon)**2)**0.5\n",
        "        time = estimate_time(map, planned[i], planned[i+1])\n",
        "        distances.append(distance)\n",
        "        times.append(time)\n",
        "    best_path = planned\n",
        "    best_distance = sum(distances)\n",
        "    from itertools import permutations\n",
        "    for path in permutations(planned):\n",
        "        new_distances = []\n",
        "        new_times = []\n",
        "        for i in range(len(path) - 1):\n",
        "            start_lat = map[path[i]]['latitude']\n",
        "            start_lon = map[path[i]]['longitude']\n",
        "            end_lat = map[path[i+1]]['latitude']\n",
        "            end_lon = map[path[i+1]]['longitude']\n",
        "            distance = ((end_lat - start_lat)**2 + (end_lon - start_lon)**2)**0.5\n",
        "            time = estimate_time(map, path[i], path[i+1])\n",
        "            new_distances.append(distance)\n",
        "            new_times.append(time)\n",
        "        new_distance = sum(new_distances)\n",
        "        new_time = sum(new_times)\n",
        "        if new_distance < best_distance:\n",
        "            best_path = path\n",
        "            best_distance = new_distance\n",
        "            best_times = new_times\n",
        "    start_index = best_path.index(start)\n",
        "    end_index = best_path.index(end)\n",
        "    if start_index < end_index:\n",
        "        return best_path[start_index:end_index+1], best_times[start_index:end_index]\n",
        "    else:\n",
        "        return best_path[end_index:start_index+1][::-1], best_times[end_index:start_index][::-1]\n"
      ],
      "metadata": {
        "id": "gWaKp0H8M9PR"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "planned =[\n",
        "\n",
        " 'Nursing Building',\n",
        "  'Library',\n",
        " 'Centennial',\n",
        " 'Rec',\n",
        " 'Dining Hall',\n",
        " 'Science Building',\n",
        " 'Student Center']\n",
        "start = 'Dining Hall'\n",
        "end='Rec'\n",
        "path = find_optimal_path(planned, start, end, map)\n",
        "print(path)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJ80FQ2GI_hG",
        "outputId": "ce607eb4-60e1-4885-bdc3-f3f00544a57d"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Function find_optimal_path took 0.10890 seconds to execute.\n",
            "('Dining Hall', 'Student Center', 'Science Building', 'Nursing Building', 'Centennial', 'Rec')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " "
      ],
      "metadata": {
        "id": "S5dFE49kJHgQ"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from math import sin, cos, sqrt, atan2, radians\n",
        "\n",
        "def estimate_time(map, start, end):\n",
        "    lat1, lon1 = map[start]['latitude'], map[start]['longitude']\n",
        "    lat2, lon2 = map[end]['latitude'], map[end]['longitude']\n",
        "    lat_diff = radians(lat2 - lat1)\n",
        "    lon_diff = radians(lon2 - lon1)\n",
        "    a = sin(lat_diff / 2) ** 2 + cos(radians(lat1)) * cos(radians(lat2)) * sin(lon_diff / 2) ** 2\n",
        "    c = 2 * atan2(sqrt(a), sqrt(1 - a))\n",
        "    distance = 3959 * c # 3959  \n",
        "\n",
        "    # Estimate time based on distance\n",
        "    walking_speed = 3.1 # miles per hour\n",
        "    time = distance / walking_speed\n",
        "    return time"
      ],
      "metadata": {
        "id": "Ve5my53LKDQj"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "estimate_time(map,'Honors Hall','Centennial')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZjiLP2D4Lkxt",
        "outputId": "a000ff12-5803-4c44-b81f-bda17e8bd142"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.198806411160471"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.array([[0 for i in range(10)] for i in range(10)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jFx1Rn4NLqcg",
        "outputId": "68586666-25e4-483e-d02d-b1ca70b306eb"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from scipy.spatial.distance import cdist\n",
        "\n",
        "def map_to_maze(map_dict):\n",
        "    # extract coordinates from map dictionary\n",
        "    coords = np.array([[loc['latitude'], loc['longitude']] for loc in map_dict.values()])\n",
        "\n",
        "    # compute distances between all pairs of points\n",
        "    dists = cdist(coords, coords)\n",
        "\n",
        "    # create empty maze array\n",
        "    maze = np.zeros((len(map_dict), len(map_dict)))\n",
        "\n",
        "    # assign random values to maze cells\n",
        "    maze.flat = np.random.randint(1, 10, size=len(maze))\n",
        "\n",
        "    # set distance values in maze array\n",
        "    for i, key in enumerate(map_dict.keys()):\n",
        "        for j, key2 in enumerate(map_dict.keys()):\n",
        "            if i != j:\n",
        "                maze[i][j] = dists[i][j]\n",
        "\n",
        "    return maze.astype(int)\n"
      ],
      "metadata": {
        "id": "sgTI3r8YYQ5o"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(map.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YU1NSpo3Zihs",
        "outputId": "b02671b1-7b31-4a62-ba2f-7d311c93afdc"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "map_to_maze(map)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gLcHBO0eYh27",
        "outputId": "7300c4ae-89ba-473a-fc89-013941174356"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5]])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "def ltlg_distance(pos1, pos2):\n",
        "\n",
        "    lat1 = math.radians(map[pos1]['latitude'])\n",
        "    lon1 = math.radians(map[pos1]['longitude'])\n",
        "\n",
        "    lat2 = math.radians(map[pos2]['latitude'])\n",
        "    lon2 = math.radians(map[pos2]['longitude'])\n",
        "    print(map[pos1]['latitude'],map[pos1]['longitude'])\n",
        "\n",
        " \n",
        "    dlon = lon2 - lon1 \n",
        "    dlat = lat2 - lat1 \n",
        "    a = math.sin(dlat / 2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2)**2\n",
        "    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\n",
        "    distance = 3961 * c  \n",
        "\n",
        "    return round(distance,1)\n"
      ],
      "metadata": {
        "id": "Q1KXC5czYjRe"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ltlg_distance('Centennial','Honors Hall')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdSg99roa1C6",
        "outputId": "21d47653-c8d2-4070-9671-dd63d2171226"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32.217553223855305 -98.2217742964382\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ytoe_9jca59B"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def llgxy(latitude, longitude):\n",
        "    x = (longitude + 180) / 360 * 10\n",
        "    y = (latitude + 90) / 180 * 10\n",
        "    return x, y\n",
        "llgxy(32.217553223855305, -98.2217742964382)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oOrjehqUfjwe",
        "outputId": "03a8ff13-f6a5-41ab-9cb5-8b0cb6864e21"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.2716173806544946, 6.789864067991961)"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xr=[]\n",
        "yr=[]\n",
        "for pos1 in map:\n",
        "  print()\n",
        "  x = round((map[pos1]['longitude'] + 180) / 360 * 10,10)\n",
        "  y = round((map[pos1]['latitude']+90) / 180 * 10,10)\n",
        "  xr.append(x)\n",
        "  yr.append(y)\n",
        "  print((x,y))\n",
        "# xr.sort()\n",
        "# yr.sort()\n",
        "print(xr)\n",
        "print(yr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d9yxbLPtfpVS",
        "outputId": "ec9dc90b-4a83-45fa-b927-c962ea5aeace"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "(2.2717278545, 6.7896809169)\n",
            "\n",
            "(2.2717415085, 6.7897628317)\n",
            "\n",
            "(2.271654619, 6.7898136601)\n",
            "\n",
            "(2.2716173807, 6.789864068)\n",
            "\n",
            "(2.2715985132, 6.789794757)\n",
            "\n",
            "(2.2716743421, 6.7898268812)\n",
            "\n",
            "(2.271706008, 6.7898712091)\n",
            "\n",
            "(2.2717246271, 6.7898791903)\n",
            "\n",
            "(2.2716844097, 6.7897850954)\n",
            "\n",
            "(2.2716734865, 6.7899308577)\n",
            "\n",
            "(2.2718154359, 6.7897980548)\n",
            "\n",
            "(2.2717573011, 6.7898164505)\n",
            "\n",
            "(2.2716321494, 6.7898735653)\n",
            "\n",
            "(2.2716535537, 6.7898868696)\n",
            "\n",
            "(2.2716363385, 6.789956598)\n",
            "\n",
            "(2.2716057608, 6.7899233376)\n",
            "[2.2717278545, 2.2717415085, 2.271654619, 2.2716173807, 2.2715985132, 2.2716743421, 2.271706008, 2.2717246271, 2.2716844097, 2.2716734865, 2.2718154359, 2.2717573011, 2.2716321494, 2.2716535537, 2.2716363385, 2.2716057608]\n",
            "[6.7896809169, 6.7897628317, 6.7898136601, 6.789864068, 6.789794757, 6.7898268812, 6.7898712091, 6.7898791903, 6.7897850954, 6.7899308577, 6.7897980548, 6.7898164505, 6.7898735653, 6.7898868696, 6.789956598, 6.7899233376]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(xr, yr,'o')\n",
        "ax.set_xlabel('Longitude')\n",
        "ax.set_ylabel('Latitude')\n",
        "ax.set_title('Campus Map')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Q1NMTE_XgID7",
        "outputId": "8055ff39-8ba2-483a-aa3c-57a38e636130"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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fwJSIeB3wPZ5/ktpDRFwVEW0R0TZhwoRhqbCZWbPIGWy6gMNK1ientLJ5JI2haP7aUmHfsumSJgBHR8SalL4MeBNARGyJiJ0p/WvA6wd3WmZmNlA5g83dwDRJUyXtR9Hhv7JPnpXAOWn5LGB1RERKn59Gq00FpgFrK5S5FRgv6chU1luBhwAkvazkeGf0ppuZ2fCpqc8mddq/C3hFRFwq6XDg0IhY298+qQ/mImAV0AJcExEPSLoUaI+IlcDVwHWSOoEnKYIHKd8NwINAD3BhROxOddmrzJT+fuAmSX+gCD7vTVX5sKQzUjlPAufWeG3MzGyIqHiQqJJJuhL4A3BKRLxa0ouB70bEG3JXsB7a2tqivb293tUwM2soktZFRFu5bbWORjs+Io6TtB4gIramZiwzM7Oqau2z2ZV+UBkAqUP+D9lqZWZmo0qtweZLwM3ASyX9A3AH8I/ZamVmZqNKTc1oEXG9pHXALEDAvIjwqC4zM6tJxWAj6SUlq78FvlW6LSKezFUxMzMbPao92ayj6KcRcDjFkGIBrcCvgKk5K2dmZqNDxT6biJgaEa8Avg/8eUQcEhEHA6cD3x2OCpqZWeOrdejzCRHx/t6ViLhN0ucz1cksqxXru1i8qoNN27qZ2DqOBbOn+zXbZpnVGmw2Sfo08M20/i48hb81oBXru1i4/D66d+0GoGtbNwuX3wfggGOWUa1Dn88GJlAMf74ZeGlKM2soi1d1PBdoenXv2s3iVR11qpFZc6h16POTwEcy18Usu03bugeUbmZDo9aJOH9Imj2gVEScMuQ1MstoYus4usoElomt4+pQG7PmUWufzcdLlg8A3kExi7JZQ1kwe/oefTYA48a2sGD29DrWymz0q7UZbV2fpJ9I6vf1AmYjVe8gAI9GMxtetTajlc4k8AKKt12Oz1Ijs8zmHTvJwcVsmNXajFY6k0AP8ChwXq5KmZnVyr+bagy1BptXR8QzpQmS9s9QHzOzmvl3U42j1t/Z/FeZtDuHsiLWvFas7+LERauZesktnLhoNSvWd9W7StYg/LupxlFt1udDgUnAOEnHUjSjARwEHJi5btYEfGdqg+HfTTWOas1os4FzgcnAF0rSnwI+malO1kQq3Zk62Fg1/t1U46gYbCJiCbBE0jsi4qZhqpM1Ed+Z2mD4d1ONo1oz2l9FxDeBKZI+1nd7RHyhzG5mNfOdqQ2GfzfVOKo1o70w/ftHZbbtNX2N2UD5ztQGy7+bagzVmtH+NS1+PyJ+UrpN0onZamVNw3emZs2h1t/ZfBk4roY0swHznanZ6Fetz+aNwJuACX36bA4CWnJWzMzMRo9qP+rcj6K/ZgzwopLPDuCsaoVLmiOpQ1KnpEvKbN9f0rK0fY2kKSXbFqb0Dkmzq5UpaZakeyRtkHSHpCOqHcPMzIZHtT6b24HbJX0jIh4bSMGSWoArgLcCG4G7Ja2MiAdLsp0HbI2IIyTNBy4D3ilpBjAfOAqYCHxf0pFpn/7KvBKYGxEPSboA+DTFb4TKHmMg52JmZoNTa5/N7yUtpvjjf0BvYpWXp80EOiPiEQBJS4G5QGmwmQv8bVq+EbhcklL60ojYCTwqqTOVR4Uyg6J5D4oZqTdVOkZEeDSdmVmSe0LTWoPN9cAy4HTgA8A5wOYq+0wCHi9Z3wgc31+eiOiRtB04OKXf1Wff3rPur8z3AbdK6qZo5juhyjGeqFJ/M7OmMBzTRtU6EefBEXE1sCsibo+I9wIj7ZXQFwOnRcRk4OvsOb1OVZLOl9QuqX3z5mpxdG+eTNLMGtVwTGhaa7DZlf79taQ/S5NyvqTSDkAXcFjJ+uSUVjaPpDEUzV9bKuxbNl3SBODoiFiT0pdRjKKrdIw9RMRVEdEWEW0TJkyocmp76r0r6NrWTfD8XYEDjpk1guGYNqrWYPO/JI0H/hr4OPA14KNV9rkbmCZpqqT9KDr8V/bJs5KiSQ6K0W2rU1/KSmB+Gkk2FZgGrK1Q5lZgfMkggrcCD1U5xpDxNOdm1sj6mx5qKKeNqqnPJiK+kxa3A28BkPTRKvv0SLoIWEXxm5xrIuIBSZcC7RGxErgauC4NAHiSIniQ8t1A0fHfA1wYEbvTcfcqM6W/H7hJ0h8ogs97U1XKHmMoeTJJM2tkwzFtlPb1Jl/SryLi8CGryQjS1tYW7e3tNec/cdHqspNJTmodx08uGWldW2ZmexuK0WiS1kVEW7lttY5GK1vuIPYdVTyZpJk1utzTRg0m2Ph3KoknkzQzq6za3GhPUT6oCPALR0p4MsnRL/eP3sxGs2rT1bxouCpiNpINx4/ezEazWoc+mzU1D283GxwHG7MaeHi72eA42JjVYDh+9GY2mjnYmNVgwezpjBu75/sCPbzdrHaDGfps1jQ8vN1scBxszGrk4e1m+87NaGZmlp2DjZmZZedgY2Zm2TnYmJlZdg42ZmaWnYONmZll52BjZmbZOdiYmVl2/lGnmQ0Lvw+ouTnYmFl2fh+QuRnNzLLz+4DMwcbMsvP7gMzBxsyy8/uAzMHGzLLz+4DMAwTMLDu/D8gcbMxsWPh9QM3NzWhmZpZd1mAjaY6kDkmdki4ps31/ScvS9jWSppRsW5jSOyTNrlampB9L2pA+myStSOknS9pesu2zOc/ZzMz2lq0ZTVILcAXwVmAjcLeklRHxYEm284CtEXGEpPnAZcA7Jc0A5gNHAROB70s6Mu1TtsyIOKnk2DcB3y45zo8j4vQ8Z2pmZtXkfLKZCXRGxCMR8SywFJjbJ89cYElavhGYJUkpfWlE7IyIR4HOVF7VMiUdBJwCrMhzWmZmNlA5g80k4PGS9Y0prWyeiOgBtgMHV9i3ljLnAT+IiB0laW+UdK+k2yQdVa6yks6X1C6pffPmzTWcnpmZ1Wo0DhA4G/hWyfo9wMsj4mjgy/TzxBMRV0VEW0S0TZgwIX8tzcyaSM5g0wUcVrI+OaWVzSNpDDAe2FJh34plSjqEoqntlt60iNgREU+n5VuBsSmfmZkNk5zB5m5gmqSpkvaj6PBf2SfPSuCctHwWsDoiIqXPT6PVpgLTgLU1lHkW8J2IeKY3QdKhqR8ISTMpznnLEJ+rmZlVkG00WkT0SLoIWAW0ANdExAOSLgXaI2IlcDVwnaRO4EmK4EHKdwPwINADXBgRuwHKlVly2PnAoj5VOQv4oKQeoBuYnwKamZkNE/nv7t7a2tqivb293tUwM2soktZFRFu5baNxgICZmY0wDjZmZpadg42ZmWXnYGNmZtk52JiZWXYONmZmlp2DjZmZZedgY2Zm2TnYmJlZdg42ZmaWnYONmZll52BjZmbZOdiYmVl2DjZmZpadg42ZmWXnYGNmZtk52JiZWXYONmZmlp2DjZmZZedgY2Zm2TnYmJlZdg42ZmaWnYONmZll52BjZmbZOdiYmVl2DjZmZpadg42ZmWWXNdhImiOpQ1KnpEvKbN9f0rK0fY2kKSXbFqb0Dkmzq5Up6ceSNqTPJkkrUrokfSnl/6mk43Kes5mZ7W1MroIltQBXAG8FNgJ3S1oZEQ+WZDsP2BoRR0iaD1wGvFPSDGA+cBQwEfi+pCPTPmXLjIiTSo59E/DttHoqMC19jgeuTP+amdkwyflkMxPojIhHIuJZYCkwt0+eucCStHwjMEuSUvrSiNgZEY8Cnam8qmVKOgg4BVhRcoxro3AX0CrpZUN8rmZmVkHOYDMJeLxkfWNKK5snInqA7cDBFfatpcx5wA8iYscA6oGk8yW1S2rfvHlztXMzM7MBGI0DBM4GvjXQnSLiqohoi4i2CRMmZKiWmVnzyhlsuoDDStYnp7SyeSSNAcYDWyrsW7FMSYdQNLXdMsB6mJlZRjmDzd3ANElTJe1H0eG/sk+elcA5afksYHVEREqfn0arTaXo3F9bQ5lnAd+JiGf6HOPdaVTaCcD2iPj10J6qmZlVkm00WkT0SLoIWAW0ANdExAOSLgXaI2IlcDVwnaRO4EmK4EHKdwPwINADXBgRuwHKlVly2PnAoj5VuRU4jWKQwe+B92Q5YTMz65eKBwkr1dbWFu3t7fWuhplZQ5G0LiLaym0bjQMEzMxshHGwMTOz7BxszMwsOwcbMzPLzsHGzMyyyzb02cwa34r1XSxe1cGmbd1MbB3HgtnTmXfsXrM9mVXlYGNmZa1Y38XC5ffRvWs3AF3bulm4/D4ABxwbMDejmVlZi1d1PBdoenXv2s3iVR11qpE1MgcbMytr07buAaWbVeJgY2ZlTWwdN6B0s0ocbMysrAWzpzNubMseaePGtrBg9vQ61cgamQcImFlZvYMAPBrNhoKDjZn1a96xkxxcbEi4Gc3MzLJzsDEzs+wcbMzMLDsHGzMzy87BxszMsvNrocuQtBl4bAiKOgR4YgjKGW18XcrzdSnP16W8kXhdXh4RE8ptcLDJSFJ7f+/jbma+LuX5upTn61Jeo10XN6OZmVl2DjZmZpadg01eV9W7AiOUr0t5vi7l+bqU11DXxX02ZmaWnZ9szMwsOwcbMzPLzsGmD0lzJHVI6pR0SZnt+0talravkTSlZNvClN4haXa1MlX4B0kPS3pI0odL0r+U8v9U0nGZT7uqEXJdTpa0XdKG9Pls5tOuapivy49Lzn2TpBUpvdm/L/1dl2b/vsySdE869zskHVHtGFlFhD/pA7QAvwBeAewH3AvM6JPnAuCraXk+sCwtz0j59wempnJaKpUJvAe4FnhBWn9p+vc04DZAwAnAGl+XADgZ+E69vyf1ui59yr0JeLe/LxWvS1N/X4CHgVeXlPuNSsfI/fGTzZ5mAp0R8UhEPAssBeb2yTMXWJKWbwRmSVJKXxoROyPiUaAzlVepzA8Cl0bEHwAi4rclx7g2CncBrZJeluOEazRSrstIM9zXBQBJBwGnACtKjtHM3xeg7HUZaYb7ugRwUFoeD2yqcoysHGz2NAl4vGR9Y0ormycieoDtwMEV9q1U5iuBd0pql3SbpGkDqMdwGinXBeCNku5N6UcN7rQGbbivS695wA8iYscA6jGcRsp1geb+vrwPuFXSRuC/A4uqHCMrB5v62h94JoopJ/4NuKbO9Rkp+rsu91DMvXQ08GVG7h1sbmcD36p3JUagvtel2b8vFwOnRcRk4OvAF+pZGQebPXUBh5WsT05pZfNIGkPxeLqlwr6VytwILE/LNwOvG0A9htOIuC4RsSMink7LtwJjJR0ymBMbpOG+LqTznQncMsB6DKcRcV2a+fsiaQJwdESsSenLgDdVOUZew9lBNtI/wBjgEYoOuN7OtqP65LmQPTvXbkjLR7FnB94jFJ13/ZZJ8Vj73rR8MnB3Wv4z9uzwXevrEgCH8vwPkWcCv+pdb4brkvb7ALCkzzGa+vtS4bo07fclpT8BHJn2Pw+4qdIxsp9/Pb+UI/FDMbLnYYoRHp9KaZcCZ6TlA4B/p+igWwu8omTfT6X9OoBTK5WZ0lsp7sTuA+6kuBMh/dG4IuW/D2jzdQmAi4AH0v9QdwFvaqbrkrb9CJjTJ62pvy8VrktTf1+At6fvw73p+ryi2jFyfjxdjZmZZec+GzMzy87BxszMsnOwMTOz7BxszMwsOwcbM7NRSNJiST9Lk7PeLKm1TJ7DJP1Q0oOSHpD0kZJty0omMf2lpA0p/eC0z9OSLq+1Pg42ZkNM0tOZy79VUmv6XLAP+58s6Ts56mb1kf6bfqNP8veA10TE6yiGRi8ss2sP8NcRMYPiN1oXSpoBEBHvjIhjIuIYiglOe39o/QzwGeDjA6mjg41Zg4mI0yJiG8XvkQYcbKw5RMR3o5j7DIrfGU0uk+fXEXFPWn4KeIg+87WlSTr/gjQVUET8LiLuoAg6NXOwMRsGko6RdFdJk8aLU/qPJF0maa2K9/eclNIPlHRDat64Ob13pC1t+2WadmUR8MrUzLG47xOLpMslnZuW56QmlXuAM0vyvFDSNen46yX1nYXYRof3Uswy0a/0XptjgTV9Np0E/CYifj6YCjjYmA2Pa4FPpCaN+4D/WbJtTETMBD5akn4BsDU1b3wGeH2ZMi8BfpGaOhb0d2BJB1BMaPrnqZxDSzZ/Clidjv8WYLGkF+7D+VkdpJuQDcDXgDNK+lhKX672KYrmsusrlPNHFE1lH409Z82GIZr4dcxgCzCzyiSNB1oj4vaUtIRiupBevW3h64ApaflPgH8BiIj7Jf10EFV4FfBo752ppG8C56dtb6P4I9Xb/n4AcDhFc4qNcBFxPBR9NsC5EXFu6fb0ZHs6MCv6mS5G0liKQHN9RCzvs20MxZNwuZudAXGwMau/nenf3Qzu/8ke9mytOKCGfQS8IyI6BnFcG4EkzQH+BnhzRPy+nzwCrgYeiohyryD4b8DPImLjYOvjZjSzzCJiO7C1tz+G4kVWt1fYBeAnFJ2ypNFBry2T5yngRSXrjwEzVLxjvhWYldJ/BkyR9Mq0fnbJPquAD/W+qVHSsTWdlDWCyym+H99LTWtfBZA0UdKtKc+JFN/HU0qa4E4rKWM+ZZrQJP2S4v0450ra2DuCrRI/2ZgNvQPT2xF7fQE4B/iqpAMppoR/T5UyvgIskfQgRbB4gOKNis+JiC2SfiLpfuC2iFgg6QbgfuBRYH3K94yk84FbJP0e+DHPB6m/B/4Z+KmkF6T9Tt/H87Y6iYgfUczsXJp2RD95N1HMFE0aVdbvK6H7NsuVpE8ZaB0967PZCCSpBRibAsUrge8D06N4z7xZw/GTjdnIdCDww9R5K+ACBxprZH6yMTOz7DxAwMzMsnOwMTOz7BxszMwsOwcbMzPLzsHGzMyy+/9pZPQ8/+41ZAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(yr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FlEpnFeEhSGK",
        "outputId": "8fd67202-b026-4f61-b573-a51e052e4178"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.789956598"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(xr)\n",
        "min(xr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iYlUC7-xjOp0",
        "outputId": "82319b2b-1616-4852-ae4c-02d77e6a1f97"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.2718154359"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round(max(xr),67)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cwxUwNi3j_qK",
        "outputId": "31631e34-d0b1-4ab8-850a-eb4f9c3698a3"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.2718154359"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round(xr[0],5)\n",
        "len(xr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HOVa6XU8kZuw",
        "outputId": "4ed24c5a-8e89-4d0d-b6ce-b1eef7855fb0"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.2717278545,\n",
              " 2.2717415085,\n",
              " 2.271654619,\n",
              " 2.2716173807,\n",
              " 2.2715985132,\n",
              " 2.2716743421,\n",
              " 2.271706008,\n",
              " 2.2717246271,\n",
              " 2.2716844097,\n",
              " 2.2716734865,\n",
              " 2.2718154359,\n",
              " 2.2717573011,\n",
              " 2.2716321494,\n",
              " 2.2716535537,\n",
              " 2.2716363385,\n",
              " 2.2716057608]"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def print_maze(maze):\n",
        "    for row in maze:\n",
        "        print(\" | \".join(str(cell) for cell in row))\n",
        "       \n",
        "\n",
        "def create_maze(xr,yr):\n",
        "  ascii_array = [chr(i) for i in range(128) if chr(i).isalnum()]\n",
        "  points = [(xr[_],yr[_]) for _ in range(len(xr))]\n",
        "  tokens={ascii_array[_]:points[_] for _ in range(len(points))}\n",
        "  #print(points)\n",
        "  maze = np.array([['#' for _ in range(len(xr))]for _ in range(len(xr))])\n",
        "  for point, token in zip(points, tokens):\n",
        "        print(point[0],point[1])\n",
        "        maze[point[0]][point[1]] = 2\n",
        "  print_maze(maze)\n",
        "  \n",
        " \n",
        "\n",
        "create_maze(xr,yr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "x-5U1PU4kiuu",
        "outputId": "f43a43dc-7a16-4629-c9fd-40cdf446a103"
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.2717278545 6.7896809169\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-164-712383952b61>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mcreate_maze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-164-712383952b61>\u001b[0m in \u001b[0;36mcreate_maze\u001b[0;34m(xr, yr)\u001b[0m\n\u001b[1;32m     12\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoints\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mmaze\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m   \u001b[0mprint_maze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaze\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ord('a')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0XcWu3d2oQCo",
        "outputId": "2889882e-c3e6-47f6-cecf-d4aacd36bb78"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "97"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_xr = np.sort(xr)\n",
        "ascii_array = [chr(i) for i in range(128) if chr(i).isalnum()]    \n",
        "    # calculate the range between each adjacent x-coordinate\n",
        "x_range = np.diff(sorted_xr)\n",
        "np.min(x_range) / (len(ascii_array) - 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "STs-u_uOobY0",
        "outputId": "86783fd8-9361-43fa-d752-6fde4723bfc6"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.4026229503907998e-08"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gOkkzXPGr5Xl",
        "outputId": "2664f0b9-e2cb-416c-d6f3-9a16a52a5425"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.2717278545,\n",
              " 2.2717415085,\n",
              " 2.271654619,\n",
              " 2.2716173807,\n",
              " 2.2715985132,\n",
              " 2.2716743421,\n",
              " 2.271706008,\n",
              " 2.2717246271,\n",
              " 2.2716844097,\n",
              " 2.2716734865,\n",
              " 2.2718154359,\n",
              " 2.2717573011,\n",
              " 2.2716321494,\n",
              " 2.2716535537,\n",
              " 2.2716363385,\n",
              " 2.2716057608]"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TymH_yFvsozv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}