spacewalk_cTSP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6b72125f",
   "metadata": {},
   "source": [
    "# Space Walk to Satellites"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7324cd0",
   "metadata": {},
   "source": [
    "In the realm of modern space exploration, satellites play a critical role in global communications, weather monitoring, GPS, and scientific research. These advanced technologies orbit Earth in precise, synchronized paths, but like any machinery, they are prone to malfunctions. When satellites experience technical issues, timely repairs are essential to prevent service disruptions and data loss. Astronauts stationed on the International Space Station (ISS) are often tasked with performing spacewalks—extravehicular activities (EVA)—to repair these satellites. Each spacewalk is meticulously planned due to the extreme conditions in space and the limited time astronauts can spend outside the ISS. \n",
    "\n",
    "Furthermore, travel between satellites and the station is time-consuming, and repairing a satellite requires specialized tools and processes, making the coordination of spacewalks a complex optimization problem. Since each satellite has a critical window for repair before its systems fail, the challenge lies in scheduling multiple spacewalks efficiently, while adhering to time and resource constraints. The importance of minimizing delays and ensuring all satellites are repaired within their operational deadlines is vital for mission success. \n",
    "\n",
    "**Scenario Description**\n",
    "\n",
    "The present task in this project is to help schedule astronauts' spacewalks to repair 7 malfunctioning satellites that are orbiting Earth. The satellites are arranged in a linear orbital path and need to be repaired before they drift too far off course. Each spacewalk team starts from the International Space Station (ISS) (considered as location 0) and can visit multiple satellites in a single spacewalk.\n",
    "\n",
    "However, spacewalks are extremely energy-consuming, and an astronaut team can only visit and repair up to 4 satellites per spacewalk. After finishing repairs, they must return to the ISS to recharge their oxygen and tools before they can conduct another spacewalk.\n",
    "\n",
    "**Parameters**\n",
    "\n",
    "1. The satellites are evenly spaced along their orbital path.\n",
    "2. It takes 1 minute for the astronaut team to travel between adjacent satellites (or between the ISS and the first satellite).\n",
    "3. Each satellite repair takes 2 minutes to complete.\n",
    "4. The satellites must be repaired by specific times (all in Coordinated Universal Time (UTC)) to avoid system failures:\n",
    "- Satellite 1: 12:19 AM\n",
    "- Satellite 2: 12:19 AM\n",
    "- Satellite 3: 12:25 AM\n",
    "- Satellite 4: 12:30 AM\n",
    "- Satellite 5: 12:20 AM\n",
    "- Satellite 6: 12:35 AM\n",
    "- Satellite 7: 12:17 AM\n",
    "\n",
    "5. Each satellite repair must be completed at least 5 minutes before its system failure time.\n",
    "6. The astronaut team can start their spacewalks at 12:00 AM from the ISS.\n",
    "\n",
    "**Objective**\n",
    "\n",
    "The goal is to schedule the astronauts’ spacewalks to minimize the total delay in repairing the satellites while ensuring all repairs are done in time to prevent system failures.\n",
    "\n",
    "**Constraints**\n",
    "\n",
    "1. Each spacewalk team can repair no more than 4 satellites per trip.\n",
    "2. Each satellite must be repaired exactly once across all spacewalks.\n",
    "3. Each spacewalk must start and end at the ISS.\n",
    "4. No two spacewalks can overlap; the astronauts must return to the ISS before starting the next spacewalk."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72fa1295",
   "metadata": {},
   "source": [
    "We will build an optimization model to solve this problem. Lets first write out the math formulation. \n",
    "\n",
    "<br>\n",
    "\n",
    "<b>Indicies and Sets: </b>\n",
    "\n",
    "Locations: $S=\\{0,..., N \\}$, $s=\\{1,..., N \\}$\n",
    "\n",
    "Trips: $\\Theta =\\{1,..., K \\}$, $\\theta =\\{2,..., K \\}$\n",
    "\n",
    "Satellite: $i, j \\in S$\n",
    "\n",
    "Trip: $k \\in \\Theta $ \n",
    "\n",
    "\n",
    "\n",
    "<br>\n",
    "\n",
    "<b>Parameters:</b>\n",
    "\n",
    "$A$: distance matrix\n",
    "\n",
    "$D_i$: failure time (deadline) for each satellite\n",
    "\n",
    "$W$: idle time required before faliure time is reached \n",
    "\n",
    "$M$: a large real number\n",
    "\n",
    "$N$: total number of satellites\n",
    "\n",
    "$T$: time at which the astronaut team can start their spacewalks (12:00 AM, 0 minutes)\n",
    " \n",
    "$U$: repair time per satellite in minutes\n",
    "\n",
    "$capacity$: the number of satellites an astronaut can service in each trip\n",
    "\n",
    "<br>\n",
    "\n",
    "<b>Decision variables:</b>\n",
    "\n",
    "$\n",
    "x_{ijk} = \\left\\{\n",
    "    \\begin{array}\\\\\n",
    "        1 & \\text{a walk from satellite } i \\text{ to } j \\ (\\forall i \\neq j, i, j \\in S), \\text{ occurs in trip } k \\in \\Theta\\\\\n",
    "        0 & \\text{otherwise} \\\\\n",
    "    \\end{array}\n",
    "\\right.\n",
    "$\n",
    "\n",
    "$\n",
    "y_{ik} = \\left\\{\n",
    "    \\begin{array}\\\\\n",
    "        1 & \\text{ satellite } i\\ (i \\in s) \\text{ is serviced during trip } k \\in \\Theta,  \\\\\n",
    "        0 & \\text{otherwise} \\\\\n",
    "    \\end{array}\n",
    "\\right.\n",
    "$\n",
    "\n",
    "$u_{ik} $: the starting time of repair work for satellite $i$ during trip $k$, $i \\in s$, $k \\in \\Theta$.\n",
    "\n",
    "$C_{k}$: the completion time of trip $k$ (when the astronaut returns to ISS from a trip), $k \\in \\Theta$.\n",
    "\n",
    "<br>\n",
    "\n",
    "The problem can be modeled as the following MIP.\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\t\\text{minimize:} \\quad & \\text{maximize}\\{0,\\ u_{ik} - U + W - D_i\\} &\\\\    \n",
    "\t\\text{subject to:} \\quad & \\sum_{j \\in s} x_{0jk} = 1, & \\forall k \\in \\Theta \\label{eq1}\\tag{1}\\\\    \n",
    "    & \\sum_{i \\in s} x_{i0k} = 1,  &\\forall k \\in \\Theta \\label{eq2}\\tag{2}\\\\\n",
    "    & \\sum_{j \\in s} x_{ijk} = y_{ik}, & \\forall i \\in s, k \\in \\Theta \\label{eq3}\\tag{3}\\\\    \n",
    "    & \\sum_{i \\in s} x_{ijk} = y_{ik},  &\\forall j \\in s, k \\in \\Theta \\label{eq4}\\tag{4}\\\\\n",
    "    & \\sum_{k \\in \\Theta} y_{ik} = 1,  & \\forall i \\in s \\label{eq5}\\tag{5}\\\\\n",
    "\t& \\sum_{i \\in s} y_{ik} \\leq capacity,  &\\forall k \\in \\Theta  \\label{eq6}\\tag{6}\\\\\n",
    "    & C_{0} = \\sum_{i \\in S} \\sum_{j \\in S} A_{ij}x_{ij0} + U \\sum_{i \\in s} y_{i0} &  \\label{eq7}\\tag{7}\\\\\n",
    "    & C_{k} = C_{k-1} + \\sum_{i \\in S} \\sum_{j \\in S} A_{ij}x_{ijk} + U \\sum_{i \\in s} y_{ik}, &\\forall k \\in \\theta \\label{eq8}\\tag{8}\\\\\n",
    "    & 0 \\leq u_{ik} \\leq M * y_{ik},  & \\forall i \\in s, k \\in \\Theta   \\label{eq9}\\tag{9}\\\\\n",
    "    & u_{ik} + M * (1- y_{ik}) \\geq C_{k-1},  & \\forall i \\in s, k \\in \\theta   \\label{eq10}\\tag{10}\\\\\n",
    "    & u_{ik} + M * (1- y_{ik}) \\geq C_{k-1} +  A_{0,i},  & \\forall i \\in s, k \\in \\theta   \\label{eq11}\\tag{11}\\\\\n",
    "    & u_{i0} + M * (1- y_{i0}) \\geq A_{0i}, \\quad\\qquad & \\forall i \\in s,   \\label{eq12}\\tag{12}\\\\\n",
    "    & u_{ik} + U + A_{ij} \\leq u_{jk} + M * (1- x_{ijk}),  & \\forall i,j \\in s, k \\in \\Theta   \\label{eq13}\\tag{13}\\\\\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a80deb6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1b1445ca-19b0-4ae3-a228-7a2e04e9c1f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################\n",
    "# Loading libraries\n",
    "################################################\n",
    "#!pip install --quiet geopy\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import seaborn as sns\n",
    "import cvxpy as cp\n",
    "import math\n",
    "from scipy.spatial import distance_matrix\n",
    "# from geopy import distance # Library for geographical calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "98f53da8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance Matrix is:\n",
      "\n",
      "[[0.     1.4142 4.4721 4.2426 4.4721 5.099  6.7082 7.2801]\n",
      " [1.4142 0.     3.1623 2.8284 3.1623 4.     5.3852 6.0828]\n",
      " [4.4721 3.1623 0.     1.4142 2.8284 4.2426 4.1231 5.3852]\n",
      " [4.2426 2.8284 1.4142 0.     1.4142 2.8284 3.     4.1231]\n",
      " [4.4721 3.1623 2.8284 1.4142 0.     1.4142 2.2361 3.    ]\n",
      " [5.099  4.     4.2426 2.8284 1.4142 0.     2.2361 2.2361]\n",
      " [6.7082 5.3852 4.1231 3.     2.2361 2.2361 0.     1.4142]\n",
      " [7.2801 6.0828 5.3852 4.1231 3.     2.2361 1.4142 0.    ]]\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "################################################\n",
    "# Building distance matrix\n",
    "################################################\n",
    "\n",
    "df = pd.read_csv('input_ctsp/locations.csv')\n",
    "sns.scatterplot(data=df, x='X', y='Y')\n",
    "plt.savefig(\"spacewalker_locations.png\")\n",
    "coords = df[['X','Y']].values  # returns a numpy array\n",
    "travel_times = distance_matrix(coords, coords)\n",
    "\n",
    "# Showing distance matrix\n",
    "print('Distance Matrix is:\\n')\n",
    "print(np.round(travel_times, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "895a0284",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6040668e-2aa9-41b9-a976-37dfcb7a2bd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal total delay: 17.87 minutes\n",
      "\n",
      "Detailed satellite-repair assignments to trips:\n",
      "\n",
      "Trip 1:\n",
      "Satellite 2 completes unloading: 6.47 minutes after 12:00 AM, late by 0.0 minutes.\n",
      "Satellite 3 completes unloading: 9.89 minutes after 12:00 AM, late by 0.0 minutes.\n",
      "Satellite 4 completes unloading: 13.30 minutes after 12:00 AM, late by 0.0 minutes.\n",
      "Satellite 5 completes unloading: 16.71 minutes after 12:00 AM, late by 1.7 minutes.\n",
      "\n",
      "Trip 2:\n",
      "Satellite 1 completes unloading: 25.23 minutes after 12:00 AM, late by 2.2 minutes.\n",
      "Satellite 6 completes unloading: 32.61 minutes after 12:00 AM, late by 7.6 minutes.\n",
      "Satellite 7 completes unloading: 36.03 minutes after 12:00 AM, late by 14.0 minutes.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "################################################\n",
    "# Prepare the input\n",
    "################################################\n",
    "\n",
    "# Indices\n",
    "n_satels = len(df) - 1\n",
    "capacity = 4\n",
    "n_trips = math.ceil( n_satels / capacity)\n",
    "satellites = np.arange(1, n_satels + 1)\n",
    "\n",
    "# Parameters\n",
    "# Departure times in minutes after 12:00 AM\n",
    "#failure_times = np.array([19, 19, 25, 30, 20, 35, 17])  \n",
    "# failure_times = np.array([34, 34, 34, 34, 34, 34, 30]) \n",
    "rng = np.random.default_rng(seed=12345)\n",
    "failure_times = rng.integers(19, 36, size =  n_satels)\n",
    "\n",
    "# Time at which the astronaut team can start their spacewalks (12:00 AM, 0 minutes)\n",
    "T = 0  \n",
    "\n",
    "# Repair time per satellite in minutes\n",
    "U = 2  \n",
    "\n",
    "M = 100000  # Big M\n",
    "\n",
    "################################################\n",
    "# Solving the integer programming problem\n",
    "################################################\n",
    "\n",
    "# Defining the decision variables\n",
    "\n",
    "# x_ijk: Binary variable for each trip, dim = 8*8*2\n",
    "x = [cp.Variable(( n_satels + 1,  n_satels + 1), boolean=True) for _ in range(n_trips)] \n",
    "\n",
    "# u_ik: Start time of repair at satellite i during trip k, dim = 7*2\n",
    "u = cp.Variable(( n_satels, n_trips), nonneg=True)  \n",
    "\n",
    "# y_ik: Whether satellite i is serviced during trip k, dim = 7 *2\n",
    "y = cp.Variable(( n_satels, n_trips), boolean=True)  \n",
    "\n",
    "# Completion time of each trip\n",
    "C = cp.Variable(n_trips, nonneg=True)  \n",
    "\n",
    "# Objective function: Minimize total delay\n",
    "delay = cp.sum(cp.maximum(0, u + U - (failure_times.reshape(-1, 1) - 5)))\n",
    "objective = cp.Minimize(delay)\n",
    "\n",
    "\n",
    "# Constraints\n",
    "constraints = []\n",
    "\n",
    "# 0. Initial constraintsx_ii = 0 \n",
    "for k in range(n_trips):\n",
    "    constraints += [cp.diag(x[k]) == 0]\n",
    "\n",
    "# 1. Flow conservation\n",
    "for k in range(n_trips):\n",
    "    # Start at International Space Station (satellite 0) and must visit each assigned satellite once\n",
    "    constraints += [ cp.sum(x[k][0, 1:]) == 1 ]\n",
    "    constraints += [ cp.sum(x[k][1:, 0]) == 1 ]    \n",
    "    # Each satellite assigned to a trip must have exactly one preceding and one succeeding satellite\n",
    "    for i in range(1,  n_satels + 1):\n",
    "        constraints += [ cp.sum(x[k][i, :]) == y[i - 1, k] ]\n",
    "        constraints += [ cp.sum(x[k][:, i]) == y[i - 1, k] ]\n",
    "\n",
    "# 2. Assignment constraints: Each satellite must be served exactly once across all trips\n",
    "for i in range(n_satels):\n",
    "    constraints += [ cp.sum(y[i, :]) == 1 ]\n",
    "\n",
    "# 3. Spaceman capacity constraints: No more than 4 satellites in each trip\n",
    "for k in range(n_trips):\n",
    "    constraints += [ cp.sum(y[:, k]) <= capacity ]\n",
    "\n",
    "# 4. Definition of Completion Time, C[k]\n",
    "constraints += [ C[0] == 0 + cp.sum( cp.multiply(travel_times, x[0]) ) + U * cp.sum(y[:,0]) ]\n",
    "\n",
    "for k in range(1, n_trips):\n",
    "    constraints += [ C[k] == C[k-1] + cp.sum( cp.multiply(travel_times, x[k]) )  + U * cp.sum(y[:,k]) ]\n",
    "\n",
    "\n",
    "# 5. Repair starting time\n",
    "# for k in range(n_trips):\n",
    "#     constraints += [u[:,k] >= 0]\n",
    "    # constraints += [u[1:,k] >= y[1:,k]]\n",
    "constraints += [u[:,:] >= 0]\n",
    "\n",
    "# If a satellite is NOT scheduled during a trip, its starting time remains 0\n",
    "# If it IS visited, then no constraint to repair starting time.\n",
    "for k in range(n_trips):\n",
    "    for i in range( n_satels):\n",
    "        constraints += [u[i,k] <= M * y[i,k] ] \n",
    "\n",
    "# This constraint is absorbed by 7.2        \n",
    "# for k in range(1, n_trips):\n",
    "#     for i in range( n_satels):\n",
    "#         constraints += [u[i,k] + M * (1-y[i,k]) >= C[k-1] ] \n",
    "\n",
    "    \n",
    "# 6. [obsolete] Trip Sequencing Constraints (trips must be sequential in time: no overlapping)\n",
    "\n",
    "# The following may be the same as 7.2\n",
    "# for i in range(n_satels):\n",
    "#     constraints += [ u[i, 0] >= 0 + y[i,0] * travel_times[0, i+1] ]\n",
    "\n",
    "# [caution] The following seemingly true constraint CANNOT hold, as u[i, k] need to be 0 sometimes.\n",
    "# for k in range(n_trips):\n",
    "#     for i in range(n_satels):\n",
    "#         constraints += [ u[i, k] >= C[k-1] + y[i,k] * travel_times[0, i+1] ]\n",
    "        \n",
    "# 7. Bounds of repair starting time, u[i,k]\n",
    "\n",
    "# 7.1 Upper bound: starting time (returning to ISS directly) must be smaller than the completion time of the same trip \n",
    "for k in range(n_trips):\n",
    "    for i in range(n_satels):\n",
    "        constraints += [ u[i,k] + U + travel_times[i+1, 0] <= C[k] ]\n",
    "\n",
    "# 7.2 Lower bound: If a satellite is vited in a trip (k), \n",
    "#     its repiar starting time () must be greater than the previous completion time.\n",
    "#     If NOT visited, however, no constraint is imposed to the starting time. \n",
    "for k in range(1, n_trips):\n",
    "    for i in range(n_satels):\n",
    "        constraints += [ C[k-1] + travel_times[0, i+1] <= u[i,k] + M * (1 - y[i,k]) ]\n",
    "\n",
    "for i in range(n_satels):\n",
    "    constraints += [ travel_times[0, i+1] <= u[i,0] + M * (1-y[i,0]) ]\n",
    "\n",
    "# 8. Subtour elimination constraints\n",
    "for k in range(n_trips):\n",
    "    for i in range(1,  n_satels + 1):\n",
    "        for j in range(1,  n_satels + 1):\n",
    "            if i != j:\n",
    "                constraints += [u[i - 1, k] + U + travel_times[i, j] <= u[j - 1, k] + M * (1 - x[k][i, j]) ]\n",
    "\n",
    "# Solve the problem\n",
    "prob = cp.Problem(objective, constraints)\n",
    "prob.solve(verbose=False)\n",
    "\n",
    "################################################\n",
    "# Results\n",
    "################################################\n",
    "\n",
    "if y.value is not None:\n",
    "    print(f\"Optimal total delay: {prob.value:.2f} minutes\")\n",
    "    print()\n",
    "    print(\"Detailed satellite-repair assignments to trips:\")\n",
    "    print()\n",
    "    \n",
    "    trip_routes = []\n",
    "    delayTable = np.zeros((n_satels, n_trips))\n",
    "    \n",
    "    for k in range(n_trips):\n",
    "        print(f\"Trip {k + 1}:\")\n",
    "        \n",
    "        # Extract the route for trip k based on x[k]\n",
    "        route = []\n",
    "        current_satellite = 0  # Start from the International Space Station (satellite 0)\n",
    "        \n",
    "        while True:\n",
    "            next_satellite = np.argmax(x[k].value[current_satellite, :])  # Find the next satellite\n",
    "            if next_satellite == 0:\n",
    "                break  # End the route when the truck returns to satellite 0 (ISS)\n",
    "            route.append(next_satellite)  # Add the gate to the route\n",
    "            current_satellite = next_satellite  # Move to the next satellite\n",
    "            \n",
    "        # Print the route in the order the satellites are visited\n",
    "        for satellite in route:\n",
    "            unload_time = u.value[satellite - 1, k] + U\n",
    "            delay = max(0, unload_time + U + 5 - failure_times[satellite - 1])\n",
    "            delayTable[satellite-1, k] = delay\n",
    "            print(f\"Satellite {satellite} completes unloading: {unload_time:.2f} minutes after 12:00 AM, late by {delay:.1f} minutes.\")\n",
    "        \n",
    "        #print(route)\n",
    "        excursion = route\n",
    "        excursion.insert(0,0)\n",
    "        excursion.append(0)\n",
    "        trip_routes.append(excursion)\n",
    "        #print(excursion)\n",
    "        #print(trip_routes)\n",
    "        print()\n",
    "        \n",
    "else:\n",
    "    print(\"No feasible solution found.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "10463def",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a directed graph\n",
    "G = nx.DiGraph()\n",
    "\n",
    "# Add nodes for the satellites and the International Space Station (ISS)\n",
    "# n_gates = n_satels\n",
    "G.add_node(0, pos=(0, 0), label=\"ISS (0)\")  # ISS\n",
    "\n",
    "# for i in range(1, n_satels + 1):\n",
    "#     G.add_node(i, pos=(i, 0), label=f\"Satellite {i}\")\n",
    "\n",
    "for i in range(1, n_satels + 1):\n",
    "    G.add_node(i, pos=tuple(coords[i]), label=f\"Satellite {i}\")\n",
    "    \n",
    "\n",
    "# Example routes for two trips (from Gantt chart data)\n",
    "# trip_routes = [\n",
    "#     [0, 1, 2, 3, 0],  # Trip 1: ISS -> Satellite 1 -> Satellite 2 -> Satellite 3 -> ISS\n",
    "#     [0, 4, 5, 6, 7, 0]  # Trip 2: ISS -> Satellite 4 -> Satellite 5 -> Satellite 6 -> Satellite 7 -> ISS\n",
    "# ]\n",
    "\n",
    "# Add edges based on the route\n",
    "for route in trip_routes:\n",
    "    for i in range(len(route) - 1):\n",
    "        G.add_edge(route[i], route[i + 1])\n",
    "\n",
    "# Get positions for nodes\n",
    "pos = nx.get_node_attributes(G, 'pos')\n",
    "\n",
    "# Plot the graph\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "# Draw nodes with labels\n",
    "nx.draw_networkx_nodes(G, pos, node_color='lightblue', node_size=1500)\n",
    "nx.draw_networkx_labels(G, pos, labels=nx.get_node_attributes(G, 'label'), font_size=10)\n",
    "\n",
    "# Draw the edges with arrows representing the trips\n",
    "nx.draw_networkx_edges(G, pos, edgelist=G.edges(), edge_color='black', arrowstyle='-|>', arrowsize=50, width=2.0)\n",
    "\n",
    "# Set the title and display the route map\n",
    "plt.title('Route Map: Astronaut Trips to Satellites')\n",
    "plt.axis('off')\n",
    "plt.savefig(\"spacewalker_routes.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0f683e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample delay data for two trips (in minutes)\n",
    "# Each row represents a satellite (gate) and each column represents a trip\n",
    "# For simplicity, you can input the actual delays computed in your solution\n",
    "# delays = np.array([\n",
    "#     [2, 0],  # Satellite 1: 2 minutes delay in Trip 1, 0 minutes in Trip 2\n",
    "#     [3, 0],  # Satellite 2\n",
    "#     [1, 0],  # Satellite 3\n",
    "#     [0, 5],  # Satellite 4\n",
    "#     [0, 2],  # Satellite 5\n",
    "#     [0, 3],  # Satellite 6\n",
    "#     [0, 1],  # Satellite 7\n",
    "# ])\n",
    "\n",
    "# Define labels for the satellites (gates) and trips\n",
    "satellites = [f\"Satellite {i}\" for i in range(1, n_satels+1)]  # [\"Satellite 1\", ..., \"Satellite 7\"]\n",
    "trips = [f\"Trip {i}\" for i in range(1, n_trips+1)]  # [\"Trip 1\", \"Trip 2\"]\n",
    "\n",
    "# Create a heatmap using Seaborn\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(delayTable, annot=False, cmap=\"crest\", xticklabels=trips, yticklabels=satellites, cbar_kws={'label': 'Delay (minutes)'})\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Delay Heatmap for Satellite Services')\n",
    "\n",
    "plt.savefig(\"spacewalker_heatmap.png\")\n",
    "\n",
    "#plt.xlabel('Trips')\n",
    "#plt.ylabel('Satellites')\n",
    "\n",
    "# Display the heatmap\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}