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    "# Multiple Gravity Assist trajectories\n",
    "## Objectives\n",
    "\n",
    "This example demonstrates how **Multiple Gravity Assist (MGA)** transfer trajectories can be simulated. Three types of transfers are analyzed:\n",
    "\n",
    "* High-thrust transfer with unpowered legs\n",
    "* High-thrust transfer with deep space maneuvers (DSMs) and manually-created legs and nodes\n",
    "* Low-thrust transfer with hodographic shaping\n",
    "\n",
    " \n",
    "In addition, this example show how the results, such as partial $\\Delta V$'s, total $\\Delta V$ and time of flight\n",
    "values can be retrieved from the transfer object.\n",
    "\n",
    "A complete guide on transfer trajectory design is given on [this page](https://docs.tudat.space/en/latest/_src_user_guide/prelim_mission_design/mga_transfer.html) of tudat user documentation."
   ]
  },
  {
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   "source": [
    "## MGA Transfer With Unpowered Legs"
   ]
  },
  {
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   "source": [
    "## Import statements\n",
    "\n",
    "The required import statements are made here, at the very beginning.\n",
    "\n",
    "Some standard modules are first loaded: numpy and matplotlib.pyplot.\n",
    "\n",
    "Then, the different modules of tudatpy that will be used are imported."
   ]
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   "source": [
    "# Load standard modules\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load tudatpy modules\n",
    "from tudatpy.trajectory_design import transfer_trajectory, shape_based_thrust\n",
    "from tudatpy.dynamics import environment_setup\n",
    "from tudatpy.util import result2array\n",
    "from tudatpy import constants"
   ]
  },
  {
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   "source": [
    "First, let's explore an MGA transfer trajectory with no thrust applied during the transfer legs. In this case, the impulsive $\\Delta V$ maneuvers are only applied during the gravity assists."
   ]
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   "source": [
    "### Setup and inputs\n",
    "\n",
    "A simplified system of bodies suffices for this application, with the Sun as central body. The planets that are visited for a gravity assist are defined in the list `transfer_body_order`. The first body in the list is the departure body and the last one is the arrival body.\n",
    "\n",
    "The departure and arrival orbit can be specified, but they are not mandatory. If not specified, the departure and arrival planets are selected to be swing-by nodes.\n",
    "Departures and arrivals at the edge of the Sphere Of Influence (SOI) of a node can be done by specifying eccentricity $e=0$ and semi-major axis $a=\\infty$.\n",
    "\n",
    "In this example, the spacecraft departs from the edge of Earth’s SOI and is inserted into a highly elliptical orbit around Saturn."
   ]
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   "source": [
    "# Create a system of simplified bodies (create all main solar system bodies with their simplest models)\n",
    "bodies = environment_setup.create_simplified_system_of_bodies()\n",
    "central_body = 'Sun'\n",
    "\n",
    "# Define the order of bodies (nodes) for gravity assists\n",
    "transfer_body_order = ['Earth', 'Venus', 'Venus', 'Earth',  'Jupiter',  'Saturn']\n",
    "\n",
    "# Define the departure and insertion orbits\n",
    "departure_semi_major_axis = np.inf\n",
    "departure_eccentricity = 0.\n",
    "\n",
    "arrival_semi_major_axis = 1.0895e8 / 0.02\n",
    "arrival_eccentricity = 0.98"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4dbd3c7",
   "metadata": {},
   "source": [
    "### Create transfer settings and transfer object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d8949de",
   "metadata": {},
   "source": [
    "The specified inputs can not be used directly, but they have to be translated to distinct settings, relating to either the nodes (departure, gravity assist, and arrival planets) or legs (trajectories in between planets). The fact that unpowered legs are used is indicated by the creation of unpowered and unperturbed leg settings.\n",
    "These settings are, in turn, used to create the transfer trajectory object."
   ]
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   "source": [
    "# Define the trajectory settings for both the legs and at the nodes\n",
    "transfer_leg_settings, transfer_node_settings = transfer_trajectory.mga_settings_unpowered_unperturbed_legs(\n",
    "    transfer_body_order,\n",
    "    departure_orbit=(departure_semi_major_axis, departure_eccentricity),\n",
    "    arrival_orbit=(arrival_semi_major_axis, arrival_eccentricity))\n",
    "\n",
    "# Create the transfer calculation object\n",
    "transfer_trajectory_object = transfer_trajectory.create_transfer_trajectory(\n",
    "    bodies,\n",
    "    transfer_leg_settings,\n",
    "    transfer_node_settings,\n",
    "    transfer_body_order,\n",
    "    central_body)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "350703d4",
   "metadata": {},
   "source": [
    "### Define transfer parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7548e4a0",
   "metadata": {},
   "source": [
    "Next, it is necessary to specify the parameters which define the transfer. The advantage of having a transfer trajectory object is that it allows analyzing many different sets of transfer parameters using the same transfer trajectory object.\n",
    "The definition of the parameters that need to be specified for this transfer can be printed using the `transfer_trajectory.print_parameter_definitions()` function."
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transfer parameter definitions:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter 0: Node time 0\n",
      "Parameter 1: Node time 1\n",
      "Parameter 2: Node time 2\n",
      "Parameter 3: Node time 3\n",
      "Parameter 4: Node time 4\n",
      "Parameter 5: Node time 5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print transfer parameter definitions\n",
    "print(\"Transfer parameter definitions:\")\n",
    "transfer_trajectory.print_parameter_definitions(transfer_leg_settings, transfer_node_settings)"
   ]
  },
  {
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   "id": "5c1572d1",
   "metadata": {},
   "source": [
    "For this transfer with unpowered legs, the transfer parameters only constitute the times at which the powered gravity assists are executed, i.e. at the nodes. \n",
    "This type of legs does not require any node free parameters or leg free parameters to be specified. Thus, they are defined as lists containing empty arrays."
   ]
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   "source": [
    "# Define times at each node\n",
    "julian_day = constants.JULIAN_DAY\n",
    "node_times = list( )\n",
    "node_times.append( ( -789.8117 - 0.5 ) * julian_day )\n",
    "node_times.append( node_times[ 0 ] + 158.302027105278 * julian_day )\n",
    "node_times.append( node_times[ 1 ] + 449.385873819743 * julian_day )\n",
    "node_times.append( node_times[ 2 ] + 54.7489684339665 * julian_day )\n",
    "node_times.append( node_times[ 3 ] + 1024.36205846918 * julian_day )\n",
    "node_times.append( node_times[ 4 ] + 4552.30796805542 * julian_day )\n",
    "\n",
    "# Define free parameters per leg (for now: none)\n",
    "leg_free_parameters = list( )\n",
    "for i in transfer_leg_settings:\n",
    "    leg_free_parameters.append( np.zeros(0))\n",
    "    \n",
    "# Define free parameters per node (for now: none)\n",
    "node_free_parameters = list( )\n",
    "for i in transfer_node_settings:\n",
    "    node_free_parameters.append( np.zeros(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a053bcc5",
   "metadata": {},
   "source": [
    "### Evaluate transfer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "755ca91c",
   "metadata": {},
   "source": [
    "The transfer parameters are now used to evaluate the transfer trajectory, which means that the semi-analytical methods used to determine the $\\Delta V$ of each leg are now applied."
   ]
  },
  {
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   "source": [
    "# Evaluate the transfer with given parameters\n",
    "transfer_trajectory_object.evaluate( node_times, leg_free_parameters, node_free_parameters )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "242f7200",
   "metadata": {},
   "source": [
    "### Extract results and plot trajectory\n",
    "Last but not least, with the transfer trajectory computed, we can now analyse it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f3fffa",
   "metadata": {},
   "source": [
    "#### Print results\n",
    "Having evaluated the transfer trajectory, it is possible to extract various transfer characteristics, such as the $\\Delta V$ and time of flight."
   ]
  },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Delta V of 4930.633 m/s and total Time of flight of 6239.107 days\n",
      "\n",
      "Delta V per leg: \n",
      " - between Earth and Venus: 0.000 m/s\n",
      " - between Venus and Venus: 0.000 m/s\n",
      " - between Venus and Earth: 0.000 m/s\n",
      " - between Earth and Jupiter: 0.000 m/s\n",
      " - between Jupiter and Saturn: 0.000 m/s\n",
      "\n",
      "Delta V per node : \n",
      " - at Earth: 2754.635 m/s\n",
      " - at Venus: 1090.659 m/s\n",
      " - at Venus: 615.765 m/s\n",
      " - at Earth: 0.009 m/s\n",
      " - at Jupiter: 0.000 m/s\n",
      " - at Saturn: 469.565 m/s\n"
     ]
    }
   ],
   "source": [
    "# Print the total DeltaV and time of Flight required for the MGA\n",
    "print('Total Delta V of %.3f m/s and total Time of flight of %.3f days\\n' % \\\n",
    "    (transfer_trajectory_object.delta_v, transfer_trajectory_object.time_of_flight / julian_day))\n",
    "\n",
    "# Print the DeltaV required during each leg\n",
    "print('Delta V per leg: ')\n",
    "for i in range(len(transfer_body_order)-1):\n",
    "    print(\" - between %s and %s: %.3f m/s\" % \\\n",
    "        (transfer_body_order[i], transfer_body_order[i+1], transfer_trajectory_object.delta_v_per_leg[i]))\n",
    "print()\n",
    "\n",
    "# Print the DeltaV required at each node\n",
    "print('Delta V per node : ')\n",
    "for i in range(len(transfer_body_order)):\n",
    "    print(\" - at %s: %.3f m/s\" % \\\n",
    "        (transfer_body_order[i], transfer_trajectory_object.delta_v_per_node[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de55429f",
   "metadata": {},
   "source": [
    "#### Plot the transfer\n",
    "The state throughout the transfer can be retrieved from the transfer trajectory object, here at 500 instances per leg, to visualize the transfer."
   ]
  },
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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the state history\n",
    "state_history = transfer_trajectory_object.states_along_trajectory(500)\n",
    "fly_by_states = np.array([state_history[node_times[i]] for i in range(len(node_times))])\n",
    "state_history = result2array(state_history)\n",
    "au = 1.5e11\n",
    "\n",
    "# Plot the transfer\n",
    "fig = plt.figure(figsize=(8,5))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "# Plot the trajectory from the state history\n",
    "ax.plot(state_history[:, 1] / au, state_history[:, 2] / au, state_history[:, 3] / au)\n",
    "# Plot the position of the nodes\n",
    "ax.scatter(fly_by_states[0, 0] / au, fly_by_states[0, 1] / au, fly_by_states[0, 2] / au, color='blue', label='Earth departure')\n",
    "ax.scatter(fly_by_states[1, 0] / au, fly_by_states[1, 1] / au, fly_by_states[1, 2] / au, color='brown', label='Venus fly-by')\n",
    "ax.scatter(fly_by_states[2, 0] / au, fly_by_states[2, 1] / au, fly_by_states[2, 2] / au, color='brown', label='Venus fly-by')\n",
    "ax.scatter(fly_by_states[3, 0] / au, fly_by_states[3, 1] / au, fly_by_states[3, 2] / au, color='green', label='Earth fly-by')\n",
    "ax.scatter(fly_by_states[4, 0] / au, fly_by_states[4, 1] / au, fly_by_states[4, 2] / au, color='peru', label='Jupiter fly-by')\n",
    "ax.scatter(fly_by_states[5, 0] / au, fly_by_states[5, 1] / au, fly_by_states[5, 2] / au, color='red', label='Saturn arrival')\n",
    "# Plot the position of the Sun\n",
    "ax.scatter([0], [0], [0], color='orange', label='Sun')\n",
    "# Add axis labels and limits\n",
    "ax.set_xlabel('x wrt Sun [AU]')\n",
    "ax.set_ylabel('y wrt Sun [AU]')\n",
    "ax.set_zlabel('z wrt Sun [AU]')\n",
    "ax.set_xlim([-10.5, 2.5])\n",
    "ax.set_ylim([-8.5, 4.5])\n",
    "ax.set_zlim([-6.5, 6.5])\n",
    "# Put legend on the right\n",
    "ax.legend(bbox_to_anchor=[1.15, 1])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45fde82c",
   "metadata": {},
   "source": [
    "## MGA transfer with DSMs and manually-created settings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2895eba8",
   "metadata": {},
   "source": [
    "This next part of the example now makes use of DSMs in between the nodes. The general approach is similar to the example without DSMs, with some modifications to the inputs and transfer parameters. Additionally, the manual creation of the nodes and legs settings is here exemplified, instead of using a factory function to get them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1781476",
   "metadata": {},
   "source": [
    "### Setup and inputs\n",
    "Again, a simplified system of bodies suffices. In this case, a transfer to Mercury is considered with gravity assists at Earth and Venus. As before, the departure and arrival orbits, and the central body (Sun) are also specified: the transfer is considered to start at the edge of Earth's SOI and end at the edge of Mercury's SOI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6159889a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T09:13:18.158883Z",
     "iopub.status.busy": "2025-09-16T09:13:18.158706Z",
     "iopub.status.idle": "2025-09-16T09:13:18.161772Z",
     "shell.execute_reply": "2025-09-16T09:13:18.161321Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create simplified bodies\n",
    "bodies = environment_setup.create_simplified_system_of_bodies()\n",
    "central_body = 'Sun'\n",
    "\n",
    "# Define a new order of bodies (nodes)\n",
    "transfer_body_order = ['Earth', 'Earth', 'Venus', 'Venus',  'Mercury']\n",
    "\n",
    "# Define the departure and insertion orbits\n",
    "departure_semi_major_axis = np.inf\n",
    "departure_eccentricity = 0.0\n",
    "\n",
    "arrival_semi_major_axis = np.inf\n",
    "arrival_eccentricity = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26107ebe",
   "metadata": {},
   "source": [
    "### Create transfer settings and transfer object\n",
    "\n",
    "Since, in this example, the DSMs are specified using the velocity formulation, one can use the `mga_settings_dsm_velocity_based_legs` factory function to create the nodes and legs settings (option 1 in the code block below). This is the same approach followed in the example without DSMs.\n",
    "\n",
    "Alternatively, one can create the nodes and legs settings manually (option 2 in the code block below): \n",
    "\n",
    "- The legs settings are a list containing the settings of each leg in the transfer. Here, only velocity-based DSM legs are used, therefore the settings of each leg are created by calling the `dsm_velocity_based_leg` factory function (this is repeated for all legs in the transfer). \n",
    "- The nodes settings are a list containing the settings of each leg in the transfer. The nodes used in this transfer are a departure node (beginning of the transfer), several swingby nodes, and an arrival node (end of the transfer). Thus, the node settings are created by calling `departure_node` once, calling `swingby_node` four times, and calling `capture_node` once.\n",
    "\n",
    "Although the manual creation of the nodes and legs settings is a bit more complex than directly calling the `mga_settings_dsm_velocity_based_legs` factory function, it also allows more flexibility in the design of the transfer. For example, with manually-created legs settings, it is possible to create a transfer which mixes high- and low-thrust arcs (this is known as a hybrid-thrust transfer, their study is still a very new research area... perhaps you can contribute to it!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9a64cce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T09:13:18.163375Z",
     "iopub.status.busy": "2025-09-16T09:13:18.163199Z",
     "iopub.status.idle": "2025-09-16T09:13:18.167062Z",
     "shell.execute_reply": "2025-09-16T09:13:18.166638Z"
    }
   },
   "outputs": [],
   "source": [
    "#########################################################################################################\n",
    "# Option 1: create the nodes and legs settings using the mga_settings_dsm_velocity_based_legs factory function\n",
    "\n",
    "# Define the MGA transfer settings\n",
    "\n",
    "# transfer_leg_settings, transfer_node_settings = transfer_trajectory.mga_settings_dsm_velocity_based_legs(\n",
    "#     transfer_body_order,\n",
    "#     departure_orbit=(departure_semi_major_axis, departure_eccentricity),\n",
    "#     arrival_orbit=(arrival_semi_major_axis, arrival_eccentricity))\n",
    "\n",
    "#########################################################################################################\n",
    "# Option 2: create the nodes and legs settings by manually calling the factory functions associated with each leg and node of the transfer\n",
    "\n",
    "# Manually create the legs settings\n",
    "\n",
    "# First create an empty list and then append to that the settings of each transfer leg\n",
    "transfer_leg_settings = []\n",
    "for i in range(len(transfer_body_order) - 1):\n",
    "    transfer_leg_settings.append( transfer_trajectory.dsm_velocity_based_leg() )\n",
    "\n",
    "    \n",
    "# Manually create the nodes settings\n",
    "\n",
    "# First create an empty list and then append to that the settings of each transfer node\n",
    "transfer_node_settings = []\n",
    "\n",
    "# Initial node: departure_node\n",
    "transfer_node_settings.append( transfer_trajectory.departure_node(departure_semi_major_axis, departure_eccentricity) )\n",
    "\n",
    "# Intermediate nodes: swingby_node\n",
    "for i in range(len(transfer_body_order) - 2):\n",
    "    transfer_node_settings.append( transfer_trajectory.swingby_node() )\n",
    "    \n",
    "# Final node: capture_node\n",
    "transfer_node_settings.append( transfer_trajectory.capture_node(arrival_semi_major_axis, arrival_eccentricity) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd46a190-94b9-4cda-b128-477a63e342b9",
   "metadata": {},
   "source": [
    "Having created the nodes and legs settings, either manually or using the factory function, it is then possible to use them to create the transfer trajectory object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8514e37f-468a-45b4-ba07-1767fdf4e000",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T09:13:18.168661Z",
     "iopub.status.busy": "2025-09-16T09:13:18.168500Z",
     "iopub.status.idle": "2025-09-16T09:13:18.171035Z",
     "shell.execute_reply": "2025-09-16T09:13:18.170555Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the transfer calculation object\n",
    "transfer_trajectory_object = transfer_trajectory.create_transfer_trajectory(\n",
    "    bodies,\n",
    "    transfer_leg_settings,\n",
    "    transfer_node_settings,\n",
    "    transfer_body_order,\n",
    "    central_body)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "599e0d14",
   "metadata": {},
   "source": [
    "### Define transfer parameters\n",
    "\n",
    "As before, it is possible to print the definition of the transfer parameters which need to be selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c1301b5a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T09:13:18.172429Z",
     "iopub.status.busy": "2025-09-16T09:13:18.172271Z",
     "iopub.status.idle": "2025-09-16T09:13:18.175249Z",
     "shell.execute_reply": "2025-09-16T09:13:18.174713Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transfer parameter definitions:\n",
      "Parameter 0: Node time 0\n",
      "Parameter 1: Node time 1\n",
      "Parameter 2: Node time 2\n",
      "Parameter 3: Node time 3\n",
      "Parameter 4: Node time 4\n",
      "Parameter 5: Node 0 Outgoing excess velocity magnitude\n",
      "Parameter 6: Node 0 Outgoing excess velocity in-plane angle\n",
      "Parameter 7: Node 0 Outgoing excess velocity out-of-plane angle\n",
      "Parameter 8: Node 1 Swingby periapsis\n",
      "Parameter 9: Node 1 Swingby orbital plane angle (with respect to the incoming velocity and node velocity)\n",
      "Parameter 10: Node 1 Swingby Delta V\n",
      "Parameter 11: Node 2 Swingby periapsis\n",
      "Parameter 12: Node 2 Swingby orbital plane angle (with respect to the incoming velocity and node velocity)\n",
      "Parameter 13: Node 2 Swingby Delta V\n",
      "Parameter 14: Node 3 Swingby periapsis\n",
      "Parameter 15: Node 3 Swingby orbital plane angle (with respect to the incoming velocity and node velocity)\n",
      "Parameter 16: Node 3 Swingby Delta V\n",
      "Parameter 17: Leg  0 DSM (velocity-based) Time-of-flight fraction\n",
      "Parameter 18: Leg  1 DSM (velocity-based) Time-of-flight fraction\n",
      "Parameter 19: Leg  2 DSM (velocity-based) Time-of-flight fraction\n",
      "Parameter 20: Leg  3 DSM (velocity-based) Time-of-flight fraction\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print transfer parameter definitions\n",
    "print(\"Transfer parameter definitions:\")\n",
    "transfer_trajectory.print_parameter_definitions(transfer_leg_settings, transfer_node_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3446f27",
   "metadata": {},
   "source": [
    "The legs with velocity-based DSMs require more transfer parameters than the unpowered legs. In particular, for legs with DSMs it is necessary to specify the leg free and node free parameters. \n",
    "\n",
    "There is a free parameter for each leg, representing the leg's time-of-flight fraction at which the DSM takes place. There are three free parameters for the departure node and each swingby node. The node free parameters represent the following:\n",
    "\n",
    "* For the departure node:\n",
    "\n",
    "  1. Magnitude of the relative velocity w.r.t. the departure planet after departure.\n",
    "  2. In-plane angle of the relative velocity w.r.t. the departure planet after departure.\n",
    "  3. Out-of-plane angle of the relative velocity w.r.t. the departure planet after departure.\n",
    "\n",
    "* For the swing-by nodes:\n",
    "\n",
    "    1. Periapsis radius.\n",
    "    2. Rotation angle.\n",
    "    3. Magnitude of $\\Delta$V applied at periapsis.\n",
    "\n",
    "* For the arrival node: no node free parameters are required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc1379fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T09:13:18.177567Z",
     "iopub.status.busy": "2025-09-16T09:13:18.177396Z",
     "iopub.status.idle": "2025-09-16T09:13:18.182705Z",
     "shell.execute_reply": "2025-09-16T09:13:18.182273Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define times at each node\n",
    "julian_day = constants.JULIAN_DAY\n",
    "node_times = list()\n",
    "node_times.append((1171.64503236 - 0.5) * julian_day)\n",
    "node_times.append(node_times[0] + 399.999999715 * julian_day)\n",
    "node_times.append(node_times[1] + 178.372255301 * julian_day)\n",
    "node_times.append(node_times[2] + 299.223139512 * julian_day)\n",
    "node_times.append(node_times[3] + 180.510754824 * julian_day)\n",
    "\n",
    "# Define the free parameters per leg\n",
    "leg_free_parameters = list()\n",
    "leg_free_parameters.append(np.array([0.234594654679]))\n",
    "leg_free_parameters.append(np.array([0.0964769387134]))\n",
    "leg_free_parameters.append(np.array([0.829948744508]))\n",
    "leg_free_parameters.append(np.array([0.317174785637]))\n",
    "\n",
    "# Define the free parameters per node\n",
    "node_free_parameters = list()\n",
    "node_free_parameters.append(np.array([1408.99421278, 0.37992647165 * 2.0 * 3.14159265358979, np.arccos(2.0 * 0.498004040298 - 1.0) - 3.14159265358979 / 2.0]))\n",
    "node_free_parameters.append(np.array([1.80629232251 * 6.378e6, 1.35077257078, 0.0]))\n",
    "node_free_parameters.append(np.array([3.04129845698 * 6.052e6, 1.09554368115, 0.0]))\n",
    "node_free_parameters.append(np.array([1.10000000891 * 6.052e6, 1.34317576594, 0.0]))\n",
    "node_free_parameters.append(np.array([]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffb6aa11",
   "metadata": {},
   "source": [
    "### Evaluate transfer\n",
    "The same approach is used to evaluate the transfer trajectory with the transfer parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f17ec066",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T09:13:18.184526Z",
     "iopub.status.busy": "2025-09-16T09:13:18.184360Z",
     "iopub.status.idle": "2025-09-16T09:13:18.186681Z",
     "shell.execute_reply": "2025-09-16T09:13:18.186315Z"
    }
   },
   "outputs": [],
   "source": [
    "# Evaluate the transfer with the given parameters\n",
    "transfer_trajectory_object.evaluate( node_times, leg_free_parameters, node_free_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c028901d",
   "metadata": {},
   "source": [
    "### Extract results and plot trajectory\n",
    "Finally, the results are extracted and used to visualize the transfer trajectory. \n",
    "\n",
    "#### Print results\n",
    "Again, the values for the $\\Delta$V and time of flight can be retrieved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1a7c8f92",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T09:13:18.188270Z",
     "iopub.status.busy": "2025-09-16T09:13:18.188111Z",
     "iopub.status.idle": "2025-09-16T09:13:18.191965Z",
     "shell.execute_reply": "2025-09-16T09:13:18.191456Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Delta V of 8630.854 m/s and total Time of flight of 1058.106 days\n",
      "\n",
      "Delta V per leg: \n",
      " - between Earth and Earth: 910.804 m/s\n",
      " - between Earth and Venus: 0.035 m/s\n",
      " - between Venus and Venus: 263.275 m/s\n",
      " - between Venus and Mercury: 1415.324 m/s\n",
      "\n",
      "Delta V per node : \n",
      " - at Earth: 1408.994 m/s\n",
      " - at Earth: 0.000 m/s\n",
      " - at Venus: 0.000 m/s\n",
      " - at Venus: 0.000 m/s\n",
      " - at Mercury: 4632.422 m/s\n"
     ]
    }
   ],
   "source": [
    "# Print the total DeltaV and time of Flight required for the MGA\n",
    "print('Total Delta V of %.3f m/s and total Time of flight of %.3f days\\n' % \\\n",
    "    (transfer_trajectory_object.delta_v, transfer_trajectory_object.time_of_flight / julian_day))\n",
    "\n",
    "# Print the DeltaV required during each leg\n",
    "print('Delta V per leg: ')\n",
    "for i in range(len(transfer_body_order)-1):\n",
    "    print(\" - between %s and %s: %.3f m/s\" % \\\n",
    "        (transfer_body_order[i], transfer_body_order[i+1], transfer_trajectory_object.delta_v_per_leg[i]))\n",
    "print()\n",
    "\n",
    "# Print the DeltaV required at each node\n",
    "print('Delta V per node : ')\n",
    "for i in range(len(transfer_body_order)):\n",
    "    print(\" - at %s: %.3f m/s\" % \\\n",
    "        (transfer_body_order[i], transfer_trajectory_object.delta_v_per_node[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cf9bb14",
   "metadata": {},
   "source": [
    "#### Plot the transfer\n",
    "The state throughout the transfer can be retrieved from the transfer trajectory object, here at 500 instances per leg, to visualize the transfer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "46d748b2",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-09-16T09:13:18.381086Z"
    },
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the state history\n",
    "state_history = transfer_trajectory_object.states_along_trajectory(500)\n",
    "fly_by_states = np.array([state_history[node_times[i]] for i in range(len(node_times))])\n",
    "state_history = result2array(state_history)\n",
    "au = 1.5e11\n",
    "\n",
    "# Plot the state history\n",
    "fig = plt.figure(figsize=(8,5))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(state_history[:, 1] / au, state_history[:, 2] / au)\n",
    "ax.scatter(fly_by_states[0, 0] / au, fly_by_states[0, 1] / au, color='blue', label='Earth departure')\n",
    "ax.scatter(fly_by_states[1, 0] / au, fly_by_states[1, 1] / au, color='green', label='Earth fly-by')\n",
    "ax.scatter(fly_by_states[2, 0] / au, fly_by_states[2, 1] / au, color='brown', label='Venus fly-by')\n",
    "ax.scatter(fly_by_states[3, 0] / au, fly_by_states[3, 1] / au, color='brown')\n",
    "ax.scatter(fly_by_states[4, 0] / au, fly_by_states[4, 1] / au, color='grey', label='Mercury arrival')\n",
    "ax.scatter([0], [0], color='orange', label='Sun')\n",
    "ax.set_xlabel('x wrt Sun [AU]')\n",
    "ax.set_ylabel('y wrt Sun [AU]')\n",
    "ax.set_aspect('equal')\n",
    "ax.legend(bbox_to_anchor=[1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da452aa3-6a49-43e5-9926-ea7ae86e138b",
   "metadata": {},
   "source": [
    "## MGA transfer with hodographic-shaping legs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ab3acd2-36b1-4b08-aa61-57f77872a42b",
   "metadata": {},
   "source": [
    "This final example shows how to setup a low-thrust MGA transfer, with the thrust profile modeled using hodographic shaping. The approach is similar to the previous examples, with only some small differences when selecting the shaping functions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "609a9ccf-dfdf-4956-bbea-ab1e521dc6fb",
   "metadata": {},
   "source": [
    "### Setup and inputs\n",
    "Again, a simplified system of bodies is used. A transfer between the Earth and Jupiter is considered, with gravity assists at Mars and the Earth. The transfer is considered to start at the edge of Earth's SOI and end at the edge of Jupiter's SOI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1051bc74-fb80-47ee-be4d-cf2066f9476e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T09:13:18.383658Z",
     "iopub.status.busy": "2025-09-16T09:13:18.383418Z",
     "iopub.status.idle": "2025-09-16T09:13:18.386564Z",
     "shell.execute_reply": "2025-09-16T09:13:18.386163Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create simplified bodies\n",
    "bodies = environment_setup.create_simplified_system_of_bodies()\n",
    "central_body = 'Sun'\n",
    "\n",
    "# Define a new order of bodies (nodes)\n",
    "transfer_body_order = ['Earth', 'Mars', 'Earth', \"Jupiter\"]\n",
    "\n",
    "# Define the departure and insertion orbits\n",
    "departure_semi_major_axis = np.inf\n",
    "departure_eccentricity = 0.0\n",
    "\n",
    "arrival_semi_major_axis = np.inf\n",
    "arrival_eccentricity = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6ba60ce-ecd7-487c-96be-696ab0459557",
   "metadata": {},
   "source": [
    "### Create transfer settings and transfer object\n",
    "\n",
    "The creation of the transfer settings for a hodographic shaping leg requires, in particular, selecting the shaping functions. Each hodographic shaping leg is defined by a series of shaping functions, which determine the evolution of the velocity profile (in cylindrical coordinates) as a function of time throughout the transfer. Hence, it is necessary to select the terms constituting the shaping functions for the radial, normal, and axial velocity. Each shaping function has the form $\\sum_i c_i \\cdot f_i(t), \\ i \\geq 3$, with $c_i$ a constant and $f_i(t)$ a function of time (e.g. sine, cosine, exponential, power, etc.). In order to meet the boundary constraints, each shaping function requires a minimum of three terms; the coefficients of these terms (i.e. $c_1$, $c_2$, and $c_3$) are determined automatically. Additional shaping terms may be specified, which will further influence the evolution of the transfer; the coefficients of these terms (i.e. $c_i, \\ i > 3$) are leg free parameters, and have to be specified by the user. \n",
    "\n",
    "If shaping functions with only three terms are specified, then each hodographic-shaping leg has only one parameter that needs to be specified by the user. The legs and nodes settings for such a transfer can be created using the `mga_settings_hodographic_shaping_legs_with_recommended_functions` factory function, which selects a set of recommended shaping functions.  \n",
    "\n",
    "To select shaping function with terms other than the recommended ones or with more terms, the nodes and legs settings can be retrieved using the `mga_settings_hodographic_shaping_legs` factory function. This approach is followed here.\n",
    "\n",
    "The first step is to select the number of revolutions and time of flight of each leg. Contrary to the previous examples, this parameters are specified before the creation of the transfer trajectory object because they are used when selecting the shaping functions. Next, one can select the shaping functions. The shaping functions used here were selected according to ([Gondelach, 2012](http://resolver.tudelft.nl/uuid:6a4f1673-88b1-4823-b2ef-9d864c84ab11), section 11.2), who finds these functions to offer the best performance for an Earth-Mars transfer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1c7989ae-801c-4d00-8393-456a3e6ef255",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# Define number of revolutions of each leg\n",
    "number_of_revolutions = [1, 0, 0]\n",
    "\n",
    "# Define the departure date of the transfer\n",
    "julian_day = constants.JULIAN_DAY\n",
    "departure_date = 9558.896403441706 * julian_day\n",
    "\n",
    "# Define the time of each leg\n",
    "time_of_flight = np.array([904.9805697730939,\n",
    "                           385.30358508711015,\n",
    "                           1100.4278671415163]) * julian_day\n",
    "\n",
    "\n",
    "# Create empty lists to save the shaping functions of each leg\n",
    "radial_velocity_function_components_per_leg = []\n",
    "normal_velocity_function_components_per_leg = []\n",
    "axial_velocity_function_components_per_leg = []\n",
    "\n",
    "# Loop over legs\n",
    "for i in range(len(transfer_body_order)-1):\n",
    "    # Compute frequency of the shaping term\n",
    "    frequency = 2.0 * np.pi / time_of_flight[i]\n",
    "    scale_factor = 1.0 / time_of_flight[i]\n",
    "\n",
    "    # Radial velocity functions: recommended functions (3 terms), scaled power sine (1 term), scaled power cosine (1 term)\n",
    "    radial_velocity_functions = shape_based_thrust.recommended_radial_hodograph_functions(time_of_flight[i])\n",
    "    radial_velocity_functions.append(shape_based_thrust.hodograph_scaled_power_sine(\n",
    "        exponent = 1.0,\n",
    "        frequency = 0.5 * frequency,\n",
    "        scale_factor = scale_factor))\n",
    "    radial_velocity_functions.append(shape_based_thrust.hodograph_scaled_power_cosine(\n",
    "        exponent = 1.0,\n",
    "        frequency = 0.5 * frequency,\n",
    "        scale_factor = scale_factor))\n",
    "\n",
    "    # Normal velocity functions: recommended functions (3 terms), scaled power sine (1 term), scaled power cosine (1 term)\n",
    "    normal_velocity_functions = shape_based_thrust.recommended_normal_hodograph_functions(time_of_flight[i])\n",
    "    normal_velocity_functions.append(shape_based_thrust.hodograph_scaled_power_sine(\n",
    "        exponent = 1.0,\n",
    "        frequency = 0.5 * frequency,\n",
    "        scale_factor = scale_factor))\n",
    "    normal_velocity_functions.append(shape_based_thrust.hodograph_scaled_power_cosine(\n",
    "        exponent = 1.0,\n",
    "        frequency = 0.5 * frequency,\n",
    "        scale_factor = scale_factor))\n",
    "\n",
    "    # Axial velocity functions: recommended functions (3 terms), scaled power sine (1 term), scaled power cosine (1 term)\n",
    "    axial_velocity_functions = shape_based_thrust.recommended_axial_hodograph_functions(time_of_flight[i], number_of_revolutions[i])\n",
    "    exponent = 4.0\n",
    "    axial_velocity_functions.append(shape_based_thrust.hodograph_scaled_power_cosine(\n",
    "        exponent = exponent,\n",
    "        frequency = (number_of_revolutions[i] + 0.5)*frequency,\n",
    "        scale_factor = scale_factor ** exponent))\n",
    "    axial_velocity_functions.append(shape_based_thrust.hodograph_scaled_power_sine(\n",
    "        exponent = exponent,\n",
    "        frequency = (number_of_revolutions[i] + 0.5)*frequency,\n",
    "        scale_factor = scale_factor ** exponent))\n",
    "    \n",
    "    # Save lists with the shaping functions of the current leg\n",
    "    radial_velocity_function_components_per_leg.append(radial_velocity_functions)\n",
    "    normal_velocity_function_components_per_leg.append(normal_velocity_functions)\n",
    "    axial_velocity_function_components_per_leg.append(axial_velocity_functions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c30c22f-7e85-4724-b5e8-5c2a48176d96",
   "metadata": {},
   "source": [
    "Having selected the shaping functions, it is now possible to create the legs and nodes settings and after that to create the transfer trajectory object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a9ea5292-d708-428a-b4cf-f1ed981a8b18",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-09-16T09:13:18.400372Z"
    }
   },
   "outputs": [],
   "source": [
    "# Get legs and nodes settings\n",
    "transfer_leg_settings, transfer_node_settings = transfer_trajectory.mga_settings_hodographic_shaping_legs(\n",
    "    transfer_body_order,\n",
    "    radial_velocity_function_components_per_leg,\n",
    "    normal_velocity_function_components_per_leg,\n",
    "    axial_velocity_function_components_per_leg,\n",
    "    departure_orbit = (departure_semi_major_axis, departure_eccentricity),\n",
    "    arrival_orbit = (arrival_semi_major_axis, arrival_eccentricity) )\n",
    "\n",
    "# Create the transfer calculation object\n",
    "transfer_trajectory_object = transfer_trajectory.create_transfer_trajectory(\n",
    "    bodies,\n",
    "    transfer_leg_settings,\n",
    "    transfer_node_settings,\n",
    "    transfer_body_order,\n",
    "    central_body)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e5c1adc-7814-480a-81d3-52ba6697b924",
   "metadata": {},
   "source": [
    "### Define transfer parameters\n",
    "\n",
    "As before, it is possible to print the definition of the transfer parameters which need to be selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "edfd2b31-9142-458b-89c6-e43412ea479e",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-09-16T09:13:18.405094Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transfer parameter definitions:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter 0: Node time 0\n",
      "Parameter 1: Node time 1\n",
      "Parameter 2: Node time 2\n",
      "Parameter 3: Node time 3\n",
      "Parameter 4: Node 0 Outgoing excess velocity magnitude\n",
      "Parameter 5: Node 0 Outgoing excess velocity in-plane angle\n",
      "Parameter 6: Node 0 Outgoing excess velocity out-of-plane angle\n",
      "Parameter 7: Node 1 Incoming excess velocity magnitude\n",
      "Parameter 8: Node 1 Incoming excess velocity in-plane angle\n",
      "Parameter 9: Node 1 Incoming excess velocity out-of-plane angle\n",
      "Parameter 10: Node 1 Swingby periapsis\n",
      "Parameter 11: Node 1 Swingby orbital plane angle (with respect to the incoming velocity and node velocity)\n",
      "Parameter 12: Node 1 Swingby Delta V\n",
      "Parameter 13: Node 2 Incoming excess velocity magnitude\n",
      "Parameter 14: Node 2 Incoming excess velocity in-plane angle\n",
      "Parameter 15: Node 2 Incoming excess velocity out-of-plane angle\n",
      "Parameter 16: Node 2 Swingby periapsis\n",
      "Parameter 17: Node 2 Swingby orbital plane angle (with respect to the incoming velocity and node velocity)\n",
      "Parameter 18: Node 2 Swingby Delta V\n",
      "Parameter 19: Node 3 Incoming excess velocity magnitude\n",
      "Parameter 20: Node 3 Incoming excess velocity in-plane angle\n",
      "Parameter 21: Node 3 Incoming excess velocity out-of-plane angle\n",
      "Parameter 22: Leg  0 Number of revolutions (integer number >= 0)\n",
      "Parameter 23: Leg  0 Radial velocity function free coefficient 0\n",
      "Parameter 24: Leg  0 Radial velocity function free coefficient 1\n",
      "Parameter 25: Leg  0 Normal velocity function free coefficient 0\n",
      "Parameter 26: Leg  0 Normal velocity function free coefficient 1\n",
      "Parameter 27: Leg  0 Axial velocity function free coefficient 0\n",
      "Parameter 28: Leg  0 Axial velocity function free coefficient 1\n",
      "Parameter 29: Leg  1 Number of revolutions (integer number >= 0)\n",
      "Parameter 30: Leg  1 Radial velocity function free coefficient 0\n",
      "Parameter 31: Leg  1 Radial velocity function free coefficient 1\n",
      "Parameter 32: Leg  1 Normal velocity function free coefficient 0\n",
      "Parameter 33: Leg  1 Normal velocity function free coefficient 1\n",
      "Parameter 34: Leg  1 Axial velocity function free coefficient 0\n",
      "Parameter 35: Leg  1 Axial velocity function free coefficient 1\n",
      "Parameter 36: Leg  2 Number of revolutions (integer number >= 0)\n",
      "Parameter 37: Leg  2 Radial velocity function free coefficient 0\n",
      "Parameter 38: Leg  2 Radial velocity function free coefficient 1\n",
      "Parameter 39: Leg  2 Normal velocity function free coefficient 0\n",
      "Parameter 40: Leg  2 Normal velocity function free coefficient 1\n",
      "Parameter 41: Leg  2 Axial velocity function free coefficient 0\n",
      "Parameter 42: Leg  2 Axial velocity function free coefficient 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print transfer parameter definitions\n",
    "print(\"Transfer parameter definitions:\")\n",
    "transfer_trajectory.print_parameter_definitions(transfer_leg_settings, transfer_node_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "305303cf-ae25-420a-af6a-87f129829992",
   "metadata": {},
   "source": [
    "In this case, there is a much larger list of free parameters than in the previous cases. As before, the first parameters are the node times, corresponding to the times when the spacecraft encounters each planet. Next, the node free parameters are required. These include the selection of the departure velocity at the departure node (3 parameters), the arrival velocity at each swingby node (3 parameters per node), the characteristics of each swingby (3 parameters per node), and the arrival velocity at the arrival node (3 parameters). Finally, it is necessary to specify the leg free parameters. These consist of the number of revolutions of each leg (1 parameter per leg), and of two free coefficients per velocity component per leg (6 parameters per leg).\n",
    "\n",
    "The parameters used in this example were determined via optimization, using PyGMO. The code used to optimize the transfer is analyzed in [this example](hodographic_shaping_mga_optimization.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b1f861c5-f3a5-4913-b730-87e4bfade5bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T09:13:18.407338Z",
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     "shell.execute_reply": "2025-09-16T09:13:18.411241Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define times at each node\n",
    "node_times = list()\n",
    "node_times.append( departure_date )\n",
    "for i, tof in enumerate(time_of_flight):\n",
    "    node_times.append( node_times[i] + tof )\n",
    "\n",
    "# Define free parameters per leg\n",
    "leg_free_parameters = list()\n",
    "leg_free_parameters.append([number_of_revolutions[0], -804.0, 5232.0, -3612.0, -6300.0, -299.0, -1731.0])\n",
    "leg_free_parameters.append([number_of_revolutions[1], -2587.0, -6648.0, 6984.0, 3438.0, -3043.0, -1027.0])\n",
    "leg_free_parameters.append([number_of_revolutions[2], -8283.0, -8114.0, -2667.0, 7128.0, 2473.0, -1471.0])\n",
    "\n",
    "# Define free parameters per node\n",
    "node_free_parameters = list()\n",
    "node_free_parameters.append(np.zeros(3))\n",
    "node_free_parameters.append([1183.979987813652, 0.0, 0.0, 10**5.565627663196208, 0.0, 0.0])\n",
    "node_free_parameters.append([3074.131807921915, 0.0, 0.0, 10**8.129202206701256, 0.0, 0.0])\n",
    "node_free_parameters.append(np.zeros(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6de95fb3-4706-4846-b868-b557fc433c9d",
   "metadata": {},
   "source": [
    "### Evaluate transfer\n",
    "Having selected the transfer parameters, it is now possible to evaluate the transfer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "79c93b17-b7df-4b16-91a6-c90329675024",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T09:13:18.413102Z",
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     "shell.execute_reply": "2025-09-16T09:13:18.430101Z"
    }
   },
   "outputs": [],
   "source": [
    "# Evaluate the transfer with the given parameters\n",
    "transfer_trajectory_object.evaluate( node_times, leg_free_parameters, node_free_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a3de19d-c655-4a76-aca7-53e63a7bb716",
   "metadata": {},
   "source": [
    "### Extract results and plot trajectory\n",
    "Finally, the results are extracted and used to analyze the transfer trajectory. \n",
    "\n",
    "#### Print results\n",
    "Again, the values for the $\\Delta V$ and time of flight can be retrieved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f298169d-4aa9-4eab-9fa6-ea96c2189dd7",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-09-16T09:13:18.435488Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Delta V of 49190.988 m/s and total Time of flight of 2390.712 days\n",
      "\n",
      "Delta V per leg: \n",
      " - between Earth and Mars: 10165.234 m/s\n",
      " - between Mars and Earth: 7489.632 m/s\n",
      " - between Earth and Jupiter: 31536.122 m/s\n",
      "\n",
      "Delta V per node : \n",
      " - at Earth: 0.000 m/s\n",
      " - at Mars: 0.000 m/s\n",
      " - at Earth: 0.000 m/s\n",
      " - at Jupiter: 0.000 m/s\n"
     ]
    }
   ],
   "source": [
    "# Print the total DeltaV and time of Flight required for the MGA\n",
    "print('Total Delta V of %.3f m/s and total Time of flight of %.3f days\\n' % \\\n",
    "    (transfer_trajectory_object.delta_v, transfer_trajectory_object.time_of_flight / julian_day))\n",
    "\n",
    "# Print the DeltaV required during each leg\n",
    "print('Delta V per leg: ')\n",
    "for i in range(len(transfer_body_order)-1):\n",
    "    print(\" - between %s and %s: %.3f m/s\" % \\\n",
    "        (transfer_body_order[i], transfer_body_order[i+1], transfer_trajectory_object.delta_v_per_leg[i]))\n",
    "print()\n",
    "\n",
    "# Print the DeltaV required at each node\n",
    "print('Delta V per node : ')\n",
    "for i in range(len(transfer_body_order)):\n",
    "    print(\" - at %s: %.3f m/s\" % \\\n",
    "        (transfer_body_order[i], transfer_trajectory_object.delta_v_per_node[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fa202c8-e919-4d65-9e70-34d63563460e",
   "metadata": {},
   "source": [
    "#### Plot the transfer\n",
    "Similarly to the previous cases, the state history throughout the transfer can be retrieved with `states_along_trajectory`. Furthermore, it is possible to retrieve the thrust acceleration history with respect to different reference frames (inertial, TNW, and RSW). Below, the thrust acceleration is retrieved with respect to a TNW frame (through `tnw_thrust_accelerations_along_trajectory`) and plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4429114f-ea11-45ca-8c16-8568a1e28d46",
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     "shell.execute_reply": "2025-09-16T09:13:18.924707Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract state and thrust acceleration history\n",
    "# state_history = transfer_trajectory_object.states_along_trajectory(500)\n",
    "thrust_acceleration_tnw_history = transfer_trajectory_object.tnw_thrust_accelerations_along_trajectory(500)\n",
    "\n",
    "thrust_acceleration_tnw_history = result2array(thrust_acceleration_tnw_history)\n",
    "\n",
    "# Plot thrust acceleration\n",
    "fig = plt.figure(figsize=(6,3))\n",
    "ax = fig.add_subplot(111)\n",
    "initial_time = thrust_acceleration_tnw_history[0, 0]\n",
    "time_of_flight = thrust_acceleration_tnw_history[:, 0] - initial_time\n",
    "ax.plot(time_of_flight / julian_day, thrust_acceleration_tnw_history[:, 1], label=\"T acceleration\" )\n",
    "ax.plot(time_of_flight / julian_day, thrust_acceleration_tnw_history[:, 2], label=\"N acceleration\" )\n",
    "ax.plot(time_of_flight / julian_day, thrust_acceleration_tnw_history[:, 3], label=\"W acceleration\" )\n",
    "ax.axvline((node_times[1] - initial_time) / julian_day, ls=\"--\", c=\"k\", alpha=0.7, label=\"Fly-by\")\n",
    "ax.axvline((node_times[2] - initial_time) / julian_day, ls=\"--\", c=\"k\", alpha=0.7, label=None)\n",
    "ax.set_xlabel('Time [day]')\n",
    "ax.set_ylabel('Thrust acceleration [m/s$^2$]')\n",
    "ax.grid()\n",
    "ax.set_axisbelow(True)\n",
    "ax.legend(bbox_to_anchor=[1, 1])\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": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}