docs/source/_src_getting_started/_src_examples/notebooks/propagation/mga_trajectories.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "35adce6f",
   "metadata": {},
   "source": [
    "# Multiple Gravity Assist trajectories\n",
    "\n",
    "Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and\n",
    "binary forms, with or without modification, are permitted exclusively\n",
    "under the terms of the Modified BSD license. You should have received\n",
    "a copy of the license with this file. If not, please or visit:\n",
    "http://tudat.tudelft.nl/LICENSE.\n",
    "\n",
    "## Context\n",
    "\n",
    "This example demonstrates how Multiple Gravity Assist (MGA) transfer trajectories can be simulated. Three types of transfers are analyzed:\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://tudat-space.readthedocs.io/en/latest/_src_user_guide/astrodynamics/trajectory_design.html) of tudat user documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf03667",
   "metadata": {},
   "source": [
    "## MGA Transfer With Unpowered Legs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe2f057",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c681e2fc",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# Load standard modules\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load tudatpy modules\n",
    "from tudatpy.kernel.trajectory_design import transfer_trajectory, shape_based_thrust\n",
    "from tudatpy.kernel.numerical_simulation import environment_setup\n",
    "from tudatpy.util import result2array\n",
    "from tudatpy.kernel import constants"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "300be7ec",
   "metadata": {},
   "source": [
    "First, let's explore an MGA transfer trajectory with no thrust applied during the transfer legs. In this case, the impulsive $\\Delta$Vs are only applied during the gravity assists."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4b08a8",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eb7a9c5d",
   "metadata": {},
   "outputs": [],
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0346652b",
   "metadata": {},
   "outputs": [],
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bee4abe2",
   "metadata": {},
   "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 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)"
   ]
  },
  {
   "cell_type": "markdown",
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce8da5f1",
   "metadata": {},
   "outputs": [],
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ebdab627",
   "metadata": {},
   "outputs": [],
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31aeaa78",
   "metadata": {},
   "outputs": [
    {
     "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d84a2821",
   "metadata": {},
   "outputs": [
    {
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\n",
      "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. Additionaly, 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": {},
   "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",
    "\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": {},
   "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": {},
   "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 paramaters which need to be selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c1301b5a",
   "metadata": {},
   "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",
    " * For the departure node:\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",
    " * For the swing-by nodes:\n",
    "    1. Periapsis radius.\n",
    "    2. Rotation angle.\n",
    "    3. Magnitude of $\\Delta$V applied at periapsis.\n",
    " * For the arrival node: no node free parameters are required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc1379fc",
   "metadata": {},
   "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": {},
   "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": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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": {},
   "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": {},
   "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": {},
   "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 paramaters which need to be selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "edfd2b31-9142-458b-89c6-e43412ea479e",
   "metadata": {},
   "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 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](https://tudat-space.readthedocs.io/en/latest/src_getting_started/_src_examples/notebooks/pygmo/hodographic_shaping_mga_optimization.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b1f861c5-f3a5-4913-b730-87e4bfade5bb",
   "metadata": {},
   "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": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Delta V of 49934.658 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: 8233.303 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",
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}