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    "# Asteroid orbit optimization with PyGMO - Custom Environment\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 binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE.\n",
    "\n",
    "## Objectives\n",
    "The aim of this tutorial is to illustrate the use of PyGMO to optimize an astrodynamics problem simulated with tudatpy. The problem describes the orbit design around a small body; the [Itokawa asteroid](https://en.wikipedia.org/wiki/25143_Itokawa). This example consists of three parts that build on each other to ultimately perform an orbit optimization. The three parts are built up as follows: Custom Environment (this page), [Design Space Exploration](aoo_design_space_exploration.ipynb), and [Optimization](aoo_optimization.ipynb).\n",
    "\n",
    "**This part of the example is focussed on the creation of a custom environment, using manually defined rotation, ephemeris, gravity field, and shape settings**. A PyGMO compatible Problem class is also created for the next parts of the example. Using this problem class, a propagation is conducted to show a possible trajectory orbiting Itokawa.\n",
    "\n",
    "\n",
    "### NOTE\n",
    "\n",
    "It is assumed that the reader of this tutorial is already familiar with the content of [this basic PyGMO tutorial](https://docs.tudat.space/en/latest/_src_advanced_topics/optimization_pygmo.html). The full PyGMO documentation is available [on this website](https://esa.github.io/pygmo2/index.html). Be careful to read the\n",
    "correct the documentation webpage (there is also a similar one for previous yet now outdated versions [here](https://esa.github.io/pygmo/index.html); as you can see, they can easily be confused).\n",
    "PyGMO is the Python counterpart of [PAGMO](https://esa.github.io/pagmo2/index.html)."
   ]
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    "## Import statements\n",
    "The required import statements are made here, at the very beginning.\n",
    "\n",
    "Some standard modules are first loaded. These are `os`, `numpy` and `matplotlib.pyplot`.\n",
    "\n",
    "Then, the different modules of `tudatpy` that will be used are imported.\n",
    "\n",
    "Finally, in this example, we also need to import the `pygmo` library."
   ]
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    "# Load standard modules\n",
    "import os\n",
    "import numpy as np\n",
    "# Uncomment the following to make plots interactive\n",
    "# %matplotlib widget\n",
    "from matplotlib import pyplot as plt\n",
    "from itertools import combinations as comb\n",
    "\n",
    "\n",
    "# Load tudatpy modules\n",
    "from tudatpy.data import save2txt\n",
    "from tudatpy import constants\n",
    "from tudatpy.interface import spice\n",
    "from tudatpy.astro import element_conversion\n",
    "from tudatpy.astro import frame_conversion\n",
    "from tudatpy.dynamics import environment_setup\n",
    "from tudatpy.dynamics import propagation_setup\n",
    "from tudatpy.dynamics import simulator\n",
    "from tudatpy.util import pareto_optimums\n",
    "\n",
    "# Load pygmo library\n",
    "import pygmo as pg\n",
    "\n",
    "current_dir = os.path.abspath('')"
   ]
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    "## Creation of Custom Environment\n",
    "\n",
    "Below a few helper functions are defined that create various custom environment models. These functions are used later in the example to create the simulation\n"
   ]
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    "### Itokawa rotation settings\n",
    "The first helper function that is setup is `get_itokawa_rotation_settings()`. This function can be called to get Itokawa rotation settings, of type [environment_setup.rotation_model.RotationModelSettings](https://py.api.tudat.space/en/latest/rotation_model.html#tudatpy.dynamics.environment_setup.rotation_model.RotationModelSettings), using a constant angular velocity.\n",
    "\n",
    "This function only take the name of the body frame of Itokawa as an input.\n",
    "In addition, some fixed parameters are defined in this function:\n",
    "\n",
    "- The orientation of Itokawa pole, as:\n",
    "    - The pole declination of -66.3 deg.\n",
    "    - The pole right ascension 90.53 deg.\n",
    "    - The meridian fixed at 0 deg.\n",
    "- The rotation rate of Itokawa of 712.143 deg/Earth day."
   ]
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    "def get_itokawa_rotation_settings(itokawa_body_frame_name):\n",
    "    # Definition of initial Itokawa orientation conditions through the pole orientation\n",
    "    pole_declination = np.deg2rad(-66.30)     # Declination\n",
    "    pole_right_ascension = np.deg2rad(90.53)  # Right ascension\n",
    "    meridian_at_epoch = 0.0                   # Meridian\n",
    "\n",
    "    # Define initial Itokawa orientation in inertial frame (equatorial plane)\n",
    "    initial_orientation_j2000 = frame_conversion.inertial_to_body_fixed_rotation_matrix(\n",
    "        pole_declination, pole_right_ascension, meridian_at_epoch)\n",
    "    \n",
    "    # Get initial Itokawa orientation in inertial frame but in the Ecliptic plane\n",
    "    initial_orientation_eclipj2000 = np.matmul(spice.compute_rotation_matrix_between_frames(\n",
    "        \"J2000\", \"ECLIPJ2000\", 0.0), initial_orientation_j2000)\n",
    "\n",
    "    # Manually check the results, if desired\n",
    "    check_results = False\n",
    "    if check_results:\n",
    "        np.set_printoptions(precision=100)\n",
    "        print(initial_orientation_j2000)\n",
    "    \n",
    "        print(initial_orientation_eclipj2000)\n",
    "\n",
    "    # Compute rotation rate\n",
    "    rotation_rate = np.deg2rad(712.143) / constants.JULIAN_DAY\n",
    "\n",
    "    # Set up rotational model for Itokawa with constant angular velocity\n",
    "    return environment_setup.rotation_model.simple(\n",
    "        \"ECLIPJ2000\", itokawa_body_frame_name, initial_orientation_eclipj2000, 0.0, rotation_rate)"
   ]
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    "### Itokawa ephemeris settings\n",
    "The next helper function defined, `get_itokawa_ephemeris_settings()`, that can be used to set the ephemeris of Itokawa.\n",
    "\n",
    "The ephemeris for Itokawa are set using a Keplerian orbit around the Sun. To do this, the initial position at a certain epoch is needed. This position is defined inside the function by the following kepler elements:\n",
    "\n",
    "- Semi-major axis of $\\approx$ 1.324 Astronomical Units ($\\approx$ 1.98E+8 km).\n",
    "- Eccentricity of $\\approx$ 0.28.\n",
    "- Inclination of $\\approx$ 1.62 deg.\n",
    "- Inclination of $\\approx$ 1.62 deg.\n",
    "- Argument of periapsis of $\\approx$ 162.8 deg.\n",
    "- Longitude of ascending node of $\\approx$ 69.1 deg.\n",
    "- Mean anomaly of $\\approx$ 187.6 deg.\n",
    "\n",
    "The only input that this function takes is the gravitational parameter of the Sun, which can be obtained using `spice.get_body_gravitational_parameter(\"Sun\")`."
   ]
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    "def get_itokawa_ephemeris_settings(sun_gravitational_parameter):\n",
    "    # Define Itokawa initial Kepler elements\n",
    "    itokawa_kepler_elements = np.array([\n",
    "        1.324118017407799 * constants.ASTRONOMICAL_UNIT,\n",
    "        0.2801166461882852,\n",
    "        np.deg2rad(1.621303507642802),\n",
    "        np.deg2rad(162.8147699851312),\n",
    "        np.deg2rad(69.0803904880264),\n",
    "        np.deg2rad(187.6327516838828)])\n",
    "    \n",
    "    # Convert mean anomaly to true anomaly\n",
    "    itokawa_kepler_elements[5] = element_conversion.mean_to_true_anomaly(\n",
    "        eccentricity=itokawa_kepler_elements[1],\n",
    "        mean_anomaly=itokawa_kepler_elements[5])\n",
    "    \n",
    "    # Get epoch of initial Kepler elements (in Julian Days)\n",
    "    kepler_elements_reference_julian_day = 2459000.5\n",
    "    \n",
    "    # Sets new reference epoch for Itokawa ephemerides (different from J2000)\n",
    "    kepler_elements_reference_epoch = (kepler_elements_reference_julian_day - constants.JULIAN_DAY_ON_J2000) \\\n",
    "                                      * constants.JULIAN_DAY\n",
    "    \n",
    "    # Sets the ephemeris model\n",
    "    return environment_setup.ephemeris.keplerian(\n",
    "        itokawa_kepler_elements,\n",
    "        kepler_elements_reference_epoch,\n",
    "        sun_gravitational_parameter,\n",
    "        \"Sun\",\n",
    "        \"ECLIPJ2000\")"
   ]
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    "### Itokawa gravity field settings\n",
    "The `get_itokawa_gravity_field_settings()` helper function can be used to get the gravity field settings of Itokawa.\n",
    "\n",
    "It creates a Spherical Harmonics gravity field model expanded up to order 4 and degree 4. Normalized coefficients are hardcoded (see `normalized_cosine_coefficients` and `normalized_sine_coefficients`), as well as the gravitational parameter (2.36). The reference radius and the Itokawa body fixed frame are to be given as inputs to this function."
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    "def get_itokawa_gravity_field_settings(itokawa_body_fixed_frame, itokawa_radius):\n",
    "    itokawa_gravitational_parameter = 2.36\n",
    "    normalized_cosine_coefficients = np.array([\n",
    "        [1.0, 0.0, 0.0, 0.0, 0.0],\n",
    "        [0.0, 0.0, 0.0, 0.0, 0.0],\n",
    "        [-0.145216, 0.0, 0.219420, 0.0, 0.0],\n",
    "        [0.036115, -0.028139, -0.046894, 0.069022, 0.0],\n",
    "        [0.087852, 0.034069, -0.123263, -0.030673, 0.150282]])\n",
    "    normalized_sine_coefficients = np.array([\n",
    "        [0.0, 0.0, 0.0, 0.0, 0.0],\n",
    "        [0.0, 0.0, 0.0, 0.0, 0.0],\n",
    "        [0.0, 0.0, 0.0, 0.0, 0.0],\n",
    "        [0.0, -0.006137, -0.046894, 0.033976, 0.0],\n",
    "        [0.0, 0.004870, 0.000098, -0.015026, 0.011627]])\n",
    "    return environment_setup.gravity_field.spherical_harmonic(\n",
    "        gravitational_parameter=itokawa_gravitational_parameter,\n",
    "        reference_radius=itokawa_radius,\n",
    "        normalized_cosine_coefficients=normalized_cosine_coefficients,\n",
    "        normalized_sine_coefficients=normalized_sine_coefficients,\n",
    "        associated_reference_frame=itokawa_body_fixed_frame)"
   ]
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    "### Itokawa shape settings\n",
    "The next helper function defined, `get_itokawa_shape_settings()` return the shape settings object for Itokawa. It uses a simple spherical model, and take the radius of Itokawa as input."
   ]
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    "def get_itokawa_shape_settings(itokawa_radius):\n",
    "    # Creates spherical shape settings\n",
    "    return environment_setup.shape.spherical(itokawa_radius)"
   ]
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    "### Simulation bodies\n",
    "Next, the `create_simulation_bodies()` function is setup, that returns an [environment.SystemOfBodies](https://py.api.tudat.space/en/latest/environment.html#tudatpy.dynamics.environment.SystemOfBodies) object. This object contains all the body settings and body objects required by the simulation. Only one input is required to this function: the radius of Itokawa.\n",
    "\n",
    "Moreover, in the system of bodies that is returned, a `Spacecraft` body is included, with a mass of 400kg, and a radiation pressure interface. This body is the one for which an orbit is to be optimised around Itokawa."
   ]
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    "def create_simulation_bodies(itokawa_radius):\n",
    "    ### CELESTIAL BODIES ###\n",
    "    # Define Itokawa body frame name\n",
    "    itokawa_body_frame_name = \"Itokawa_Frame\"\n",
    "\n",
    "    # Create default body settings for selected celestial bodies\n",
    "    bodies_to_create = [\"Sun\", \"Earth\", \"Jupiter\", \"Saturn\", \"Mars\"]\n",
    "\n",
    "    # Create default body settings for bodies_to_create, with \"Earth\"/\"J2000\" as\n",
    "    # global frame origin and orientation. This environment will only be valid\n",
    "    # in the indicated time range [simulation_start_epoch --- simulation_end_epoch]\n",
    "    body_settings = environment_setup.get_default_body_settings(\n",
    "        bodies_to_create,\n",
    "        \"SSB\",\n",
    "        \"ECLIPJ2000\")\n",
    "\n",
    "    # Add Itokawa body\n",
    "    body_settings.add_empty_settings(\"Itokawa\")\n",
    "\n",
    "    # Adds Itokawa settings\n",
    "    # Gravity field\n",
    "    body_settings.get(\"Itokawa\").gravity_field_settings = get_itokawa_gravity_field_settings(itokawa_body_frame_name,\n",
    "                                                                        itokawa_radius)\n",
    "    # Rotational model\n",
    "    body_settings.get(\"Itokawa\").rotation_model_settings = get_itokawa_rotation_settings(itokawa_body_frame_name)\n",
    "    # Ephemeris\n",
    "    body_settings.get(\"Itokawa\").ephemeris_settings = get_itokawa_ephemeris_settings(\n",
    "        spice.get_body_gravitational_parameter( 'Sun') )\n",
    "    # Shape (spherical)\n",
    "    body_settings.get(\"Itokawa\").shape_settings = get_itokawa_shape_settings(itokawa_radius)\n",
    "\n",
    "    ### VEHICLE BODY ###\n",
    "    # Create vehicle object\n",
    "    body_settings.add_empty_settings(\"Spacecraft\")\n",
    "    body_settings.get(\"Spacecraft\").constant_mass = 400\n",
    "\n",
    "    # Create radiation pressure settings\n",
    "    reference_area_radiation = (4*0.3*0.1+2*0.1*0.1)/4  # Average projection area of a 3U CubeSat\n",
    "    radiation_pressure_coefficient = 1.2\n",
    "    occulting_bodies_dict = dict()\n",
    "    occulting_bodies_dict[\"Sun\"] = [\"Itokawa\"]\n",
    "    vehicle_target_settings = environment_setup.radiation_pressure.cannonball_radiation_target(\n",
    "        reference_area_radiation, radiation_pressure_coefficient, occulting_bodies_dict )\n",
    "\n",
    "    # Add the radiation pressure interface to the body settings\n",
    "    body_settings.get(\"Spacecraft\").radiation_pressure_target_settings = vehicle_target_settings\n",
    "\n",
    "    # Create system of selected bodies\n",
    "    bodies = environment_setup.create_system_of_bodies(body_settings)\n",
    "\n",
    "    return bodies"
   ]
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    "### Acceleration models\n",
    "The `get_acceleration_models()` helper function returns the acceleration models to be used during the astrodynamic simulation. The following accelerations are included:\n",
    "\n",
    "- Gravitational acceleration modelled as a Point Mass from the Sun, Jupiter, Saturn, Mars, and the Earth.\n",
    "- Gravitational acceleration modelled as Spherical Harmonics up to degree and order 4 from Itokawa.\n",
    "- Radiation pressure from the Sun using a simplified cannonball model.\n",
    "\n",
    "This function takes as input the list of bodies that will be propagated, the list of central bodies related to the propagated bodies, and the system of bodies used."
   ]
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    "def get_acceleration_models(bodies_to_propagate, central_bodies, bodies):\n",
    "    # Define accelerations acting on Spacecraft\n",
    "    accelerations_settings_spacecraft = dict(\n",
    "        Sun =     [ propagation_setup.acceleration.radiation_pressure(),\n",
    "                    propagation_setup.acceleration.point_mass_gravity() ],\n",
    "        Itokawa = [ propagation_setup.acceleration.spherical_harmonic_gravity(3, 3) ],\n",
    "        Jupiter = [ propagation_setup.acceleration.point_mass_gravity() ],\n",
    "        Saturn =  [ propagation_setup.acceleration.point_mass_gravity() ],\n",
    "        Mars =    [ propagation_setup.acceleration.point_mass_gravity() ],\n",
    "        Earth =   [ propagation_setup.acceleration.point_mass_gravity() ]\n",
    "    )\n",
    "\n",
    "    # Create global accelerations settings dictionary\n",
    "    acceleration_settings = {\"Spacecraft\": accelerations_settings_spacecraft}\n",
    "\n",
    "    # Create acceleration models\n",
    "    return propagation_setup.create_acceleration_models(\n",
    "        bodies,\n",
    "        acceleration_settings,\n",
    "        bodies_to_propagate,\n",
    "        central_bodies)"
   ]
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    "### Termination settings\n",
    "The termination settings for the simulation are defined by the `get_termination_settings()` helper.\n",
    "\n",
    "Nominally, the simulation terminates when a final epoch is reached. However, this can happen in advance if the\n",
    "spacecraft breaks out of the predefined altitude range.\n",
    "This is defined by the four inputs that this helper function takes, related to the mission timing and the mission altitude range."
   ]
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    "def get_termination_settings(mission_initial_time, \n",
    "                             mission_duration,\n",
    "                             minimum_distance_from_com,\n",
    "                             maximum_distance_from_com):\n",
    "    # Mission duration\n",
    "    time_termination_settings = propagation_setup.propagator.time_termination(\n",
    "        mission_initial_time + mission_duration,\n",
    "        terminate_exactly_on_final_condition=False\n",
    "    )\n",
    "    # Upper altitude\n",
    "    upper_altitude_termination_settings = propagation_setup.propagator.dependent_variable_termination(\n",
    "        dependent_variable_settings=propagation_setup.dependent_variable.relative_distance('Spacecraft', 'Itokawa'),\n",
    "        limit_value=maximum_distance_from_com,\n",
    "        use_as_lower_limit=False,\n",
    "        terminate_exactly_on_final_condition=False\n",
    "    )\n",
    "    # Lower altitude\n",
    "    lower_altitude_termination_settings = propagation_setup.propagator.dependent_variable_termination(\n",
    "        dependent_variable_settings=propagation_setup.dependent_variable.altitude('Spacecraft', 'Itokawa'),\n",
    "        limit_value=minimum_distance_from_com,\n",
    "        use_as_lower_limit=True,\n",
    "        terminate_exactly_on_final_condition=False\n",
    "    )\n",
    "\n",
    "    # Define list of termination settings\n",
    "    termination_settings_list = [time_termination_settings,\n",
    "                                 upper_altitude_termination_settings,\n",
    "                                 lower_altitude_termination_settings]\n",
    "\n",
    "    return propagation_setup.propagator.hybrid_termination(termination_settings_list,\n",
    "                                                           fulfill_single_condition=True)"
   ]
  },
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    "### Dependent variables to save\n",
    "Finally, the `get_dependent_variables_to_save()` helper function returns a pre-defined list of dependent variables to save during the propagation, alongside the propagated state. This function can be expanded, but contains by default only the position of the spacecraft with respect to the Itokawa asteroid expressed in spherical coordinates."
   ]
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    "def get_dependent_variables_to_save():\n",
    "    dependent_variables_to_save = [\n",
    "        propagation_setup.dependent_variable.central_body_fixed_spherical_position(\n",
    "            \"Spacecraft\", \"Itokawa\"\n",
    "        )\n",
    "    ]\n",
    "    return dependent_variables_to_save"
   ]
  },
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    "## Optimisation problem formulation \n",
    "The optimisation problem can now be defined. This has to be done in a class that is compatible to what the PyGMO library can expect from this User Defined Problem (UDP). See [this page](https://esa.github.io/pygmo2/problem.html#pygmo.problem) from the PyGMO documentation as a reference. In this example, this class is called `AsteroidOrbitProblem`.\n",
    "\n",
    "The `AsteroidOrbitProblem.__init__()` method is used to setup the problem. Most importantly, many problem-related objects are saved through it: the system of bodies, the propagator settings, the parameters that will later be used for the termination settings, and the design variables boundaries. The input arguments that refer to functions are defined using a lambda function. While this seems unnecessary, it is **essential**: PyGMO internally pickles its user defined objects and some objects including the ones here cannot be pickled properly without using lambda functions.\n",
    "\n",
    "Then, the `AsteroidOrbitProblem.get_bounds()` function is used by PyGMO to define the search space. This function returns the boundaries of each design variable, as defined in the [pygmo.problem.get_bounds](https://esa.github.io/pygmo2/problem.html#pygmo.problem.get_bounds) documentation.\n",
    "\n",
    "The `AsteroidOrbitProblem.get_nobj()` function is also used by PyGMO. It returns the number of objectives in the problem, in this case 2.\n",
    "\n",
    "The last function used by PyGMO is `AsteroidOrbitProblem.fitness()`. As mentioned in the [pygmo.problem.fitness](https://esa.github.io/pygmo2/problem.html#pygmo.problem.fitness) documentation, PyGMO will input a given set of design variable to this function, that is expected to return a score associated with them. This is thus the cost function of the problem. In this case, this `AsteroidOrbitProblem.fitness()` runs a simulation using TudatPy based on the orbital elements that PyGMO inputs as design variables. Then, the score relative to the two optimisation objectives is computed and returned. Note that, in PyGMO, this fitness function will always be **minimised**. To **maximise** objectives instead, the fitness that is returned will have to be for instance inversed. This is why, because we want to maximise the coverage, the fitness for this objective is computed as the inverse of the mean latitudes. If the mean of the latitudes is high, the coverage is high, which is closer to the optimum. Because this better value is higher than worse values, we return the fitness as the inverse of the mean latitudes.\n",
    "\n",
    "One more function is included, `AsteroidOrbitProblem.get_last_run_dynamics_simulator()`. This allows to get the dynamic simulator of the last simulation that was run in the problem."
   ]
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    "class AsteroidOrbitProblem:\n",
    "    \n",
    "    def __init__(self,\n",
    "                 bodies,\n",
    "                 propagator_settings,\n",
    "                 mission_initial_time,\n",
    "                 mission_duration,\n",
    "                 design_variable_lower_boundaries,\n",
    "                 design_variable_upper_boundaries):\n",
    "        \n",
    "        # Sets input arguments as lambda function attributes\n",
    "        # NOTE: this is done so that the class is \"pickable\", i.e., can be serialized by pygmo\n",
    "        self.bodies_function = lambda: bodies\n",
    "        self.propagator_settings_function = lambda: propagator_settings\n",
    "        \n",
    "        # Initialize empty dynamics simulator\n",
    "        self.dynamics_simulator_function = lambda: None\n",
    "        \n",
    "        # Set other input arguments as regular attributes\n",
    "        self.mission_initial_time = mission_initial_time\n",
    "        self.mission_duration = mission_duration\n",
    "        self.mission_final_time = mission_initial_time + mission_duration\n",
    "        self.design_variable_lower_boundaries = design_variable_lower_boundaries\n",
    "        self.design_variable_upper_boundaries = design_variable_upper_boundaries\n",
    "\n",
    "    def get_bounds(self):\n",
    "        return (list(self.design_variable_lower_boundaries), list(self.design_variable_upper_boundaries))\n",
    "\n",
    "    def get_nobj(self):\n",
    "        return 2\n",
    "\n",
    "    def fitness(self,\n",
    "                orbit_parameters):\n",
    "        # Retrieves system of bodies\n",
    "        current_bodies = self.bodies_function()\n",
    "        \n",
    "        # Retrieves Itokawa gravitational parameter\n",
    "        itokawa_gravitational_parameter = current_bodies.get(\"Itokawa\").gravitational_parameter\n",
    "        \n",
    "        # Reset the initial state from the design variable vector\n",
    "        new_initial_state = element_conversion.keplerian_to_cartesian_elementwise(\n",
    "            gravitational_parameter=itokawa_gravitational_parameter,\n",
    "            semi_major_axis=orbit_parameters[0],\n",
    "            eccentricity=orbit_parameters[1],\n",
    "            inclination=np.deg2rad(orbit_parameters[2]),\n",
    "            argument_of_periapsis=np.deg2rad(235.7),\n",
    "            longitude_of_ascending_node=np.deg2rad(orbit_parameters[3]),\n",
    "            true_anomaly=np.deg2rad(139.87))\n",
    "        \n",
    "        # Retrieves propagator settings object\n",
    "        propagator_settings = self.propagator_settings_function()\n",
    "        \n",
    "        # Reset the initial state\n",
    "        propagator_settings.initial_states = new_initial_state\n",
    "\n",
    "        # Propagate orbit\n",
    "        dynamics_simulator = simulator.create_dynamics_simulator(current_bodies,\n",
    "                                                                        propagator_settings)\n",
    "        # Update dynamics simulator function\n",
    "        self.dynamics_simulator_function = lambda: dynamics_simulator\n",
    "\n",
    "        # Retrieve dependent variable history\n",
    "        dependent_variables = dynamics_simulator.propagation_results.dependent_variable_history\n",
    "        dependent_variables_list = np.vstack(list(dependent_variables.values()))\n",
    "        \n",
    "        # Retrieve distance\n",
    "        distance = dependent_variables_list[:, 0]\n",
    "        # Retrieve latitude\n",
    "        latitudes = dependent_variables_list[:, 1]\n",
    "        \n",
    "        # Compute mean latitude\n",
    "        mean_latitude = np.mean(np.absolute(latitudes))\n",
    "        # Computes fitness as mean latitude\n",
    "        current_fitness = 1.0 / mean_latitude\n",
    "\n",
    "        # Exaggerate fitness value if the spacecraft has broken out of the selected distance range\n",
    "        current_penalty = 0.0\n",
    "        if (max(dynamics_simulator.propagation_results.dependent_variable_history.keys()) < self.mission_final_time):\n",
    "            current_penalty = 1.0E2\n",
    "\n",
    "        return [current_fitness + current_penalty, np.mean(distance) + current_penalty * 1.0E3]\n",
    "\n",
    "    def get_last_run_dynamics_simulator(self):\n",
    "        return self.dynamics_simulator_function()"
   ]
  },
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   "source": [
    "### Setup orbital simulation\n",
    "Before running the optimisation, some aspect of the orbital simulation around Itokawa still need to be setup.\n",
    "Most importantly, the simulation bodies, acceleration models, integrator settings, and propagator settings, all have to be defined. To do so, the helpers that were defined above are used."
   ]
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    "#### Simulation settings\n",
    "The simulation settings are first defined.\n",
    "\n",
    "The SPICE kernels are loaded, so that we can access the gravitational parameter of the Sun in the `create_simulation_bodies()` function.\n",
    "\n",
    "The definition of the termination parameters follows, with a maximum mission duration of 5 Earth days. The altitude range above Itokawa is also defined between 150 meters and 5 km.\n",
    "\n",
    "Follows the definition of the design variable range, that PyGMO will use during the optimisation. This range is as follows:\n",
    "\n",
    "- Initial semi-major axis between 300 and 2000 meters.\n",
    "- Initial eccentricity between 0 and 0.3.\n",
    "- Initial inclination between 0 and 180 deg.\n",
    "- Initial longitude of the ascending node between 0 and 360 deg.\n",
    " \n",
    "The system of bodies is then setup using the `create_simulation_bodies()` helper, and a radius for Itokawa of 161.915 meters.\n",
    "\n",
    "Finally, the acceleration models are setup using the `get_acceleration_models()` helper."
   ]
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    "# Load spice kernels\n",
    "spice.load_standard_kernels()\n",
    "\n",
    "# Set simulation start and end epochs\n",
    "mission_initial_time = 0.0\n",
    "mission_duration = 5.0 * constants.JULIAN_DAY\n",
    "\n",
    "# Define Itokawa radius\n",
    "itokawa_radius = 161.915\n",
    "\n",
    "# Set altitude termination conditions\n",
    "minimum_distance_from_com = 150.0 + itokawa_radius\n",
    "maximum_distance_from_com = 5.0E3 + itokawa_radius\n",
    "\n",
    "# Set boundaries on the design variables\n",
    "design_variable_lb = (300, 0.0, 0.0, 0.0)\n",
    "design_variable_ub = (2000, 0.3, 180, 360)\n",
    "\n",
    "# Create simulation bodies\n",
    "bodies = create_simulation_bodies(itokawa_radius)\n",
    "\n",
    "# Define bodies to propagate and central bodies\n",
    "bodies_to_propagate = [\"Spacecraft\"]\n",
    "central_bodies = [\"Itokawa\"]\n",
    "\n",
    "# Create acceleration models\n",
    "acceleration_models = get_acceleration_models(bodies_to_propagate, central_bodies, bodies)"
   ]
  },
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    "#### Dependent variables, termination settings, and orbit parameters\n",
    "To define the propagator settings in the subsequent sections, we first call the `get_dependent_variables_to_save()` and `get_termination_settings()` helpers to define the dependent variables and termination settings.\n",
    "\n",
    "The `orbit_parameters` list is defined from a `final_population.dat` file from the optimization. These values are not crucial, but do help for a 'relatively' stable orbit."
   ]
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    "# Define list of dependent variables to save\n",
    "dependent_variables_to_save = get_dependent_variables_to_save()\n",
    "\n",
    "# Create propagation settings\n",
    "termination_settings = get_termination_settings(\n",
    "    mission_initial_time, mission_duration, minimum_distance_from_com, maximum_distance_from_com)\n",
    "\n",
    "orbit_parameters = [1.20940330e+03, 2.61526215e-01, 7.53126558e+01, 2.60280587e+02]"
   ]
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    "#### Integrator and Propagator settings\n",
    "Let's now define the integrator settings. In this case, a variable step integration scheme is used, with the followings:\n",
    "\n",
    "- RKF7(8) coefficient set.\n",
    "- Initial time step of 1 sec.\n",
    "- Minimum and maximum time steps of 1E-6 sec and 1 Earth day.\n",
    "- Relative and absolute error tolerances of 1E-8.\n",
    "\n",
    "Then, we define pure translational propagation settings with a Cowell propagator. The initial state is set to 0 for both the position and the velocity. This is because the initial state will later be changed in the `AsteroidOrbitProblem.fitness()` function during the optimisation."
   ]
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    "# Create numerical integrator settings\n",
    "integrator_settings = propagation_setup.integrator.runge_kutta_variable_step_size(\n",
    "    initial_time_step=1.0,\n",
    "    coefficient_set=propagation_setup.integrator.CoefficientSets.rkf_78,\n",
    "    minimum_step_size=1.0E-6,\n",
    "    maximum_step_size=constants.JULIAN_DAY,\n",
    "    relative_error_tolerance=1.0E-8,\n",
    "    absolute_error_tolerance=1.0E-8)\n",
    "\n",
    "# Get current propagator, and define translational state propagation settings\n",
    "propagator = propagation_setup.propagator.cowell\n",
    "# Define propagation settings\n",
    "initial_state = np.zeros(6)\n",
    "propagator_settings = propagation_setup.propagator.translational(central_bodies,\n",
    "                                                                         acceleration_models,\n",
    "                                                                         bodies_to_propagate,\n",
    "                                                                         initial_state,\n",
    "                                                                         mission_initial_time,\n",
    "                                                                         integrator_settings,\n",
    "                                                                         termination_settings,\n",
    "                                                                         propagator,\n",
    "                                                                         dependent_variables_to_save)"
   ]
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   "source": [
    "## Propagating Orbit\n",
    "\n",
    "To finalise this part of the example, a simple orbit is propagated to show the plausibility of the simulation. The PyGMO compatible problem class is created, after which the fitness function is called which creates the dynamics simulator and subsequently returns the data that can be used for post-processing.\n",
    "\n",
    "The 4 design variables are:\n",
    "\n",
    "- initial values of the semi-major axis.\n",
    "- initial eccentricity.\n",
    "- initial inclination.\n",
    "- initial longitude of the ascending node."
   ]
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   "source": [
    "orbitProblem = AsteroidOrbitProblem(bodies,\n",
    "                                    propagator_settings,\n",
    "                                    mission_initial_time,\n",
    "                                    mission_duration,\n",
    "                                    design_variable_lb,\n",
    "                                    design_variable_ub)\n",
    "design_variable_vector = np.array([1.20940330e+03, 2.61526215e-01, 7.53126558e+01, 2.60280587e+02])\n",
    "orbitProblem.fitness(design_variable_vector)"
   ]
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    "### Visualizing orbit\n",
    "With a little bit of post-processing, the orbit can be plotted. You can see that with only 2 full orbits that the trajectory is already very perturbed. This has to do with all the perturbations that are in the model, which poses a challenge for the optimization in the next two parts of the example."
   ]
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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "state_history = orbitProblem.get_last_run_dynamics_simulator().propagation_results.state_history\n",
    "state_history = np.vstack(list(state_history.values()))\n",
    "state_history = np.vstack(state_history)\n",
    "\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "ax = fig.add_subplot(1, 1, 1, projection='3d')\n",
    "\n",
    "u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]\n",
    "x = itokawa_radius * np.cos(u)*np.sin(v)\n",
    "y = itokawa_radius * np.sin(u)*np.sin(v)\n",
    "z = itokawa_radius * np.cos(v)\n",
    "ax.plot_wireframe(x, y, z, color=\"red\")\n",
    "\n",
    "ax.plot(state_history[:, 0], state_history[:, 1], state_history[:, 2])\n",
    "ax.set_xlabel('X [m]')\n",
    "ax.set_ylabel('Y [m]')\n",
    "ax.set_zlabel('Z [m]')\n",
    "ax.set_xlim([-1000,1000])\n",
    "ax.set_ylim([-1000,1000])\n",
    "ax.set_zlim([-1000,1000])\n",
    "ax.grid()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tudat-examples",
   "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
}