{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5661202a-d735-425b-8b13-3e7e38266e65",
   "metadata": {},
   "source": [
    "# Parameter Estimation with DELFI-C3\n",
    "## Objectives\n",
    "This example will guide you through the set-up of an orbit estimation routine, which usually comprises the estimation of the covariance, as well as the estimation of the initial parameters. In this example we will focus on the latter, and you'll learn: \n",
    " \n",
    "* how to set up and perform the **full estimation** of a spacecraft's initial state, drag coefficient, and radiation pressure coefficient\n",
    " \n",
    "For the **covariance analysis** over the course of the spacecraft's orbit see the [Delfi-C3 Covariance Analysis example](covariance_estimated_parameters.ipynb).\n",
    " \n",
    "If you have already followed the covariance example, you might want to **skip the first part of this example** dealing with the setup of all relevant (environment, propagation, and estimation) modules, and dive straight in to the full estimation of all chosen parameters.\n",
    " \n",
    "To simulate the orbit of a spacecraft, we will fall back and reiterate on all aspects of orbit propagation that are important within the scope of orbit estimation. Further, we will highlight all relevant features of modelling a tracking station on Earth. Using this station, we will simulate a tracking routine of the spacecraft using a series of open-loop Doppler range-rate measurements at 1 mm/s every 60 seconds. To assure an uninterrupted line-of-sight between the station and the spacecraft, a minimum elevation angle of more than 15 degrees above the horizon - as seen from the station - will be imposed as constraint on the simulation of observations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58c530e7-a396-49af-8136-4a5cfca02e29",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Import statements\n",
    "Typically - in the most pythonic way - all required modules are imported 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. Most notably, the `estimation`, `dynamics.parameters_setup`, and `observations` modules will be used and demonstrated within this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e2109e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load required standard modules\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4b1ccb21",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Load required tudatpy modules\n",
    "from tudatpy import constants\n",
    "from tudatpy.interface import spice\n",
    "from tudatpy import dynamics\n",
    "from tudatpy.dynamics import environment, environment_setup\n",
    "from tudatpy.dynamics import propagation_setup, parameters_setup, simulator\n",
    "from tudatpy import estimation\n",
    "from tudatpy.estimation import observable_models_setup, observable_models, observations_setup, observations, estimation_analysis\n",
    "from tudatpy.astro.time_representation import DateTime\n",
    "from tudatpy.astro import element_conversion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a6cf2de-a838-4d02-90d8-f39f8eb1494d",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Configuration\n",
    "First, NAIF's `SPICE` kernels are loaded, to make the positions of various bodies such as the Earth, the Sun, or the Moon known to `tudatpy`.\n",
    " \n",
    "Subsequently, the start and end epoch of the simulation are defined. Note that using `tudatpy`, the times are generally specified in seconds since J2000. Hence, setting the start epoch to `0` corresponds to the 1st of January 2000. The end epoch specifies a total duration of the simulation of three days.\n",
    " \n",
    "For more information on J2000 and the conversion between different temporal reference frames, please refer to the [API documentation](https://py.api.tudat.space/en/latest/time_representation.html) of the `time_representation` module.\n",
    "\n",
    "NOTE: \n",
    "To avoid issues with the interpolator, we define a time buffer and add we subtract it and add it to the simulation start and end epoch, respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b34ed1f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load spice kernels\n",
    "spice.load_standard_kernels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "baedd9b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set simulation start and end epochs\n",
    "time_buffer = 10\n",
    "simulation_start_epoch = DateTime(2000, 1, 1).epoch() - time_buffer\n",
    "simulation_end_epoch   = DateTime(2000, 1, 4).epoch() + time_buffer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88b59af3-7322-4499-9e34-43279524f3b4",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Set up the environment\n",
    "We will now create and define the settings for the environment of our simulation. In particular, this covers the creation of (celestial) bodies, vehicle(s), and environment interfaces.\n",
    " \n",
    "### Create the main bodies\n",
    "To create the systems of bodies for the simulation, one first has to define a list of strings of all bodies that are to be included. Note that the default body settings (such as atmosphere, body shape, rotation model) are taken from the `SPICE` kernel.\n",
    " \n",
    "These settings, however, can be adjusted. Please refer to the [Available Environment Models](https://docs.tudat.space/en/latest/_src_user_guide/state_propagation/environment_setup/environment_models.html#available-model-types) in the user guide for more details.\n",
    " \n",
    "Finally, the system of bodies is created using the settings. This system of bodies is stored into the variable `bodies`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aadda7d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create default body settings for \"Sun\", \"Earth\", \"Moon\", \"Mars\", and \"Venus\"\n",
    "bodies_to_create = [\"Sun\", \"Earth\", \"Moon\", \"Mars\", \"Venus\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a614fb86",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Create default body settings for bodies_to_create, with \"Earth\"/\"J2000\" as the global frame origin and orientation\n",
    "global_frame_origin = \"Earth\"\n",
    "global_frame_orientation = \"J2000\"\n",
    "body_settings = environment_setup.get_default_body_settings(\n",
    "    bodies_to_create, global_frame_origin, global_frame_orientation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca04e4b-3d0f-43f4-8b68-03bc55dad6a3",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Create the vehicle and its environment interface\n",
    "We will now create the satellite - called Delfi-C3 - for which an orbit will be simulated. Using an `empty_body` as a blank canvas for the satellite, we define mass of 2.2 kg, a reference area (used both for aerodynamic and radiation pressure) of 4m$^2$, and a aerodynamic drag coefficient of 1.2. Idem for the radiation pressure coefficient. Finally, when setting up the radiation pressure interface, the Earth is set as a body that can occult the radiation emitted by the Sun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7a62fcf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create empty body settings for the satellite\n",
    "body_settings.add_empty_settings(\"Delfi-C3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f2adc93b",
   "metadata": {},
   "outputs": [],
   "source": [
    "body_settings.get(\"Delfi-C3\").constant_mass = 2.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "946300f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create aerodynamic coefficient interface settings\n",
    "reference_area_drag = (4*0.3*0.1+2*0.1*0.1)/4  # Average projection area of a 3U CubeSat\n",
    "drag_coefficient = 1.2\n",
    "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n",
    "    reference_area_drag, [drag_coefficient, 0.0, 0.0]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "94ae971f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the aerodynamic interface to the body settings\n",
    "body_settings.get(\"Delfi-C3\").aerodynamic_coefficient_settings = aero_coefficient_settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "487accd3",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# 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\"] = [\"Earth\"]\n",
    "vehicle_target_settings = environment_setup.radiation_pressure.cannonball_radiation_target(\n",
    "    reference_area_radiation, radiation_pressure_coefficient, occulting_bodies_dict )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "50b585f0",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Add the radiation pressure interface to the body settings\n",
    "body_settings.get(\"Delfi-C3\").radiation_pressure_target_settings = vehicle_target_settings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31634d0d-a2db-42bb-9fc8-4b158529f297",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Finally, the system of bodies is created using the settings. This system of bodies is stored into the variable `bodies`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4c91f38e",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Create system of bodies\n",
    "bodies = environment_setup.create_system_of_bodies(body_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3dde466-5a33-48e6-a5cc-550f8d2a1505",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Set up the propagation\n",
    "Having the environment created, we will define the settings for the propagation of the spacecraft. First, we have to define the body to be propagated - here, the spacecraft - and the central body - here, Earth - with respect to which the state of the propagated body is defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "81c78df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define bodies that are propagated\n",
    "bodies_to_propagate = [\"Delfi-C3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c55a987f",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Define central bodies of propagation\n",
    "central_bodies = [\"Earth\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a735abe5-5bdd-46f2-8397-81fed28b6f4a",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Create the acceleration model\n",
    "Subsequently, all accelerations (and there settings) that act on `Delfi-C3` have to be defined. In particular, we will consider:\n",
    "\n",
    "* Gravitational acceleration using a spherical harmonic approximation up to 8th degree and order for Earth.\n",
    "* Aerodynamic acceleration for Earth.\n",
    "* Gravitational acceleration using a simple point mass model for:\n",
    "\n",
    "  - The Sun\n",
    "  - The Moon\n",
    "  - Mars\n",
    "\n",
    "* Radiation pressure experienced by the spacecraft - shape-wise approximated as a spherical cannonball - due to the Sun.\n",
    "\n",
    "The defined acceleration settings are then applied to `Delfi-C3` by means of a dictionary, which is finally used as input to the propagation setup to create the acceleration models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6e3a7da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the accelerations acting on Delfi-C3\n",
    "accelerations_settings_delfi_c3 = dict(\n",
    "    Sun=[\n",
    "        propagation_setup.acceleration.radiation_pressure(),\n",
    "        propagation_setup.acceleration.point_mass_gravity()\n",
    "    ],\n",
    "    Mars=[\n",
    "        propagation_setup.acceleration.point_mass_gravity()\n",
    "    ],\n",
    "    Moon=[\n",
    "        propagation_setup.acceleration.point_mass_gravity()\n",
    "    ],\n",
    "    Earth=[\n",
    "        propagation_setup.acceleration.spherical_harmonic_gravity(8, 8),\n",
    "        propagation_setup.acceleration.aerodynamic()\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "68a9217d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create global accelerations dictionary\n",
    "acceleration_settings = {\"Delfi-C3\": accelerations_settings_delfi_c3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "35f6b381",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Create acceleration models\n",
    "acceleration_models = propagation_setup.create_acceleration_models(\n",
    "    bodies,\n",
    "    acceleration_settings,\n",
    "    bodies_to_propagate,\n",
    "    central_bodies)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b62eaa6b-5b33-4d4e-8840-69dbee593ce8",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Define the initial state\n",
    "Realise that the initial state of the spacecraft always has to be provided as a cartesian state - i.e. in the form of a list with the first three elements representing the initial position, and the three remaining elements representing the initial velocity.\n",
    "\n",
    "Within this example, we will retrieve the initial state of Delfi-C3 using its Two-Line-Elements (TLE) the date of its launch (April the 28th, 2008). The TLE strings are obtained from [space-track.org](https://www.space-track.org)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e381be97",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Retrieve the initial state of Delfi-C3 using Two-Line-Elements (TLEs)\n",
    "delfi_tle = environment.Tle(\n",
    "    \"1 32789U 07021G   08119.60740078 -.00000054  00000-0  00000+0 0  9999\",\n",
    "    \"2 32789 098.0082 179.6267 0015321 307.2977 051.0656 14.81417433    68\",\n",
    ")\n",
    "delfi_ephemeris = environment.TleEphemeris(\"Earth\", \"J2000\", delfi_tle, False)\n",
    "initial_state = delfi_ephemeris.cartesian_state( simulation_start_epoch )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421b088d-bc86-4fcc-929d-8ef5f490bd92",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Create the integrator settings\n",
    "For the problem at hand, we will use an RKF78 integrator with a fixed step-size of 60 seconds. This can be achieved by tweaking the implemented RKF78 integrator with variable step-size such that both the minimum and maximum step-size is equal to 60 seconds and a tolerance of 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "71a59f8a",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Create numerical integrator settings\n",
    "integrator_settings = propagation_setup.integrator.\\\n",
    "    runge_kutta_fixed_step_size(initial_time_step=60.0,\n",
    "                                coefficient_set=propagation_setup.integrator.CoefficientSets.rkdp_87)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c1b7ab6-43d4-4069-bc04-2d66b241e985",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Create the propagator settings\n",
    "By combining all of the above-defined settings we can define the settings for the propagator to simulate the orbit of `Delfi-C3` around Earth. A termination condition needs to be defined so that the propagation stops as soon as the specified end epoch is reached. Finally, the translational propagator's settings are created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1da80a50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create termination settings\n",
    "termination_condition = propagation_setup.propagator.time_termination(simulation_end_epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c1860975",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Create propagation settings\n",
    "propagator_settings = propagation_setup.propagator.translational(\n",
    "    central_bodies,\n",
    "    acceleration_models,\n",
    "    bodies_to_propagate,\n",
    "    initial_state,\n",
    "    simulation_start_epoch,\n",
    "    integrator_settings,\n",
    "    termination_condition\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76cb64fc-f438-4dce-aca9-bbd54bf72411",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Set up the observations\n",
    "Having set the underlying dynamical model of the simulated orbit, we can define the observational model. Generally, this entails the addition all required ground stations, the definition of the observation links and types, as well as the precise simulation settings.\n",
    "\n",
    "### Add a ground station\n",
    "Trivially, the simulation of observations requires the extension of the current environment by at least one observer - a ground station. For this example, we will model a single ground station located in Delft, Netherlands, at an altitude of 0m, 52.00667°N, 4.35556°E.\n",
    "\n",
    "More information on how to use the `add_ground_station()` function can be found in the respective [API documentation](https://py.api.tudat.space/en/latest/environment_setup.html#tudatpy.dynamics.environment_setup.add_ground_station)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "237254ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the position of the ground station on Earth\n",
    "station_altitude = 0.0\n",
    "delft_latitude = np.deg2rad(52.00667)\n",
    "delft_longitude = np.deg2rad(4.35556)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "390bd11e",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Add the ground station to the environment\n",
    "environment_setup.add_ground_station(\n",
    "    bodies.get_body(\"Earth\"),\n",
    "    \"TrackingStation\",\n",
    "    [station_altitude, delft_latitude, delft_longitude],\n",
    "    element_conversion.geodetic_position_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbbadcef-dae3-4781-896b-498116cf3c31",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Define Observation Links and Types\n",
    "To establish the links between our ground station and `Delfi-C3`, we will make use of the [observation module](https://py.api.tudat.space/en/latest/observation.html#observation) of tudat. During the link definition, each member is assigned a certain function within the link, for instance as \"transmitter\", \"receiver\", or \"reflector\". Once two (or more) members are connected to a link, they can be used to simulate observations along this particular link. The precise type of observation made along this link - e.g., range, range-rate, angular position, etc. - is then determined by the chosen observable type.\n",
    "\n",
    "To fully define an observation model for a given link, we have to create a list of the observation model settings of all desired observable types and their associated links. This list will later be used as input to the actual estimator object.\n",
    "\n",
    "Each observable type has its own function for creating observation model settings - in this example we will use the `one_way_doppler_instantaneous()` function to model a series of one-way open-loop (i.e. instantaneous) Doppler observations. Realise that the individual observation model settings can also include corrective models or define biases for more advanced use-cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5d31a2bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the uplink link ends for one-way observable\n",
    "link_ends = dict()\n",
    "link_ends[observable_models_setup.links.receiver] = observable_models_setup.links.body_reference_point_link_end_id(\"Earth\", \"TrackingStation\")\n",
    "link_ends[observable_models_setup.links.transmitter] = observable_models_setup.links.body_origin_link_end_id(\"Delfi-C3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3bf3ffed",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Create observation settings for each link/observable\n",
    "link_definition = observable_models_setup.links.LinkDefinition(link_ends)\n",
    "observation_settings_list = [observable_models_setup.model_settings.one_way_doppler_instantaneous(link_definition)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e6ea8f6-3522-4522-a938-116e60d8869e",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Define Observation Simulation Settings\n",
    "We now have to define the times at which observations are to be simulated. To this end, we will define the settings for the simulation of the individual observations from the previously defined observation models. Bear in mind that these observation simulation settings are not to be confused with the ones to be used when setting up the estimator object, as done just above.\n",
    "\n",
    "Finally, for each observation model, the observation simulation settings set the times at which observations are simulated and defines the viability criteria and noise of the observation.\n",
    "\n",
    "Note that the actual simulation of the observations requires `Observation Simulators`, which are created automatically by the `Estimator` object. Hence, one cannot simulate observations before the creation of an estimator.\n",
    "\n",
    "Also, we recall we had applied a time buffer to our start and end epoch, so it is now the time to remove it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "edfabcab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define observation simulation times for each link (separated by steps of 1 minute)\n",
    "observation_times = np.arange(simulation_start_epoch + time_buffer, simulation_end_epoch - time_buffer, 60.0)\n",
    "observation_simulation_settings = observations_setup.observations_simulation_settings.tabulated_simulation_settings(\n",
    "    observable_models_setup.model_settings.one_way_instantaneous_doppler_type,\n",
    "    link_definition,\n",
    "    observation_times\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ec0595f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add noise levels of roughly 3.3E-12 [s/m] and add this as Gaussian noise to the observation\n",
    "noise_level = 1.0E-3\n",
    "observations_setup.random_noise.add_gaussian_noise_to_observable(\n",
    "    [observation_simulation_settings],\n",
    "    noise_level,\n",
    "    observable_models_setup.model_settings.one_way_instantaneous_doppler_type\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cc3cb908",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Create viability settings\n",
    "viability_setting = observations_setup.viability.elevation_angle_viability([\"Earth\", \"TrackingStation\"], np.deg2rad(15))\n",
    "observations_setup.viability.add_viability_check_to_all(\n",
    "    [observation_simulation_settings],\n",
    "    [viability_setting]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "443cfbc6-51f1-43d3-a493-2ac41039c712",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Set up the estimation\n",
    "Using the defined models for the environment, the propagator, and the observations, we can finally set the actual presentation up. In particular, this consists of defining all parameter that should be estimated, the creation of the estimator, and the simulation of the observations.\n",
    "\n",
    "### Defining the parameters to estimate\n",
    "For this example estimation, we decided to estimate the initial state of `Delfi-C3`, its drag coefficient, and the gravitational parameter of Earth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "97c59b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup parameters settings to propagate the state transition matrix\n",
    "parameter_settings = parameters_setup.initial_states(propagator_settings, bodies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "87ba68de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add estimated parameters to the sensitivity matrix that will be propagated\n",
    "parameter_settings.append(parameters_setup.gravitational_parameter(\"Earth\"))\n",
    "parameter_settings.append(parameters_setup.constant_drag_coefficient(\"Delfi-C3\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fc43913f",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Create the parameters that will be estimated\n",
    "parameters_to_estimate = parameters_setup.create_parameter_set(parameter_settings, bodies)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d35a5b0f-05c9-4807-bc00-35379c950904",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Creating the Estimator object\n",
    "Ultimately, the `Estimator` object consolidates all relevant information required for the estimation of any system parameter:\n",
    "\n",
    "* the environment (bodies)\n",
    "* the parameter set (parameters_to_estimate)\n",
    "* observation models (observation_settings_list)\n",
    "* dynamical, numerical, and integrator setup (propagator_settings)\n",
    "\n",
    "Underneath its hood, upon creation, the estimator automatically takes care of setting up the relevant Observation Simulator and Variational Equations which will subsequently be required for the simulation of observations and the estimation of parameters, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "673eedf0",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Create the estimator\n",
    "estimator = estimation_analysis.Estimator(\n",
    "    bodies,\n",
    "    parameters_to_estimate,\n",
    "    observation_settings_list,\n",
    "    propagator_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d09ef07c-bf42-45d9-b0ff-491e607a2213",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Perform the observations simulation\n",
    "Using the created `Estimator` object, we can perform the simulation of observations by calling its `simulate_observations()` method, see the [API reference](https://py.api.tudat.space/en/latest/estimation/observations_setup/observations_wrapper.html#tudatpy.estimation.observations_setup.observations_wrapper.simulate_observations). Note that to know about the time settings for the individual types of observations, this function makes use of the earlier defined observation simulation settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "860dbd16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulate required observations\n",
    "simulated_observations = observations_setup.observations_wrapper.simulate_observations(\n",
    "    [observation_simulation_settings],\n",
    "    estimator.observation_simulators,\n",
    "    bodies)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2616cee-f2fd-4d00-b14d-a964aab7b779",
   "metadata": {},
   "source": [
    "## Perform the estimation\n",
    "Having simulated the observations and created the `Estimator` object - containing the **variational equations** for the parameters to estimate - we have defined everything to conduct the actual estimation. Realise that up to this point, we have not yet specified whether we want to perform a **covariance analysis** or the **full estimation** of all parameters. It should be stressed that the general setup for either path to be followed is entirely **identical**.\n",
    "\n",
    "### Set up the inversion\n",
    "To set up the inversion of the problem, we collect all relevant inputs in the form of a estimation input object and define some basic settings of the inversion. Most crucially, this is the step where we can account for different **weights** - if any - of the different observations in the `ObservationCollection`, to give the estimator knowledge about the quality of the individual types of observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e5e0c531",
   "metadata": {},
   "outputs": [],
   "source": [
    "simulated_observations.set_constant_weight(noise_level ** -2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4113e805",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the true parameters to later analyse the error\n",
    "truth_parameters = parameters_to_estimate.parameter_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5e8ee36d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perturb the initial state estimate from the truth (10 m in position; 0.1 m/s in velocity)\n",
    "perturbed_parameters = truth_parameters.copy( )\n",
    "for i in range(3):\n",
    "    perturbed_parameters[i] += 10.0\n",
    "    perturbed_parameters[i+3] += 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "08b28924",
   "metadata": {},
   "outputs": [],
   "source": [
    "perturbed_parameters[6] += 1e5\n",
    "perturbed_parameters[7] += 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0464c9c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters_to_estimate.parameter_vector = perturbed_parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "64171e8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create input object for the estimation\n",
    "convergence_checker = estimation_analysis.estimation_convergence_checker(maximum_iterations=4)\n",
    "estimation_input = estimation_analysis.EstimationInput(\n",
    "    simulated_observations,\n",
    "    convergence_checker=convergence_checker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3954a803",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Set methodological options\n",
    "estimation_input.define_estimation_settings(\n",
    "    reintegrate_variational_equations=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e54224e7-c785-4e75-8d12-cda00f7d099c",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Estimate the individual parameters\n",
    "Using the just defined inputs, we can ultimately run the estimation of the selected parameters. After a pre-defined maximum number of iterations (the default value is set to a total of five), the least squares estimator - ideally having reached a sufficient level of convergence - will stop with the process of iterating over the problem and updating the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b2e73616",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating residuals and partials 59\n",
      "Current residual: 14.0605\n",
      "Parameter update   -25.6766    -13.3906    -16.7706 0.000114141  -0.0193511  -0.0245252 1.91636e+09 -0.00859325\n",
      "Calculating residuals and partials 59\n",
      "Current residual: 0.0279783\n",
      "Parameter update     15.7989      3.37573      6.61665   -0.0101477   0.00969979    0.0146291 -1.90335e+09  -0.00153857\n",
      "Calculating residuals and partials 59\n",
      "Current residual: 0.000793695\n",
      "Parameter update 8.80432e-05 -6.22713e-05  5.52397e-06  1.31626e-07 -2.19443e-07  -1.3405e-08     -6611.18 -1.74629e-07\n",
      "Calculating residuals and partials 59\n",
      "Current residual: 0.000778498\n",
      "Maximum number of iterations reached\n",
      "Parameter update-8.21334e-07 -3.46212e-07  2.48737e-07 -4.19638e-10 -4.19364e-11 -1.03043e-09      16.4804 -6.39593e-09\n",
      "Final residual: 0.000778498\n"
     ]
    }
   ],
   "source": [
    "# Perform the estimation\n",
    "estimation_output = estimator.perform_estimation(estimation_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e29bd859-e6da-4b34-835b-88209dc00916",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## True Errors, Formal Errors\n",
    "\n",
    "Since we have now estimated the actual parameters - unlike when only getting the initial covariance matrix over the course of the orbit, as done in [Delfi-C3 Covariance Analysis example](covariance_estimated_parameters.ipynb) - we are able to qualitatively compare the goodness-of-fit of the found parameters with the known ground truth ones. \n",
    "\n",
    "The common way to perform this comparison is by following these 3 simple steps:\n",
    "\n",
    "1) Compute the formal errors. These are the **diagonal entries of the covariance matrix (variances)**\n",
    "2) Take the difference between the true parameters (known) and the estimated ones (estimated)\n",
    "3) Take the ratio between the results obtained in 2) and 1). This is the true-to-formal-error ratio.\n",
    "\n",
    "Doing this might help highlight that the formal errors one gets as the result of a covariance analysis tend to sketch a too optimistic version of reality - typically, (all or some of) the true errors are by a certain factor larger than the formal ones. In this example, we see that the 3rd, 5th and 8th parameters estimates might be too optimistic, as their true-to-formal ratio is bigger than one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0ab7078c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perturb the initial state estimate from the truth (10 m in position; 0.1 m/s in velocity)\n",
    "# Save the true parameters to later analyse the error\n",
    "truth_parameters = parameters_to_estimate.parameter_vector\n",
    "perturbed_parameters = truth_parameters.copy( )\n",
    "for i in range(3):\n",
    "    perturbed_parameters[i] += 10.0\n",
    "    perturbed_parameters[i+3] += 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "25720bfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "perturbed_parameters[6] += 1e5\n",
    "perturbed_parameters[7] += 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3fe1aec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters_to_estimate.parameter_vector = perturbed_parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fd909aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create input object for the estimation\n",
    "convergence_checker = estimation_analysis.estimation_convergence_checker(maximum_iterations=4)\n",
    "estimation_input = estimation_analysis.EstimationInput(\n",
    "    simulated_observations,\n",
    "    convergence_checker=convergence_checker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9db4f427",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set methodological options\n",
    "estimation_input.define_estimation_settings(\n",
    "    reintegrate_variational_equations=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2802ac77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating residuals and partials 59\n",
      "Formal Errors:\n",
      "\n",
      "[2.01309420e-01 1.57844271e-01 1.07627792e-01 1.24859080e-04\n",
      " 1.66318994e-04 1.55529465e-04 1.24090054e+07 1.09842298e-04]\n",
      "\n",
      "Current residual: 14.0603\n",
      "Parameter update   -25.7996    -13.3759    -16.6165  0.00014703  -0.0196994  -0.0246295 1.90325e+09 -0.00846097\n",
      "Calculating residuals and partials 59\n",
      "Current residual: 0.0279782\n",
      "Parameter update     15.7995      3.37595      6.61647   -0.0101472   0.00969959    0.0146295 -1.90335e+09  -0.00153885\n",
      "Calculating residuals and partials 59\n",
      "Current residual: 0.000793698\n",
      "Parameter update  8.9458e-05 -6.12395e-05  3.45307e-06  1.32837e-07 -2.19374e-07 -1.14678e-08     -6483.44 -1.74178e-07\n",
      "Calculating residuals and partials 59\n",
      "Current residual: 0.000778498\n",
      "Maximum number of iterations reached\n",
      "Parameter update-7.71114e-07 -6.10572e-07  1.08048e-06  -7.1014e-10  6.72426e-12 -1.08527e-09      -66.854  -6.4455e-10\n",
      "Final residual: 0.000778498\n"
     ]
    }
   ],
   "source": [
    "# Print the formal errors (diagonal entries of the covariance matrix) and the true-to-formal-errors.\n",
    "# Perform the estimation\n",
    "estimation_output = estimator.perform_estimation(estimation_input)\n",
    "formal_errors = estimation_output.formal_errors #formal errors\n",
    "print(f'Formal Errors:\\n\\n{formal_errors}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a6df0722",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Errors:\n",
      "\n",
      "[-7.07339495e-07 -1.57247996e-07  1.83470547e-07 -2.26464181e-10\n",
      " -1.58706825e-10 -7.50787876e-10  1.71250000e+01 -4.80411821e-09]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "true_errors = truth_parameters - parameters_to_estimate.parameter_vector #true_parameters - estimated_parameters = true error\n",
    "print(f'True Errors:\\n\\n{true_errors}\\n') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "244f2ab4",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True-To-Formal-Error Ratio:\n",
      "\n",
      "[-3.51369297e-06 -9.96222383e-07  1.70467631e-06 -1.81375821e-06\n",
      " -9.54231515e-07 -4.82730315e-06  1.38004614e-06 -4.37365050e-05]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "true_to_formal_ratio = true_errors/formal_errors #true-to-formal-error ratio\n",
    "print(f'True-To-Formal-Error Ratio:\\n\\n{true_to_formal_ratio}\\n') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4ff9f81-4b85-4496-974a-099e2054d4ae",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Post-Processing Results\n",
    "Perfect, we have got our results. Now it is time to make sense of them. To further process them, one can - exemplary - plot:\n",
    "\n",
    "1) the **behaviour of the simulated observations over time**;\n",
    "2) the **history of the residuals**;\n",
    "3) the **statistical interpretation of the final residuals**.\n",
    "\n",
    "### Range-rate over time\n",
    "First, we will thus plot all simulations we have simulated over time. One can clearly see how the satellite slowly emerges from the horizon and then more 'quickly' passes the station, until the visibility criterion is not fulfilled anymore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "70768252",
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_times = np.array(simulated_observations.concatenated_times)\n",
    "observations_list = np.array(simulated_observations.concatenated_observations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "30378060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11fd92dd0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 5))\n",
    "plt.title(\"Observations as a function of time\")\n",
    "plt.scatter(observation_times / 3600.0, observations_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6dc07801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Time [hr]\")\n",
    "plt.ylabel(\"Range rate [m/s]\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9a7579e5",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a1cda95-da56-49d5-aff0-feb792b46f38",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Residuals history\n",
    "One might also opt to instead plot the **behaviour of the residuals** per iteration of the estimator. To this end, we have thus plotted the residuals of the individual observations as a function of time. Note that we can observe a seemingly equal spread around zero. As expected - since we have not defined it this way - the observation is thus not biased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "df49832e",
   "metadata": {},
   "outputs": [],
   "source": [
    "residual_history = estimation_output.residual_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b06b77ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(9, 6))\n",
    "subplots_list = [ax1, ax2, ax3, ax4]\n",
    "for i in range(4):\n",
    "    subplots_list[i].scatter(observation_times, residual_history[:, i])\n",
    "    subplots_list[i].set_ylabel(\"Observation Residual [m/s]\")\n",
    "    subplots_list[i].set_title(\"Iteration \"+str(i+1))\n",
    "\n",
    "ax3.set_xlabel(\"Time since J2000 [s]\")\n",
    "ax4.set_xlabel(\"Time since J2000 [s]\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a70d1a8f-a879-4dcf-8f2b-b73c2a363ca9",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Final residuals\n",
    "Finally, one can plot the **statistical distribution of the final residuals** between the simulated observations and the estimated orbit. Ideally, given the type of observable we have used (i.e. free of any bias) as well as a statistically sufficient high number of observations, we would expect to see a Gaussian distribution with zero mean here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "78181718",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_residuals = estimation_output.final_residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cb1d2457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,5))\n",
    "plt.hist(final_residuals, 25)\n",
    "plt.xlabel('Final iteration range-rate residual [m/s]')\n",
    "plt.ylabel('Occurrences [-]')\n",
    "plt.title('Histogram of residuals on final iteration')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "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
}