{
 "cells": [
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    "# Simulation and Estimation Using Different Dynamical Models\n",
    "## Objectives\n",
    "Within this example, we will go beyond the earlier introduced basic steps of setting up an orbit estimation routine. In particular, using several orbits of the **Mars Express (MEX)** spacecraft around the Red Planet, we will introduce different **new types of observables**, **observation constraints**, and finally focus on how to apply **different dynamical models** to the simulation of observations and the estimation, respectively. Since no further explanation with respect to already introduced functionalities will be given in this example, the reader is advised - if not already done so - to first browse to the [Parameter estimation example](full_estimation_example.ipynb).\n",
    "\n",
    "Using **different dynamical models** for the simulation of observations and the subsequent estimation comes in handy when trying to **emulate what effects an imperfect dynamical model will have on the estimation** of selected parameters based on real-world data. \n",
    "\n",
    "**Here is why**: while real-world data stems from a perfectly 'modelled' environment, **the estimator will always be subject to modelling shortcomings**. So we will never be able to perfectly model reality when setting up our estimation model. \n",
    "\n",
    "When we are dealing with *simulated observed data* (which is still different from the *real world data*, it being simulated!), we need to mimic this above-mentioned discrepancy. So how do we do that? We use a **slightly less advanced model for the estimation than for the simulation of observations**. This way, we are able to artificially mock the discrepancy between the real world and the estimation model available, and gain insights into the behaviour and sensitivity of the found solution to **modelling imperfections**."
   ]
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   "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`. Moreover, we import `os` to be able to tell our system where it can find the downloaded SPICE kernel for Mars Express.\n",
    "\n",
    "Again, mainly (new) functionalities of the `estimation`, `estimation_setup`, and `observations` modules of the imported `tudatpy`packages will be used and demonstrated within this example.\n"
   ]
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  {
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   "source": [
    "# Load required standard modules\n",
    "import os\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "\n",
    "# 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\n",
    "# Retrieve current directory\n",
    "current_directory = os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Simulation Settings\n",
    "After having defined the general configuration of our simulation (i.e. importing required `SPICE` kernels, defining start and end epoch of the simulation) we will create the main celestial bodies involved in the simulation (mainly Mars, its two moons, the two neighbouring planets, and the Sun), the spacecraft itself, and its environment interface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load standard spice kernels as well as the one describing the orbit of Mars Express\n",
    "spice.load_standard_kernels()\n",
    "spice.load_kernel(current_directory + \"/ORMM_T19_031222180906_00052.BSP\")\n",
    "\n",
    "# Set simulation start (January 1st, 2004 - 00:00) and end epochs (January 11th, 2004 - 00:00)\n",
    "simulation_start_epoch = DateTime(2004, 1,  1).epoch()\n",
    "simulation_end_epoch   = DateTime(2004, 1, 11).epoch()\n",
    "\n",
    "### CELESTIAL BODIES ###\n",
    "# Create default body settings for \"Mars\", \"Phobos\", \"Deimos\", \"Sun\", \"Jupiter\", \"Earth\"\n",
    "bodies_to_create = [\"Mars\", \"Phobos\", \"Deimos\", \"Sun\", \"Jupiter\", \"Earth\"]\n",
    "\n",
    "# Create default body settings for bodies_to_create, with \"Mars\"/\"J2000\" as the global frame origin and orientation\n",
    "global_frame_origin = \"Mars\"\n",
    "global_frame_orientation = \"ECLIPJ2000\"\n",
    "body_settings = environment_setup.get_default_body_settings(\n",
    "    bodies_to_create, global_frame_origin, global_frame_orientation)\n",
    "### VEHICLE BODY ###\n",
    "# Create vehicle object\n",
    "body_settings.add_empty_settings(\"MEX\")\n",
    "body_settings.get(\"MEX\").constant_mass = 1000.0\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()\n",
    "occulting_bodies[\"Sun\"] = [\"Mars\"]\n",
    "radiation_pressure_settings = environment_setup.radiation_pressure.cannonball_radiation_target(\n",
    "    reference_area_radiation, radiation_pressure_coefficient, occulting_bodies)\n",
    "\n",
    "# Add the radiation pressure interface to the environment\n",
    "body_settings.get(\"MEX\").radiation_pressure_target_settings = radiation_pressure_settings\n",
    "# Create system of bodies\n",
    "\n",
    "bodies = environment_setup.create_system_of_bodies(body_settings)\n",
    "\n",
    "# Define bodies that are propagated\n",
    "bodies_to_propagate = [\"MEX\"]\n",
    "\n",
    "# Define central bodies of propagation\n",
    "central_bodies = [\"Mars\"]"
   ]
  },
  {
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   "execution_count": 3,
   "metadata": {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.\n",
      "Ignoring fixed x limits to fulfill fixed data aspect with adjustable data limits.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time2plt = np.arange(simulation_start_epoch, simulation_end_epoch, 60)\n",
    "mex2plt = list()\n",
    "for epoch in time2plt:\n",
    "    mex2plt.append(spice.get_body_cartesian_position_at_epoch(\n",
    "        target_body_name=\"MEX\",\n",
    "        observer_body_name=\"Mars\",\n",
    "        reference_frame_name=\"ECLIPJ2000\",\n",
    "        aberration_corrections=\"none\",\n",
    "        ephemeris_time=epoch))\n",
    "mex2plt = np.array(mex2plt)\n",
    "radius_mars = spice.get_average_radius(\"Mars\")\n",
    "\n",
    "fig, ax1 = plt.subplots(1, 1, figsize=(9, 6))\n",
    "ax1.axis('equal')\n",
    "\n",
    "ax1.plot(mex2plt[:, 1]*1E-3, mex2plt[:, 2]*1E-3, color=\"dodgerblue\", label=\"Mars Express\")\n",
    "ax1.add_patch(plt.Circle((0, 0), radius_mars*1E-3, color=\"firebrick\", label=\"Mars\"))\n",
    "\n",
    "ax1.set_xlabel(r'$\\Delta y$ [km]')\n",
    "ax1.set_ylabel(r'$\\Delta z$ [km]')\n",
    "ax1.set_xlim(-1.0E4, 5.5E4)\n",
    "ax1.set_ylim(-2.0E4, 2.0E4)\n",
    "ax1.ticklabel_format(style='sci', scilimits=(0, 0), axis='both')\n",
    "ax1.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Set Up the Observations\n",
    "Having set the underlying environment model of the simulated orbit, we can define the observational model. This entails the addition all required ground stations, the definition of the observation links and types, as well as the precise simulation settings.\n",
    "### Add a ground station\n",
    "Following its real-world counterpart, our simulated Mars Express spacecraft will also be tracked using ESA's New Norcia (NNO) ESTRACK ground station. Located in North-East Australia, it will be set up with an altitude of 252m, 31.0482°S, 116.191°E."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the position of the New Norcia (NNO) ESTRACK Earth station\n",
    "station_altitude = 252.0\n",
    "new_norcia_latitude = np.deg2rad(-31.0482)\n",
    "new_norcia_longitude = np.deg2rad(116.191)\n",
    "\n",
    "# Add the ground station to the environment\n",
    "environment_setup.add_ground_station(\n",
    "    bodies.get_body(\"Earth\"),\n",
    "    \"NNO\",\n",
    "    [station_altitude, new_norcia_latitude, new_norcia_longitude],\n",
    "    element_conversion.geodetic_position_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "### Define Observation Model Settings\n",
    "Within this example - as it is common practice when tracking deep-space missions using the ESTRACK system - Mars Express will be tracked using an **n-way Doppler measurement** (realised as two-way link ends in this example). This means that the signal travels from Earth to the spacecraft where it gets **re-transmitted** and subsequently has to travel back to Earth where it is **recorded and processed**. In particular, we will model **two-way range** and **range-rate (Doppler) observables**.\n",
    "\n",
    "Moreover, expanding upon the knowledge from the [Parameter estimation example](full_estimation_example.ipynb), we will introduce how to introduce the settings for the light time correction of the signal due to the **relativistic effects of the Sun**, as well as how to impose a **constant bias** on one of the two observables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the uplink link ends for one-way observable\n",
    "one_way_nno_mex_link_ends = dict( )\n",
    "one_way_nno_mex_link_ends[observable_models_setup.links.transmitter] = observable_models_setup.links.body_reference_point_link_end_id(\"Earth\", \"NNO\")\n",
    "one_way_nno_mex_link_ends[observable_models_setup.links.receiver] = observable_models_setup.links.body_origin_link_end_id(\"MEX\")\n",
    "one_way_nno_mex_link_definition = observable_models_setup.links.LinkDefinition(one_way_nno_mex_link_ends)\n",
    "\n",
    "# Define settings for light-time calculations\n",
    "light_time_correction_settings = observable_models_setup.light_time_corrections.first_order_relativistic_light_time_correction([\"Sun\"])\n",
    "\n",
    "# Define settings for range bias\n",
    "range_bias_settings = observable_models_setup.biases.absolute_bias([0.01])\n",
    "\n",
    "# Create list of observation settings\n",
    "observation_settings_list = list()\n",
    "observation_settings_list.append(observable_models_setup.model_settings.one_way_range(\n",
    "    one_way_nno_mex_link_definition,\n",
    "    light_time_correction_settings = [light_time_correction_settings],\n",
    "    bias_settings = range_bias_settings))\n",
    "observation_settings_list.append(observable_models_setup.model_settings.one_way_doppler_instantaneous(\n",
    "    one_way_nno_mex_link_definition,\n",
    "    light_time_correction_settings = [light_time_correction_settings]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "### Define Observation Simulation Settings\n",
    "Finally, for each above-defined observation model, we will define **the noise of the observation-type** and general viability criteria. We impose the spacecraft to be trackable only when at a certain minimum angle (15 degrees) of elevation above the horizon as seen from the ground station. We also introduce Mars as a body that can potentially **occult** the line-of-sight between Mars Express and **New Norcia** (i.e. when the spacecraft dives 'behind' Mars, we will not simulate any observations)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define observation simulation times for each link (separated by steps of one minute)\n",
    "observation_times = np.arange(simulation_start_epoch, simulation_end_epoch, 60.0)\n",
    "observation_simulation_settings = list()\n",
    "\n",
    "observation_simulation_settings.append(observations_setup.observations_simulation_settings.tabulated_simulation_settings(\n",
    "    observable_models_setup.model_settings.one_way_instantaneous_doppler_type,\n",
    "    one_way_nno_mex_link_definition,\n",
    "    observation_times))\n",
    "\n",
    "observation_simulation_settings.append(observations_setup.observations_simulation_settings.tabulated_simulation_settings(\n",
    "    observable_models_setup.model_settings.one_way_range_type,\n",
    "    one_way_nno_mex_link_definition,\n",
    "    observation_times))\n",
    "\n",
    "# Add noise levels of roughly 1.0E-3 [m/s] and add this as Gaussian noise to the observation\n",
    "noise_level_doppler = 1.0E-3\n",
    "observations_setup.random_noise.add_gaussian_noise_to_observable(\n",
    "    observation_simulation_settings,\n",
    "    noise_level_doppler,\n",
    "    observable_models_setup.model_settings.one_way_instantaneous_doppler_type\n",
    ")\n",
    "\n",
    "noise_level_range = 1.0\n",
    "observations_setup.random_noise.add_gaussian_noise_to_observable(\n",
    "    observation_simulation_settings,\n",
    "    noise_level_range,\n",
    "    observable_models_setup.model_settings.one_way_range_type\n",
    ")\n",
    "\n",
    "# Create viability settings\n",
    "viability_settings = [observations_setup.viability.elevation_angle_viability([\"Earth\", \"NNO\"], np.deg2rad(15)),\n",
    "                      observations_setup.viability.body_occultation_viability([\"NNO\", \"MEX\"], \"Mars\")]\n",
    "observations_setup.viability.add_viability_check_to_all(\n",
    "    observation_simulation_settings,\n",
    "    viability_settings\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Define the Dynamical Model(s)\n",
    "Note that unlike it has usually been the case so far - be it with examples dealing with propagation or the prior estimation ones - we have always defined a mere single dynamical model. The modular structure of tudat, however, enables us to simulate the observations using a dynamical model that is (theoretically entirely) different from the one used to perform the estimation. Hence, we will now first define the model that will be used during the simulation of observations. In particular, we will consider:\n",
    "\n",
    "* Gravitational acceleration using a spherical harmonic approximation up to 4th degree and order for Mars.\n",
    "* Gravitational acceleration using a simple point mass model for:\n",
    "\n",
    "    - Mars' two moons Phobos and Deimos\n",
    "    - Earth\n",
    "    - Jupiter\n",
    "    - The Sun\n",
    "\n",
    "* Radiation pressure experienced by the spacecraft - shape-wise approximated as a spherical cannonball - due to the Sun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Define the accelerations acting on Mars Express during the observation simulation\n",
    "accelerations_settings_mars_express_simulation = dict(\n",
    "    Mars=[\n",
    "        propagation_setup.acceleration.spherical_harmonic_gravity(4, 4)\n",
    "    ],\n",
    "    Phobos=[\n",
    "        propagation_setup.acceleration.point_mass_gravity()\n",
    "    ],\n",
    "    Deimos=[\n",
    "        propagation_setup.acceleration.point_mass_gravity()\n",
    "    ],\n",
    "    Earth=[\n",
    "        propagation_setup.acceleration.point_mass_gravity()\n",
    "    ],\n",
    "    Jupiter=[\n",
    "        propagation_setup.acceleration.point_mass_gravity()\n",
    "    ],\n",
    "    Sun=[\n",
    "        propagation_setup.acceleration.point_mass_gravity(),\n",
    "        propagation_setup.acceleration.radiation_pressure()\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "### Perform the observations simulation\n",
    "Following the known - trivial - estimation pipeline, the observations are simulated using the `simulation_observations()` function of the respective `Estimator` object. However, to avoid having to create two distinct estimators, we will manually implement a set of observation simulators upfront, before altering the dynamical model and creating the actual estimator.\n",
    "\n",
    "The way custom-implemented observation simulators are implemented is that they do not propagate any bodies themselves but simulate the observations based on the (tabulated) ephemerides of all involved bodies. However, for this example, we wish to use \"mock\" ephemeris - coming from the propagation of an initial state using the propagation model we chose for simulating our observations, in place of the real (SPICE) ones. This means that we have to update the ephemeris of MEX such that it stems from the dynamical model of choice and not its `SPICE` kernel. In simple terms, these are the steps to follow:\n",
    "\n",
    "1) Select the **initial MEX state**. This (*and only this!*) is taken from the **SPICE ephemeris.\n",
    "2) Using the **chosen model and propagator, **propagate the initial MEX state**.\n",
    "3) Save the **propagated states**. **These will be used as ephemeris** for our example.\n",
    "\n",
    "Having updated the tabulated ephemeris of Mars Express, one can create the required `observation simulator` object and finally simulate the observations according to the above-defined settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "### Accelerations ###\n",
    "# Create global accelerations dictionary\n",
    "acceleration_settings_simulation = {\"MEX\": accelerations_settings_mars_express_simulation}\n",
    "# Create acceleration models\n",
    "acceleration_models_simulation = propagation_setup.create_acceleration_models(\n",
    "    bodies,\n",
    "    acceleration_settings_simulation,\n",
    "    bodies_to_propagate,\n",
    "    central_bodies)\n",
    "\n",
    "### Initial State ###\n",
    "initial_state = spice.get_body_cartesian_state_at_epoch(\n",
    "    target_body_name=\"MEX\",\n",
    "    observer_body_name=\"Mars\",\n",
    "    reference_frame_name=\"ECLIPJ2000\",\n",
    "    aberration_corrections=\"none\",\n",
    "    ephemeris_time=simulation_start_epoch)\n",
    "\n",
    "### Integrator and Termination Settings ###\n",
    "# Create 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)\n",
    "# Create termination settings\n",
    "termination_settings = propagation_setup.propagator.time_termination(simulation_end_epoch)\n",
    "\n",
    "### Propagator Settings ###\n",
    "propagator_settings_simulation = propagation_setup.propagator.translational(\n",
    "                  central_bodies=central_bodies,\n",
    "                  acceleration_models=acceleration_models_simulation,\n",
    "                  bodies_to_integrate=bodies_to_propagate,\n",
    "                  initial_states=initial_state,\n",
    "                  initial_time=simulation_start_epoch,\n",
    "                  integrator_settings=integrator_settings,\n",
    "                  termination_settings=termination_settings)\n",
    "# Create or update the ephemeris of all propagated bodies (here: MEX) to match the propagated results\n",
    "propagator_settings_simulation.processing_settings.set_integrated_result = True\n",
    "# Run propagation\n",
    "dynamics_simulator = simulator.create_dynamics_simulator(bodies, propagator_settings_simulation)\n",
    "state_history_simulated_observations = dynamics_simulator.propagation_results.state_history\n",
    "\n",
    "# Create observation simulators\n",
    "observation_simulators = observations_setup.observations_simulation_settings.create_observation_simulators(\n",
    "    observation_settings_list, bodies)\n",
    "# Get MEX simulated observations as ObservationCollection\n",
    "mex_simulated_observations = observations_setup.observations_wrapper.simulate_observations(\n",
    "    observation_simulation_settings,\n",
    "    observation_simulators,\n",
    "    bodies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "### Alter the Dynamical Model for Mars\n",
    "Based on what we already mentioned in the Objectives section, we want to create a discrepancy between the estimation model and the simulation model. Therefore, we will now select a **worse dynamical model for the estimation** with respect to the one used for the **observation simulations**.  The previously defined dynamical model of accelerations acting on `MEX` was taking into consideration the gravity of Phobos and Deimos. We will now make the model worse by removing the gravitational pull of both of Mars' moons from the acceleration settings of the spacecraft. This is simply achieved via the `pop` command. \n",
    "\n",
    "All remaining settings remain untouched."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Copy and subsequently alter the accelerations acting on Mars Express used during the estimation\n",
    "accelerations_settings_mars_express_estimation = accelerations_settings_mars_express_simulation\n",
    "accelerations_settings_mars_express_estimation.pop('Phobos')\n",
    "accelerations_settings_mars_express_estimation.pop('Deimos')\n",
    "\n",
    "# Create updated global accelerations dictionary\n",
    "acceleration_settings_estimation = {\"MEX\": accelerations_settings_mars_express_estimation}\n",
    "\n",
    "# Create updated acceleration models\n",
    "acceleration_models_estimation = propagation_setup.create_acceleration_models(\n",
    "    bodies,\n",
    "    acceleration_settings_estimation,\n",
    "    bodies_to_propagate,\n",
    "    central_bodies)\n",
    "\n",
    "# Create updated propagator settings\n",
    "propagator_settings_estimation = propagation_setup.propagator.translational(\n",
    "                  central_bodies=central_bodies,\n",
    "                  acceleration_models=acceleration_models_estimation,\n",
    "                  bodies_to_integrate=bodies_to_propagate,\n",
    "                  initial_states=initial_state,\n",
    "                  initial_time=simulation_start_epoch,\n",
    "                  integrator_settings=integrator_settings,\n",
    "                  termination_settings=termination_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Perform the estimation\n",
    "Having altered the dynamical model as well as the propagator settings, we create the `Estimator` object and subsequently set up the inversion of the problem - in particular, one has to define which **parameters are to be estimated**, could potentially include any a-priori information in the form of an a-priori covariance matrix, and **define the weights** associated with the individual types of observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning, function setConstantPerObservableWeightsMatrix is deprecated, weights should preferably be defined at the observation collection level."
     ]
    }
   ],
   "source": [
    "# Setup parameters settings to propagate the state transition matrix\n",
    "parameter_settings = parameters_setup.initial_states(propagator_settings_estimation, bodies)\n",
    "\n",
    "# Add estimated parameters to the sensitivity matrix that will be propagated\n",
    "parameter_settings.append(parameters_setup.gravitational_parameter(\"Mars\"))\n",
    "\n",
    "# Create the parameters that will be estimated\n",
    "parameters_to_estimate = parameters_setup.create_parameter_set(parameter_settings, bodies)\n",
    "\n",
    "# Create the estimator\n",
    "estimator = estimation_analysis.Estimator(\n",
    "    bodies,\n",
    "    parameters_to_estimate,\n",
    "    observation_settings_list,\n",
    "    propagator_settings_estimation)\n",
    "\n",
    "# Save the true parameters to later analyse the error\n",
    "truth_parameters = parameters_to_estimate.parameter_vector\n",
    "\n",
    "# Define weighting of the observations in the inversion\n",
    "weights_per_observable = {observations.observations_processing.observation_parser(\n",
    "    observable_models_setup.model_settings.one_way_instantaneous_doppler_type ): noise_level_doppler ** -2,\n",
    "                          observations.observations_processing.observation_parser(\n",
    "                              observable_models_setup.model_settings.one_way_range_type ): noise_level_range ** -2}\n",
    "mex_simulated_observations.set_constant_weight_per_observation_parser(weights_per_observable)\n",
    "\n",
    "\n",
    "# Create input object for the estimation\n",
    "estimation_input = estimation_analysis.EstimationInput(\n",
    "    mex_simulated_observations)\n",
    "\n",
    "# Set methodological options\n",
    "estimation_input.define_estimation_settings(\n",
    "    reintegrate_variational_equations=False,\n",
    "    save_state_history_per_iteration=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "### Estimate the individual parameters\n",
    "Finally, the actual estimation can be performed - ideally having reached a sufficient level of convergence, the least squares estimator will have found the most suitable parameters for the problem at hand.\n",
    "\n",
    "We will again qualitatively compare the goodness-of-fit of the found parameters with the known ground truth ones (**realise that this cannot typically be done when working with real-world observations, since the ground truth is not known, and is exactly what one would like to know!**). However, since we conveniently know these parameters, it serves as a handy measure to shed light onto the estimation process. In particular, this highlights the fact that with increasing discrepancy between the dynamical models used within the simulation and estimation routines, the true-to-formal error ratio has to increase, since - besides to the pure estimation of parameters - the (artificially) introduced modelling imperfections have to be implicitly mitigated altering the values of the same set of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating residuals and partials 11070\n",
      "Current residual: 32.304\n",
      "Parameter update    -10.5916      62.9584      430.765 -1.82732e-05 -0.000141081 -0.000550749  6.57045e+06\n",
      "Calculating residuals and partials 11070\n",
      "Current residual: 0.714447\n",
      "Parameter update  0.00133845  -0.00754336   -0.0666343  5.83863e-09 -2.77461e-08  9.81874e-08     -5876.66\n",
      "Calculating residuals and partials 11070\n",
      "Current residual: 0.714448\n",
      "Parameter update-4.29788e-05  0.000229027   0.00255811 -2.34708e-10  1.16753e-09  -3.6591e-09      191.013\n",
      "Calculating residuals and partials 11070\n",
      "Current residual: 0.714448\n",
      "Parameter update -3.3294e-05  0.000183865   0.00177283 -1.52623e-10  7.35536e-10 -2.55983e-09      146.442\n",
      "Calculating residuals and partials 11070\n",
      "Current residual: 0.714448\n",
      "Maximum number of iterations reached\n",
      "Parameter update 1.50816e-05 -8.26822e-05  -0.00105331  9.76057e-11 -4.75803e-10   1.4969e-09     -65.2812\n",
      "Final residual: 0.714447\n"
     ]
    }
   ],
   "source": [
    "# Perform the covariance analysis\n",
    "estimation_output = estimator.perform_estimation(estimation_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.38261233e+00 7.65771158e+00 6.85405703e+01 6.01196528e-06\n",
      " 2.90993288e-05 1.00220617e-04 6.31730489e+06]\n",
      "[ 1.05902865e+01 -6.29511490e+01 -4.30701837e+02  1.82676342e-05\n",
      "  1.41107376e-04  5.50655066e-04 -6.56484655e+06]\n"
     ]
    }
   ],
   "source": [
    "# Print the covariance matrix\n",
    "print(estimation_output.formal_errors)\n",
    "print(truth_parameters - parameters_to_estimate.parameter_vector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Post-processing\n",
    "Finally, to further illustrate the impact certain differences between the applied dynamical models have, we will first plot the behaviour of the simulated observations over time, as well as show how the discrepancy between our estimated solution and the 'ground truth' builds up over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_residuals = estimation_output.final_residuals\n",
    "observation_times = np.array(mex_simulated_observations.concatenated_times)\n",
    "\n",
    "fig, ax1 = plt.subplots(1, 1, figsize=(9, 6))\n",
    "\n",
    "ax1.scatter((observation_times[0:int(len(observation_times)/2)] - observation_times[0]) / (3600*24),\n",
    "            final_residuals[0:int(len(final_residuals)/2)])\n",
    "\n",
    "ax1.set_title(\"Observations as a function of time\")\n",
    "ax1.set_xlabel(r'Time [days]')\n",
    "ax1.set_ylabel(r'Final Residuals [m]')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simulator_object = estimation_output.simulation_results_per_iteration[-1]\n",
    "state_history = simulator_object.dynamics_results.state_history\n",
    "\n",
    "time2plt = np.vstack(list(state_history.keys()))\n",
    "time2plt_normalized = (time2plt - time2plt[0]) / (3600*24)\n",
    "\n",
    "mex_prop = np.vstack(list(state_history.values()))\n",
    "mex_sim_obs = np.vstack(list(state_history_simulated_observations.values()))\n",
    "\n",
    "fig, ax1 = plt.subplots(1, 1, figsize=(9, 6))\n",
    "\n",
    "ax1.plot(time2plt_normalized, (mex_prop[:, 0] - mex_sim_obs[:, 0]), label=r'$\\Delta x$')\n",
    "ax1.plot(time2plt_normalized, (mex_prop[:, 1] - mex_sim_obs[:, 1]), label=r'$\\Delta y$')\n",
    "ax1.plot(time2plt_normalized, (mex_prop[:, 2] - mex_sim_obs[:, 2]), label=r'$\\Delta z$')\n",
    "ax1.plot(time2plt_normalized, np.linalg.norm((mex_prop[:, 0:3] - mex_sim_obs[:, 0:3]), axis=1), label=r'$||\\Delta X||$')\n",
    "\n",
    "ax1.set_title(\"Element-wise difference between true and estimated states\")\n",
    "ax1.set_xlabel(r'$Time$ [days]')\n",
    "ax1.set_ylabel(r'$\\Delta X$ [m]')\n",
    "ax1.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}