{
 "cells": [
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   "metadata": {},
   "source": [
    "# Linear sensitivity analysis using variational equations\n",
    "\n",
    "## Objectives\n",
    "This example is an extension of the Perturbed Satellite Orbit Application. It adopts the simulation setup from the Perturbed Satellite Orbit, considering a slightly reduced set of perturbing accelerations for the propagation of the vehicle.\n",
    "\n",
    "The script demonstrates how the basic numerical simulation setup (aiming to propagate the state of the system) can swiftly be extended to enable a study of the system's sensitivity.\n",
    "\n",
    "Via the `dynamics.parameters_setup` module, the system parameters w.r.t. which the sensitivity is to be studied are defined and a `create_variational_equations_solver` function from the `dynamics.simulator` module is used in order to setup and integrate the system's variational equations. After obtaining the state transition matrices from the integrated variational equations, the system's response to small perturbations can be tested via simple matrix multiplication.\n",
    "\n",
    "The availability of variational equations in tudat enables many more, advanced functionalities, such as covariance analysis and precise orbit determination."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004e05ee",
   "metadata": {},
   "source": [
    "## Import statements\n",
    "The required import statements are made here, at the very beginning.\n",
    "\n",
    "Some standard modules are first loaded. These are `numpy` and `matplotlib.pyplot`.\n",
    "\n",
    "Then, the different modules of `tudatpy` that will be used are imported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "11c3b0e7",
   "metadata": {
    "execution": {
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   "source": [
    "# Load standard modules\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Load tudatpy modules\n",
    "from tudatpy.interface import spice\n",
    "from tudatpy import dynamics\n",
    "from tudatpy.dynamics import environment\n",
    "from tudatpy.dynamics import parameters_setup, environment_setup, propagation_setup, simulator\n",
    "from tudatpy.astro import element_conversion\n",
    "from tudatpy import constants\n",
    "from tudatpy.util import result2array\n",
    "from tudatpy.astro.time_representation import DateTime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411f6a69",
   "metadata": {},
   "source": [
    "## Configuration\n",
    "NAIF's `SPICE` kernels are first loaded, so that the position of various bodies such as the Earth, the Sun, the Moon, Venus, or Mars, can be make known to `tudatpy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "233d5642",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T07:28:06.420472Z",
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     "iopub.status.idle": "2025-09-16T07:28:06.448099Z",
     "shell.execute_reply": "2025-09-16T07:28:06.447822Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load spice kernels\n",
    "spice.load_standard_kernels()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af015069",
   "metadata": {},
   "source": [
    "## Environment setup\n",
    "Let’s create the environment for our simulation. This setup covers the creation of (celestial) bodies, vehicle(s), and environment interfaces.\n",
    "\n",
    "### Create the bodies\n",
    "Bodies can be created by making a list of strings with the bodies that is to be included in the simulation.\n",
    "\n",
    "The default body settings (such as atmosphere, body shape, rotation model) are taken from `SPICE`.\n",
    "\n",
    "These settings 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc8a7f23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T07:28:06.449394Z",
     "iopub.status.busy": "2025-09-16T07:28:06.449290Z",
     "iopub.status.idle": "2025-09-16T07:28:06.512644Z",
     "shell.execute_reply": "2025-09-16T07:28:06.512356Z"
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   "outputs": [],
   "source": [
    "# Create default body settings for \"Sun\", \"Earth\", \"Moon\", \"Mars\", \"Venus\"\n",
    "bodies_to_create = [\"Sun\", \"Earth\", \"Moon\", \"Mars\", \"Venus\"]\n",
    "\n",
    "# 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": "df74a0b3",
   "metadata": {},
   "source": [
    "### Create the vehicle and its environment interface\n",
    "Let's now create the satellite for which an orbit will be simulated.\n",
    "\n",
    "This satellite is setup to have mass of 2.2 kg, a reference area (used both for aerodynamic and radiation pressure) of 4m$^2$, a radiation pressure coefficient of 1.2, and a drag coefficient also of 1.2.\n",
    "\n",
    "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": 4,
   "id": "5f534f3a",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-09-16T07:28:06.516147Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create empty body settings for the satellite\n",
    "body_settings.add_empty_settings(\"Delfi-C3\")\n",
    "\n",
    "body_settings.get(\"Delfi-C3\").constant_mass = 2.2\n",
    "\n",
    "# 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",
    ")\n",
    "\n",
    "# Add the aerodynamic interface to the body settings\n",
    "body_settings.get(\"Delfi-C3\").aerodynamic_coefficient_settings = aero_coefficient_settings\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\"] = [\"Earth\"]\n",
    "vehicle_target_settings = environment_setup.radiation_pressure.cannonball_radiation_target(\n",
    "    reference_area_radiation, radiation_pressure_coefficient, occulting_bodies_dict )\n",
    "\n",
    "\n",
    "# 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": "890bfba3",
   "metadata": {},
   "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": 5,
   "id": "6eeae3d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T07:28:06.517229Z",
     "iopub.status.busy": "2025-09-16T07:28:06.517138Z",
     "iopub.status.idle": "2025-09-16T07:28:06.527367Z",
     "shell.execute_reply": "2025-09-16T07:28:06.527118Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the system of bodies\n",
    "bodies = environment_setup.create_system_of_bodies(body_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "150ee92d",
   "metadata": {},
   "source": [
    "## Propagation setup\n",
    "Now that the environment is created, the propagation setup is defined.\n",
    "\n",
    "First, the bodies to be propagated and the central bodies will be defined.\n",
    "Central bodies are the bodies with respect to which the state of the respective propagated bodies is defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43fe0e1b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T07:28:06.528166Z",
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     "shell.execute_reply": "2025-09-16T07:28:06.529378Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define bodies that are propagated\n",
    "bodies_to_propagate = [\"Delfi-C3\"]\n",
    "\n",
    "# Define central bodies of propagation\n",
    "central_bodies = [\"Earth\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f79b428c",
   "metadata": {},
   "source": [
    "### Create the acceleration model\n",
    "First off, the acceleration settings that act on `Delfi-C3` are to be defined.\n",
    "In this case, these consist in the followings:\n",
    "\n",
    "* Gravitational acceleration using a Point Mass approximation from:\n",
    "\n",
    "    - The Sun\n",
    "    - The Moon\n",
    "    - Mars\n",
    "    - Venus\n",
    "\n",
    "* Gravitational acceleration using a Spherical Harmonic approximation up to degree and order 5 from Earth.\n",
    "* Aerodynamic acceleration from Earth.\n",
    "* Acceleration caused by the radiation pressure of the Sun on the vehicle approximated as a cannonball.\n",
    "\n",
    "The acceleration settings defined are then applied to `Delfi-C3` in a dictionary.\n",
    "\n",
    "This dictionary is finally input to the propagation setup to create the acceleration models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c20e8588",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T07:28:06.530525Z",
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     "shell.execute_reply": "2025-09-16T07:28:06.532526Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define unique (Sun, Earth) 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",
    "    Earth=[\n",
    "        propagation_setup.acceleration.spherical_harmonic_gravity(5, 5),\n",
    "        propagation_setup.acceleration.aerodynamic()\n",
    "    ])\n",
    "\n",
    "# Define other point mass accelerations acting on Delfi-C3\n",
    "for other in set(bodies_to_create).difference({\"Sun\", \"Earth\"}):\n",
    "    accelerations_settings_delfi_c3[other] = [\n",
    "        propagation_setup.acceleration.point_mass_gravity()]\n",
    "\n",
    "# Create global accelerations dictionary\n",
    "acceleration_settings = {\"Delfi-C3\": accelerations_settings_delfi_c3}\n",
    "\n",
    "# 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": "e9c93c50",
   "metadata": {},
   "source": [
    "### Define simulation start and end epoch\n",
    "\n",
    "Next, the start and end simulation epochs are specified.\n",
    "In Tudat, all epochs are defined as seconds since J2000.\n",
    "For ease of use, the start and end epochs are derived from calender dates using the `DateTime` class.\n",
    "Please refer to the [API documentation](https://py.api.tudat.space/en/latest/time_representation.html) of the `time_representation` module for more information on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a1e6c455",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T07:28:06.533544Z",
     "iopub.status.busy": "2025-09-16T07:28:06.533452Z",
     "iopub.status.idle": "2025-09-16T07:28:06.535156Z",
     "shell.execute_reply": "2025-09-16T07:28:06.534905Z"
    }
   },
   "outputs": [],
   "source": [
    "# Set simulation start and end epochs\n",
    "simulation_start_epoch = DateTime(2008, 4, 28).epoch()\n",
    "simulation_end_epoch   = DateTime(2008, 4, 29).epoch()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e717487",
   "metadata": {},
   "source": [
    "### Define the initial state\n",
    "The initial state of the vehicle that will be propagated is now defined. \n",
    "\n",
    "This initial state always has to be provided as a cartesian state, 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": 9,
   "id": "4a4527e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T07:28:06.536087Z",
     "iopub.status.busy": "2025-09-16T07:28:06.536002Z",
     "iopub.status.idle": "2025-09-16T07:28:06.537863Z",
     "shell.execute_reply": "2025-09-16T07:28:06.537593Z"
    }
   },
   "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": "0cd77c22",
   "metadata": {},
   "source": [
    "### Create the propagator settings\n",
    "The propagator is finally setup.\n",
    "\n",
    "First, a termination condition is defined so that the propagation will stop when the end epochs that was defined is reached.\n",
    "\n",
    "Subsequently, the integrator settings are defined using a RK4 integrator with the fixed step size of 10 seconds.\n",
    "\n",
    "Then, the translational propagator settings are defined. These are used to simulate the orbit of `Delfi-C3` around Earth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8a451ab1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T07:28:06.538669Z",
     "iopub.status.busy": "2025-09-16T07:28:06.538574Z",
     "iopub.status.idle": "2025-09-16T07:28:06.540615Z",
     "shell.execute_reply": "2025-09-16T07:28:06.540385Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create termination settings\n",
    "termination_condition = propagation_setup.propagator.time_termination(simulation_end_epoch)\n",
    "\n",
    "# Create numerical integrator settings\n",
    "integrator_settings = propagation_setup.integrator.runge_kutta_fixed_step(\n",
    "    time_step = 10.0,\n",
    "    coefficient_set = propagation_setup.integrator.CoefficientSets.rk_4 )\n",
    "\n",
    "# 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": "e2c93354",
   "metadata": {},
   "source": [
    "### Setup the variational equations\n",
    "In addition to the state of the satellite, variation equations will also be propagated.\n",
    "A detailed explanation on variational equations is given in [tudatpy user guide](https://docs.tudat.space/en/latest/_src_user_guide/state_propagation/propagating_variational_simulation.html).\n",
    "\n",
    "In this example, both the initial state transition matrix and the sensitivity matrix are to be propagated.\n",
    "The list of the available estimated parameters for the sensitivity matrix are also given in [tudatpy user guide](https://docs.tudat.space/en/latest/_src_user_guide/state_propagation/propagating_variational_equations/available_parameters.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f49f443",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T07:28:06.541500Z",
     "iopub.status.busy": "2025-09-16T07:28:06.541416Z",
     "iopub.status.idle": "2025-09-16T07:28:06.543287Z",
     "shell.execute_reply": "2025-09-16T07:28:06.543041Z"
    }
   },
   "outputs": [],
   "source": [
    "# Setup parameters settings to propagate the state transition matrix\n",
    "parameter_settings = parameters_setup.initial_states(propagator_settings, bodies)\n",
    "\n",
    "# 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\"))\n",
    "\n",
    "# Create the parameters that will be estimated\n",
    "parameters_to_estimate = parameters_setup.create_parameter_set(parameter_settings, bodies)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9869855c",
   "metadata": {},
   "source": [
    "## Propagate the dynamics\n",
    "In this example, since we wish to propagate the variational equations in addition to the satellite state, we use the `create_variational_equations_solver()` function (instead of the `create_dynamics_simulator()` function that we would normally use).\n",
    "\n",
    "This function takes additional arguments: the parameters that have to be estimated, and a boolean to specify that the parameters will be integrated immediately when the function is called."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4ccda476",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T07:28:06.544098Z",
     "iopub.status.busy": "2025-09-16T07:28:06.544004Z",
     "iopub.status.idle": "2025-09-16T07:28:07.463546Z",
     "shell.execute_reply": "2025-09-16T07:28:07.463210Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the variational equation solver and propagate the dynamics\n",
    "variational_equations_solver = simulator.create_variational_equations_solver(\n",
    "    bodies, propagator_settings, parameters_to_estimate, simulate_dynamics_on_creation=True\n",
    ")\n",
    "\n",
    "# Extract the resulting state history, state transition matrix history, and sensitivity matrix history\n",
    "states = variational_equations_solver.state_history\n",
    "state_transition_matrices = variational_equations_solver.state_transition_matrix_history\n",
    "sensitivity_matrices = variational_equations_solver.sensitivity_matrix_history"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db22a959",
   "metadata": {},
   "source": [
    "## Perform the sensitivity analysis\n",
    "Now that the state transition matrix history and sensitivity matrix history are known, we can perform the actual sensitivity analysis by varying the estimated parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "42a6f45b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T07:28:07.464729Z",
     "iopub.status.busy": "2025-09-16T07:28:07.464624Z",
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     "shell.execute_reply": "2025-09-16T07:28:07.494405Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define the linear variation in the initial state\n",
    "initial_state_variation = [1, 0, 0, 1.0E-3, 0, 0]\n",
    "# Define the linear variation in the Earth gravitational parameter\n",
    "earth_standard_param_variation = [-2.0E+5, 0.0]\n",
    "# Define the linear variation in the drag coefficient\n",
    "drag_coeff_variation = [0.0, 0.05]\n",
    "\n",
    "# Define dictionary to contain the state that has been varied to each variation\n",
    "delta_initial_state_dict = dict()\n",
    "earth_standard_param_dict = dict()\n",
    "delta_drag_coeff_dict = dict()\n",
    "\n",
    "# Compute the deviation in state due to each variation\n",
    "for epoch in state_transition_matrices:\n",
    "    delta_initial_state_dict[epoch] = np.dot(state_transition_matrices[epoch], initial_state_variation)\n",
    "    earth_standard_param_dict[epoch] = np.dot(sensitivity_matrices[epoch], earth_standard_param_variation)\n",
    "    delta_drag_coeff_dict[epoch] = np.dot(sensitivity_matrices[epoch], drag_coeff_variation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00998f8f",
   "metadata": {},
   "source": [
    "## Post-process the results\n",
    "First, extract the time, and deviation in position and velocity associated with the system response to each variation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "52137983",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T07:28:07.495991Z",
     "iopub.status.busy": "2025-09-16T07:28:07.495883Z",
     "iopub.status.idle": "2025-09-16T07:28:07.512620Z",
     "shell.execute_reply": "2025-09-16T07:28:07.512300Z"
    }
   },
   "outputs": [],
   "source": [
    "# Convert the dictionaries to numpy ndarrays\n",
    "delta_initial_state_array = result2array(delta_initial_state_dict)\n",
    "delta_earth_standard_param_array = result2array(earth_standard_param_dict)\n",
    "delta_drag_coefficient_array = result2array(delta_drag_coeff_dict)\n",
    "\n",
    "# Extract the time, and convert it to hours\n",
    "time = delta_initial_state_array[:,0]\n",
    "time_hours = time / 3600\n",
    "\n",
    "# Compute the deviation in position and velocity associated with the variation in initial state\n",
    "delta_r1 = np.linalg.norm(delta_initial_state_array[:, 1:4], axis=1)\n",
    "delta_v1 = np.linalg.norm(delta_initial_state_array[:, 4:8], axis=1)\n",
    "\n",
    "# Compute the deviation in position and velocity associated with the variation in Earth gravitational parameter\n",
    "delta_r2 = np.linalg.norm(delta_earth_standard_param_array[:, 1:4], axis=1)\n",
    "delta_v2 = np.linalg.norm(delta_earth_standard_param_array[:, 4:8], axis=1)\n",
    "\n",
    "# Compute the deviation in position and velocity associated with the variation in drag coefficient\n",
    "delta_r3 = np.linalg.norm(delta_drag_coefficient_array[:, 1:4], axis=1)\n",
    "delta_v3 = np.linalg.norm(delta_drag_coefficient_array[:, 4:8], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37c41a70",
   "metadata": {},
   "source": [
    "### Plot the deviation in position\n",
    "Make a plot of the deviation in position over time, in response to all parameter variations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e915f2c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T07:28:07.513709Z",
     "iopub.status.busy": "2025-09-16T07:28:07.513611Z",
     "iopub.status.idle": "2025-09-16T07:28:07.753910Z",
     "shell.execute_reply": "2025-09-16T07:28:07.753600Z"
    },
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot deviations of position\n",
    "plt.figure(figsize=(9, 5))\n",
    "plt.title('Trajectory deviation (position only) in response to indicated parameter variation')\n",
    "plt.grid()\n",
    "plt.plot(time_hours, delta_r1, color='tomato', label='variation initial state')\n",
    "plt.plot(time_hours, delta_r2, color='orange', label='variation grav. parameter (Earth)')\n",
    "plt.plot(time_hours, delta_r3, color='cyan', label='variation drag coefficient')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('Time [hr]')\n",
    "plt.ylabel('$\\Delta r$ [m]')\n",
    "plt.xlim([min(time_hours), max(time_hours)])\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dd5b7f6",
   "metadata": {},
   "source": [
    "### Plot the deviation in velocity\n",
    "Make a plot of the deviation in velocity over time, in response to all parameter variations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8c5a18bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T07:28:07.755275Z",
     "iopub.status.busy": "2025-09-16T07:28:07.755169Z",
     "iopub.status.idle": "2025-09-16T07:28:08.042811Z",
     "shell.execute_reply": "2025-09-16T07:28:08.042530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot deviations of speed\n",
    "plt.figure(figsize=(9, 5))\n",
    "plt.title('Trajectory deviation (velocity only) in response to indicated parameter variation.')\n",
    "plt.grid()\n",
    "plt.plot(time_hours, delta_v1, color='tomato', label='variation initial state')\n",
    "plt.plot(time_hours, delta_v2, color='orange', label='variation grav. parameter (Earth)')\n",
    "plt.plot(time_hours, delta_v3, color='cyan', label='variation drag coefficient')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('Time [hr]')\n",
    "plt.ylabel('$\\Delta v$ [m/s]')\n",
    "plt.xlim([min(time_hours), max(time_hours)])\n",
    "plt.legend()\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": 5
}