Merge pull request #70 from luigigisolfi/feature/restore_lars_changes…

…_fix_NO_API Feature/restore lars changes fix (no api)
tudat-team · Oct 11, 2024 · 218e30c · 218e30c
2 parents ebc3588 + e0a1aef
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diff --git a/estimation/covariance_estimated_parameters.ipynb b/estimation/covariance_estimated_parameters.ipynb
@@ -20,27 +20,6 @@
     "The first part of this example deals with the setup of all relevant (environment, propagation, and estimation) modules, so feel free to skip these steps if you're already familiar with them!"
    ]
   },
-  {
-   "cell_type": "raw",
-   "id": "cdc4c9a7-f484-4600-ba59-f4a99863088a",
-   "metadata": {},
-   "source": [
-    "## Key API References\n",
-    " Here's a comprehensive list of the  _modules_ and _methods_ that are relevant to this example, or that will be introduced here for the first time.\n",
-    "\n",
-    "| Module | | Methods |➡️ | | | \n",
-    "| --- | --- | --- | --- | --- | --- |\n",
-    "| [numerical_simulation.estimation_setup.observation](https://py.api.tudat.space/en/latest/observation.html) | [**body_reference_point_link_end_id**](https://py.api.tudat.space/en/latest/observation.html#tudatpy.numerical_simulation.estimation_setup.observation.body_reference_point_link_end_id) | [**LinkDefinition**](https://py.api.tudat.space/en/latest/observation.html#tudatpy.numerical_simulation.estimation_setup.observation.LinkDefinition) | [**one_way_doppler_instantaneous**](https://py.api.tudat.space/en/latest/observation.html#tudatpy.numerical_simulation.estimation_setup.observation.one_way_doppler_instantaneous) | [**add_gaussian_noise_to_observable**](https://py.api.tudat.space/en/latest/observation.html#tudatpy.numerical_simulation.estimation_setup.observation.add_gaussian_noise_to_observable) | [**elevation_angle_viability**](https://py.api.tudat.space/en/latest/observation.html#tudatpy.numerical_simulation.estimation_setup.observation.elevation_angle_viability) |[**tabulated_simulation_settings**](https://py.api.tudat.space/en/latest/observation.html#tudatpy.numerical_simulation.estimation_setup.observation.tabulated_simulation_settings)|\n",
-    "| [numerical_simulation.estimation_setup](https://py.api.tudat.space/en/latest/estimation_setup.html)|[**parameter**](https://py.api.tudat.space/en/latest/parameter.html)  |[**create_parameter_set**](https://py.api.tudat.space/en/latest/estimation_setup.html#tudatpy.numerical_simulation.estimation_setup.create_parameter_set) | | | | |\n",
-    "| [numerical_simulation.Estimator](https://py.api.tudat.space/en/latest/numerical_simulation.html#tudatpy.numerical_simulation.Estimator)  | [**observation_simulators**](https://py.api.tudat.space/en/latest/numerical_simulation.html#tudatpy.numerical_simulation.Estimator.observation_simulators) | [**compute_covariance**](https://py.api.tudat.space/en/latest/numerical_simulation.html#tudatpy.numerical_simulation.Estimator.compute_covariance) | [**perform_estimation**](https://py.api.tudat.space/en/latest/numerical_simulation.html#tudatpy.numerical_simulation.Estimator.perform_estimation) | [**state_transition_interface**](https://py.api.tudat.space/en/latest/numerical_simulation.html#tudatpy.numerical_simulation.Estimator.state_transition_interface) | |\n",
-    "| [numerical_simulation.estimation](https://py.api.tudat.space/en/latest/estimation.html) | [**simulate_observations**](https://py.api.tudat.space/en/latest/estimation.html#tudatpy.numerical_simulation.estimation.simulate_observations) | [**CovarianceAnalysisInput**](https://py.api.tudat.space/en/latest/estimation.html#tudatpy.numerical_simulation.estimation.CovarianceAnalysisInput) | [**estimation_convergence_checker**](https://py.api.tudat.space/en/latest/estimation.html#tudatpy.numerical_simulation.estimation.EstimationConvergenceChecker) |[**EstimationInput**](https://py.api.tudat.space/en/latest/estimation.html#tudatpy.numerical_simulation.estimation.EstimationInput)|\n",
-    "| [astro.time_conversion](https://py.api.tudat.space/en/latest/time_conversion.html) | [**DateTime**]() | \n",
-    "| [astro.element_conversion](https://py.api.tudat.space/en/latest/element_conversion.html) | [**geodetic_position_type**]() | \n",
-    "\n",
-    "\n",
-    "🧐 **Missing something**? You can find out more on the [TudatPy API Reference](https://py.api.tudat.space/en/latest/), or you can [reach out to us](https://docs.tudat.space/en/latest/_src_about/contribute_to_tudat.html)!"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "e99cbeda",
@@ -122,9 +101,7 @@
     "### 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://tudat-space.readthedocs.io/en/latest/_src_user_guide/state_propagation/environment_setup/create_models/available.html#available-environment-models) 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`."
+    "These settings, however, can be adjusted. Please refer to the [Available Environment Models](https://tudat-space.readthedocs.io/en/latest/_src_user_guide/state_propagation/environment_setup/create_models/available.html#available-environment-models) in the user guide for more details."
    ]
   },
   {
@@ -165,10 +142,10 @@
     "body_settings.get(\"Delfi-C3\").constant_mass = 2.2 #kg\n",
     "\n",
     "# Create aerodynamic coefficient interface settings\n",
-    "reference_area = (4*0.3*0.1+2*0.1*0.1)/4  # Average projection area of a 3U CubeSat\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_coefficient, 0.0, 0.0]\n",
+    "    reference_area_drag, [drag_coefficient, 0.0, 0.0]\n",
     ")\n",
     "# Add the aerodynamic interface to the environment\n",
     "body_settings.get(\"Delfi-C3\").aerodynamic_coefficient_settings = aero_coefficient_settings\n",
@@ -218,12 +195,15 @@
    "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."
@@ -704,7 +684,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Calculating residuals and partials 39\n",
       "Initial_covariance:\n",
       "\n",
       "[[ 2.16831937e-01  5.77721311e-02 -7.35943531e-02  8.62442596e-05\n",
@@ -723,6 +702,7 @@
       "   2.56615163e+03  2.77851575e+02  2.94641955e+14 -1.66514596e+02]\n",
       " [ 1.06862152e-04  5.15555071e-05 -4.04930006e-05  4.58235148e-08\n",
       "  -9.60473866e-10  1.28824286e-07 -1.66514596e+02  9.66866983e-08]]\n",
+      "Calculating residuals and partials 39\n",
       "\n",
       "Formal Error Matrix:\n",
       "\n",
@@ -784,7 +764,7 @@
     "print(f'Formal Error Matrix:\\n\\n{diagonal_covariance}\\n')\n",
     "\n",
     "sigma = 3  # Confidence level\n",
-    "original_eigenvalues, original_eigenvectors = np.linalg.eig(initial_covariance)\n",
+    "original_eigenvalues, original_eigenvectors = np.linalg.eig(diagonal_covariance)\n",
     "original_diagonal_eigenvalues, original_diagonal_eigenvectors = np.linalg.eig(diagonal_covariance)\n",
     "print(f'Estimated state and parameters:\\n\\n {parameters_to_estimate.parameter_vector}\\n')\n",
     "print(f'Eigenvalues of Covariance Matrix:\\n\\n {original_eigenvalues}\\n')\n",
@@ -976,20 +956,9 @@
     "plt.legend()\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "24d05573-d1a8-4ac0-b9e3-7e7a74b2bb14",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "4a4d53b53330cd83e1499268313a4bcd5eafe4bf50523883929af79f2dd687b2"
-  },
   "kernelspec": {
    "display_name": "tudat-bundle",
    "language": "python",
@@ -1005,7 +974,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

diff --git a/estimation/covariance_estimated_parameters.py b/estimation/covariance_estimated_parameters.py
@@ -72,8 +72,6 @@
 # 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.
 # 
 # These settings, however, can be adjusted. Please refer to the [Available Environment Models](https://tudat-space.readthedocs.io/en/latest/_src_user_guide/state_propagation/environment_setup/create_models/available.html#available-environment-models) in the user guide for more details.
-# 
-# Finally, the system of bodies is created using the settings. This system of bodies is stored into the variable `bodies`.
 
 # In[3]:
 
@@ -99,10 +97,10 @@
 body_settings.get("Delfi-C3").constant_mass = 2.2 #kg
 
 # Create aerodynamic coefficient interface settings
-reference_area = (4*0.3*0.1+2*0.1*0.1)/4  # Average projection area of a 3U CubeSat
+reference_area_drag = (4*0.3*0.1+2*0.1*0.1)/4  # Average projection area of a 3U CubeSat
 drag_coefficient = 1.2
 aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(
-    reference_area, [drag_coefficient, 0.0, 0.0]
+    reference_area_drag, [drag_coefficient, 0.0, 0.0]
 )
 # Add the aerodynamic interface to the environment
 body_settings.get("Delfi-C3").aerodynamic_coefficient_settings = aero_coefficient_settings
@@ -137,12 +135,15 @@
 
 # ### Create the acceleration model
 # Subsequently, all accelerations (and there settings) that act on `Delfi-C3` have to be defined. In particular, we will consider:
+# 
 # * Gravitational acceleration using a spherical harmonic approximation up to 8th degree and order for Earth.
 # * Aerodynamic acceleration for Earth.
 # * Gravitational acceleration using a simple point mass model for:
+# 
 #     - The Sun
 #     - The Moon
 #     - Mars
+# 
 # * Radiation pressure experienced by the spacecraft - shape-wise approximated as a spherical cannonball - due to the Sun.
 # 
 # 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.
@@ -464,7 +465,7 @@
 print(f'Formal Error Matrix:\n\n{diagonal_covariance}\n')
 
 sigma = 3  # Confidence level
-original_eigenvalues, original_eigenvectors = np.linalg.eig(initial_covariance)
+original_eigenvalues, original_eigenvectors = np.linalg.eig(diagonal_covariance)
 original_diagonal_eigenvalues, original_diagonal_eigenvectors = np.linalg.eig(diagonal_covariance)
 print(f'Estimated state and parameters:\n\n {parameters_to_estimate.parameter_vector}\n')
 print(f'Eigenvalues of Covariance Matrix:\n\n {original_eigenvalues}\n')
@@ -589,9 +590,3 @@
 plt.legend()
 plt.show()
 
-
-# In[ ]:
-
-
-
-
diff --git a/estimation/covariance_propagation_example.ipynb b/estimation/covariance_propagation_example.ipynb
@@ -11,28 +11,6 @@
     "This example will show you how to **propagate the covariance**. We will simulate a series of **one-way**  open loop observing sessions of the ``Starlink-32101`` satellite, and we will see simulating a different number of observations affects the evolution of the **formal errors** over time. This example builds up on the [Starlink-32101 Covariance Analysis example](https://docs.tudat.space/en/latest/_src_getting_started/_src_examples/notebooks/estimation/covariance_estimated_parameters.html), so go check that out if you haven't already! "
    ]
   },
-  {
-   "cell_type": "raw",
-   "id": "77d50ba7-f440-4e36-9215-6f2d1bb069da",
-   "metadata": {},
-   "source": [
-    "## Key API References\n",
-    " Here's a comprehensive list of the  _modules_ and _methods_ that are relevant to this example, or that will be introduced here for the first time.\n",
-    "\n",
-    "| Module | | Methods | ➡️ || | || | || |\n",
-    "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n",
-    "| [numerical_simulation.estimation_setup.observation](https://py.api.tudat.space/en/latest/observation.html) | [**body_reference_point_link_end_id**](https://py.api.tudat.space/en/latest/observation.html#tudatpy.numerical_simulation.estimation_setup.observation.body_reference_point_link_end_id) | [**LinkDefinition**](https://py.api.tudat.space/en/latest/observation.html#tudatpy.numerical_simulation.estimation_setup.observation.LinkDefinition) | [**one_way_doppler_instantaneous**](https://py.api.tudat.space/en/latest/observation.html#tudatpy.numerical_simulation.estimation_setup.observation.one_way_doppler_instantaneous) | [**add_gaussian_noise_to_observable**](https://py.api.tudat.space/en/latest/observation.html#tudatpy.numerical_simulation.estimation_setup.observation.add_gaussian_noise_to_observable) | [**elevation_angle_viability**](https://py.api.tudat.space/en/latest/observation.html#tudatpy.numerical_simulation.estimation_setup.observation.elevation_angle_viability)|[**tabulated_simulation_settings**](https://py.api.tudat.space/en/latest/observation.html#tudatpy.numerical_simulation.estimation_setup.observation.tabulated_simulation_settings)|\n",
-    "|[numerical_simulation.estimation_setup](https://py.api.tudat.space/en/latest/estimation_setup.html)| [**parameter**](https://py.api.tudat.space/en/latest/parameter.html) |[**create_parameter_set**](https://py.api.tudat.space/en/latest/estimation_setup.html#tudatpy.numerical_simulation.estimation_setup.create_parameter_set) | | | | |\n",
-    "| [numerical_simulation.Estimator](https://py.api.tudat.space/en/latest/numerical_simulation.html#tudatpy.numerical_simulation.Estimator) | [**observation_simulators**](https://py.api.tudat.space/en/latest/numerical_simulation.html#tudatpy.numerical_simulation.Estimator.observation_simulators) | [**compute_covariance**](https://py.api.tudat.space/en/latest/numerical_simulation.html#tudatpy.numerical_simulation.Estimator.compute_covariance) | [**perform_estimation**](https://py.api.tudat.space/en/latest/numerical_simulation.html#tudatpy.numerical_simulation.Estimator.perform_estimation) | [**state_transition_interface**](https://py.api.tudat.space/en/latest/numerical_simulation.html#tudatpy.numerical_simulation.Estimator.state_transition_interface) | |\n",
-    "| [numerical_simulation.estimation](https://py.api.tudat.space/en/latest/estimation.html)  | [**simulate_observations**](https://py.api.tudat.space/en/latest/estimation.html#tudatpy.numerical_simulation.estimation.simulate_observations) | [**CovarianceAnalysisInput**](https://py.api.tudat.space/en/latest/estimation.html#tudatpy.numerical_simulation.estimation.CovarianceAnalysisInput) | [**estimation_convergence_checker**](https://py.api.tudat.space/en/latest/estimation.html#tudatpy.numerical_simulation.estimation.EstimationConvergenceChecker) |[**EstimationInput**](https://py.api.tudat.space/en/latest/estimation.html#tudatpy.numerical_simulation.estimation.EstimationInput)| [**propagate_formal_errors_split_output**]| [**propagate_covariance_split_output**]()|\n",
-    "| [astro.frame_conversion](https://py.api.tudat.space/en/latest/frame_conversion.html#) | [**inertial_to_rsw_rotation_matrix**](https://py.api.tudat.space/en/latest/frame_conversion.html#tudatpy.astro.frame_conversion.inertial_to_rsw_rotation_matrix) | \n",
-    "| [astro.time_conversion](https://py.api.tudat.space/en/latest/time_conversion.html) | [**DateTime**]() | \n",
-    "| [astro.element_conversion](https://py.api.tudat.space/en/latest/element_conversion.html) | [**geodetic_position_type**]() | \n",
-    "\n",
-    "\n",
-    "🧐 **Missing something**? You can find out more on the [TudatPy API Reference](https://py.api.tudat.space/en/latest/), or you can [reach out to us](https://docs.tudat.space/en/latest/_src_about/contribute_to_tudat.html)! "
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "e99cbeda",
@@ -158,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 4,
    "id": "1585b93e",
    "metadata": {},
    "outputs": [],
@@ -234,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 6,
    "id": "ae320f6d",
    "metadata": {},
    "outputs": [],
@@ -280,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 7,
    "id": "12458abf",
    "metadata": {},
    "outputs": [],
@@ -305,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 8,
    "id": "44edeb70",
    "metadata": {},
    "outputs": [],
@@ -327,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 9,
    "id": "facb90c1",
    "metadata": {},
    "outputs": [],
@@ -363,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 10,
    "id": "4eebd846",
    "metadata": {},
    "outputs": [],
@@ -396,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 11,
    "id": "8a19c012",
    "metadata": {},
    "outputs": [],
@@ -444,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 12,
    "id": "48d30336",
    "metadata": {},
    "outputs": [
@@ -609,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 13,
    "id": "7d1256d3-0526-4773-a4e0-99fa4df47878",
    "metadata": {},
    "outputs": [
@@ -678,7 +656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 14,
    "id": "fa338460-f014-4490-8df1-e80c1fb2add1",
    "metadata": {},
    "outputs": [
@@ -750,14 +728,6 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6a8d645d-c170-4ad1-9565-7a91bb8b2f18",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -779,7 +749,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

diff --git a/estimation/covariance_propagation_example.py b/estimation/covariance_propagation_example.py
@@ -546,6 +546,3 @@
 plt.tight_layout()
 plt.show()
 
-
-
-