Repository: aidotse/PASEOS (language: python)

Issue:
Integrate orekit
# Feature
## Desired Behavior / Functionality

Long-term it will probably make sense to integrate [orekit](https://gitlab.orekit.org/orekit-labs/python-wrapper/blob/master/examples/The_Basics.ipynb) for the dynamics part.

## What Needs to Be Done
- Replace pykep dependency with orekit
- Create some tests to try it out

## How Can It Be Tested
- Tests
- Run a simulation

Allow using TLEs
# Description

Summary of changes

* Provide option to create actors from two-line elements which also implicitly allows using SGP4
* Added a tests
* Added docs for it
* Minor refactoring in ActorBuilder

## Resolved Issues

- [x] fixes #62 

## How Has This Been Tested?

CI/CD

## Related Pull Requests

N/A



Discussion / hints:
As a start we could also instead integrate this https://github.com/esa/polyhedral-gravity-model with pykep and thereby model irregular bodies with homogeneous density distributions
Short-term not relevant, can reopen if necessary


Fix patch:
diff --git a/.gitignore b/.gitignore
index d3ad94d1..c2d2a1aa 100644
--- a/.gitignore
+++ b/.gitignore
@@ -139,3 +139,4 @@ thermal_test.csv
 results
 pytest-coverage.txt
 pytest.xml
+examples/Orekit_example/orekit-data.zip
diff --git a/README.md b/README.md
index 26f90c25..770710b0 100644
--- a/README.md
+++ b/README.md
@@ -244,7 +244,11 @@ sim.add_known_actor(grndStation)
 
 #### Set an orbit for a PASEOS SpacecraftActor
 
-Once you have defined a [SpacecraftActor](#spacecraftactor), you can assign a [Keplerian orbit](https://en.wikipedia.org/wiki/Kepler_orbit) to it. To this aim, you need to define the central body the [SpacecraftActor](#spacecraftactor) is orbiting around and specify its position and velocity (in the central body's [inertial frame](https://en.wikipedia.org/wiki/Inertial_frame_of_reference)) and an epoch. In this case, we will use `Earth` as a central body.
+Once you have defined a [SpacecraftActor](#spacecraftactor), you can assign a [Keplerian orbit](https://en.wikipedia.org/wiki/Kepler_orbit) or use [SGP4 (Earth orbit only)](https://en.wikipedia.org/wiki/Simplified_perturbations_models). 
+
+##### Keplerian Orbit
+
+To this aim, you need to define the central body the [SpacecraftActor](#spacecraftactor) is orbiting around and specify its position and velocity (in the central body's [inertial frame](https://en.wikipedia.org/wiki/Inertial_frame_of_reference)) and an epoch. In this case, we will use `Earth` as a central body.
 
 ```py
 import pykep as pk
@@ -264,6 +268,61 @@ ActorBuilder.set_orbit(actor=sat_actor,
                        epoch=pk.epoch(0), central_body=earth)
 ```
 
+##### SGP4 / Two-line element (TLE) 
+
+For using SGP4 / [Two-line element (TLE)](https://en.wikipedia.org/wiki/Two-line_element_set) you need to specify the TLE of the [SpacecraftActor](#spacecraftactor). In this case, we will use the TLE of the [Sentinel-2A](https://en.wikipedia.org/wiki/Sentinel-2) satellite from [celestrak](https://celestrak.com/).
+
+```py
+from paseos import ActorBuilder, SpacecraftActor
+# Define an actor of type SpacecraftActor
+sat_actor = ActorBuilder.get_actor_scaffold(name="Sentinel-2A",
+                                       actor_type=SpacecraftActor,
+                                       epoch=pk.epoch(0))
+
+# Specify your TLE
+line1 = "1 40697U 15028A   23188.15862373  .00000171  00000+0  81941-4 0  9994"
+line2 = "2 40697  98.5695 262.3977 0001349  91.8221 268.3116 14.30817084419867"
+
+# Set the orbit of the actor
+ActorBuilder.set_TLE(sat_actor, line1, line2)
+```
+
+##### Custom Propagators
+
+You can define any kind of function you would like to determine actor positions and velocities. This allows integrating more sophisticated propagators such as [orekit](https://www.orekit.org/). A dedicated example on this topic can be found in the `examples` folder.
+
+In short, you need to define a propagator function that returns the position and velocity of the actor at a given time. The function shall take the current epoch as arguments. You can then set the propagator function with
+
+```py
+import pykep as pk
+from paseos import ActorBuilder, SpacecraftActor
+# Create a SpacecraftActor
+starting_epoch = pk.epoch(42)
+my_sat = ActorBuilder.get_actor_scaffold(
+    name="my_sat", actor_type=SpacecraftActor, epoch=starting_epoch
+)
+
+# Define a custom propagator function that just returns a sinus position
+def my_propagator(epoch: pk.epoch):
+  position,velocity = your_external_propagator(epoch)
+  return position,velocity
+
+# Set the custom propagator
+ActorBuilder.set_custom_orbit(my_sat, my_propagator, starting_epoch)
+```
+
+##### Accessing the orbit
+You can access the orbit of a [SpacecraftActor](#spacecraftactor) with
+
+```py
+# Position, velocity and altitude can be accessed like this
+t0 = pk.epoch("2022-06-16 00:00:00.000") # Define the time (epoch)
+print(sat_actor.get_position(t0)) 
+print(sat_actor.get_position_velocity(t0))
+print(sat_actor.get_altitude(t0))
+```
+
+
 #### How to add a communication device
 
 The following code snippet shows how to add a communication device to a [SpacecraftActors] (#spacecraftactor). A communication device is needed to model the communication between [SpacecraftActors] (#spacecraftactor) or a [SpacecraftActor](#spacecraftactor) and [GroundstationActor](#ground-stationactor). Currently, given the maximum transmission data rate of a communication device, PASEOS calculates the maximum data that can be transmitted by multiplying the transmission data rate by the length of the communication window. The latter is calculated by taking the period for which two actors are in line-of-sight into account.
diff --git a/examples/Orekit_example/orekit_integration.ipynb b/examples/Orekit_example/orekit_integration.ipynb
new file mode 100644
index 00000000..631e1ae0
--- /dev/null
+++ b/examples/Orekit_example/orekit_integration.ipynb
@@ -0,0 +1,256 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PASEOS Orekit integration example\n",
+    "\n",
+    "This example requires an [orekit installation](https://gitlab.orekit.org/orekit-labs/python-wrapper/-/wikis/installation) via conda. (Note that orekit requires Java)\n",
+    "\n",
+    "It showcases how to adapt an example from the [orekit documentation](https://gitlab.orekit.org/orekit-labs/python-wrapper/-/blob/master/examples/Example_numerical_prop.ipynb ) to utilize orekit for position and velocity calculations inside PASEOS.\n",
+    "\n",
+    "Additionally, you will need to place the orekit [data](https://gitlab.orekit.org/orekit/orekit-data) as `orekit-data.zip` in the same folder as this notebook. (N.B. don't decompress the zip file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(\"..\")\n",
+    "sys.path.append(\"../..\")\n",
+    "\n",
+    "import paseos\n",
+    "from paseos import ActorBuilder, SpacecraftActor\n",
+    "\n",
+    "import pykep as pk\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import orekit\n",
+    "\n",
+    "from orekit.pyhelpers import setup_orekit_curdir\n",
+    "from orekit_propagator import OrekitPropagator\n",
+    "from org.orekit.time import AbsoluteDate, TimeScalesFactory\n",
+    "from orekit.pyhelpers import absolutedate_to_datetime\n",
+    "\n",
+    "\n",
+    "# Initialize the orekit virtual machine and set the current directory\n",
+    "vm = orekit.initVM()\n",
+    "setup_orekit_curdir()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize the propagator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set a start date\n",
+    "utc = TimeScalesFactory.getUTC()\n",
+    "epoch = AbsoluteDate(2020, 1, 1, 0, 0, 00.000, utc)\n",
+    "\n",
+    "# Let's define some orbit and a satellite mass of 1000 kg\n",
+    "# We use an orbit with a semi-major axis of 10000 km, a eccentricity of 0.75 and an inclination of 1e-4°\n",
+    "orbital_elements = [10000000.0, 0.75, 0.0001, 0.0, 0.0, 0.0]\n",
+    "satellite_mass = 1000.0\n",
+    "\n",
+    "# Initialize the propagator\n",
+    "propagator = OrekitPropagator(orbital_elements, epoch, satellite_mass)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check the orbit propagation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can check it works by propagating a bit\n",
+    "r,v = [], []\n",
+    "for t in range(0, 10000, 100):\n",
+    "    state = propagator.eph(float(t))\n",
+    "    r.append(state.getPVCoordinates().getPosition())\n",
+    "    v.append(state.getPVCoordinates().getVelocity())\n",
+    "\n",
+    "# Plot the orbit and velocity magnitude\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax[0].plot([i.getX() for i in r], [i.getY() for i in r])\n",
+    "ax[0].set_xlabel(\"X [m]\")\n",
+    "ax[0].set_ylabel(\"Y [m]\")\n",
+    "ax[0].set_title(\"Orbit\")\n",
+    "ax[0].set_aspect(\"equal\", \"box\")\n",
+    "ax[1].plot([i.getNorm() for i in v])\n",
+    "ax[1].set_xlabel(\"Time [s]\")\n",
+    "ax[1].set_ylabel(\"Velocity [m/s]\")\n",
+    "ax[1].set_title(\"Velocity magnitude\")\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using it with PASEOS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert to pykep epoch\n",
+    "date_string = absolutedate_to_datetime(epoch).strftime(\"%Y-%m-%d %H:%M:%S\")\n",
+    "pykep_epoch = pk.epoch_from_string(date_string)\n",
+    "\n",
+    "# Create a spacecraft actor\n",
+    "my_sat = ActorBuilder.get_actor_scaffold(name=\"my_sat\", actor_type=SpacecraftActor, epoch=pykep_epoch)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we need to define a function that returns position and velocity given an epoch. We will use our propagator for this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def propagator_function(epoch: pk.epoch):\n",
+    "    \"\"\"Propagator function for the spacecraft actor.\n",
+    "\n",
+    "    Args:\n",
+    "        epoch (pk.epoch): Epoch to propagate to. (can be expensive!)\n",
+    "\n",
+    "    Returns:\n",
+    "        list: List of position and velocity.\n",
+    "    \"\"\"\n",
+    "    # Compute the time since the start of the simulation\n",
+    "    time_since_start = (epoch.mjd2000 - pykep_epoch.mjd2000) * pk.DAY2SEC\n",
+    "\n",
+    "    # Propagate the orbit\n",
+    "    state = propagator.eph(time_since_start)\n",
+    "\n",
+    "    # Return the position and velocity as a list\n",
+    "    r = list(state.getPVCoordinates().getPosition().toArray())\n",
+    "    v = list(state.getPVCoordinates().getVelocity().toArray())\n",
+    "    \n",
+    "    return r, v\n",
+    "\n",
+    "# Set the custom orbit\n",
+    "ActorBuilder.set_custom_orbit(my_sat, propagator_function, pykep_epoch)\n",
+    "\n",
+    "# Create a PASEOS instance\n",
+    "instance = paseos.init_sim(local_actor=my_sat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's check if it works by running the PASEOS simulation for this actor and get the position as before.\n",
+    "\n",
+    "If everything went well, you should see the same plot again, except this time it's happening inside PASEOS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "# We can check it works by propagating a bit\n",
+    "r,v = [], []\n",
+    "for _ in range(0, 10000, 100):\n",
+    "    current_epoch = instance.local_time\n",
+    "    r_t,v_t = my_sat.get_position_velocity(current_epoch)\n",
+    "    r.append(r_t)\n",
+    "    v.append(v_t)\n",
+    "    instance.advance_time(time_to_advance=100.0, current_power_consumption_in_W=0.0)\n",
+    "\n",
+    "# Plot the orbit and velocity magnitude\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax[0].plot([i[0] for i in r], [i[1] for i in r])\n",
+    "ax[0].set_xlabel(\"X [m]\")\n",
+    "ax[0].set_ylabel(\"Y [m]\")\n",
+    "ax[0].set_title(\"Orbit\")\n",
+    "ax[0].set_aspect(\"equal\", \"box\")\n",
+    "ax[1].plot([np.linalg.norm(i) for i in v])\n",
+    "ax[1].set_xlabel(\"Time [s]\")\n",
+    "ax[1].set_ylabel(\"Velocity [m/s]\")\n",
+    "ax[1].set_title(\"Velocity magnitude\")\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "paseos",
+   "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": "3.10.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/Orekit_example/orekit_propagator.py b/examples/Orekit_example/orekit_propagator.py
new file mode 100644
index 00000000..5617b843
--- /dev/null
+++ b/examples/Orekit_example/orekit_propagator.py
@@ -0,0 +1,108 @@
+from org.orekit.orbits import KeplerianOrbit, PositionAngle
+from org.orekit.time import AbsoluteDate
+from org.orekit.utils import Constants
+from org.orekit.frames import FramesFactory
+from org.orekit.orbits import OrbitType
+from org.orekit.propagation.numerical import NumericalPropagator
+from org.hipparchus.ode.nonstiff import DormandPrince853Integrator
+from org.orekit.propagation import SpacecraftState
+from org.orekit.utils import IERSConventions
+from org.orekit.forces.gravity.potential import GravityFieldFactory
+from org.orekit.forces.gravity import HolmesFeatherstoneAttractionModel
+
+from orekit import JArray_double
+
+
+class OrekitPropagator:
+    """This class serves as a wrapper to orekit. It initializes the orekit
+    virtual machine and provides a method to propagate a satellite orbit.
+
+    It follows the example from the orekit documentation:
+
+    https://gitlab.orekit.org/orekit-labs/python-wrapper/-/blob/master/examples/Propagation.ipynb
+    """
+
+    # Constants for the numerical propagator, see orekit docs for details
+    minStep = 0.0001
+    maxstep = 1000.0
+    initStep = 60.0
+    positionTolerance = 1.0
+
+    def __init__(self, orbital_elements: list, epoch: AbsoluteDate, satellite_mass: float) -> None:
+        """Initialize the propagator.
+
+        Args:
+            orbital_elements (list): List of orbital elements.
+            epoch (AbsoluteDate): Epoch of the orbit.
+            satellite_mass (float): Mass of the satellite.
+        """
+
+        # Inertial frame where the satellite is defined
+        inertialFrame = FramesFactory.getEME2000()
+
+        # Unpack the orbital elements
+        a, e, i, omega, raan, lv = orbital_elements
+
+        self.initialDate = epoch
+
+        # Orbit construction as Keplerian
+        initialOrbit = KeplerianOrbit(
+            a,
+            e,
+            i,
+            omega,
+            raan,
+            lv,
+            PositionAngle.TRUE,
+            inertialFrame,
+            epoch,
+            Constants.WGS84_EARTH_MU,
+        )
+
+        # Set up the numerical propagator tolerance
+        tolerances = NumericalPropagator.tolerances(
+            self.positionTolerance, initialOrbit, initialOrbit.getType()
+        )
+
+        # Set up the numerical integrator
+        integrator = DormandPrince853Integrator(
+            self.minStep,
+            self.maxstep,
+            JArray_double.cast_(
+                tolerances[0]
+            ),  # Double array of doubles needs to be casted in Python
+            JArray_double.cast_(tolerances[1]),
+        )
+        integrator.setInitialStepSize(self.initStep)
+
+        # Define the initial state of the spacecraft
+        satellite_mass = 100.0  # The models need a spacecraft mass, unit kg.
+        initialState = SpacecraftState(initialOrbit, satellite_mass)
+
+        # Set up the numerical propagator
+        self.propagator_num = NumericalPropagator(integrator)
+        self.propagator_num.setOrbitType(OrbitType.CARTESIAN)
+        self.propagator_num.setInitialState(initialState)
+
+        # Add the force models
+        gravityProvider = GravityFieldFactory.getNormalizedProvider(10, 10)
+        self.propagator_num.addForceModel(
+            HolmesFeatherstoneAttractionModel(
+                FramesFactory.getITRF(IERSConventions.IERS_2010, True), gravityProvider
+            )
+        )
+
+    def eph(self, time_since_epoch_in_seconds: float):
+        """Get the position and velocity of the satellite at a given time since epoch.
+
+        Args:
+            time_since_epoch_in_seconds (float): Time since epoch in seconds.
+
+        Returns:
+            orekit SpacecraftState: The position and velocity of the satellite.
+        """
+        state = self.propagator_num.propagate(
+            self.initialDate, self.initialDate.shiftedBy(time_since_epoch_in_seconds)
+        )
+
+        return state
diff --git a/examples/Sentinel_2_example_notebook/Sentinel2_example_notebook.ipynb b/examples/Sentinel_2_example_notebook/Sentinel2_example_notebook.ipynb
index 20103021..52a24775 100644
--- a/examples/Sentinel_2_example_notebook/Sentinel2_example_notebook.ipynb
+++ b/examples/Sentinel_2_example_notebook/Sentinel2_example_notebook.ipynb
@@ -25,7 +25,6 @@
     "import os\n",
     "sys.path.insert(1, os.path.join(\"..\",\"..\"))\n",
     "import pykep as pk\n",
-    "import numpy as np\n",
     "import paseos\n",
     "from paseos import ActorBuilder, SpacecraftActor\n",
     "from utils import s2pix_detector, acquire_data\n",
@@ -55,8 +54,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "#Define today as pykep epoch (27-10-22)\n",
+    "#please, refer to https://esa.github.io/pykep/documentation/core.html#pykep.epoch\n",
+    "today = pk.epoch_from_string('2022-10-27 12:00:00.000')\n",
+    "\n",
     "# Define local actor\n",
-    "S2B = ActorBuilder.get_actor_scaffold(name=\"Sentinel2-B\", actor_type=SpacecraftActor, epoch=pk.epoch(0))"
+    "S2B = ActorBuilder.get_actor_scaffold(name=\"Sentinel2-B\", actor_type=SpacecraftActor, epoch=today)"
    ]
   },
   {
@@ -65,30 +68,7 @@
    "metadata": {},
    "source": [
     "#### 1.a) - Add an orbit for S2B\n",
-    "\n",
-    "Since **S2B** is orbiting around Earth, let's define `earth` as `pykep.planet` object. \n",
-    "\n",
-    "Internally, paseos uses pykep.planet objects to model gravitational acceleration and Keplerian orbits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "30691b25",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define central body\n",
-    "earth = pk.planet.jpl_lp(\"earth\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "013bf2e2",
-   "metadata": {},
-   "source": [
-    "To find realistic orbits for **S2B**, we can exploit `Two Line Elements (TLEs)` (Downloaded on 27-10-2022). This would allow finding their ephemerides at time = 27-10-2022 12:00:00.\n",
-    "Please, refer to [Two-line_element_set](https://en.wikipedia.org/wiki/Two-line_element_set) to know more about TLEs."
+    "We can make use of the [two-line element](https://en.wikipedia.org/wiki/Two-line_element_set) actor creation [inside PASEOS](https://github.com/aidotse/PASEOS/tree/allow_using_TLE#sgp4--two-line-element-tle) for set the orbit of the PASEOS actor (TLE Downloaded on 27-10-2022)."
    ]
   },
   {
@@ -98,38 +78,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Define today as pykep epoch (27-10-22)\n",
-    "#please, refer to https://esa.github.io/pykep/documentation/core.html#pykep.epoch\n",
-    "today = pk.epoch_from_string('2022-10-27 12:00:00.000')\n",
-    "\n",
     "sentinel2B_line1 = \"1 42063U 17013A   22300.18652110  .00000099  00000+0  54271-4 0  9998\"\n",
     "sentinel2B_line2 = \"2 42063  98.5693  13.0364 0001083 104.3232 255.8080 14.30819357294601\"\n",
-    "sentinel2B = pk.planet.tle(sentinel2B_line1, sentinel2B_line2)\n",
     "\n",
-    "#Calculating S2B ephemerides.\n",
-    "sentinel2B_eph=sentinel2B.eph(today)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8eab58f6",
-   "metadata": {},
-   "source": [
-    "Now we define the actor's orbit for **S2B**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3567ebb8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Adding orbit around Earth based on previously calculated ephemerides\n",
-    "ActorBuilder.set_orbit(actor=S2B, \n",
-    "                       position=sentinel2B_eph[0], \n",
-    "                       velocity=sentinel2B_eph[1], \n",
-    "                       epoch=today, central_body=earth)"
+    "ActorBuilder.set_TLE(S2B, sentinel2B_line1, sentinel2B_line2)"
    ]
   },
   {
@@ -463,7 +415,9 @@
    "source": [
     "### 3.d) - Showing detected volcanic eruptions\n",
     "\n",
-    "The next plot will show an example of onboard coarse volcanic eruptions detection on some Sentinel-2 L1C tiles. The different eruptions will be surrounded a bounding box, and their coordinates will be printed to raise an alert."
+    "The next plot will show an example of onboard coarse volcanic eruptions detection on some Sentinel-2 L1C tiles. The different eruptions will be surrounded a bounding box, and their coordinates will be printed to raise an alert.\n",
+    "\n",
+    "The execution and rendering of the images may take a few minutes."
    ]
   },
   {
@@ -480,7 +434,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "85bbd3ac",
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [],
    "source": [
     "fig, ax=plt.subplots(1,3,figsize=(10,4))\n",
@@ -502,7 +458,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.6 ('paseos')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/paseos/actors/actor_builder.py b/paseos/actors/actor_builder.py
index c56f5d2b..63a068e0 100644
--- a/paseos/actors/actor_builder.py
+++ b/paseos/actors/actor_builder.py
@@ -1,6 +1,7 @@
 from typing import Callable, Any
 
 from loguru import logger
+import numpy as np
 from dotmap import DotMap
 import pykep as pk
 from skyfield.api import wgs84
@@ -16,18 +17,19 @@
 class ActorBuilder:
     """This class is used to construct actors."""
 
-    def __new__(self):
-        if not hasattr(self, "instance"):
-            self.instance = super(ActorBuilder, self).__new__(self)
+    def __new__(cls):
+        if not hasattr(cls, "instance"):
+            cls.instance = super(ActorBuilder, cls).__new__(cls)
         else:
             logger.debug(
                 "Tried to create another instance of ActorBuilder. Keeping original one..."
             )
-        return self.instance
+        return cls.instance
 
     def __init__(self):
         logger.trace("Initializing ActorBuilder")
 
+    @staticmethod
     def get_actor_scaffold(name: str, actor_type: object, epoch: pk.epoch):
         """Initiates an actor with minimal properties.
 
@@ -50,6 +52,7 @@ def get_actor_scaffold(name: str, actor_type: object, epoch: pk.epoch):
 
         return actor_type(name, epoch)
 
+    @staticmethod
     def set_ground_station_location(
         actor: GroundstationActor,
         latitude: float,
@@ -81,6 +84,74 @@ def set_ground_station_location(
         )
         actor._minimum_altitude_angle = minimum_altitude_angle
 
+    @staticmethod
+    def set_custom_orbit(actor: SpacecraftActor, propagator_func: Callable, epoch: pk.epoch):
+        """Define the orbit of the actor using a custom propagator function.
+        The custom function has to return position and velocity in meters
+        and meters per second respectively. The function will be called with the
+        current epoch as the only parameter.
+
+        Args:
+            actor (SpacecraftActor): Actor to update.
+            propagator_func (Callable): Function to propagate the orbit.
+            epoch (pk.epoch): Current epoch.
+        """
+        assert callable(propagator_func), "propagator_func has to be callable."
+        assert isinstance(epoch, pk.epoch), "epoch has to be a pykep epoch."
+        assert isinstance(actor, SpacecraftActor), "Orbit only supported for SpacecraftActors"
+        assert actor._orbital_parameters is None, "Actor already has an orbit."
+        assert np.isclose(
+            actor.local_time.mjd2000, epoch.mjd2000
+        ), "The initial epoch has to match actor's local time."
+        actor._custom_orbit_propagator = propagator_func
+
+        # Try evaluating position and velocity to check if the function works
+        try:
+            position, velocity = actor.get_position_velocity(epoch)
+            assert len(position) == 3, "Position has to be list of 3 floats."
+            assert all(
+                [isinstance(val, float) for val in position]
+            ), "Position has to be list of 3 floats."
+            assert len(velocity) == 3, "Velocity has to be list of 3 floats."
+            assert all(
+                [isinstance(val, float) for val in velocity]
+            ), "Velocity has to be list of 3 floats."
+        except Exception as e:
+            logger.error(f"Error evaluating custom orbit propagator function: {e}")
+            raise RuntimeError("Error evaluating custom orbit propagator function.")
+
+        logger.debug(f"Added custom orbit propagator to actor {actor}")
+
+    @staticmethod
+    def set_TLE(
+        actor: SpacecraftActor,
+        line1: str,
+        line2: str,
+    ):
+        """Define the orbit of the actor using a TLE. For more information on TLEs see
+        https://en.wikipedia.org/wiki/Two-line_element_set .
+
+        TLEs can be obtained from https://www.space-track.org/ or https://celestrak.com/NORAD/elements/
+
+        Args:
+            actor (SpacecraftActor): Actor to update.
+            line1 (str): First line of the TLE.
+            line2 (str): Second line of the TLE.
+
+        Raises:
+            RuntimeError: If the TLE could not be read.
+        """
+        try:
+            actor._orbital_parameters = pk.planet.tle(line1, line2)
+            # TLE only works around Earth
+            actor._central_body = pk.planet.jpl_lp("earth")
+        except RuntimeError:
+            logger.error("Error reading TLE \n", line1, "\n", line2)
+            raise RuntimeError("Error reading TLE")
+
+        logger.debug(f"Added TLE to actor {actor}")
+
+    @staticmethod
     def set_orbit(
         actor: SpacecraftActor,
         position,
@@ -115,8 +186,9 @@ def set_orbit(
 
         logger.debug(f"Added orbit to actor {actor}")
 
+    @staticmethod
     def set_position(actor: BaseActor, position: list):
-        """Sets the actors position. Use this if you do not want the actor to have a keplerian orbit around a central body.
+        """Sets the actors position. Use this if you do *not* want the actor to have a keplerian orbit around a central body.
 
         Args:
             actor (BaseActor): Actor set the position on.
@@ -133,6 +205,7 @@ def set_position(actor: BaseActor, position: list):
         actor._position = position
         logger.debug(f"Setting position {position} on actor {actor}")
 
+    @staticmethod
     def set_power_devices(
         actor: SpacecraftActor,
         battery_level_in_Ws: float,
@@ -182,6 +255,7 @@ def set_power_devices(
             + f"ChargingRate={charging_rate_in_W}W to actor {actor}"
         )
 
+    @staticmethod
     def set_radiation_model(
         actor: SpacecraftActor,
         data_corruption_events_per_s: float,
@@ -215,6 +289,7 @@ def set_radiation_model(
         )
         logger.debug(f"Added radiation model to actor {actor}.")
 
+    @staticmethod
     def set_thermal_model(
         actor: SpacecraftActor,
         actor_mass: float,
@@ -310,6 +385,7 @@ def set_thermal_model(
             power_consumption_to_heat_ratio=power_consumption_to_heat_ratio,
         )
 
+    @staticmethod
     def add_comm_device(actor: BaseActor, device_name: str, bandwidth_in_kbps: float):
         """Creates a communication device.
 
@@ -327,6 +403,7 @@ def add_comm_device(actor: BaseActor, device_name: str, bandwidth_in_kbps: float
 
         logger.debug(f"Added comm device with bandwith={bandwidth_in_kbps} kbps to actor {actor}.")
 
+    @staticmethod
     def add_custom_property(
         actor: BaseActor, property_name: str, initial_value: Any, update_function: Callable
     ):
diff --git a/paseos/actors/base_actor.py b/paseos/actors/base_actor.py
index d72d0fd7..fa9dd2cf 100644
--- a/paseos/actors/base_actor.py
+++ b/paseos/actors/base_actor.py
@@ -25,6 +25,9 @@ class BaseActor(ABC):
     # Orbital parameters of the actor, stored in a pykep planet object
     _orbital_parameters = None
 
+    # Custom function for orbital propagation
+    _custom_orbit_propagator = None
+
     # Position if not defined by orbital parameters
     _position = None
 
@@ -262,24 +265,24 @@ def get_position(self, epoch: pk.epoch):
             np.array: [x,y,z] in meters
         """
         logger.trace(
-            "Computing "
-            + self._orbital_parameters.name
-            + " position at time "
-            + str(epoch.mjd2000)
-            + " (mjd2000)."
+            "Computing " + self.name + " position at time " + str(epoch.mjd2000) + " (mjd2000)."
         )
 
-        if self._orbital_parameters is not None and self._position is not None:
+        if (
+            self._orbital_parameters is not None or self._custom_orbit_propagator is not None
+        ) and self._position is not None:
             raise ValueError(
                 "Ambiguous position definition. Either set an orbit OR position with ActorBuilder."
             )
 
         # If the actor has no orbit, return position
-        if self._orbital_parameters is None:
+        if self._orbital_parameters is None and self._custom_orbit_propagator is None:
             if self._position is not None:
                 self._previous_position = self._position
                 self._time_of_previous_position = epoch.mjd2000
                 return self._position
+        elif self._custom_orbit_propagator is not None:
+            return self._custom_orbit_propagator(epoch)[0]
         else:
             return self._orbital_parameters.eph(epoch)[0]
 
@@ -296,19 +299,27 @@ def get_position_velocity(self, epoch: pk.epoch):
         Returns:
             np.array: [x,y,z] in meters
         """
-        if self._orbital_parameters is None:
+
+        if self._orbital_parameters is None and self._custom_orbit_propagator is None:
             raise NotImplementedError(
                 "No suitable way added to determine actor velocity. Set an orbit with ActorBuilder."
             )
 
         logger.trace(
             "Computing "
-            + self._orbital_parameters.name
+            + self.name
             + " position / velocity at time "
             + str(epoch.mjd2000)
             + " (mjd2000)."
         )
-        pos, vel = self._orbital_parameters.eph(epoch)
+
+        # Use either custom propagator or pykep to compute position / velocity
+        if self._custom_orbit_propagator is not None:
+            pos, vel = self._custom_orbit_propagator(epoch)
+        else:
+            pos, vel = self._orbital_parameters.eph(epoch)
+
+        # Store the position / velocity for later use
         self._previous_position = pos
         self._previous_velocity = vel
         self._time_of_previous_position = epoch.mjd2000


Test patch:
diff --git a/paseos/tests/actor_builder_test.py b/paseos/tests/actor_builder_test.py
index b5160922..7dafab5b 100644
--- a/paseos/tests/actor_builder_test.py
+++ b/paseos/tests/actor_builder_test.py
@@ -5,11 +5,53 @@
 
 sys.path.append("../..")
 
-from paseos import ActorBuilder
+from paseos import ActorBuilder, SpacecraftActor
 
 from test_utils import get_default_instance
 
 
+def test_set_TLE():
+    """Check if we can set a TLE correctly"""
+
+    _, sentinel2a, earth = get_default_instance()
+    # Set the TLE
+    line1 = "1 40697U 15028A   23188.15862373  .00000171  00000+0  81941-4 0  9994"
+    line2 = "2 40697  98.5695 262.3977 0001349  91.8221 268.3116 14.30817084419867"
+    ActorBuilder.set_TLE(sentinel2a, line1, line2)
+
+    # Check that get_altitude returns a sensible value
+    earth_radius = 6371000
+    assert sentinel2a.get_altitude() > earth_radius + 780000
+    assert sentinel2a.get_altitude() < earth_radius + 820000
+
+    # Check that get_position_velocity returns sensible values
+    position, velocity = sentinel2a.get_position_velocity(sentinel2a.local_time)
+    assert position is not None
+    assert velocity is not None
+
+    # Create an actor with a keplerian orbit and check that the position and velocity
+    # diverge over time
+    s2a_kep = ActorBuilder.get_actor_scaffold("s2a_kep", SpacecraftActor, sentinel2a.local_time)
+    ActorBuilder.set_orbit(s2a_kep, position, velocity, sentinel2a.local_time, earth)
+
+    # After some orbits the differences should be significant
+    # since the TLE uses SGP4 and the other actor uses Keplerian elements
+    t0_later = pk.epoch(sentinel2a.local_time.mjd2000 + 1)
+    r, v = sentinel2a.get_position_velocity(t0_later)
+    r_kep, v_kep = s2a_kep.get_position_velocity(t0_later)
+    print("r,v SGP4 after  1 day")
+    print(r)
+    print(v)
+    print("r,v Kep  after  1 day")
+    print(r_kep)
+    print(v_kep)
+    print("Differences in r and v")
+    print(np.linalg.norm(np.array(r) - np.array(r_kep)))
+    print(np.linalg.norm(np.array(v) - np.array(v_kep)))
+    assert np.linalg.norm(np.array(r) - np.array(r_kep)) > 100000
+    assert np.linalg.norm(np.array(v) - np.array(v_kep)) > 400
+
+
 def test_set_orbit():
     """Check if we can specify an orbit correctly"""
     _, sat1, earth = get_default_instance()
diff --git a/paseos/tests/custom_propagator_test.py b/paseos/tests/custom_propagator_test.py
new file mode 100644
index 00000000..293c12c6
--- /dev/null
+++ b/paseos/tests/custom_propagator_test.py
@@ -0,0 +1,48 @@
+"""Test the ability to use a custom propagator in a paseos simulation."""
+import sys
+
+sys.path.append("../..")
+
+import numpy as np
+import pykep as pk
+from paseos import SpacecraftActor, ActorBuilder
+
+
+def test_custom_propagator():
+    """Test if we can just determine position as static with a custom propagator"""
+
+    # Create a spacecraft actor
+    starting_epoch = pk.epoch(42)
+    my_sat = ActorBuilder.get_actor_scaffold(
+        name="my_sat", actor_type=SpacecraftActor, epoch=starting_epoch
+    )
+
+    # Define a custom propagator function that just returns a sinus position
+    def my_propagator(epoch: pk.epoch):
+        """Custom propagator that returns a sinus position"""
+        time_since_epoch_in_seconds = (epoch.mjd2000 - starting_epoch.mjd2000) * pk.DAY2SEC
+
+        r = [np.sin(time_since_epoch_in_seconds), 0.0, 0.0]
+        v = [42.0, 42.0, 42.0]
+
+        return r, v
+
+    # Set the custom propagator
+    ActorBuilder.set_custom_orbit(my_sat, my_propagator, starting_epoch)
+
+    # Check that the position is correct
+    r = my_sat.get_position(starting_epoch)
+    assert np.allclose(r, [0, 0, 0])
+
+    r, v = my_sat.get_position_velocity(starting_epoch)
+    assert np.allclose(r, [0, 0, 0])
+    assert np.allclose(v, [42, 42, 42])
+
+    # Check that the position is correct at a later time
+    later = pk.epoch(42 + 0.5 * np.pi * pk.SEC2DAY)
+    r = my_sat.get_position(later)
+    assert np.allclose(r, [1, 0, 0])
+
+    r, v = my_sat.get_position_velocity(later)
+    assert np.allclose(r, [1, 0, 0])
+    assert np.allclose(v, [42, 42, 42])
diff --git a/paseos/tests/time_multiplier_test.py b/paseos/tests/time_multiplier_test.py
index 0f43a1dd..1623a5ab 100644
--- a/paseos/tests/time_multiplier_test.py
+++ b/paseos/tests/time_multiplier_test.py
@@ -54,4 +54,4 @@ async def func(args):
     # 1s real time equals 5s simulation
     # And discharge 10W per second
     # So should be roughly 50W - 5W consumed from starting 500
-    assert sat1.battery_level_in_Ws > 350 and sat1.battery_level_in_Ws <= 455.1
+    assert sat1.battery_level_in_Ws > 350 and sat1.battery_level_in_Ws <= 456