diff --git a/examples/Frames and Coordinate Systems.ipynb b/examples/Frames and Coordinate Systems.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Frames and Coordinate Systems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Authors\n",
+    "\n",
+    "Lots of parts are from the orekit documentation, with some updates, simplifications and Pythonification by Petrus Hyvönen, SSC\n",
+    "\n",
+    "## Learning Goals\n",
+    "* *What are frames*: How to work with frames and transformations in Orekit\n",
+    "* *How can I use pre-defined frames*: How to use and specify typical pre-defined frames \n",
+    "\n",
+    "## Keywords\n",
+    "orekit, frames"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "source": [
+    "Initialize orkit and bring up the python-java interface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import orekit\n",
+    "vm = orekit.initVM()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now set up the pointer to the orekit-data.zip file, using one of the helper files. The file should be in current directory if not specified otherwise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from orekit.pyhelpers import setup_orekit_curdir, absolutedate_to_datetime\n",
+    "setup_orekit_curdir()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "source": [
+    "Now we are set up to import and use objects from the orekit library. Packages can be imported as they were native Python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from org.hipparchus.geometry.euclidean.threed import Vector3D, SphericalCoordinates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from org.orekit.data import DataProvidersManager, ZipJarCrawler\n",
+    "from org.orekit.frames import FramesFactory, TopocentricFrame\n",
+    "from org.orekit.bodies import OneAxisEllipsoid, GeodeticPoint, CelestialBodyFactory\n",
+    "from org.orekit.time import TimeScalesFactory, AbsoluteDate, DateComponents, TimeComponents\n",
+    "from org.orekit.utils import IERSConventions, Constants, PVCoordinates, PVCoordinatesProvider, AbsolutePVCoordinates\n",
+    "\n",
+    "from org.orekit.propagation.analytical.tle import TLE, TLEPropagator\n",
+    "from java.io import File\n",
+    "\n",
+    "from math import radians, pi, degrees\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "attachments": {
+    "b6e2cb99-2618-4790-962b-e394e9d408f3.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Frames\n",
+    "\n",
+    "A coordinate system is anchored in specified reference points, called frames. The definition of these can be of vital importance to space missions.\n",
+    "\n",
+    "The orekit package supports a number of pre-defined frames, and also advanced user defined frames. All frames but the root frame are related to a parent frame, so that it is possible to transform between any frames.\n",
+    "\n",
+    "![image.png](attachment:b6e2cb99-2618-4790-962b-e394e9d408f3.png)\n",
+    "\n",
+    "_Image from orekit documentation_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For example, if we request a pre-defined frame with a center located at the Sun's gravity center, and rotating with the Sun rotation (according to implemented models), we can trace the different frames that has been used, all the way to the root frame, the GCRF."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sun_frame = CelestialBodyFactory.getSun().getBodyOrientedFrame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sun/rotating \n",
+      " Sun/inertial \n",
+      " ICRF \n",
+      " Earth-Moon barycenter/inertial \n",
+      " GCRF \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sun_frame, '\\n',\n",
+    "      sun_frame.getParent(), '\\n',\n",
+    "      sun_frame.getParent().getParent(), '\\n',\n",
+    "      sun_frame.getParent().getParent().getParent(), '\\n',\n",
+    "      sun_frame.getParent().getParent().getParent().getParent(),'\\n',\n",
+    "     )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Example of pre-defined frames\n",
+    "\n",
+    "There are a number of pre-defined frames in Orekit, and for most usages there will be little need to define own frames, although it is possible."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Celestial & Inertial Frames"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An inertial frame is a frame where there is no rotation of the frame with respect to the stars, and there is no acceleration of the origin of the frame. An object, where no forces are acting on it will continue to move along its velocity.\n",
+    "\n",
+    "The definition of the orientation of the frame, and which stars or model that is use to define it (stars can move..) varies between different frames of this type."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## International Celestial Reference Frame\n",
+    "\n",
+    "The [ICRF frame](https://www.iers.org/IERS/EN/DataProducts/ICRF/ICRF/icrf.html) is a modern intertial frame managed by te International Earth Rotation Service (IERS). The definition of the fram is based on the location of 295 extragalactic radio sources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "icrf_frame = FramesFactory.getICRF()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Frame: GCRF>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "icrf_frame.getParent().getParent()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Legacy frames EME2000 / J2000\n",
+    "\n",
+    "The EME2000 / J2000 frame has been common in space and astronomy for decades, and is commonly occuring in the industry. The EME2000 frame in Orekit is close to the classical J2000 frame.\n",
+    "\n",
+    "This frame is based on the Earth's equator and equinox, determined from observation of plaetary motion and other data.\n",
+    "\n",
+    "- $X_{J2000}$: Defined by the intersection equatorial and ecliptic planes, called vernal equinox\n",
+    "- $Y_{J2000}$: The cross product of Z and X\n",
+    "- $Z_{J2000}$: Approximately the Earth's spin axis orientation (north) at 1 January 2000 12:00:00 ET/TBD\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eme_frame = FramesFactory.getEME2000()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us create a position-velocity pair at an instance, as an example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "position = Vector3D(3220103., 69623., 6449822.)\n",
+    "velocity = Vector3D(6414.7, -2006., -3180.)\n",
+    "pv_eme = PVCoordinates(position, velocity)\n",
+    "initDate = AbsoluteDate.J2000_EPOCH.shiftedBy(584.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<PVCoordinates: {P(3220103.0, 69623.0, 6449822.0), V(6414.7, -2006.0, -3180.0), A(0.0, 0.0, 0.0)}>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pv_eme"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Non-Inertial Frames \n",
+    "\n",
+    "Non-inertial frames has some kind of acceleration in the frame, including rotation. Examples are body fixed frames, where the axis are fixed to the features of a body (such as a planet, a satellite, the Moon etc)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  International Terrestrial Reference Frame (ITRF)\n",
+    "\n",
+    "This frame is an Earth centric frame that rotates with the Earth and based on the International Terrestrial Reference System. This system is describe at the [IERS webpage](https://www.iers.org/IERS/EN/Science/ITRS/ITRS.html) and the frame is described at [ITRF page](https://www.iers.org/IERS/EN/DataProducts/ITRF/itrf.html)\n",
+    "\n",
+    "It is based on the following conditions (from webpage):\n",
+    "\n",
+    "- It is geocentric, the center of mass being defined for the whole earth, including oceans and atmosphere.\n",
+    "- The unit of length is the metre (SI). This scale is consistent with the TCG time coordinate for a geocentric local frame, in agreement with IAU and IUGG (1991) resolutions. This is obtained by appropriate relativistic modelling.\n",
+    "- Its orientation was initially given by the BIH orientation at 1984.0.\n",
+    "- The time evolution of the orientation is ensured by using a no-net-rotation condition with regards to horizontal tectonic motions over the whole earth.\n",
+    "\n",
+    "\n",
+    "The frame is depending on the Earth orientation parameters provided in the orekit-data.zip file. Details and options of this frame is available in the Orekit API documentation. This is a recommended frame if a terrestrial frame is needed.\n",
+    "\n",
+    "The frame can be created with the method:\n",
+    "\n",
+    "    getITRF(IERSConventions conventions, boolean simpleEOP)\n",
+    "    \n",
+    "Where the IERSConventions defines which convention to apply, and if simpleEOP is True then tidal effects are ignored when interpolating the Earth Orientation Parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<FactoryManagedFrame: CIO/2010-based ITRF simple EOP>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ITRF = FramesFactory.getITRF(IERSConventions.IERS_2010, True)\n",
+    "ITRF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<PVCoordinates: {P(636230.8642043588, 3157560.6363997906, 6449739.1915731225), V(3590.7818598711724, 5774.1369400248705, -3180.1063781893704), A(0.8454957292958571, -0.5068975962973081, -1.0598439902712802E-6)}>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p2 = eme_frame.getTransformTo(ITRF, initDate).transformPVCoordinates(pv_eme)\n",
+    "p2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p3 = ITRF.getTransformTo(eme_frame, initDate).transformPVCoordinates(p2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Topocentric Frame "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This frame is associated with any position at the surface of a body shape, such as the Earth shape. The origin of the frame is at a GeodeticPoint and the right-handed trihedra is:\n",
+    "\n",
+    "- X axis in the local horizontal plane (normal to zenith direction) and following the local parallel towards East\n",
+    "- Y axis in the horizontal plane (normal to zenith direction) and following the local meridian towards North\n",
+    "- Z axis towards Zenith direction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "earthFrame = FramesFactory.getITRF(IERSConventions.IERS_2010, True)\n",
+    "earth = OneAxisEllipsoid(Constants.WGS84_EARTH_EQUATORIAL_RADIUS,\n",
+    "                                       Constants.WGS84_EARTH_FLATTENING,\n",
+    "                                       earthFrame)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "longitude = radians(21.063)\n",
+    "latitude  = radians(67.878)\n",
+    "altitude  = 341.0\n",
+    "station_point = GeodeticPoint(latitude, longitude, altitude)\n",
+    "station_frame = TopocentricFrame(earth, station_point, \"Esrange\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To transform from other frame to topocentric, a transform class is created, on which the method transformPVCoordinates is applied:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pv_topo = eme_frame.getTransformTo(station_frame, initDate).transformPVCoordinates(pv_eme)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can see the (Cartesian) coordinates of our point in the topocentric frame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<PVCoordinates: {P(2717933.0799277574, 842497.7138440607, 265735.66483865934), V(4097.836310353481, -6224.178656863627, -902.6549534192495), A(-0.7768959573335615, -0.5621567085919928, 0.22851850659067963)}>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pv_topo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Vector3D: {2,717,933.0799277574; 842,497.7138440607; 265,735.6648386593}>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pv_topo.getPosition()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the in the EME2000 frame that is inertial, the acceleration was zero, while in this non-inertial frame there is an acceleration component."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 1\n",
+    "\n",
+    "Calculate the elevation, azimuth and range to the Moon, the Sun and Mars, as seen from a local observer on the Earth, expressed in topocentric spherical coordinates."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The position and velocity of a body can be obtained through:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sun = CelestialBodyFactory.getSun()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sun_pv = PVCoordinatesProvider.cast_(sun).getPVCoordinates(initDate, eme_frame)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<TimeStampedPVCoordinates: {2000-01-01T12:08:39.816, P(2.6516438177770767E10, -1.3275448589123846E11, -5.755544530649549E10), V(29793.62724231183, 5021.298523191532, 2176.795939264307), A(-0.0010836747309132, 0.005554775314372023, 0.0024052918163242304)}>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sun_pv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are different ways to calculate this, either through the spherical coordinates of the topocentric frame, or by using convencince functions available in the topocentric frame object. Check the API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Orbit Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from org.orekit.orbits import KeplerianOrbit, PositionAngle\n",
+    "from org.orekit.propagation.analytical import KeplerianPropagator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "utc = TimeScalesFactory.getUTC()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ra = 400 * 1000         #  Perigee\n",
+    "rp = 2000 * 1000         #  Apoee\n",
+    "i = math.radians(90.0)      # inclinationa\n",
+    "omega = math.radians(90.0)   # perigee argument\n",
+    "raan = math.radians(0.0)  # right ascension of ascending node\n",
+    "lv = math.radians(0.0)    # True anomaly\n",
+    "\n",
+    "epochDate = AbsoluteDate(2016, 1, 1, 0, 0, 00.000, utc)\n",
+    "initialDate = epochDate\n",
+    "\n",
+    "a = (rp + ra + 2 * Constants.WGS84_EARTH_EQUATORIAL_RADIUS) / 2.0    \n",
+    "e = 1.0 - (rp + Constants.WGS84_EARTH_EQUATORIAL_RADIUS) / a\n",
+    "\n",
+    "## Inertial frame where the satellite is defined\n",
+    "inertialFrame = FramesFactory.getEME2000()\n",
+    "\n",
+    "## Orbit construction as Keplerian\n",
+    "initialOrbit = KeplerianOrbit(a, e, i, omega, raan, lv,\n",
+    "                              PositionAngle.TRUE,\n",
+    "                              inertialFrame, epochDate, Constants.WGS84_EARTH_MU)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "propagator = KeplerianPropagator(initialOrbit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "el=[]\n",
+    "pv=[]\n",
+    "t = []\n",
+    "s = []\n",
+    "\n",
+    "extrapDate = initialDate;\n",
+    "finalDate = extrapDate.shiftedBy(60.0*60*24*1) #seconds\n",
+    "\n",
+    "while (extrapDate.compareTo(finalDate) <= 0.0):  \n",
+    "    s.append(propagator.propagate(extrapDate))\n",
+    "    t.append(extrapDate)\n",
+    "    extrapDate = extrapDate.shiftedBy(10.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_inert  = [tmp.getPVCoordinates().getPosition().getX()/1000 for tmp in s]\n",
+    "y_inert  = [tmp.getPVCoordinates().getPosition().getY()/1000 for tmp in s]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\phy\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
+      "  \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x_inert,y_inert)\n",
+    "plt.axes().set_aspect('equal', 'datalim')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot in Earth reference frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\phy\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
+      "  if sys.path[0] == '':\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "target_frame = earthFrame\n",
+    "x_earth = []\n",
+    "y_earth = []\n",
+    "\n",
+    "for time, tmp_s in zip(t,s):\n",
+    "    trans = inertialFrame.getTransformTo(target_frame, time)\n",
+    "    pos = trans.transformPosition(tmp_s.getPVCoordinates().getPosition())\n",
+    "    x_earth.append(pos.getX()/1000)\n",
+    "    y_earth.append(pos.getY()/1000)\n",
+    "    \n",
+    "plt.plot(x_earth,y_earth);\n",
+    "plt.axes().set_aspect('equal', 'datalim')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sun oriented inertial frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1b51cb3d278>]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sun = CelestialBodyFactory.getSun()\n",
+    "target_frame = sun.getInertiallyOrientedFrame()\n",
+    "\n",
+    "x_earth = []\n",
+    "y_earth = []\n",
+    "\n",
+    "for tmp_t, tmp_s in zip(t,s):\n",
+    "    trans = inertialFrame.getTransformTo(target_frame, tmp_t)\n",
+    "    pos = trans.transformPosition(tmp_s.getPVCoordinates().getPosition())\n",
+    "    x_earth.append(pos.getX()/1000)\n",
+    "    y_earth.append(pos.getY()/1000)\n",
+    "\n",
+    "plt.plot(x_earth,y_earth)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Moon centered moon body oriented frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\phy\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
+      "  \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "moon = CelestialBodyFactory.getMoon()\n",
+    "target_frame = moon.getBodyOrientedFrame()\n",
+    "\n",
+    "x_earth = []\n",
+    "y_earth = []\n",
+    "\n",
+    "for tmp_t, tmp_s in zip(t,s):\n",
+    "    trans = inertialFrame.getTransformTo(target_frame, tmp_t)\n",
+    "    pos = trans.transformPosition(tmp_s.getPVCoordinates().getPosition())\n",
+    "    x_earth.append(pos.getX()/1000)\n",
+    "    y_earth.append(pos.getY()/1000)\n",
+    "    \n",
+    "plt.plot(x_earth,y_earth)\n",
+    "plt.axes().set_aspect('equal', 'datalim')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using AbsolutePVCoordinates\n",
+    "\n",
+    "There is a class in Orekit AbsolutePVCoordinates that contains all parameters to specify a PV coordinate in space and time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AbsolutePVCoordinates: {2000-01-01T12:08:39.816, P(3220103.0, 69623.0, 6449822.0), V(6414.7, -2006.0, -3180.0), A(0.0, 0.0, 0.0)}>"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "apv = AbsolutePVCoordinates(eme_frame, initDate, position, velocity)\n",
+    "apv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From this a PVCoordinate can be fetched in any frame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<TimeStampedPVCoordinates: {2000-01-01T12:08:39.816, P(2717933.0799277574, 842497.7138440607, 265735.66483865934), V(4097.836310353481, -6224.178656863627, -902.6549534192495), A(-0.7768959573335615, -0.5621567085919928, 0.22851850659067963)}>"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "apv.getPVCoordinates(station_frame)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.09311743352952996"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "station_frame.getElevation(apv.getPosition(), apv.getFrame(), apv.getDate())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}