# User guide¶

## Defining the orbit: `Orbit`

objects¶

The core of poliastro are the `Orbit`

objects
inside the `poliastro.twobody`

module. They store all the required
information to define an orbit:

- The body acting as the central body of the orbit, for example the Earth.
- The position and velocity vectors or the orbital elements.
- The time at which the orbit is defined.

First of all, we have to import the relevant modules and classes:

```
import numpy as np
import matplotlib.pyplot as plt
plt.ion() # To immediately show plots
from astropy import units as u
from poliastro.bodies import Earth, Mars, Sun
from poliastro.twobody import Orbit
plt.style.use("seaborn") # Recommended
```

### From position and velocity¶

There are several methods available to create
`Orbit`

objects. For example, if we have the
position and velocity vectors we can use
`from_vectors()`

:

```
# Data from Curtis, example 4.3
r = [-6045, -3490, 2500] * u.km
v = [-3.457, 6.618, 2.533] * u.km / u.s
ss = Orbit.from_vectors(Earth, r, v)
```

And that’s it! Notice a couple of things:

Defining vectorial physical quantities using Astropy units is very easy. The list is automatically converted to a

`astropy.units.Quantity`

, which is actually a subclass of NumPy arrays.If we display the orbit we just created, we get a string with the radius of pericenter, radius of apocenter, inclination and attractor:

>>> ss 7283 x 10293 km x 153.2 deg orbit around Earth (♁)

If no time is specified, then a default value is assigned:

>>> ss.epoch <Time object: scale='utc' format='jyear_str' value=J2000.000> >>> ss.epoch.iso '2000-01-01 12:00:00.000'

If we’re working on interactive mode (for example, using the wonderful IPython notebook) we can immediately plot the current state:

```
from poliastro.plotting import plot
plot(ss)
```

This plot is made in the so called *perifocal frame*, which means:

- we’re visualizing the plane of the orbit itself,
- the \(x\) axis points to the pericenter, and
- the \(y\) axis is turned \(90 \mathrm{^\circ}\) in the direction of the orbit.

The dotted line represents the *osculating orbit*:
the instantaneous Keplerian orbit at that point. This is relevant in the
context of perturbations, when the object shall deviate from its Keplerian
orbit.

Warning

Be aware that, outside the Jupyter notebook (i.e. a normal Python interpreter
or program) you might need to call `plt.show()`

after the plotting
commands or `plt.ion()`

before them or they won’t show. Check out the
Matplotlib FAQ for more information.

### From classical orbital elements¶

We can also define a `Orbit`

using a set of
six parameters called orbital elements. Although there are several of
these element sets, each one with its advantages and drawbacks, right now
poliastro supports the *classical orbital elements*:

- Semimajor axis \(a\).
- Eccentricity \(e\).
- Inclination \(i\).
- Right ascension of the ascending node \(\Omega\).
- Argument of pericenter \(\omega\).
- True anomaly \(\nu\).

In this case, we’d use the method
`from_classical()`

:

```
# Data for Mars at J2000 from JPL HORIZONS
a = 1.523679 * u.AU
ecc = 0.093315 * u.one
inc = 1.85 * u.deg
raan = 49.562 * u.deg
argp = 286.537 * u.deg
nu = 23.33 * u.deg
ss = Orbit.from_classical(Sun, a, ecc, inc, raan, argp, nu)
```

Notice that whether we create a `Orbit`

from \(r\) and \(v\) or from
elements we can access many mathematical properties individually using the
`state`

property of
`Orbit`

objects:

```
>>> ss.state.period.to(u.day)
<Quantity 686.9713888628166 d>
>>> ss.state.v
<Quantity [ 1.16420211, 26.29603612, 0.52229379] km / s>
```

To see a complete list of properties, check out the
`poliastro.twobody.orbit.Orbit`

class on the API reference.

## Moving forward in time: propagation¶

Now that we have defined an orbit, we might be interested in computing
how is it going to evolve in the future. In the context of orbital
mechanics, this process is known as **propagation**, and can be
performed with the `propagate`

method of
`Orbit`

objects:

```
>>> from poliastro.examples import iss
>>> iss
6772 x 6790 km x 51.6 deg orbit around Earth (♁)
>>> iss.epoch
<Time object: scale='utc' format='iso' value=2013-03-18 12:00:00.000>
>>> iss.nu.to(u.deg)
<Quantity 46.595804677061956 deg>
>>> iss.n.to(u.deg / u.min)
<Quantity 3.887010576192155 deg / min>
```

Using the `propagate()`

method
we can now retrieve the position of the ISS after some time:

```
>>> iss_30m = iss.propagate(30 * u.min)
>>> iss_30m.epoch # Notice we advanced the epoch!
<Time object: scale='utc' format='iso' value=2013-03-18 12:30:00.000>
>>> iss_30m.nu.to(u.deg)
<Quantity 163.1409357544868 deg>
```

For more advanced propagation options, check out the
`poliastro.twobody.propagation`

module.

## Changing the orbit: `Maneuver`

objects¶

poliastro helps us define several in-plane and general out-of-plane
maneuvers with the `Maneuver`

class inside the
`poliastro.maneuver`

module.

Each `Maneuver`

consists on a list of impulses \(\Delta v_i\)
(changes in velocity) each one applied at a certain instant \(t_i\). The
simplest maneuver is a single change of velocity without delay: you can
recreate it either using the `impulse()`

method or instantiating it directly.

```
from poliastro.maneuver import Maneuver
dv = [5, 0, 0] * u.m / u.s
man = Maneuver.impulse(dv)
man = Maneuver((0 * u.s, dv)) # Equivalent
```

There are other useful methods you can use to compute common in-plane
maneuvers, notably `hohmann()`

and
`bielliptic()`

for Hohmann and
bielliptic transfers respectively. Both return the corresponding
`Maneuver`

object, which in turn you can use to calculate the total cost
in terms of velocity change (\(\sum |\Delta v_i|\)) and the transfer
time:

```
>>> ss_i = Orbit.circular(Earth, alt=700 * u.km)
>>> ss_i
7078 x 7078 km x 0.0 deg orbit around Earth (♁)
>>> hoh = Maneuver.hohmann(ss_i, 36000 * u.km)
>>> hoh.get_total_cost()
<Quantity 3.6173981270031357 km / s>
>>> hoh.get_total_time()
<Quantity 15729.741535747102 s>
```

You can also retrieve the individual vectorial impulses:

```
>>> hoh.impulses[0]
(<Quantity 0 s>, <Quantity [ 0. , 2.19739818, 0. ] km / s>)
>>> hoh[0] # Equivalent
(<Quantity 0 s>, <Quantity [ 0. , 2.19739818, 0. ] km / s>)
>>> tuple(val.decompose([u.km, u.s]) for val in hoh[1])
(<Quantity 15729.741535747102 s>, <Quantity [ 0. , 1.41999995, 0. ] km / s>)
```

To actually retrieve the resulting `Orbit`

after performing a maneuver, use
the method `apply_maneuver()`

:

```
>>> ss_f = ss_i.apply_maneuver(hoh)
>>> ss_f
36000 x 36000 km x 0.0 deg orbit around Earth (♁)
```

## More advanced plotting: `OrbitPlotter`

objects¶

We previously saw the `poliastro.plotting.plot()`

function to easily
plot orbits. Now we’d like to plot several orbits in one graph (for example,
the maneuver we computed in the previous section). For this purpose, we
have `OrbitPlotter`

objects in the
`plotting`

module.

These objects hold the perifocal plane of the first `Orbit`

we plot in
them, projecting any further trajectories on this plane. This allows to
easily visualize in two dimensions:

```
from poliastro.plotting import OrbitPlotter
op = OrbitPlotter()
ss_a, ss_f = ss_i.apply_maneuver(hoh, intermediate=True)
op.plot(ss_i, label="Initial orbit")
op.plot(ss_a, label="Transfer orbit")
op.plot(ss_f, label="Final orbit")
```

Which produces this beautiful plot:

## Where are the planets? Computing ephemerides¶

New in version 0.3.0.

Thanks to Astropy and jplephem, poliastro can now read Satellite Planet Kernel (SPK) files, part of NASA’s SPICE toolkit. This means that we can query the position and velocity of the planets of the Solar System.

The method `get_body_ephem()`

will return
a planetary orbit using low precision ephemerides available in
Astropy and an `astropy.time.Time`

:

```
from astropy import time
epoch = time.Time("2015-05-09 10:43") # UTC by default
```

And finally, retrieve the planet orbit:

```
>>> from poliastro import ephem
>>> Orbit.from_body_ephem(Earth, epoch)
1 x 1 AU x 23.4 deg orbit around Sun (☉)
```

This does not require any external download. If on the other hand we want to use higher precision ephemerides, we can tell Astropy to do so:

```
>>> from astropy.coordinates import solar_system_ephemeris
>>> solar_system_ephemeris.set("jpl")
Downloading http://naif.jpl.nasa.gov/pub/naif/generic_kernels/spk/planets/de430.bsp
|==========>-------------------------------| 23M/119M (19.54%) ETA 59s22ss23
```

This in turn will download the ephemerides files from NASA and use them for future computations. For more information, check out Astropy documentation on ephemerides.

Note

The position and velocity vectors are given with respect to the
Solar System Barycenter in the **International Celestial Reference Frame**
(ICRF), which means approximately equatorial coordinates.

## Traveling through space: solving the Lambert problem¶

The determination of an orbit given two position vectors and the time of
flight is known in celestial mechanics as **Lambert’s problem**, also
known as two point boundary value problem. This contrasts with Kepler’s
problem or propagation, which is rather an initial value problem.

The package `poliastro.iod`

allows as to solve Lambert’s problem,
provided the main attractor’s gravitational constant, the two position
vectors and the time of flight. As you can imagine, being able to compute
the positions of the planets as we saw in the previous section is the
perfect complement to this feature!

For instance, this is a simplified version of the example Going to Mars with Python using poliastro, where the orbit of the Mars Science Laboratory mission (rover Curiosity) is determined:

```
date_launch = time.Time('2011-11-26 15:02', scale='utc')
date_arrival = time.Time('2012-08-06 05:17', scale='utc')
tof = date_arrival - date_launch
ss0 = Orbit.from_body_ephem(Earth, date_launch)
ssf = Orbit.from_body_ephem(Mars, date_arrival)
from poliastro import iod
(v0, v), = iod.lambert(Sun.k, ss0.r, ssf.r, tof)
```

And these are the results:

```
>>> v0
<Quantity [-29.29150998, 14.53326521, 5.41691336] km / s>
>>> v
<Quantity [ 17.6154992 ,-10.99830723, -4.20796062] km / s>
```

## Working with NEOs¶

NEOs (Near Earth Objects) are asteroids and comets whose orbits are near to earth (obvious, isn’t it?).
More correctly, their perihelion (closest approach to the Sun) is less than 1.3 astronomical units (≈ 200 * 10^{6} km).
Currently, they are being an important subject of study for scientists around the world, due to their status as the relatively
unchanged remains from the solar system formation process.

Because of that, a new module related to NEOs has been added to `poliastro`

as part of SOCIS 2017 project.

For the moment, it is possible to search NEOs by name (also using wildcards),
and get their orbits straight from NASA APIs, using `orbit_from_name()`

.
For example, we can get Apophis asteroid (99942 Apophis) orbit with one command, and plot it:

```
from poliastro.neos import neows
apophis_orbit = neows.orbit_from_name('apophis') # Also '99942' or '99942 apophis' works
earth_orbit = Orbit.from_body_ephem(Earth)
op = OrbitPlotter()
op.plot(earth_orbit, label='Earth')
op.plot(apophis_orbit, label='Apophis')
```

*Per Python ad astra* ;)