# Separations, Catalog Matching, and Related Functionality¶

`astropy.coordinates`

contains commonly-used tools for comparing or
matching coordinate objects. Of particular importance are those for
determining separations between coordinates and those for matching a
coordinate (or coordinates) to a catalog. These are mainly implemented
as methods on the coordinate objects.

## Separations¶

The on-sky separation is easily computed with the
`astropy.coordinates.BaseCoordinateFrame.separation()`

or
`astropy.coordinates.SkyCoord.separation()`

methods,
which computes the great-circle distance (*not* the small-angle
approximation):

```
>>> import numpy as np
>>> from astropy import units as u
>>> from astropy.coordinates import SkyCoord
>>> c1 = SkyCoord('5h23m34.5s', '-69d45m22s', frame='icrs')
>>> c2 = SkyCoord('0h52m44.8s', '-72d49m43s', frame='fk5')
>>> sep = c1.separation(c2)
>>> sep
<Angle 20.74611447604398 deg>
```

The returned object is an `Angle`

instance, so it
is straightforward to access the angle in any of several equivalent angular
units:

```
>>> sep.radian
0.36208800460262575
>>> sep.hour
1.3830742984029323
>>> sep.arcminute
1244.7668685626388
>>> sep.arcsecond
74686.01211375833
```

Also note that the two input coordinates were not in the same frame - one is automatically converted to match the other, ensuring that even though they are in different frames, the separation is determined consistently.

In addition to the on-sky separation described above,
`astropy.coordinates.BaseCoordinateFrame.separation_3d()`

or
`astropy.coordinates.SkyCoord.separation_3d()`

methods will
determine the 3D distance between two coordinates that have `distance`

defined:

```
>>> from astropy.coordinates import SkyCoord
>>> c1 = SkyCoord('5h23m34.5s', '-69d45m22s', distance=70*u.kpc, frame='icrs')
>>> c2 = SkyCoord('0h52m44.8s', '-72d49m43s', distance=80*u.kpc, frame='icrs')
>>> sep = c1.separation_3d(c2)
>>> sep
<Distance 28.743988157814094 kpc>
```

## Matching Catalogs¶

`coordinates`

supports leverages the coordinate framework to make it
straightforward to find the closest coordinates in a catalog to a desired set
of other coordinates. For example, assuming `ra1`

/`dec1`

and
`ra2`

/`dec2`

are numpy arrays loaded from some file:

```
>>> from astropy.coordinates import SkyCoord
>>> from astropy import units as u
>>> c = SkyCoord(ra=ra1*u.degree, dec=dec1*u.degree)
>>> catalog = SkyCoord(ra=ra2*u.degree, dec=dec2*u.degree)
>>> idx, d2d, d3d = c.match_to_catalog_sky(catalog)
```

You can also find the nearest 3d matches, different from the on-sky
separation shown above only when the coordinates were initialized with
a `distance`

:

```
>>> c = SkyCoord(ra=ra1*u.degree, dec=dec1*u.degree, distance=distance1*u.kpc)
>>> catalog = SkyCoord(ra=ra2*u.degree, dec=dec2*u.degree, distance=distance2*u.kpc)
>>> idx, d2d, d3d = c.match_to_catalog_3d(catalog)
```

Now `idx`

are indices into `catalog`

that are the closest objects to each
of the coordinates in `c`

, `d2d`

are the on-sky distances between them, and
`d3d`

are the 3-dimensional distances. Because coordinate objects support
indexing, `idx`

enables easy access to the matched set of coordinates in
the catalog:

```
>>> matches = catalog[idx]
>>> (matches.separation_3d(c) == d3d).all()
True
>>> dra = (matches.ra - c.ra).arcmin
>>> ddec = (matches.dec - c.dec).arcmin
```

This functionality can also be accessed from the
`match_coordinates_sky()`

and
`match_coordinates_3d()`

functions. These
will work on either `SkyCoord`

objects *or* the lower-level frame classes:

```
>>> from astropy.coordinates import match_coordinates_sky
>>> idx, d2d, d3d = match_coordinates_sky(c, catalog)
>>> idx, d2d, d3d = match_coordinates_sky(c.frame, catalog.frame)
```

## Searching Around Coordinates¶

Closely-related functionality can be used to search for *all* coordinates within
a certain distance (either 3D distance or on-sky) of another set of coordinates.
The `search_around_*`

methods (and functions) provide this functionality,
with an interface very similar to `match_coordinates_*`

:

```
>>> idxc, idxcatalog, d2d, d3d = catalog.search_around_sky(c, 1*u.deg)
>>> np.all(d2d < 1*u.deg)
True
>>> idxc, idxcatalog, d2d, d3d = catalog.search_around_3d(c, 1*u.kpc)
>>> np.all(d3d < 1*u.kpc)
True
```

The key difference for these methods is that there can be multiple (or no)
matches in `catalog`

around any locations in `c`

. Hence, indices into both
`c`

and `catalog`

are returned instead of just indices into `catalog`

.
These can then be indexed back into the two `SkyCoord`

objects, or, for that
matter, any array with the same order:

```
>>> np.all(c[idxc].separation(catalog[idxcatalog]) == d2d)
True
>>> np.all(c[idxc].separation_3d(catalog[idxcatalog]) == d3d)
True
>>> print catalog_objectnames[idxcatalog]
['NGC 1234' 'NGC 4567' ...]
```

Note, though, that this dual-indexing means that `search_around_*`

does not
work well if one of the coordinates is a scalar, because the returned index
would not make sense for a scalar:

```
>>> scalarc = SkyCoord(1*u.deg, 2*u.deg)
>>> idxscalarc, idxcatalog, d2d, d3d = catalog.search_around_sky(scalarc, 1*u.deg) # THIS DOESN'T ACTUALLY WORK
>>> scalarc[idxscalarc]
IndexError: 0-d arrays can't be indexed
```

As a result (and because the `search_around_*`

algorithm is inefficient in
the scalar case, anyway), the best approach for this scenario is to instead
use the `separation*`

methods:

```
>>> d2d = scalarc.separation(catalog)
>>> catalogmsk = d2d < 1*u.deg
>>> d3d = scalarc.separation_3d(catalog)
>>> catalog3dmsk = d3d < 1*u.kpc
```

The resulting `catalogmsk`

or `catalog3dmsk`

variables are boolean arrays
rather than arrays of indices, but in practice they usually can be used in
the same way as `idxcatalog`

from the above examples. If you definitely do
need indices instead of boolean masks, you can do:

```
>>> idxcatalog = np.where(catalogmsk)[0]
>>> idxcatalog3d = np.where(catalog3dmsk)[0]
```